Sustainable Chemistry Research: Chemical and Biochemical Aspects 9783111071435, 9783111070902

Table of contents :
Preface of the Book of Proceedings of the Virtual Conference on Chemistry and its Applications (VCCA-2022).
Contents
List of contributing authors
1 Dipeptidyl peptidase IV: a multifunctional enzyme with implications in several pathologies including cancer
2 A mini review on the prospects of Fagara zanthoxyloides extract based composites: a remedy for COVID-19 and associated replica?
3 Triterpenoids of antibacterial extracts from the leaves of Bersama abyssinica Fresen (Francoaceae)
4 Physicochemical assessment and insilico studies on the interaction of 5-HT2c receptor with herbal medication bioactive compounds used in the treatment of premature ejaculation
5 Xanthoangelol, geranilated chalcone compound, isolation from pudau leaves (Artocarpus kemando Miq.) as antibacterial and anticancer
6 Exploration of bioactive compounds from Mangifera indica (Mango) as probable inhibitors of thymidylate synthase and nuclear factor kappa-B (NF-Κb) in colorectal cancer management
7 Identification of potential inhibitors of thymidylate synthase (TS) (PDB ID: 6QXH) and nuclear factor kappa-B (NF–κB) (PDB ID: 1A3Q) from Capsicum annuum (bell pepper) towards the development of new therapeutic drugs against colorectal cancer (CRC)
8 Synthesis, characterization and in vitro activity study of some organotin(IV) carboxylates against leukemia cancer cell, L-1210
9 Phytochemicals from Annona muricata (Sour Sop) as potential inhibitors of SARS-CoV-2 main protease (Mpro) and spike receptor protein: a structure-based drug design studies and chemoinformatics analyses
10 Identification of novel inhibitors of P13K/AKT pathways: an integrated in-silico study towards the development of a new therapeutic agent against ovarian cancer
11 Immobilization of α-amylase from Aspergillus fumigatus using adsorption method onto zeolite
12 Phytochemical components and GC–MS analysis of Petiveria alliaceae L. fractions and volatile oils
13 Characterization of crude saponins from stem bark extract of Parinari curatellifolia and evaluation of its antioxidant and antibacterial activities
14 Physicochemical and free radical scavenging activity of Adansonia digitata seed oil
15 Photoprotection strategies with antioxidant extracts: a new vision
16 A systematic DFT study of arsenic doped iron cluster AsFen (n = 1–4)
17 Effect of case-based learning, team-based learning and regular teaching methods on secondary school students’ self-concept in chemistry in Maara sub-county, Tharaka Nithi county, Kenya
18 Random and block architectures of N-arylitaconimide monomers with methyl methacrylate
19 Evaluation of phytochemicals and amino acid profiles of four vegetables grown on a glyphosate contaminated soil in Southwestern Nigeria
Index

Citation preview

Ponnadurai Ramasami (Ed.) Sustainable Chemistry Research

Also of interest Sustainable Chemistry Research Volume : Computational and Industrial Aspects Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Sustainable Chemistry Research Volume : Analytical Aspects Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Physical Sciences Reviews. e-ISSN -X

Sustainable Chemistry Research Volume 1: Chemical and Biochemical Aspects Edited by Ponnadurai Ramasami

Editor Prof. Dr. Ponnadurai Ramasami Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius E-mail address: [email protected]

ISBN 978-3-11-107090-2 e-ISBN (PDF) 978-3-11-107143-5 e-ISBN (EPUB) 978-3-11-107164-0 Library of Congress Control Number: 2023940883 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the internet at http://dnb.dnb.de. © 2023 Walter de Gruyter GmbH, Berlin/Boston Cover image: Petmal / iStock / Getty Images Plus Typesetting: TNQ Technologies Pvt. Ltd. Printing and binding: CPI books GmbH, Leck www.degruyter.com

Preface of the Book of Proceedings of the Virtual Conference on Chemistry and its Applications (VCCA-2022). A virtual conference on chemistry and its applications (VCCA-2022) was organized online from 8th to 12th August 2022. The theme of the virtual conference was “Resilience and Sustainable Research through Basic Sciences”. There were 210 presentations for the virtual conference with 500 participants from 55 countries. A secured platform was used for virtual interactions of the participants. After the virtual conference, there was a call for full papers to be considered for publication in the conference proceedings. Manuscripts were received and they were processed and reviewed as per the policy of De Gruyter. This book, volume 1, is a collection of the nineteen accepted manuscripts covering chemical and biochemical aspects. I hope that these chapters of this volume 1 will add to literature and they will be useful references for researchers. To conclude, VCCA-2022 was a successful event and I would like to thank all those who have contributed. I would also like to thank the Organising and International Advisory committee members, the participants and the reviewers. Prof. Ponnadurai Ramasami, UNESCO Chair in Computational Chemistry Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius E-mail address: [email protected]

https://doi.org/10.1515/9783111071435-201

Contents Preface V List of contributing authors

XIX

Yarini Arrebola Sánchez, Fabiola Almeida García, Daniel Ojeda del Sol, Mario E. Valdés-Tresanco, Carlos David Ortiz, Belinda Sánchez Ramírez and Isel Pascual Alonso 1 Dipeptidyl peptidase IV: a multifunctional enzyme with implications in 1 several pathologies including cancer 1.1 Introduction: cancer is coming and we need more and more effective 1 weapons to stop or ameliorate it 1.2 DPP-IV general characteristics: an interesting peptidase that is more than 3 just an enzyme 5 1.2.1 DPP-IV gene and protein expression: a vast picture across human tissues 1.2.2 DPP-IV active site structure: catalytic triad, residues of interest and substrate 7 specificity 7 1.2.3 DPP-IV β-propeller domain: a singular structure from a singular protein 9 1.2.4 DPP-IV cellular expression: a very wide spread molecule 9 1.3 DPP-IV main functions: how versatile protein is DPP-IV! 11 1.3.1 DPP-IV and metabolism: an old and well-documented story 1.3.2 DPP-IV and the immune system: a story of a dual role and multiple 15 effects 16 1.3.3 DPP-IV and viral infections: the deadly MERS-CoV and SARS-Cov-2 17 1.3.4 DPP-IV and asthma: promising evidences (but in mice) 17 1.3.5 DPP-IV and pulmonary hypertension: a field not very explored 1.3.6 DPP-IV and pulmonary fibrosis: is this a story of inflammation, senescence or 18 both? 1.3.7 DPP-IV and cardiovascular system: cardio-protector effects of its inhibitions and 18 possible causes 19 1.4 DPP- IV and cancer: friend or foe? 19 1.4.1 DPP-IV and leukemia: a protein often linked to worst prognosis 1.4.2 DPP-IV in skin malignancies: a protein that vanishes in melanoma and rises in 20 keratinocyte tumours 21 1.4.3 DPP-IV in lung cancer: a voluble expression pattern 22 1.4.4 DPP-IV and endometrial adenocarcinoma: an unsolved question 22 1.4.5 DPP-IV in ovarian cancer: the importance of fibronectin 23 1.4.6 DPP-IV in prostate cancer: a possible dependency on DPP-IV levels 1.4.7 DPP-IV and thyroid gland: a very useful tool to discriminate among neoplasias, 24 papillary and follicular carcinomas 25 1.4.8 DPP-IV and neural tissue: gliomas, meningiomas and neuroblastomas

VIII

1.4.9 1.4.10 1.4.11 1.4.12 1.4.13 1.4.14 1.4.15 1.5

Contents

DPP-IV and mesothelioma: a promising field to be explored 26 DPP-IV and hepatocarcinoma: a rising not only in cancer, but precancerous 26 pathologies DPP-IV and gastro-oesophageal junction adenocarcinoma (EGJA): a shortcut to 27 early diagnosis DPP-IV and colorectal cancers: a protein that remains upregulated from early 27 stages to metastasis DPP-IV and extracellular matrix proteins or cytoskeleton proteins: a possible 28 way into metastasis 31 DPP-IV, GLP-1 and cancer: concerns about pancreas 32 DPP-IV inhibitors and cancer: a tenue light at the end of the tunnel 36 Conclusions 36 References

Abiodun Oladipo, Onome Ejeromedoghene, Ademola Adebayo, Olakunle Ogunyemi, and George Egejuru 2 A mini review on the prospects of Fagara zanthoxyloides extract based 57 composites: a remedy for COVID-19 and associated replica? 58 2.1 Introduction 59 2.2 The state of Covid-19, mode of transmission and treatment 61 2.3 Herbal remedies 63 2.4 Fagara zanthoxyloides extracts 65 2.5 The broad biomedical application of fagara extracts 65 2.5.1 Antibacterial activities 66 2.5.2 Antiviral activities 66 2.6 Composites of Fagara zanthoxyloides extracts 69 2.7 Conclusion and prospects 69 References Danielle Sinkam Gozo, Calixte Tonbou and Jean Momeni 3 Triterpenoids of antibacterial extracts from the leaves of Bersama abyssinica 75 Fresen (Francoaceae) 75 3.1 Introduction 76 3.2 Material and methods 76 3.2.1 Chemical reagents and equipment 76 3.2.2 Bacterial strains 77 3.2.3 Plant material 77 3.2.4 Extraction and isolation of secondary metabolites 77 3.2.5 Determination of total polyphenols by the Folin–Ciocalteu method 78 3.2.6 Antibacterial activity 78 3.2.7 Data analysis

IX

Contents

3.3 3.3.1 3.3.2 3.3.3 3.4

Results and discussion 79 Results of extraction, screening phytochemical and total polyphenol 80 Result of isolation 82 Result of antibacterial activity 83 Conclusions 84 References

79

Charles I. Aghanwa, Uche E. Ekpunobi and Adaora Ogbuagu 4 Physicochemical assessment and insilico studies on the interaction of 5-HT2c receptor with herbal medication bioactive compounds used in the treatment 87 of premature ejaculation 88 4.1 Introduction 90 4.2 Methods 90 4.2.1 Sampling of herbal medicine 90 4.2.2 Proximate analysis 92 4.2.3 Phytochemical analysis 94 4.2.4 Heavy metal analysis 94 4.2.5 Gas Chromatography–Mass Spectrometry Analysis (GC-MS) [38] 95 4.2.6 In Silico Docking (Molecular Docking) 96 4.3 Results and discussions 96 4.3.1 Proximate analysis 97 4.3.2 Phytochemical analysis 99 4.3.3 Heavy metals analysis 100 4.3.4 Chemical constituents analysis 103 4.3.5 Molecular docking analysis 107 4.3.6 ADMET Studies 116 4.4 Conclusions 116 References Tati Suhartati, Novita Andriyani, Yandri Yandri and Sutopo Hadi 5 Xanthoangelol, geranilated chalcone compound, isolation from pudau leaves 121 (Artocarpus kemando Miq.) as antibacterial and anticancer 121 5.1 Introduction 122 5.2 Methods 122 5.2.1 General 123 5.2.2 Sample preparation 123 5.2.3 Anti-bacterial and anti-cancer test 123 5.3 Results and discussions 123 5.3.1 Isolation of flavonoid compounds 124 5.3.2 Structural analysis 130 5.3.3 Antibacterial bioactivity test

X

5.3.4 5.4

Contents

Anticancer bioactivity test 135 Conclusions 135 References

134

Misbaudeen Abdul-Hammed, Isah Adewale Bello, Monsurat Olajide, Ibrahim Olaide Adedotun, Tolulope Irapada Afolabi, Ayobami Abimbola Ibironke and Barakat Dasola Adebayo 6 Exploration of bioactive compounds from Mangifera indica (Mango) as probable inhibitors of thymidylate synthase and nuclear factor kappa-B 137 (NF-Κb) in colorectal cancer management 138 6.1 Introduction 140 6.2 Materials and methods 6.2.1 Preparation of the target receptor: Thymidylate synthase (PDB ID: 6QXH) and 140 (NF–κB) (PDB ID: 1A3Q) 141 6.2.2 Preparation of ligands and geometry optimization 141 6.2.3 Determination of active sites of the target receptors 141 6.2.4 ADMET predictions 141 6.2.5 Drug–likeness predictions 141 6.2.6 Molecular docking protocol 142 6.2.7 Oral bioavailability and PASS predictions 142 6.3 Results and discussions 142 6.3.1 Validation of the active sites of the receptors 144 6.3.2 ADMET/pharmacokinetic prediction analysis 153 6.3.3 Drug–likeness prediction 153 6.3.4 Molecular docking analysis 159 6.3.5 Oral bioavailability analysis 161 6.3.6 Prediction of activity spectra for substances (PASS) 161 6.4 Conclusions 162 References Monsurat Olajide, Misbaudeen Abdul-Hammed, Isah Adewale Bello, Ibrahim Olaide Adedotun and Tolulope Irapada Afolabi 7 Identification of potential inhibitors of thymidylate synthase (TS) (PDB ID: 6QXH) and nuclear factor kappa-B (NF–κB) (PDB ID: 1A3Q) from Capsicum annuum (bell pepper) towards the development of new therapeutic 165 drugs against colorectal cancer (CRC) 166 7.1 Introduction 168 7.2 Materials and methods 168 7.2.1 Preparation of target receptors 169 7.2.2 Preparation and geometry optimization of the ligands 169 7.2.3 Determination of active sites of the target receptors

Contents

7.2.4 7.2.5 7.2.6 7.2.7 7.3 7.3.1 7.3.2 7.3.3 7.3.4 7.3.5 7.3.6 7.3.7 7.4

XI

Predictions of ADMET properties of the compounds 169 169 Drug-likeness predictions of the compounds 169 Oral bioavailability and PASS analysis 170 Molecular docking studies 170 Result and discussions 170 Validation of the active sites in the target receptors 172 ADMET predictions 173 Drug-likeness analysis 173 Molecular docking analysis 189 Oral bioavailability of the ligands and standard drugs 192 Bioactivity of the selected compounds and standard drugs 193 PASS analysis 195 Conclusions 195 References

Sutopo Hadi, Ermin Katrin Winarno, Hendig Winarno, Khairun Nisa Berawi, Tati Suhartati, Yandri Yandri and Wasinton Simanjuntak 8 Synthesis, characterization and in vitro activity study of some 199 organotin(IV) carboxylates against leukemia cancer cell, L-1210 199 8.1 Introduction 200 8.2 Experimental 200 8.2.1 Materials 200 8.2.2 Characterization techniques 200 8.2.3 Preparation of organotin(IV) carboxylates 201 8.2.4 Bioassay anticancer activity test against leukemia cancer cell, L-1210 201 8.3 Results and discussion 205 8.4 Conclusions 205 References Ibrahim Olaide Adedotun, Misbaudeen Abdul-Hammed, Bamidele Toheeb Towolawi, Tolulope Irapada Afolabi, Karimot Motunrayo Mufutau and Hadijat Motunrayo Adegoke 9 Phytochemicals from Annona muricata (Sour Sop) as potential inhibitors of SARS-CoV-2 main protease (Mpro) and spike receptor protein: a 207 structure-based drug design studies and chemoinformatics analyses 208 9.1 Introduction 208 9.2 Materials and methods 208 9.2.1 Ligand preparation 209 9.2.2 Protein structure preparation 209 9.2.3 Drug-likeliness and ADMET profiling analysis 9.2.4 Prediction of activity spectra for substances (PASS) and oral bioactivity 210 assessment

XII

9.2.5 9.3 9.3.1 9.3.2 9.3.3 9.4

Contents

Molecular docking studies 210 210 Results and discussion 210 ADMET and drug-likeness analyses Bioactivity and oral-bioavailability assessment 215 Virtual screening analysis 219 Conclusion 230 References

214

Ibrahim Olaide Adedotun, Misbaudeen Abdul-Hammed, Basirat Temidayo Egunjobi, Ubeydat Temitope Ismail, Jemilat Yetunde Yusuf, Tolulope Irapada Afolabi and Ibrahim Olajide Gbadebo 10 Identification of novel inhibitors of P13K/AKT pathways: an integrated in-silico study towards the development of a new therapeutic agent against ovarian 231 cancer 232 10.1 Introduction 233 10.2 Materials and methods 233 10.2.1 Ligand preparation 239 10.2.2 Preparation of target receptor 240 10.2.3 Determination of (5DXT and 2JDR) active sites 240 10.2.4 Molecular docking simulation 240 10.2.5 Prediction of Activity Spectra for Substances (PASS) 241 10.2.6 Assessment of pharmacokinetic properties 241 10.3 Results and discussion 10.3.1 Protein kinase B (PKB Beta/Akt2) and Phosphoinositide-3-kinase (PI3K) 241 structure and active site analysis 241 10.3.2 ADMET assay of the ligands 242 10.3.3 Drug likeness analysis of the selected compounds 249 10.3.4 Molecular docking analysis 249 10.3.5 Oral bioavailability of the passed compounds 254 10.3.6 Prediction of activity spectra for substances (PASS) 254 10.3.7 Bioactivity of the selected compounds 256 10.3.8 Binding mode and Molecular interactions 258 10.4 Conclusions 258 References Yandri Yandri, Hendri Ropingi, Tati Suhartati, Bambang Irawan and Sutopo Hadi 11 Immobilization of α-amylase from Aspergillus fumigatus using adsorption 261 method onto zeolite 261 11.1 Introduction 263 11.2 Materials and methods 263 11.2.1 Materials

Contents

11.2.2 11.3 11.3.1 11.3.2 11.3.3 11.3.4 11.4

XIII

Research Procedures 264 265 Results and discussions 265 Determination of optimum temperature 267 Determination of KM and Vmax values 268 Thermal stability 270 Reusability assay 270 Conclusions 271 References

Enitan Omobolanle Adesanya, Olubunkunola Oluwole Oyesiku, Olumide Olatunde Adesanya, Akingbolabo Daniel Ogunlakin, Adeshina Isaiah Odugbemi and Samuel Ayodele Egieyeh 12 Phytochemical components and GC–MS analysis of Petiveria alliaceae L. 273 fractions and volatile oils 274 12.1 Introduction 275 12.2 Materials and methods 275 12.2.1 Materials 277 12.2.2 Methods 279 12.3 Results and discussion 283 12.4 Conclusions 284 References Emmanuel Mshelia Halilu 13 Characterization of crude saponins from stem bark extract of Parinari curatellifolia and evaluation of its antioxidant and antibacterial 287 activities 288 13.1 Introduction 289 13.2 Methodology 289 13.2.1 Collection, identification and preparation 289 13.2.2 Extraction of crude saponins 290 13.2.3 Solubility study 290 13.2.4 Phytochemical screening for saponins 291 13.2.5 Thin layer chromatography 291 13.2.6 Fluorescence analysis of crude saponins 291 13.2.7 UV finger and FTIR finger printing 291 13.2.8 Synthesis of silver nanoparticles 292 13.2.9 Antioxidant studies 292 13.2.10 Antibacterial studies 293 13.2.11 Statistical analysis 293 13.3 Results and discussion 293 13.3.1 Extraction and solubility of crude saponins 293 13.3.2 Phytochemical studies

XIV

13.3.3 13.3.4 13.3.5 13.3.6 13.3.7 13.4

Contents

Fluorescence analysis and thin layer chromatographic separation profile of the 297 crude saponins UV finger printing of crude saponin and FTIR finger printing crude 298 saponin 299 Synthesis of silver nanoparticles and characterization of nanoparticles 301 Antioxidant studies 302 Antibacterial activity 303 Conclusions 303 References

Emmanuel Mshelia Halilu and Nafisa Kudu Muhammad 14 Physicochemical and free radical scavenging activity of Adansonia digitata 307 seed oil 307 14.1 Introduction 309 14.2 Methodology 309 14.2.1 Collection and identification of Adansonia digitata seeds 309 14.2.2 Preparation of seed for oil extraction 309 14.2.3 Organoleptic evaluation of powdered sample of Adansonia digitata 310 14.2.4 Physicochemical Studies 310 14.2.5 Extraction of oil 310 14.2.6 Evaluation of organoleptic characters of the fixed oil 310 14.2.7 Solubility testing 310 14.2.8 Specific gravity determination 311 14.2.9 Qualitative phytochemical screening on the oil 311 14.2.10 Oil Analyses 311 14.2.11 GC-MS analysis 312 14.2.12 Antioxidant studies 312 14.2.13 Toxicity study 313 14.2.14 Data analysis 313 14.3 Results and discussion 14.3.1 Organoleptic evaluation of powdered sample of Adansonia digitata 313 seeds 313 14.3.2 Physicochemical evaluation 314 14.3.3 Extraction of oil 314 14.3.4 Organoleptic evaluation of the oil 314 14.3.5 Solubility studies 314 14.3.6 Qualitative phytochemical screening 315 14.3.7 TLC profile of Adansonia digitata oil 315 14.3.8 GC-MS analysis of Adasonia digitata oil 316 14.3.9 Oil analyses 317 14.3.10 Acute toxicity (LD50) studies

Contents

14.3.11 14.4

Antioxidant studies 318 Conclusions 318 References

XV

317

Radia Ayad, Mostefa Lefahal, El Hani Makhloufi and Salah Akkal 15 Photoprotection strategies with antioxidant extracts: a new vision 321 15.1 Introduction 323 15.2 Material and methods 323 15.3 Results and discussions 323 15.3.1 Photostability 323 15.3.2 Adverse effects in human beings 324 15.3.3 Adverse effects on environment and marine organisms 15.3.4 Plant extracts as sustainable ingredients for sunscreens and cosmetic 324 formulations 326 15.3.5 Antioxidant polyphenols as photoprotective agents 15.3.6 Application of green extraction processes for the cosmetic industries 329 15.3.7 Green extraction, green chemistry, and green cosmetics 330 15.4 Conclusions 330 References Shayeri Das, Prabhat Ranjan and Tanmoy Chakraborty 16 A systematic DFT study of arsenic doped iron cluster AsFen (n = 1–4) 335 16.1 Introduction 336 16.2 Computational details 337 16.3 Results and discussion 337 16.3.1 Equilibrium geometry 338 16.3.2 CDFT based descriptors 340 16.4 Conclusions 340 References

321

327

335

Antony Mugiira Arimba, David Kuria Wamukuru and Zachary Orato Anditi 17 Effect of case-based learning, team-based learning and regular teaching methods on secondary school students’ self-concept in chemistry in Maara 345 sub-county, Tharaka Nithi county, Kenya 346 17.1 Introduction 348 17.2 Purpose of the study 348 17.3 Objective of the study 348 17.4 Hypotheses of study 348 17.5 Research design 349 17.6 Population of study 349 17.7 Instrumentation

XVI

17.8 17.9 17.10 17.11 17.12 17.13 17.14 17.15 17.16 17.17 17.18

Contents

Students’ self-concept questionnaire (SSCQ) 349 350 Validity of instrument 350 Reliability of instrument 350 Treatment of study 351 Data collection procedures 351 Analysis of data Effects of CBL, TBL and RTM on students’ chemistry self-concept 354 Summary of findings 355 Conclusions 355 Recommendations for improvement 355 Suggestions for further rlesearch 355 References

351

Chetana Deoghare 18 Random and block architectures of N-arylitaconimide monomers with 359 methyl methacrylate 360 18.1 Introduction 361 18.2 Conventional FRP 361 18.3 Conventional FRP of IIs 362 18.4 RDRPs 363 18.5 ATRP 364 18.5.1 Kinetics of ATRP 366 18.6 Components of ATRP and their effects on KATRP and kp 366 18.6.1 Monomers 366 18.6.2 Initiators 367 18.6.3 Catalysts 368 18.6.4 Temperature and Solvents 368 18.6.5 Modifications on ATRP 369 18.7 Synthesis of IIs 371 18.8 Mechanism of copolymerization 371 18.8.1 Terminal model 372 18.8.2 Penultimate model 373 18.8.3 Complex participation model 374 18.8.4 Discrimination between TM and PM 375 18.8.5 Microstructure analysis of copolymers of NAI and MMA 375 18.9 Mechanism and kinetics of copolymerizations of IIs 376 18.10 MIs and their copolymerization via RDRPs 378 18.11 ATRP of MIs 379 18.12 Living polymerizations of IIs 379 18.12.1 Anionic polymerization 380 18.12.2 RDRPs of IIs

Contents

18.13 18.13.1 18.13.2 18.13.3 18.14

XVII

Computational study on FRP 381 382 Methods of molecular modeling 383 DFT methods 384 Basis sets 385 Summary and future directions 387 References

Enitan Omobolanle Adesanya, Olumide Olatunde Adesanya and Samuel Ayodele Egieyeh 19 Evaluation of phytochemicals and amino acid profiles of four vegetables 399 grown on a glyphosate contaminated soil in Southwestern Nigeria 400 19.1 Introduction 401 19.2 Materials and methods 401 19.2.1 Materials 402 19.2.2 Methods 404 19.3 Results and discussion 406 19.4 Discussion 407 19.5 Conclusion and recommendation 407 References Index

411

List of contributing authors Misbaudeen Abdul-Hammed Computational Biophysical Chemistry Unit Department of Pure and Applied Chemistry Ladoke Akintola University of Technology LAUTECH Ogbomoso Oyo State Nigeria E-mail: [email protected] Ademola Adebayo Department of Forest and Conservation Sciences Faculty of Forestry University of British Columbia Vancouver Canada Barakat Dasola Adebayo Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Faculty of Pure and Applied Science Ogbomoso Nigeria Ibrahim Olaide Adedotun Computational Biophysical Chemistry Unit Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Faculty of Pure and Applied Science LAUTECH Ogbomoso; Department of Chemistry University of Ibadan; Foresight Institute of Research and Translation Ibadan Oyo State Nigeria Hadijat Motunrayo Adegoke Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Ogbomoso Nigeria

https://doi.org/10.1515/9783111071435-202

Enitan Omobolanle Adesanya Department of Biochemistry Olabisi Onabanjo University Sagamu Ogun State Nigeria E-mail: [email protected] Olumide Olatunde Adesanya Plant Science Department Olabisi Onabanjo University Agp-Iwoye Ogun State Nigeria E-mail: [email protected] Tolulope Irapada Afolabi Computational Biophysical Chemistry Unit Department of Pure and Applied Chemistry Ladoke Akintola University of Technology LAUTECH Ogbomoso Oyo State Nigeria Charles I. Aghanwa Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Nigeria E-mail: [email protected] Zachary Orato Anditi Department of Curriculum Instruction and Educational Management Egerton University Njoro Kenya Novita Andriyani Department of Chemistry Faculty of Mathematics and Natural Sciences University of Lampung Bandar Lampung 35145 Indonesia

XX

List of contributing authors

Antony Mugiira Arimba Department of Curriculum Instruction and Educational Management Egerton University Njoro Kenya E-mail: [email protected] Radia Ayad Department of Chemistry Valorization of Natural Resources Bioactive Molecules and Biological Analysis Unit University Frères Mentouri Constantine 1 Constantine 25017 Algeria and Department of Chemistry Laboratory of Phytochemistry and Pharmacology Faculty of Exact Sciences and Informatics University Mohammed Seddik Benyahia of Jijel Jijel 18000 Algeria E-mail: [email protected] Isah Adewale Bello Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Faculty of Pure and Applied Science Ogbomoso Nigeria Khairun Nisa Berawi Medical Faculty Universitas Lampung Bandar Lampung 35145 Indonesia Tanmoy Chakraborty Department of Chemistry and Biochemistry School of Basic Sciences and Research Sharda University Greater Noida-201310 India E-mail: [email protected] Shayeri Das Department of Mechatronics Engineering Manipal University Jaipur Dehmi Kalan-303007 India

Chetana Deoghare Department of Chemistry Institute of Sciences Humanities & Liberal Studies Indus University Rancharda via Shilaj Ahmedabad 382115 Gujarat India E-mail: [email protected] George Egejuru School of Public Health Southeast University Jiangning District Nanjing 211189 P. R. China Samuel Ayodele Egieyeh University of the Western Cape School of Pharmacy Robert Sobukwe Road Bellville Cape Town Western Cape ZA 7535 South Africa E-mail: [email protected] Basirat Temidayo Egunjobi Department of Chemistry University of Ibadan Ibadan Oyo State Nigeria Onome Ejeromedoghene School of Chemistry and Chemical Engineering Southeast University Jiangning District Nanjing P. R. China E-mail: [email protected] Uche E. Ekpunobi Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Nigeria

List of contributing authors

Fabiola Almeida García Center for Protein Studies Faculty of Biology University of Havana Havana Cuba

Ayobami Abimbola Ibironke Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Faculty of Pure and Applied Science Ogbomoso Nigeria

Ibrahim Olajide Gbadebo Computational Biophysical Chemistry Unit Department of Pure and Applied Chemistry Ladoke Akintola University of Technology LAUTECH Ogbomoso Oyo State Nigeria

Bambang Irawan Department of Biology Faculty of Mathematics and Natural Sciences University of Lampung Bandar Lampung 35145 Indonesia

Danielle Sinkam Gozo Laboratory of Organic Chemistry and Applications Department of Chemistry Faculty of Science University of Ngaoundere Ngaoundere Cameroon E-mail: [email protected] Sutopo Hadi Department of Chemistry University of Lampung Bandar Lampung 35145 Indonesia E-mail: [email protected] Emmanuel Mshelia Halilu Faculty of Pharmacy Cyprus International University Haspolat/Nicosia North-Cyprus Türkiye and Department of Pharmacognosy and Ethnomedicine, Faculty of Pharmaceutical Sciences Usmanu Danfodiyo University Sokoto Nigeria E-mail: [email protected]

XXI

Ubeydat Temitope Ismail Computational Biophysical Chemistry Unit Department of Pure and Applied Chemistry Ladoke Akintola University of Technology LAUTECH Ogbomoso Oyo State Nigeria Mostefa Lefahal El Hani Makhloufi and Salah Akkal Department of Chemistry Bioactive Molecules and Biological Analysis Unit University Frères Mentouri Constantine 1 Constantine 25017 Algeria Jean Momeni Laboratory of Organic Chemistry and Applications Department of Chemistry Faculty of Science University of Ngaoundere Ngaoundere Cameroon E-mail: [email protected] Karimot Motunrayo Mufutau Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Ogbomoso Nigeria

XXII

List of contributing authors

Nafisa Kudu Muhammad Department of Pharmacognosy and Ethnomedicine Faculty of Pharmaceutical Sciences Usmanu Danfodiyo University Sokoto Nigeria Adeshina Isaiah Odugbemi Phytomedicine, Molecular Toxicology and Computational Biochemistry Research Group, Biochemistry Programmes Bowen University Iwo Nigeria and South African National Bioinformatics Institute Faculty of Natural Sciences University of the Western Cape Cape Town South Africa E-mail: [email protected] Adaora Ogbuagu Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Nigeria Akingbolabo Daniel Ogunlakin Phytomedicine, Molecular Toxicology and Computational Biochemistry Research Group Biochemistry Programmes Bowen University Iwo Nigeria E-mail: [email protected] Olakunle Ogunyemi Department of Forestry and Wildlife Management Federal University of Agriculture PMB 2240 Abeokuta Ogun State Nigeria

Abiodun Oladipo Co-Innovation Center for Sustainable Forestry in Southern China College of Forestry Nanjing Forestry University 210037 Nanjing P. R. China E-mail: [email protected] Monsurat Olajide Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Faculty of Pure and Applied Science Ogbomoso Nigeria; Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Faculty of Pure and Applied Science Ogbomoso Nigeria and Department of Chemical Sciences Crescent University Abeokuta Ogun State Nigeria Carlos David Ortiz Center for Protein Studies Faculty of Biology University of Havana Havana Cuba Olubunkunola Oluwole Oyesiku Department of Plant Science Olabisi Onabanjo University Sagamu Ogun State Nigeria E-mail: [email protected]

List of contributing authors

Isel Pascual Center for Protein Studies Faculty of Biology University of Havana 25# 455, between J and I Plaza de la Revolución Havana CP 10 400 Cuba E-mail: [email protected] Belinda Sánchez Ramírez Centro de Inmunología Molecular La Habana Cuba Prabhat Ranjan Department of Mechatronics Engineering Manipal University Jaipur Dehmi Kalan-303007 India E-mail: [email protected] Hendri Ropingi Department of Chemistry Faculty of Mathematics and Natural Sciences University of Lampung Bandar Lampung 35145 Indonesia Yarini Arrebola Sánchez Center for Protein Studies Faculty of Biology University of Havana Havana Cuba Wasinton Simanjuntak Department of Chemistry University of Lampung Bandar Lampung 35145 Indonesia Daniel Ojeda del Sol Center for Protein Studies Faculty of Biology University of Havana Havana Cuba

XXIII

Tati Suhartati Department of Chemistry University of Lampung Bandar Lampung 35145 Indonesia Calixte Tonbou Department of Chemistry Faculty of Science University of Yaounde I Yaounde Cameroon E-mail: [email protected] Bamidele Toheeb Towolawi Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology Ogbomoso Nigeria Mario E. Valdés-Tresanco Center for Protein Studies Faculty of Biology University of Havana Havana Cuba and Department of Biological Sciences University of Calgary Calgary Canada David Kuria Wamukuru Department of Curriculum Instruction and Educational Management Egerton University Njoro Kenya Ermin Katrin Winarno Research Center for Radiation Process Technology Research Organization for Nuclear Energy (BATAN) – National Research and Innovation Agency (NRIA) Banten 15314 Indonesia

XXIV

List of contributing authors

Hendig Winarno Research Center for Radiation Process Technology Research Organization for Nuclear Energy (BATAN) – National Research and Innovation Agency (NRIA) Banten 15314 Indonesia Yandri Yandri Department of Chemistry University of Lampung Bandar Lampung 35145 Indonesia E-mail: [email protected]

Jemilat Yetunde Yusuf Universiti of Teknologi Petronas Perak Malaysia

Yarini Arrebola Sánchez, Fabiola Almeida García, Daniel Ojeda del Sol, Mario E. Valdés-Tresanco, Carlos David Ortiz, Belinda Sánchez Ramírez and Isel Pascual Alonso*

1 Dipeptidyl peptidase IV: a multifunctional enzyme with implications in several pathologies including cancer

Abstract: Ectopeptidases are particularly interesting due to their potential to regulate/ dysregulate the peptide mediated signaling cellular pathways because the active site located to the extracellular space. Dipeptidyl peptidase IV (DPP-IV, EC 3.4.14.5) is currently one of the ectopeptidases that has a great and complex influence on important physiological and pathological processes. Due to its influence on the immune system, type 2 diabetes mellitus, pulmonary pathologies, cardiovascular system, viral infections and cancer, DPP-IV is very attractive as a possible therapeutic target. However, its versatility makes such expectations very difficult. The aim of this work is to summarize relevant structural and functional aspects of DPP-IV and the role of this protein in several pathologies with special emphasis on cancer. DPP-IV role in cancer seems to depend on specific location, histologic type of tumour, tumour microenvironment, and presence/absence of molecules able to interact with DPP-IV. Because of DPP-IV controversial effects, generalizations are difficult and most of the time the role of DPP-IV must be analyzed case by case. However, new evidences in cell lines, animal models and clinical studies suggest that DPP-IV inhibitors open a promissory window through new therapeutic strategies against some cancers. Keywords: cancer; dipeptidyl peptidase IV; ectopeptidases; human diseases; inhibitors.

1.1 Introduction: cancer is coming and we need more and more effective weapons to stop or ameliorate it Nowadays cancer is responsible about 1/6 of deaths worldwide, being the second leading cause of death. In 2020, nearly 9.6 million of people lost their lives at the behest of cancer

*Corresponding author: Isel Pascual, Center for Protein Studies, Faculty of Biology, University of Havana, 25 # 455, between J and I, Plaza de la Revolución, Havana, CP 10 400, Cuba, E-mail: [email protected] Yarini Arrebola Sánchez, Fabiola Almeida García, Daniel Ojeda del Sol and Carlos David Ortiz, Center for Protein Studies, Faculty of Biology, University of Havana, Havana, Cuba Mario E. Valdés-Tresanco, Center for Protein Studies, Faculty of Biology, University of Havana, Havana, Cuba; and Department of Biological Sciences, University of Calgary, Calgary, Canada Belinda Sánchez Ramírez, Centro de Inmunología Molecular, La Habana, Cuba As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: Y. A. Sánchez, F. A. García, D. O. del Sol, M. E. Valdés-Tresanco, C. D. Ortiz, B. S. Ramírez and I. P. Alonso “Dipeptidyl peptidase IV: a multifunctional enzyme with implications in several pathologies including cancer” “Physical Sciences Reviews” [Online] 2023. DOI: 10.1515/psr-2022-0288 | https://doi.org/10.1515/9783111071435-001

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1 Dipeptidyl peptidase IV and implications in cancer

[1]. Countries with small and medium economies account for around 70 % of cancer deaths. Globally, the most frequently diagnosed cancers are three: lung cancer (11.6 % of total cases), women breast cancer (11.6 %), and colorectal malignancies (10.2 %). Regarding to mortality caused by cancers, once again lung cancer is the one that contributes the most (18.4 %), followed a little further away by colorectal (9.2 %) and stomach malignancies (8.2 %) [1]. Predictions of the World Health Organization do not hide nor underestimate a gray near horizon: for the next 20 years, cancer incidences are predicted to increase by at least 60 %. Among the approximately 18 million people diagnosed with cancer worldwide in 2018, close to 10 million lost their lives. It is expected that, by 2040, these new cases will increase to an estimated number between 27 and 39 million. Once again, the most affected will be the countries with small and medium-sized economies, where more than 2/3 of all new cases are expected to be diagnosed [1]. The integral management of cancer demands a large financial support, sophisticated equipment, and multiple strategies not only for prevention and diagnosis, but also for treatment and for palliative cares. Numbers speaks without hesitation: more than 600 types of cancer appear on the International Classification of Diseases; some of them require refined and specific diagnoses and/or treatments [1]. One of the promissory target for cancer treatment is the modulation of the activity of the particular enzymes involved in critical events during carcinogenesis and metastasis [2–4]. It is known that carcinogenesis is accompanied by the increasing loss of regulatory mechanisms at the molecular and cellular levels. This set of dysregulations (also known as the hallmarks of cancer) will ultimately determine the appearance of the malignant phenotype. Significant advances have been made in the understanding of cancer in recent years; therefore, the cancer hallmarks list has grown since it was first proposed several years ago. Towards the end of 2022, a referential author like Hanahan has proposed the following list, which is, in our opinion, the most up-to-date and complete. According to him, a cancerous lineage must be able to: metabolically reprogram itself, evade different forms of cell death, sustainably proliferate, evade growth inhibitory signals, gain access to the vasculature and/or promote angiogenesis, escape “surveillance” of the immune system, invade other tissues, and metastasize. In addition, disrupted differentiation and cell plasticity seem to be emerging as two new but (for now at least) weak or discrete hallmarks [2]. Most of the hallmarks are directly conditioned by anomalies in the cell signaling circuits. Among these anomalies, the most notorious are the constitutive expression/overexpression of oncogenes and the reduced/null expression of genes involved in tumour suppression. Several molecular signals secreted by the tumour and/or its microenvironment usually trigger most of these aberrant circuits. In this context, the modulation of cell signalling (autocrine, paracrine and juxtacrine) by signaling peptides, hormones, cytokines, and growth factors are relevant for the altered above mentioned processes. Being determinant in the levels of most of these ligands, the rate of extracellular proteolysis will therefore be determinant during tumour genesis and progression [3]. In that way, peptidases (enzymes that cleavage peptide bounds) are

1.2 DPP-IV general characteristics: an interesting peptidase that is more than just an enzyme

3

particularly interesting due to their potential to regulate/dysregulate the peptide mediated signaling cellular pathways [3–7]. The crucial importance of these enzymes during tumour evolution is noticed by the fact that their expression pattern often changes (increases or decreases) in many tumour cells. Through their influence on the balance of peptide signals, peptidases are capable of modulating (directly or indirectly) key processes for the tumour such as proliferation, immortalization, angiogenic capacity, migration and metastasis [3]. In particular, some peptidases are anchored to the cell membrane, are multifunctional and expose their active site to the outside of the cell: they are called ectopeptidases. Since they expose the catalytic site into the extracellular medium, they show relevant physiological and/or pathological roles (including the processing of various peptide signals such as growth factors, hormones and neuropeptides) [3–7]. Thus, nowadays ectopeptidase has emerged as potential therapeutic targets against several diseases in general, and against cancer in particular [3, 8–11]. A remarkable example of an ectopeptidase extensively studied in recent years is the dipeptidyl peptidase IV (DPP-IV, EC 3.4.14.5) [12], also called CD26 or adenosine deaminase binding protein (ADABP) [13–15]. In humans, it is widely distributed in several cellular types from diverse organs [16]. DPP-IV has a relevant role in several physiological or pathological processes. It contributes to control: energetic metabolism, immune response, cell adhesion, apoptosis, inflammation and several types of cancer [17–21]. Due to the importance of DPP-IV in glucose homeostasis, several DPP-IV inhibitors have been used successfully in the treatment of type 2 diabetes mellitus (T2DM) in humans [20, 22–27]. Due to the high complexity of cancer, research on DPP-IV and cancer is not as advanced as it is for DPP-IV and T2DM [26]. However, in recent years, much evidence has emerged pointing to this enzyme as a possible target against certain types of cancer [28–37]. In the following sections, we will review relevant structural and functional aspects of DPPIV, as well as its relationship with various pathologies, including (with particular emphasis) cancer.

1.2 DPP-IV general characteristics: an interesting peptidase that is more than just an enzyme The enzyme dipeptidyl peptidase IV (DPP-IV or DPP4, EC 3.4.14.5) [12] is a highly conserved type II transmembrane glycoprotein mainly expressed as a dimer. Each monomer is 766 residues in length and it is formed by the succession of a cytoplasmic tail of six residues located towards the N-terminus, a short transmembrane segment and a voluminous extracellular domain [38] (Figure 1.1). In body fluids (semen, blood and bile) a soluble isoform of DPP-IV has been found that has lost the intracellular and transmembrane sections, but retains enzymatic activity [39].

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1 Dipeptidyl peptidase IV and implications in cancer

Figure 1.1: Primary structure of human DPPIV. It consists of a short cytoplasmic tail, a single transmembrane region, and a large extracellular domain composed of a glycosylated region, a cysteine-rich region, and a catalytic region. Here they are labeled in yellow, green, cyan, blue and violet, respectively.

DPP-IV is a serine peptidase belonging to the S9 family of the SC clan [12]. It can acts as an enzyme, as a cell surface marker and as a protein able to associate/interact with other membrane or extracellular proteins (Figure 1.2). Its extracellular domain possesses the active site, which hydrolyzes dipeptides from the N-terminus of small peptides that contain alanine (Ala) or, even better, proline (Pro) in the second position counting from the N-terminus [40]. Often, the binding of several proteins to human DPP-IV results in extra-enzymatic interactions. Among these proteins able to strongly bind DPP-IV are adenosine deaminase (ADA), caveolin-1, mannose 6-phosphate/insulinlike growth factor II receptor, caspase recruitment domain-containing protein 11 (CARD11), the CD45 tyrosine phosphatase, the plasminogen receptor, the Na+/H+ exchanger isoform NHE3, the chemokine receptor CXCR4, MERS-CoV spike proteins, TAT and gp-120 HIV proteins, and the extracellular matrix proteins fibronectin (FN), collagen and vitronectin [41–46].

Figure 1.2: DPP-IV functions. Gray solid lines indicate direct effects of DPP-IV on a physiologic and/or pathologic process via one or more of its three main roles: Enzyme, surface marker molecule and protein– protein interacting. Gray discontinue lines indicate DPP-IV indirect influence due to DPP-IV pleiotropic effects.

1.2 DPP-IV general characteristics: an interesting peptidase that is more than just an enzyme

5

1.2.1 DPP-IV gene and protein expression: a vast picture across human tissues The gene of human DPP-IV (hDPP-IV) is contained on the long arm of chromosome 2 (2q24.3). It has approximately 70 kb with 16 exons (with sizes between 45 bp and 1.5 kb) [19] as well as binding sites for transcription factors that are typical of housekeeping genes [47]. mRNA for DPP-IV has been found in cells from gastrointestinal and proximal digestive tract, kidney, skin, liver and gallbladder, endocrine glands like pancreas and ovarian, prostate, urinary bladder, muscle, respiratory system, connective tissues, eye, brain and lymphoid tissues including bone marrow [48]. In humans, the DPP-IV membrane isoform has four variants, all of them encoded by different transcripts (differential splicing) of the same gene. Among these four variants, there are three very close forms (with 766, 765 and 764 residues respectively) and a slightly smaller fourth, with 748 residues [49]. As there are specific variations in size, the glycosylation sites are not exactly in the same position for the four variants, although in all cases it is an N-glycosylation with N-acetyl glucosamine (GlcNAc) in asparagine residues. Table 1.1 summarizes some features of the four variants. Until now, the variant encoded by transcript 1 (766 residues variant) has been the most observed and it is referred to as the major variant [51]. In addition to the position of the glycosylations, variant two differs from variant one in the replacement of the T32 substitution by N32, as well as the loss of D33 [52]. Variant 3 is characterized by the absence of G31 and T32 with respect to variant 1 [53]. In variant 4, the changes are slightly more drastic (it has eight fewer residues than variant 1) [54]. It would be interesting to know what physiological/pathological conditions could modify the preponderance of variant 1. Does variant 1 prevail in all cells or only in those observed so far? Can this preponderance be altered depending on the cell cycle, the stage of cell differentiation, or the emergence of a tumour phenotype? More studies are needed to achieve the answers.

Table .: pDPP-IV and hDPP-IV: size, molecular weight and glycosylation sites. DPP-IV variant

Size

Molecular weight of the monomer (Da)

pDPP-IV hDPP-IV (variant ) hDPP-IV (variant ) hDPP-IV (variant ) hDPP-IV (variant )

    

, , , , ,

N-Glicosilation sites (GlcNAc) N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N No data available

References [] [] [] [] []

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1 Dipeptidyl peptidase IV and implications in cancer

There seems to be no immediate correlation between DPP-IV mRNA levels and the enzyme detected in the cells membrane; for example, the pancreas and lung have similar levels of DPP-IV expression, but the amount of DPP-IV mRNA detected in the lung is significantly less than that detected in the pancreas [55]. Furthermore, in the brain, eye, skin, muscle, bone marrow and connective tissues there is DPP-IV mRNA, but its expression is not observed under physiological conditions [48]. As it was mentioned earlier, DPP-IV is expressed as a highly glycosylated, type II integral membrane protein [38]. The protein is normally found as a homodimer of 220–290 kDa molecular weight [56–59], but also can form tetramers of around 900 kDa. Each monomer is made up of two domains: a β-propeller domain (residues 58 to 497) and an αβ-hydrolase domain (residues 39 to 51 plus residues 501 to 706) (Figure 1.3A). hDPP-IV has nine N-glycosylation sites, most of which are located in the β-propeller region, close to the dimerization site. These glycosylations probably protect the mature protein from the extracellular proteolytic machinery [57]. hDPP-IV and porcine DPP-IV (pDPP-IV) are structurally very close: the two have 766 residues and share 88 % sequence identity; therefore, both enzymes not only show extremely similar functional behaviours against pH and temperature, but are also susceptible to being inhibited by the same molecules, including various divalent cations [60]. Only three glycosylatons change: there is no glycosylation at position N520, there is one glycosylation at N179, and another at N279 [50, 51]. This makes pDPP-IV an adequate surrogate model when the human enzyme is not available due to ethical or economic reasons [60]. pDPP-IV is included in Table 1.1 for better visualization of its structural similarities to hDPP-IV (variant 1). In recent years, rat DPP-IV has been shown to possess several of the properties described for its human and pig counterparts, suggesting a high structure–function similarity in mammals [61].

Figure 1.3: Structural elements of the human DPP-IV. (A) Monomeric structure of a DPP-IV. Each monomer is composed of one αβ-hydrolase and one β-propeller domain. (B) Access to the active site of DPP-IV. Discontinuous ellipse indicates the access zone for substrates. The violet arrow indicates the exit for the dipeptide resulting from the catalytic hydrolysis. (C) Active site of DPP-IV. Residues of the catalytic triad (S630, H740 and D708) and those involved in substrate binding (E205, E206 and R125) are highlighted.

1.2 DPP-IV general characteristics: an interesting peptidase that is more than just an enzyme

7

The dimeric and soluble isoform of DPP-IV begins from S39 and it originates from endothelial membrane DPP-IV, which probably undergoes proteolytic cleavage [62–65].

1.2.2 DPP-IV active site structure: catalytic triad, residues of interest and substrate specificity The DPP-IV catalytic domain, also known as the αβ-hydrolase domain, has a unique architecture, as it consists of an eight-stranded β-sheet surrounded by 12 α-helixes (Figure 1.3A) [66]. A lateral opening of about 15 Å allows access to the cavity where the active site of hDPP-IV is located, so that only small substrates of a peptide nature (fragments of unfolded proteins and unfolded peptides) can gain access to the active site. A tunnel formed by the β-propeller domain allows the products of the reaction to leave the active site (Figure 1.3B) [67]. The catalytic triad (S630, D708 and H740) (Figure 1.3C) is located in the interphase between the β-propeller and αβ-hydrolase domains. Although it does not belong to the catalytic triad, the Y547 residue appears to stabilize the reaction intermediate tetrahedral oxyanion and is therefore essential during catalysis [61]. At the binding site, there are two glutamate residues (E205 and E206) that contribute to linearize the peptide substrate through salt bridges that are established with the N-terminus of the substrate (Figure 1.3C). These residues, whose presence is characteristic of the DPP-IV family, only leave space for two amino acids before the peptide reaches the reactive serine residue in the active center, which explains why the enzyme has dipeptidyl aminopeptidasetype activity. The importance of both residues has been confirmed by site-directed mutagenesis experiments [68, 69]. The DPP-IV S1 site is narrow because it is made up of bulky residues: Y631, V656, W659, 662 Y , Y666 and V711. This means that, for a peptide to be hydrolyzed by DPP-IV, it must not only be small and unfolded, but in the second position from its N-terminus it must contain a small residue (proline, alanine or glycine) that can be accommodated in such a restricted space [61]. Since few peptides possess proline at the mentioned position, this strongly determines the specificity of DPP-IV. Homodimerization, which requires the αβ-hydrolase domain as well as a protrusion from the fourth sheet of the β-propeller [69], is essential for catalysis. Proof of this is that point mutations that prevent dimerization (example: change of H750 to E) also prevent catalysis [70].

1.2.3 DPP-IV β-propeller domain: a singular structure from a singular protein Usually a β-propeller domain is formed by the clustering of a minimum of four and a maximum of eight β-sheets, each of which contains between 30 and 50 residues arranged in four anti-parallel strands [71]. The structure is highly symmetric, as the β-sheets are

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1 Dipeptidyl peptidase IV and implications in cancer

arranged radially and form a central tunnel of about 30–45 Å [71–75]. Due to their relative structural flexibility, β-propeller may be involved both in enzyme catalysis and in the establishment of protein-protein interactions in those proteins that possess them [76–81]. Interestingly, alterations in several of these proteins have been related to the appearance and evolution of pathologies such as: arterial hypertension, arthritis, infections, Alzheimer’s disease, familial hypercholesterolemia, Huntington disease, retinitis and cancer [82]. Among the β-propells of leukocyte membrane proteins, the β-propell of DPP-IV is unique both in number of β-sheets (eight) and in organization, being one of the least organized described to date. Note that the other two leukocyte surface antigens that carry β-propeller domains (CD100 [83] and the α-chain integrins [84]) have much more organized seven β-sheet β-propellers [83–85]. In the case of DPP-IV, an antiparallel β-sheet penetrates between strands one and two of the second β-sheet of the domain. This “intrusive sheet” possesses, in turn, a residue (R125) that stabilizes (via a salt bridge with E205) the C-terminal turn of an α-helix that is inserted between the third and fourth β-sheet of the domain. Another antiparallel β-sheet is introduced between the third and fourth strands of the fourth β-sheet. This complex arrangement is highly relevant both for dimerization and for catalysis and interaction with other proteins of physiological and/or pathological relevance such as ADA, FN, collagen and HIV gp120 [86]. Interestingly, it has been observed that the R125 residue is capable of interacting not only with substrates, but also with certain inhibitors, which makes it a frequent target during the in silico design of strategies to inhibit DPP-IV [57, 58, 61, 66, 87–89]. From bacteria to humans, R125 is highly conserved. Similarly, E205, which, as we said, is stabilized by R125 via a salt bridge, is highly conserved. In fact, the helix to which E205 belongs (D-W-X-Y-E-E205-E-X) is conserved in all members of the DPP-IV family [90] (Figure 1.4).

Figure 1.4: β-propeller domain of human DPP-IV. (A) Front view of the β-propeller domain, showing the displacement of β-sheets around a central tunnel of 30–45 Å in diameter. (B) Side view.

1.3 DPP-IV main functions: how versatile protein is DPP-IV!

9

1.2.4 DPP-IV cellular expression: a very wide spread molecule Various DPP-IV homologues have been reported in bacteria [91–99], fungi [100, 101] and mammals [40, 102–105]. Evidence suggests that the overall structure of DPP-IV from bacteria is similar to that of DPP-IV from mammals, but they differ in several respects. In all cases, a few variations of residues have been observed around the active site resulting from insertions or deletions at the genetic level. In general, bacteria DPP-IV and mammal DPP-IV shares several properties in spite of structural differences [91–99], but we need more crystals from bacteria of different groups to be able to reach more complete conclusions. In mammals, DPP-IV is abundantly expressed in tissues of various organs such as kidney, intestine, lung, brain, liver, pancreas, bladder, pancreas, spleen, thymus, lymph nodes, uterus, skin, as well as in sperm, lymphocytes and endothelium [86]. In humans, it has enhanced expression inparathyroid gland, kidney, placenta, prostate gland, intestine [48], semen [106] and several immune cells [86]. Under normal conditions, the kidney and seminal fluid have the highest specific activity of hDPP-IV. Table 1.2 summarizes the main cellular types expressing human DPP-IV found in tissues of high expression of this protein.

1.3 DPP-IV main functions: how versatile protein is DPP-IV! As we have said before, various DPP-IV homologues have been reported in bacteria [91–99], fungi [100, 101] and mammals [40, 102–105], where shows a very high expression at the brush border of the gastrointestinal tract [86]. This suggests that the ancestral function of DPP-IV might be nutritional via the partial digestion of peptides. It is reasonable to believe that, during the evolution, this putative pristine function (the mere digestion) moved into others more sophisticated (like regulating signals mediated by peptides) while organisms were increasing their complexity. In humans, this ancestral role may still be preserved in those DPP-IV from saliva, enterocytes, and bile [108]. DPP-IV can act as peptidase, receptor/cell surface marker as well as a member of protein-protein interactions, depending on the cell type and the intracellular and extracellular conditions of those cells where it is expressed (Figure 1.2). It can be involved in complex process associated to cellular cycle such as the proliferation of normal smooth muscle cell [109] or the apoptosis of wide cellular types. To date, its main natural protein ligands are ADA [110], Na+/H+ ion exchanger [111], FN [112], caveolin 1, and chymosin CXC receptor 4 (CXCR4) [113]. Within the functions of DPP-IV, those related the implication of its peptidase activity in different pathologies have been the most studied to design new therapeutic schemes based on a specific and selective inhibition of hDPP-IV [10, 14, 16, 18, 20, 26, 30, 35, 114–117].

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ⊗ ✓ ✓ ✓ ✓ ✓

T-cells

Plasma cells ✓ ✓ ⊗ ✓ ⊗ ⊗ ⊗ ✓ ⊗ ⊗ ⊗ ✓ ⊗ ⊗ ⊗

Mast cells ⊗ ✓ ⊗ ✓ ✓ ⊗ ✓ ✓ ⊗ ⊗ ✓ ⊗ ✓ ⊗ ✓

Neutrophils ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Macrofages

Cellular type

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ⊗ ✓

Fibroblasts

Cite: The Human Protein Atlas (Accessed September , , at http://www.proteinatlas.org/ENSG-DPP/tissue+cell+type).

Adipose subcutaneous Adipose visceral Breast Colon Heart muscle Kidney Liver Lung Pancreas Prostate Skeletal muscle Skin Stomach Testis Tyroid

Organ or tissue

✓ ✓ ✓ ✓ ✓ ⊗ ⊗ ✓ ⊗ ✓ ✓ ✓ ⊗ ⊗ ✓

Smooth muscle cells

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Endotelial cells

Table .: Main types of human cells expressing human DPP-IV. Dependence of this expression on various tissues and/or organs. Check marks indicate confirmed DPP-IV expression. Crucified circles indicate non-confirmed expression to date. Adapted from The Human Protein Atlas [].

10 1 Dipeptidyl peptidase IV and implications in cancer

1.3 DPP-IV main functions: how versatile protein is DPP-IV!

11

In humans, several cytokines, growth factors and some neuropeptides have proline or alanine at its penultimate position, a structural motif that influences both their biological activities and protection against non-specific protelytic degradation [118]. Therefore, these peptides are natural substrates of DPP-IV, which further regulates their biological functions [119, 120]. Natural substrates of DPP-IV include at least nine chymosins, glucagon like peptides 1 (GLP-1) and 2 (GLP-2), neuropeptide Y (NPY), peptide YY (PYY), substance P (SP), glucose insulinotropic peptide (GIP) [110], human chorionic gonadotropin (hCG), human pancreatic polypeptide (hPP), corticotropin-like intermediate lobe peptide (CLIP), gastrin releasing peptide (GRP) [121] and adenylate cyclase activating polypeptide (PACAP) [122]. Soluble DPP-IV also can hydrolyze oxyntomodulin and stromal cell-derived factor 1 (SDF-1), also named as CXCL12 [123, 124]. It has been suggested that hDPP-IV is capable of degrading collagen and thus facilitating cell traffic through the extracellular matrix (in this sense, it is known that DPP-IV is one of the key molecules during lymphocyte migration from the cortex to the medulla of the thymus) [108]. Furthermore, DPP-IV from rat is known to possess gelatinase activity on collagen [125]. Table 1.3 summarizes natural substrates of DPP-IV as well as their halflife time and kinetic behaviour versus DPP-IV. The large number of substrates and their differences in kinetics and half-life time gives a first idea of the complex network of interactions mediated by the enzymatic activity of DPP-IV.

1.3.1 DPP-IV and metabolism: an old and well-documented story Among the natural DPP-IV substrates, there are two incretine hormones especially relevant the glucose metabolism in mammalian: GLP-1 and GIP [127–130, 151, 152] (Table 1.3). 85 % of the secreted GLP-1 never reaches the systemic circulation [153] because it is rapidly degraded, with DPP-IV being the major responsible for its degradation (>95 %) [66]. Currently, one of the most recent and promising methods to treat T2DM is to use DPP-IV inhibitors, which are also called gliptins [22–28]. Table 1.4 summarizes relevant aspects of most extensively commercialized gliptins worldwide, including several benefits beyond T2DM reported in animal models. In patients with T2DM, gliptins increase the half-life of GL-1 and GIP, thereby decreasing appetite, increasing feelings of fullness by decreasing gastric motility, inhibiting glucagon secretion, and, in longer terms, they reduce body weight and may even reverse some damage to the beta cells of the pancreas [22–29, 177, 178]. Recently, a direct correlation between glycosylated hemoglobin (HbA1c) and soluble DPP-IV levels has been observed [179]. Pérez-Macedonio et al. (2022) have found that DPP-IV from serum exosomes is higher in diabetic than in normal individuals; those exosomes also were smaller in T2DM patients [180]. More studies are required to know how relevant this particular finding may be for T2DM management.

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1 Dipeptidyl peptidase IV and implications in cancer

Table .: Main natural substrates of human DPP-IV: region where hydrolysis occurs, half-life time and kinetic behaviour versus human DPP-IV. DPP-IV substrate

N-terminal sequence

Half-life time (min)

kcat/KM ( M− s−)

CXCL MDC MDC I-TAC IP- Mig Eotaxin RANTES LDβ GLP- GLP- GIP PACAP PACAP Oxyntomodulin Secretin VIP PHM GHRH GRP SP NPY PYY hCG hPP CLIP

KPVSLSYRCGPYGANMEDYGANMEDFPMFKRGRCVPLSRTVRCTPVVRKGRCGPASVPTTCSPYSSDTTPAPLAADTPTHAEGTFTSHADGSFSDYAEGTFISHSDGIFTDHSDGIFTDHSQGTFTSHSDGTFTSHSDAVFTDHADGVFTSYADAIFTNVPLPAGGGRPKPQQFFYPSKPDNPGYPAKPEAPGSKEPLNCNPAPLEPVYPGRPVKVYPNG-

Pi.

expected to be less than 10 [8]. All the selected compounds show favourable oral bioavailability values as depicted in Table 9.4.

9.3.3 Virtual screening analysis Molecular Docking Simulation is a structure-based drug design approach used to predict the binding conformations and binding relationship between the ligand and the target receptor in the active site [13]. It uses virtual screening to identify the correct positioning of a ligand within the target receptor binding site and evaluate how the ligand can bind to the receptor thereby predicting the binding mode of the ligand to the target receptor. The molecular docking analysis was carried out on the 9 compounds that passed the chemoinformatics screening. The molecular docking studies of the compounds with 6LU7 showed that Roseoside was the only compound that showed lower binding energy (ΔG)

216

9 Anti-SARS-CoV-2 agents from Annona muricata

Figure 9.3: The bioavailability radar of the selected compounds, L-1, Roseoside; L-2, Coreximine; L-3, Annoionol B; L-4, Annoionol A; L-5, Vomifoliol; L-6, Gallic acid; L-7, Loliolide; L-8, Blumenol C; L-9, Muricatacin B; SD, Remdesivir.

than Remdesivir (standard), Coreximine had the same binding energy (ΔG) with Remdesivir while the rest showed higher binding energy (ΔG) than the standard. The molecular docking studies of the selected compounds with 6LZG showed that all the

9.3 Results and discussion

217

Figure 9.3: Continued.

selected compounds showed higher binding energy (ΔG) than Remdesivir. The docking results are shown in Tables 9.5 and 9.6. Similarly, in Table 9.5, it can be seen that Roseoside had the lowest docking score (−7.50 kcal/mol) with 7 amino acids residue (Gln 189, Glu 166, Leu 141, His 163, Gly 143, Ser 144, Cys 145), whileCoreximine (−7.0 kcal/mol) (Table 9.6) showed the lowest docking score with six amino acid residues (Ala 348, Tyr 385, Arg 393, Asp 350, Tyr 347, Glu 375). Various molecular interactions and binding modes observed in the identified compounds are shown in Tables 9.7 and 9.8. As compared with the standard (Remdesivir), all the selected compounds interacted with almost all the amino acids present in the active sites both in the main protease (6LU7) responsible for the replication of the virus and the spike glycoprotein (6LZG) responsible for the entry of the virus into the host cell.

CHNO −. . .  . . −.  .  . .

CHO −. . .  −. −. −.  .  . .

Formula VINA score Mass TPSA #Rotatable bonds XLOGP WLOGP ESOL Log S Lipinski #violations Bioavailability score PAIN #alerts Fraction Csp Synthetic Accessibility

CHO −. . .  . . −.  .  . .

CP- CHO −. . .  . . −.  .  . .

CP- CHO −. . .  . . −.  .  . .

CP- CHO −. . .  . . −.  .  . .

CP- CHO −. . .  . . −.  .  . .

CP- CHO −. . .  . . −.  .  . .

CP- CHO . . .  . . −.  .  . .

CP-

CHNOP −. . .  . . −.  .  . .

SD

CP-, Roseoside; CP-, Coreximine; CP-, Annoionol B; CP-, Annoionol A; CP-, Vomifoliol; CP-, Gallic acid; CP-, Loliolide; CP-, Blumenol C; CP-, Muricatacin B; SD, Remdesivir.

CP-

CP-

LIGANDS

Table .: Oral-bioavailability of the selected compounds

218 9 Anti-SARS-CoV-2 agents from Annona muricata

9.4 Conclusion

219

Table .: Molecular docking studies for the main protease LU using Pyrex. Compounds

Roseoside

Coreximine

Annoionol B

Annoionol A

Vomifoliol

Gallic acid

Loliolide Blumenol C Muricatacin

Binding LU receptor amino acids affinity (ΔG), forming H–bond ligands kcal/mol . Gln  (. Å), Glu  (. Å, . Å), Leu  (. Å, . Å), His  (. Å, . Å), Gly  (. Å, . Å), Ser  (. Å), cys  (. Å) . Gly  (. Å), Arg  (. Å), Thr (. Å+, . Å), Gln  (. Å) . His  (. Å), Leu  (. Å), Glu  (. Å), Ser  (. Å), cys  (. Å), Asn  (. Å) . His  (. Å), Leu  (, Å, . Å), His  (. Å), Ser  (. Å), Gly  (.) . Glu  (. Å, . Å), Ser  (. Å) Asn  (. Å), Gln  (. Å), cys  (. Å) . Gln  (. Å), Glu  (. Å), Gly  (. Å), Ser  (. Å), Leu  (. Å, . Å), His  (. Å) . Cys  (. Å), Leu  (. Å), Gly  (. Å), Ser  (. Å) . Leu  (. Å), Ser  (. Å), cys  (. Å), Gly  (. Å) . Cys  (. Å), Gly  (. Å, . Å), Ser  (. Å), Leu  (. Å)

Electrostatic/hydrophobic interactions involved

Inhibition constant (Ki), µM

Cys , His , Phe , Pro 

.

Met , Asn 

.

Cys 

.

Cys , Gly 

.

His 

.

Cys 

.

Nil

.

His , met , Ser 

.

Met , His 

.

Standard Remdesivir

. Asn  (. Å), His  (. Å), Thr  (. Å)

Met 

.

9.4 Conclusion This research investigated the antiviral activities of phytochemicals from Annona muricata against SARS-CoV-2 and its Spike Glycoprotein. Chemoinformatics (ADMET, Druglikeness, Oral bioavailability, and PASS) analyses show that 9 out of 132 compounds

220

9 Anti-SARS-CoV-2 agents from Annona muricata

Table .: Molecular docking studies of the spike protein LZG using Pyrex. Compounds

Coreximine

Muricatacin

Binding LU receptor amino Electrostatic/hydrophobic inaffinity acids forming H– teractions involved (ΔG), kcal/ bond ligands mol −. Ala , Tyr , Arg , Asp , Tyr , Glu  −. Asp , Ala , Gly , Glu , His , His , Ser 

Roseoside

−. Asp ,

Loliolide

−. His , Leu 

Annoionol B

−. His , Arg .

Annoionol A

−. His , Arg , Trp , Glu .

Vomifoliol

−. Lys , Glu 

Gallic acid

−. Glu 

Blumenol C

−. –

Asp  (., .), Arg  (.), Tyr  (.), Ala  (., .) Pro  (.)Glu  (.) His  (.), Glu  (.), Ser  (., .), Glu  (.), His  (.), Glu  (.), Asp  (., .) Tyr  (.) Asp  (.) Asn  (.), Ala  (.), Asp  (.), Asp  (., .), His  (.), Leu  (.), Gly  (.), Asn  (.) His  (., ., .), Glu  (.), Thr  (.), Tyr  (.), Arg  (., .) Glu  (.) His  (., ., .), Glu  (.), Thr  (.), Tyr  (.), Arg  (., .) Glu  (.) His  (.), Glu (.), Thr  (.) Glu  (., .), Glu  (.), Ala  (., .), Asp  (., .,) Glu  (.), Leu  (.)

Inhibition constant (Ki), µM .

.

. . .

.

. . .

Standard Remdesivir

−. Asn , Asp , Asp Glu  (.), Asn  (.), Asn   (., .), Asp  (.), Ala  (.).

.

screened displayed the desired pharmacokinetics properties. The results of the molecular docking revealed that two out of the nine compounds, Roseoside and Coreximine revealed the desired affinity for the amino acid residue present at the active site of the main protease and its spike glycoprotein, thereby interacting and sharing the same pocket with them respectively. The overall data revealed both compounds could be investigated further as potent antiviral agents towards the design of a novel antiviral drug against renowned coronavirus disease.

Coreximine

Roseoside

Ligands

Binding pockets

Table .: Binding mode and molecular interaction of the selected hits against LU. Interactions

9.4 Conclusion

221

Annoionol A

Annoionol B

Ligands

Table .: (continued)

Binding pockets

Interactions

222 9 Anti-SARS-CoV-2 agents from Annona muricata

Gallic acid

Vomifoliol

Ligands

Table .: (continued)

Binding pockets

Interactions

9.4 Conclusion

223

Blumenol C

Loliolide

Ligands

Table .: (continued)

Binding pockets

Interactions

224 9 Anti-SARS-CoV-2 agents from Annona muricata

SD

Muricatacin

Ligands

Table .: (continued)

Binding pockets

Interactions

9.4 Conclusion

225

Annoionol B

Coreximine

Roseoside

Ligands

Binding pockets

Table .: Binding mode and molecular interaction of the selected hits against LZG. Interactions

226 9 Anti-SARS-CoV-2 agents from Annona muricata

Gallic acid

Vomifoliol

Annoionol A

Ligands

Table .: (continued)

Binding pockets

Interactions

9.4 Conclusion

227

Muricatacin

Blumenol C

Loliolide

Ligands

Table .: (continued)

Binding pockets

Interactions

228 9 Anti-SARS-CoV-2 agents from Annona muricata

Remdesivir

Ligands

Table .: (continued)

Binding pockets

Interactions

9.4 Conclusion

229

230

9 Anti-SARS-CoV-2 agents from Annona muricata

Acknowledgements: The authors would like to thank the editor for his guidance and review of this article before its publication.

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Ibrahim Olaide Adedotun, Misbaudeen Abdul-Hammed*, Basirat Temidayo Egunjobi, Ubeydat Temitope Ismail, Jemilat Yetunde Yusuf, Tolulope Irapada Afolabi and Ibrahim Olajide Gbadebo

10 Identification of novel inhibitors of P13K/ AKT pathways: an integrated in-silico study towards the development of a new therapeutic agent against ovarian cancer Abstract: Ovarian cancer is a crucial gynaecological unmet medical disease with a high mortality rate. According to recent research, the phosphoinositol 3 kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathways are hyper-activated in the majority of ovarian cancer patients, necessitating the use of inhibitors. Over the years, phytochemicals have been used as alternative sources of therapeutic agents due to their reported biological activities and limited side effects. Curcuma longa (Tumeric), a reported ayurvedic medicine has also been noted for its anti-cancer properties. Thus, 155 phytochemicals from this plant and 2 reference drugs were evaluated for their inhibitory prowess against P13K/AKT receptor using a computer-aided drug design approach. The binding scores and inhibiting efficiencies were obtained via virtual screening. Molinspiration Chemoinformatics and SwissADME tools were used to investigate the drug-likeness properties and oral bioavailability of the compounds selected, while the ADMET SAR-2 website was used to conduct the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis. Other analyses performed on the selected compounds include bioactivity, activity spectra for substances (PASS) prediction, binding mode, and molecular interaction. The results revealed that Hopenone 1 (−8.8 kcal mol−1) and Epriprocurcumenol (−7.8 kcal mol−1) are potent inhibitors of the P13K receptor, while Epiprocurcumenol (−9.0 kcal mol−1), Procurcuminol (−8.6 kcal mol−1) and Curlone

*Corresponding author: Misbaudeen Abdul-Hammed, Computational Biophysical Chemistry Unit, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, LAUTECH, Ogbomoso, Oyo State, Nigeria, E-mail: [email protected]. https://orcid.org/0000-0002-5453-5858 Ibrahim Olaide Adedotun, Computational Biophysical Chemistry Unit, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, LAUTECH, Ogbomoso, Oyo State, Nigeria; Department of Chemistry, University of Ibadan, Ibadan, Oyo State, Nigeria; and Foresight Institute of Research and Translation, Ibadan, Oyo State, Nigeria Basirat Temidayo Egunjobi, Department of Chemistry, University of Ibadan, Ibadan, Oyo State, Nigeria Ubeydat Temitope Ismail, Tolulope Irapada Afolabi and Ibrahim Olajide Gbadebo, Computational Biophysical Chemistry Unit, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, LAUTECH, Ogbomoso, Oyo State, Nigeria Jemilat Yetunde Yusuf, Universiti of Teknologi Petronas, Perak, Malaysia As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: I. O. Adedotun, M. Abdul-Hammed, B. T. Egunjobi, U. T. Ismail, J. Y. Yusuf, T. I. Afolabi and I. O. Gbadebo “Identification of novel inhibitors of P13K/AKT pathways: an integrated in-silico study towards the development of a new therapeutic agent against ovarian cancer” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0341 | https://doi.org/10.1515/9783111071435-010

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(−8.3 kcal mol−1) are potential inhibitors of AKT receptor. In comparison to Topotecan and Melphalan, they have better binding affinities, oral bioavailability, drug-likeness characteristics, ADMET properties, bioactivities, PASS properties, binding mechanism, and also interact well with the active site of the target receptor. As a result, this preliminary investigation suggests that these chemicals should be studied further for the design of novel ovarian cancer therapeutics. Keywords: anti-neoplastic; molecular docking; ovarian cancer; turmeric.

10.1 Introduction Cancer, according to WHO 2020 is the leading cause of death worldwide, breast cancer being the leading type of cancer. Ovarian cancer (OC) is the seventh most frequently diagnosed and the eighth deadliest form of cancer among females worldwide [1]. Three histological types of OC are associated with the disease, epithelial OC (EOC) being the most common type, forming nearly 90 % of all OC cases [2], with a survival rate of 45.6 % for 5 years. Ovarian cancers are mostly diagnosed at an advanced stage with a low survival rate of 35 %. Early-stage detection rate is as low as 20 % for this disease while the survival rate is 70 % [3]. It has earlier been reported that age, nulliparity, obesity, history of ovarian or breast cancer in a family, fertility treatments, early menarche, and menopausal hormone therapy among others are major risk factors for developing OC while a reduced risk of emergence of OC has been associated with factors like breastfeeding, oral contraceptives and some selected surgical procedures e.g. tubal ligation [2]. Apart from the risk factors, there exist, alterations of some specific pathways that promote disease progression, recurrence, and resistance to chemotherapy. Data revealed by the Cancer Genome Altas (TCGA) indicate the hyperactivation of phosphoinositol 3 kinases (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) (PI3K/AKT/mTOR) pathway in Ovarian Cancer patients of about 60 % [4]. This pathway ordinarily plays significant roles in cancer cell growth, survival, and metabolic programming, among others [5]. In patients with high-grade serous cancer (HGSC), different activating mutations with increased DNA copy numbers of p110α (PIK3CA) and p110β (PIK3CB) subunits of PI3K have been identified [5, 6]. Mutations in Ovarian Cancer also have other subunits which are PI3K p85 (PIK3R1), PTEN, AKT1, AKT2, INPP4B, and MTOR [7]. Traditionally, the combination of chemotherapy and surgery together with radiotherapy are commonly used treatments of ovarian cancer, firstly with the surgical staging of affected tissue, tumor debulking surgery followed by chemotherapy (platinum-based) [8]. With chemo and radiotherapies, intense side effects have been reported. In particular, disease recurrence has also been noticed in platinum-resistant OC patients, as a

10.2 Materials and methods

233

result of the low response rate [9]. The limited achievement has been reported in the anticancer drug discovery against OC over the years with several preclinical and clinical trials being investigated to detect drug-like compounds with therapeutic properties, hence the need to target the pathway. Several drugs aiming to hit members of the PI3K axis have emerged and have been evaluated both in preclinical studies and clinical trials (Table 10.1). These drugs include isoform-specific (p110 α, p110 β, p110 γ, p110 δ) or pan-class I PI3K inhibitors, dual PI3K/ mTOR inhibitors, mTOR inhibitors, and AKT inhibitors [10]. However, further studies are needed to determine the relationship between the resistance/sensitivity of drugs and specific mutants. Medicinal plants have been an integral part of the healthcare system for centuries. Turmeric (Curcuma longa), of the ginger family, Zingiberaceae is a rhizomatous herbaceous plant [11]. It is local to tropical South Asia yet is presently generally developed in the tropical and subtropical districts of the world. The turmeric, an orange-yellow powder is ready from the bubbled and dried rhizomes of the plant. In Ayurveda medication, turmeric is principally utilized as a treatment for provocative circumstances, and in conventional Chinese medication, it is utilized as an energizer, hopeful, carminative, cordial, emmenagogue, astringent, cleanser, diuretic, and martinet [12–14]. It has been used widely for millennia since it is non-toxic and has some medicinal characteristics such as antioxidant, analgesic, anti-inflammatory, anti-microbial and antiseptic properties [15, 16]. Curcumin has just been discovered as well to have anticancer properties due to its influence on a range of cancers Mutagenesis, oncogene expression, and cell division are all examples of biological processes. Regulation of the cell cycle, apoptosis, cancer, and metastasis [16–18]. Because of this, the present work is aimed at investigating the use of phytochemicals in the treatment of ovarian cancer via targeted pathway P13k/Akt using molecular docking analysis coupled with ADMET-studies, pharmacokinetic, oral-bioavailability and drug-likeness analysis among other analyses.

10.2 Materials and methods 10.2.1 Ligand preparation One hundred and fifty-five (155) phytochemicals Table 10.2 isolated from Curcuma longa and two standards (Topotecan and Melphalan) were used for this study. The selected plant has been reported to possess antioxidant, analgesic, anti-inflammatory, anti-microbial, anti-cancer, and antiseptic properties, these biological activities awakened the zeal to examine these phytochemicals as potential inhibitors of the PI3K/AKT signalling pathway in ovarian cancer. The 3D structures of the ligands as well as the standards were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). Using Spartan

GDC- GSK IPI- CAL- (idelalisib) BYL

GDC- BKM PX BAY - XL-

BEZ GDC- NVP-BEZ PF-

Isoform specific

Pan-class I PK

Dual PK/ mTOR

Compound

PK and mTOR PK and mTOR PK and mTOR PK and mTOR

Class I PK Class I PK Class I PK Class I PIK Class I PIK

pα pβ pγ and pδ p δ p α

Isoform selectivity

Clinical trial status

I-II, solid tumors I-IIa, advanced solid cancers with PTEN deficiency I-IIa, advanced hematological malignancies, III, CLL, SLL I-II-III, AML, CLL, Hodgkin’s and nonHodgkin’s lymphoma, MCL, MM I-II, advanced solid tumor, CRC Breast, melanomas, MM, non-Hodg- I-II, breast, non-Hodgkin’s lymphoma, kin’s lymphoma, NSCLC, ovarian NSCLC Breast, CRC, endometrial, GIST, GBM, I-II, breast, CRC, endometrial, GIST, GBM, leukemia, Melanoma, NSCLC, leukaemia, melanoma, NSCLC, pancreatic, Pancreatic, prostate renal cell, HNSCC, TCC Breast, CRC, MM, NSCLC, pancreatic, I-II, CRC, GBM, NSCLC, HNSCC Prostate, ovarian I_II, advanced solid Advanced solid cancers, lymphoma, glioblastoma, nonLymphoma, glioblastoma, NCLC, small-cell lung cancetumours; I-II, breast solid, breast Breast, ovarian I-II, breast Non-Hodgkin’s lymphpma, solid can- I, Non-Hodgkin’s lymphoma, solid cancers, cers, breast breast Advanced breast I-II, advanced breast cancer Advanced solid I, advanced solid cancers

Sloid Advanced solid Hematological malignancies AML, CLL, Hodgkin’s and non-Hodgkin’s lymphoma, MCL Solid, CRC

Cancer type

Table .: Some list of developed and evaluated drugs in preclinical studies and clinical trials. targeting members of the PIK axis.

www.clinicaltrials.gov www.clinicaltrials.gov www.clinicaltrials.gov ()

www.clinicaltrials.gov () www.clinicaltrials.gov www.clinicaltrials.gov ()

() www.clinicaltrials.gov (, ) (, ) www.clinicaltrials.gov

Reference

234 10 Identification of novel inhibitors of P13K/AKT pathways

CCI- (temsirolimus) RAD- (everolimus) AZD INK OSI-

AZD GDC- GSK MK-

mTOR

AKT

Compound

Table .: (continued)

Akt Akt Akt AKt

mTOR mTOR mTOR mTOR mTOR

Isoform selectivity mRCC, solid, refractory diffuse large B-cell lymphoma, breast, mRCC Advanced breast, pNET, nonmalignant kidney and brain, advanced melanoma, CRC, SCLC Solid Advanced solid, breast Solid, lymphoma Advanced breast Solid Lymphoma Solid, breast

Cancer type

I-II, advanced breast I, solid cancer I, lymphoma I, solid cancer and breast

I, solid tumors; II, refractory diffuse large B-cell lymphoma and breast; III, mRCC I, advanced breast cancer; II, advanced melanoma, CRC, SCLC I, solid tumors I, advanced solid tumors; I-II, breast I, solid tumor and lymphoma

Clinical trial status

() www.clinicaltrials.gov www.clinicaltrials.gov ()

(–, , ) (, , –) () www.clinicaltrials.gov www.clinicaltrials.gov

Reference

10.2 Materials and methods

235

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10 Identification of novel inhibitors of P13K/AKT pathways

Table .: Studied ligands and their classifications. S/N

Name of phytochemicals

Classification

                                            

Aristolene Z-Alpha Bergamotane Ar-turmerone Turmerone Ar-curcumene Ar-turmerol Bisabola-,-diene--one Bisabolone Bisacurone Bisacurone A Bisacurone B Bisacurone C Bisacumol Turmeronol A Turmeronol B Zingiberene Xanthorrhizol Zingerone Dehydrozingerone Beta-bisabolene Alpha curcumene Beta-sesquiphellandrene Curcuphenol Curlone Curculonone A Curculonone B Curculonone C Curculonone D Beta-atlantone Dihydro-ar-turmerone Alpha-bisabolol (E)-Caryophyllene Caryophyllene Di-epi-Cedrane Phytol Geranyllinalool (E,E,E)-,,,-Tetramethylhexadeca-,,,,-pentaene ,,,-Tetramethyl-hexadeca-,,,,-pentaene Curcumin Demethoxycurcumin -(-Hydroxyphenyl)--(,-dihydroxyphenyl)-,-heptadiene-,-dione Bisdemethoxycurcumin Tetrahydroxycurcumin ,-Bis(-hydroxyphenyl)--heptene-,-dione -Hydroxy-,-bis-(-hydroxyphenyl)--heptene-,-dione

Aristolene Bergamotane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Bisabolane Caryophyllane Caryophyllane Cedrane Diterpenoid Diterpenoid Diterpenoid Diterpenoid Diarylheptanoid Diarylheptanoid Diarylheptanoid Diarylheptanoid Diarylheptanoid Diarylheptanoid Diarylheptanoid

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Table .: (continued) S/N

Name of phytochemicals

Classification

                                            

Cyclocurcumin ,-Bis(-hydroxy--methoxyphenyl)-,,-heptatrien--one ,-Bis(-hydroxyphenyl)-,,-heptatrien--one Beta-Elemene Gamma-Elemene Linoleic acid Palmitic acid Oleic acid Stearic acid Germacrene D Germacrone Germacrone--al Dehydrocurdione (S,S)-germacrone-,-epoxide Curcumenol Epiprocurcumenol Procurcumenol Isoprocurcumenol Procurcumadiol Zedoaronediol Himachalene p-Cymene m-Cymene Alpha-terpinene Alpha-terpineol Beta -phellandrene Limonene Terpinolene Thymol Carvacrol E-Carveol Carvone Menthol O-Cymene P-Methylacetophenone Piperitone Thujene P-Cymen--ol Sylvestrene Menthofuran Camphor Teresantalol Benzene, -methyl--(-methylpropyl) -Carene Alpha-pinene

Diarylheptanoid Diarylheptanoid Diarylheptanoid Elemane Elemane Fatty acid Fatty acid Fatty acid Fatty acid Germacrane Germacrane Germacrane Germacrane Germacrane Guanine Guanine Guanine Guanine Guanine Guanine Himachalene Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid

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10 Identification of novel inhibitors of P13K/AKT pathways

Table .: (continued) S/N

Name of phytochemicals

Classification

                                            

Geranial Geraniol Neral Nerol Myrcene R-citronellene Citronellyl pentanoate Iso-artemisia ketone Trans-ocimene Linalool Neryl acetate Geranic acid Geranyl acetate -Bornanone ,-Dimethyl-,-nonadien--ol ,,,-Tetramethyl-,-octadiene ,-Dimethyl--nonenal ,-Dimethyl-,-octadiene-,-diol ,-Dimethyl-,-octadiene Vanillic acid Vanillin Calebin A (E)-Ferulic acid (E)--(-Hydroxy--methoxyphenyl)but--en--one Alpha-cubebene -Epi-sesquithujene Curcumanolide A Curcumanolide B Alpha-Humulene -Oxabicyclo[..]dodeca-,-diene, ,,,-tetramethy ,,-Dodecatrien--ol, ,,-trimethyl Adoxal Alpha-Farnesene ,-Undecadien--one, ,-dimethyl Hexadecane-,-diol Cis-Nerolidal Cis-Beta-Farnesene Nerolidyl propionate Beta sitosterol Stigmasterol Gitoxigenin -Oxopregn--en--yl acetate Hopenone  Hop-()-en--ol Dicumyl peroxide

Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Monoterpenoid Phenolic Phenolic Phenylpropene Phenylpropene Phenylpropene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Sesquiterpene Steroid Steroid Steroid Steroid Triterpenoid Triterpenoid

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239

Table .: (continued) S/N

Name of phytochemicals

                   

Cyclohexyl formate Methyleugenol ,,-Trimethylcyclohexyl acetate ,-Dimethyl--oxabicyclo[..]oct--en--one Bicyclo[..]nonan--one, ,-dimethyl--nitro-(exo) Z-Gamma bisabolene ,-Octadiene Alpha turmerone Dehydrocurcumene Benzyl isothiocyanate (,,-Trimethyl-cyclopent--enyl)-methanol (E)-Sesquisabinene hydrate ,′-oxybis[octahydro-,,-trimethyl-,-methanobenzofuran] (-Methylpropyl)-benzene Pyrazolo[,-a]pyridine, ,a,,-tetrahydro-,-dimethyl Ent-Zedoaronediol Beta-Sitosterol Corymbolone Alpha-Santalene (Z)-Ferulic acid

Classification

14 Conformer Distribution with Molecular Mechanics/MMFF, the conformational search was performed and the most stable conformers were chosen and optimized. Using the equilibrium geometry density functional theory (DFT) method with the B3LYP functional and the 6–31 + G* as the basis set, optimization was carried out on Spartan 14′ software.

10.2.2 Preparation of target receptor Phosphoinositide3-kinase (PI3K) and Protein Kinase B (PKB/AKT) with PDB ID: 5DXT and 2JDR respectively were used as the target protein for this study (Figure 10.1). The structures were retrieved from the protein data bank (RCSB) (https://www.rcsb.org/pdb). Water molecules and other unwanted complexes were also removed from the downloaded protein to avoid interactions and interference with the potential binding site of the target protein during the docking simulation using Biovia Discovery Studio. The binding pocket X, Y, and Z coordinates of 5DXT and 2JDR were defined as 13.39, 15.12, 26.90, and 22.42, 6.12, and 46.04 respectively using Autodock tool-1.5.6 program [19].

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10 Identification of novel inhibitors of P13K/AKT pathways

Figure 10.1: The Crystal Structure and binding pocket of (A) protein kinase B (PKB/Akt) (PDB ID: 2JDR) and (B) phosphoinositide-3-kinase (PI3K) (PDB ID: 5DXT).

10.2.3 Determination of (5DXT and 2JDR) active sites Computed Atlas for Surface Topography of Proteins (CASTp) (http://sts.bioe.uic.edu/castp/ index.html?2011) [20] and Biovia Discovery Studio (2019) was used in determining binding pocket, amino acids and all ligands interactions in the active PI3K and PKB/Akt active site. The results obtained were validated or affirmed with the report of a previously examined experiment on PI3K bound to inhibitor GDC-0326 [21] and PKB/Akt bound to inhibitor A-443654 [22].

10.2.4 Molecular docking simulation Docking simulations were done using Pyrx [23]. The docking simulation (consensus docking) was performed in four places. Using the binding affinities ((kcal/mol)) of the ligands and standards respectively, their inhibiting abilities against the target receptor were computed using Eq. (10.1), while other molecular interactions that occurred during the simulation were studied using Biovia-2019 Discovery Studio and PyMol. K i = 10(B.E/1:366)

(10.1)

Ki is the inhibition constant in µM and B.E. is the binding energy in kcal/mol

10.2.5 Prediction of Activity Spectra for Substances (PASS) Prediction of Activity Spectra for Substances (PASS) webserver http://www.pharmaexpert. ru/passonline/ [24] was used to presage the biological activities of the ligands under study. It

10.3 Results and discussion

241

was feasible to uncover the effects of a molecule entirely based on the molecular formula utilizing MNA (multilevel neighbours of atoms) descriptors using the PASS, indicating that biological activity is a function of its chemical structure [25, 26].

10.2.6 Assessment of pharmacokinetic properties ADMET SAR2 was used to predict the absorption, distribution, metabolism, and toxicity (ADMET) properties of the selected compounds [27]. SwissADME webserver was used to research the oral bioavailability properties of the compounds while drug-likeness features of the compounds selected were examined using Molinspiration online tool (http://molinspiration.com/).

10.3 Results and discussion 10.3.1 Protein kinase B (PKB Beta/Akt2) and Phosphoinositide3-kinase (PI3K) structure and active site analysis The structure of Protein kinase B (PDB ID:2JDR) (Figure 10.1A) contains 342 amino acids complexed with an inhibitor A-443654 ((2s)-1-(1h-indol-3-yl)-3-{[5-(3-methyl-1h-indazol5-yl)pyridin-3-yl]oxy}propan-2-amine). The X-ray crystallographic structure reveals its resolution to be 2.30 Å, crystal dimension to be a = 44.93 Å, b = 61.00 Å, c = 125.00 Å with angles α = 90°, β = 90° c = 90° respectively. PKB/Akt2 has its R-values to be (free = 0.252, work = 0.193, observed = 0.196). Protein kinase B (PKB) is located in the ATP cleft with the following amino acids Leu158, Gly159, Phe163, Val166, Ala179, Lys181, Thr213, Met229, Glu230, Ala232, Glu236, Glu279, Asn280, Thr292, Asp293 [22]. Also, Phosphatidylinositol 4,5-biphosphate-3-kinase catalytic subunit alpha isoform (p10α) structure (PDB ID: 5DXT) (Figure 10.1B) contains 962 amino acid residue complexed with inhibitor GDC-0326((2S)-2-({2-[1-(propan-2-yl)-1H-1,2,4-triazol-5-yl]5,6-dihydroimidazo[1,2-d][1,4]benzoxazepin-9-yl}oxy)propanamide). The X-ray crystallographic structure reveals its resolution to be 2.25 Å while its crystal dimension is a = 58.51 Å, b = 133.67 Å, c = 141.34 Å with angles α = 90°, β = 90°, c = 90° respectively. PI3K has its R-values to be (free, work and observed) are 0.262, 0.227 and 0.228. The active residue amino acids of 5DXT include Ser774, Trp780, Tyr836, Ile848, Glu849, Val851, Ser854, His855, Thr856, Gln859, Met922, Phe930, Ile932, Asp933 [21].

10.3.2 ADMET assay of the ligands In drug discovery and development, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of chemical compounds play a vital role. Thus, it is

242

10 Identification of novel inhibitors of P13K/AKT pathways

important to find potent molecules with better ADMET properties in the early stage of the discovery process. Of all 155 bioactive compounds (ligands) from Curcuma longa (Turmeric) analyzed. 39 of the ligands passed (13 Bisabolane, 7 monoterpenoid, 5 guanine, 4 sesquiterpenoid, 2 triterpenoid, 2 phenylpropene, 1 caryophellene, 1 aristolene, 1 cedrane, and 3 others). As shown in Table 10.3, these ligands exhibit good ADMET properties and are thereby selected for further analysis. From the ADMET profile of these compounds shown in Table 10.3, it could be seen that all the selected compounds cross the bloodbrain barrier (BBB+) and also have the tendency of being absorbed in the human intestine (HIA+). Also, they express good aqueous solubility (Log S) as they fall within the proposed range of −1 to −5. This is an indication of a drug candidate with excellent absorption and distribution potential. Furthermore, the Cytochrome P450 inhibitors (microsomal enzymes) used in predicting the metabolic activities of drug-like compounds from this ADMET profile point out that all the selected hits does not inhibit any of the cytochrome P450, thus an indication of good metabolism of the therapeutic agent. The selected compounds are non-carcinogenic though, not bio-degradable. The AMES toxicity indicates the mutagenic abilities of a drug, interestingly, all the selected compounds are non-AMES toxic. Though some compounds show eye irritation and corrosion, all the selected compounds exhibit type III except two compounds with type IV acute oral toxicity, this type III is slightly toxic and can be further improved to type IV which is non-toxic during lead optimization. The hERG (human ether a-go-go-related gene) potassium ion channel is an essential biomarker for a potential drug candidate, it helps to coordinate the heart’s beating by mediating the repolarizing current in the cardiac action potential. Surprisingly, all the selected compounds are non-blockers of the hERG potassium channel. In comparison, the ADMET profile of the selected compounds is found to have better properties than that of the standards. Topotecan (S1) was found to be an inhibitor of Cyp450 (1A2) while Melphelan (S2) also is a Cyp450 (1A2), (3A4) inhibitor and it also exhibits type I acute oral toxicity. With this, further investigation should be done on Melphalan by the right agency, as it might also cause a great effect on the body.

10.3.3 Drug likeness analysis of the selected compounds The drug-likeness analysis of a compound is an essential factor during the first phases of drug discovery. As stated in Lipinski’s rule of five (RO5), an effective drug-like compound must have a molecular weight (MW) ≤ 500 Da, hydrogen bond donor (HBD’s) ≤ 5, hydrogen bond acceptor (HBAs) ≤ 10 and octanol-water partition coefficient (log P) ≤ 5 with not more than one violation allowed. These are indications of a good oral bioavailability index of a compound [28]. The drug-likeness of the compounds selected as presented in Table 10.4 indicate that none had more than one violation as

Log S

. (−)

. (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−)

HIA

.(+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

. (+)

L

L

L

L

L

L

L

L

L

L

. (+)

. (−)

. (+)

. (+)

. (+)

. (+)

. (−)

. (−)

. (−)

. (−)

. (+)

. (+)

CaCo-

Absorption and distribution

BBB (+/−) ARISTOLENE ADMET L . (+) BISABOLANE ADMET L . (+)

Compounds

–

–

–

–

–

–

–

–

–

–

–

–

C

–

–

–

–

–

–

–

–

–

–

–

–

A

–

–

–

–

–

–

–

–

–

–

–

–

A

Metabolism

Table .: ADMET profiling of the selected Hit compounds and standard drug.

–

–

–

–

–

–

–

–

–

–

–

–

C

–

–

–

–

–

–

–

–

–

–

–

–

D

–

–

–

–

–

–

–

–

–

–

–

–

B

Extn

–

–

–

–

–

–

–

–

–

–

–

–

AM

III

III

III

III

III

III

III

III

III

III

III

III

AOT

–

–

–

–

+

–

–

–

–

–

+

–

EI

–

–

–

–

+

–

–

–

–

–

–

–

EC

–

–

–

–

–

–

–

–

–

–

–

–

HI

–

–

–

–

–

–

–

–

–

–

–

–

C

Toxicity

.

−.

.

.

.

.

−.

−.

−.

−.

.

.

TP

–

–

–

–

–

–

–

–

–

–

–

–

HP

−.

−.

−.

−.

−.

−.

−.

−.

−.

−.

−.

−.

SP

10.3 Results and discussion

243

. (−)

. (−)

. (−) . (−) . (−) . (−) . (−)

. (−) . (−) . (−)

. (+)

. (+) . (+) . (+) . (+) . (+)

. (+) . (+) . (+)

. (−) . (−)

. (+)

. (+)

. (+)

L

CARYOPHYLLANE ADMET L . (+) CEDRANE ADMET L . (+) GUANINE ADMET L . (+) L . (+) L . (+) L . (+) L . (+) MONOTERPENOID ADMET L . (+) L . (+) L . (+)

. (+)

. (+)

. (+) . (+) . (+)

. (+) . (−) . (+) . (+) . (+)

. (+)

. (+)

. (+)

. (+)

Absorption and distribution

L

Compounds

Table .: (continued)

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

Metabolism

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

Extn

–

–

–

–

–

–

–

–

–

–

–

–

III

III

III

III

III

III

III

III

III

III

III

III

+

+

+

+

–

+

+

–

+

–

–

–

+

+

+

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

Toxicity

.

.

.

.

.

.

.

.

.

.

.

.

–

–

–

–

–

–

–

–

–

–

–

–

−.

−.

−.

−.

−.

−.

−.

−.

−.

−.

−.

−.

244 10 Identification of novel inhibitors of P13K/AKT pathways

L

. (−) . (−) . (−) . (−) −. −.

. (−) . (−)

. (−) . (−) . (−) . (−)

. (−)

. (+) . (+) . (+) . (+) . (+) . (+)

. (+) . (+)

. (+) . (+) . (+) . (+)

. (+)

. (+)

. (+) . (+) . (+) . (+)

. (+) . (+)

. (+) . (+) . (+) . (−) . (+) . (+)

Absorption and distribution

. (+) L . (+) L . (+) L . (+) L . (+) L . (+) PHENYLPROPENE ADMET L . (+) L . (+) SESQUITERPENE ADMET L . (+) L . (+) L . (+) L . (+) TRITERPENOID ADMET L . (+) L

Compounds

Table .: (continued)

– –

– –

–

–

–

–

–

–

–

–

–

–

–

–

–

–

+

–

–

–

–

–

–

–

+

–

–

–

–

–

–

–

–

–

– –

–

–

–

–

Metabolism

–

–

–

–

–

–

–

–

– –

–

–

–

–

–

–

–

–

–

–

–

–

– –

–

–

–

–

–

–

–

–

–

–

–

–

– –

–

–

–

–

Extn

–

–

–

–

–

–

–

–

– –

–

–

–

–

III

III

IV

III

III

III

IV

III

III III

III

III

III

III

–

–

–

+

+

–

+

+

+ +

+

+

+

+

–

–

–

+

–

–

+

+

+ +

–

+

–

–

–

–

–

–

–

–

–

–

– –

–

–

–

–

–

–

–

–

–

–

–

–

– –

–

–

–

–

Toxicity

.

.

.

.

.

.

. (+)

.

. .

.

.

.

.

–

–

–

–

–

–

–

–

– –

–

–

–

–

−.

−.

−.

−.

−.

−.

−.

−.

−. −.

−.

−.

−.

−.

10.3 Results and discussion

245

. (+)

.(−) .(−)

.(+) .(+)

.(−) .(−)

. (+) . (+) . (+)

. (+)

–

–

+ +

–

–

– –

–

–

– +

–

–

–

Metabolism

– –

–

–

–

– –

–

–

–

– –

–

–

–

Extn

– –

–

–

–

III I

III

III

IV

– –

+

+

+

– –

–

+

+

– –

–

–

–

– –

–

–

–

Toxicity

. .

.

.

.

– +

–

–

–

−. −.

−.

−.

−.

ARISTOLENE L = Aristolene. BISABOLANE L = Bisabolone L = Bisacurone, L = bisacurone A L = bisacurone B L = bisacurone C, L = Bisacumol, L = Dehydrozingerone, L = Curlone, L = curculonone C, L-curculonone D L = curculonone B, L = curculonone A, L = beta-atlantone, CARYOPHYLLANE L =(E)-caryophyllene CEDRANE L = di-epi-Cedrane. GUANINE L = Curcumenol, L = Epiprocurcumenol, L = isoprocurcumenol, L = procurcumadiol, L = procurcumenol, MONOTERPENOID L = m-cymene L = menthol. L = o-cymene, L = teresantalol. L = benzene, -methyl--(-methylpropyl), L = ,,,-tetramethyl-,-octadiene, L = ,-dimethyl-,-octadiene, PHENYLPROPENE L= (E)--(-hydroxy--methoxyphenyl)but--en--one., L=(E)-ferulic acid SESQUITERPENE L = Alpha-cubebene, L = alpha-Humulene, L = ,,-dodecatrien--ol, ,,-trimethyl, L = ,-undecadien--one, ,-dimethyl-, (Z) TRITERPENOID L = Hopenone , L = Hop-()-en--ol OTHERS L = Cyclohexyl formate, L = ,-dimethyl--oxabicyclo[..]oct--en--one, L = Pyrazolo[,-a]pyridine, ,a,,-tetrahydro-,-dimethyl. STANDARD DRUGS S = Topotecan, S = Melphelan.

. (−) . (−) . (−)

. (−)

. (+) . (+) . (+)

. (+)

Absorption and distribution

OTHERS ADMET L . (+) L . (+) L . (+) STANDARD DRUGS ADMET SD .(−) SD .(−)

Compounds

Table .: (continued)

246 10 Identification of novel inhibitors of P13K/AKT pathways

Molecular weight . . . . . . . . . . . . . . . . . . . . . . . .

Heavy atoms (HA)



            





    

  

L Bisabolane drug-likeness L L L L L L L L L L L L L Caryophyllane drug-likeness L Cedrane drug-likeness L Guanine drug-likeness L L L L L Monoterpenoid drug-likeness L L L

Compounds

  

    





            



RO violation

Aristolene drug-likeness

Table .: Drug likeness properties of the best hits and the standard drug (SD).

  

    





            



Hydrogen bond donor (HBD)

  

    





            



Hydrogen acceptor (HBA)

. . .

. . . . .

.

.

. . . . . . . . . . . . .

.

miLogP

10.3 Results and discussion

247

Molecular weight . . . . . . . . . . . . . . . . .

Heavy atoms (HA)

   

 

   

 

  

 

 

  

 

   

 

   

RO violation

Aristolene drug-likeness

 

  

 

   

 

   

Hydrogen bond donor (HBD)

 

  

 

   

 

   

Hydrogen acceptor (HBA)

. .

. . .

. .

. . . .

. .

. . . .

miLogP

ARISTOLENE L = Aristolene. BISABOLANE L = Bisabolone L = Bisacurone, L = bisacurone An L = bisacurone B L = bisacurone C, L = Bisacumol, L = Dehydrozingerone, L = Curlone, L = curculonone C, L-curculonone D L = curculonone B, L = curculonone A, L = beta-atlantone, CARYOPHYLLANE L =(E)-caryophyllene CEDRANE L = di-epi-Cedrane. GUANINE L = Curcumenol, L = Epiprocurcumenol, L = isoprocurcumenol, L = procurcumadiol, L = procurcumenol, MONOTERPENOID L = m-cymene L = menthol. L = o-cymene, L = teresantalol. L = benzene, -methyl-(-methylpropyl), L = ,,,-tetramethyl-,-octadiene, L = ,-dimethyl-,-octadiene, PHENYLPROPENE L= (E)--(-hydroxy--methoxyphenyl)but--en--one. , L=(E)-ferulic acid SESQUITERPENE L = Alpha-cubebene, L = alpha-Humulene, L = ,,-dodecatrien--ol, ,,-trimethyl, L = ,-undecadien--one, ,-dimethyl-,(Z)TRITERPENOID L = Hopenone , L = Hop-()-en--ol OTHERS L = Cyclohexyl formate, L = ,-dimethyl--oxabicyclo[..]oct--en--one, L = Pyrazolo[,-a]pyridine, ,a,,-tetrahydro-,-dimethyl. STANDARD DRUGS S = Topotecan, S = Melphelan.

L L L L Phenylpropene drug-likeness L L Sesquiterpene drug-likeness L L L L Triterpenoid drug-likeness L L Others drug-likeness L L L Standard drug SD SD

Compounds

Table .: (continued)

248 10 Identification of novel inhibitors of P13K/AKT pathways

10.3 Results and discussion

249

well as the standards (Topotecan S1 and Melphalan S2) which is an indication that the selected hit compounds are potential drug compounds as they possessed good drug-like properties.

10.3.4 Molecular docking analysis Molecular docking is a well-known structure-based drug design approach that predicts the interactions between ligand and target receptor as well as identifies possible inhibitors of the target receptor with accuracy and speed [29]. Figure 10.1 shows the structure of phosphoinositide 3-kinases (PI3Kα) with PDB ID:5DXT and the structure of PKB-BETA (AKT2) with PDB ID: 2JDR that was used as the target proteins for this research. 39 isolated phytochemicals from Curcuma longa, after passing ADMET and drug-likeness analysis were docked with the two target proteins 5DXT and 2JDR. The docking results of the passed ligands within the Ki range of 0.3 and 1.93 were selected and reported in Table 10.5. Hop-17(21)-en-3-o had −8.9 J K/mol, Hopenone was −8.8 JK/ mol and Epiprocurcumeno was −7.8 JK/mol while Topotecan and Melphalan had −8.7 JK/mol and −6.1 JK/mol respectively with PI3Kα. For AKT2, Topotecan had the highest binding affinity of −9.4 JK/mol, while Epiprocurcumenol, Procurcumenol, Hopenone, curlone, Hop-17(21)-en-3-ol, bisacurone_B, curcumenol, isoprocurcumenol and Melphalan had −9.0 JK/mol, −8.6 JK/mol, −8.4 JK/mol, −8.3 JK/mol, −8.3 JK/mol, −8.1 JK/ mol, −8.1 JK/mol, −8.1 JK/mol and −6.6 JK/mol respectively. Furthermore, as reported by Hopkins et al. 2014, the binding affinity conformed to the vina results and is used to calculate the inhibition constant value conformed (Ki) (Equation (10.1)) [30].

10.3.5 Oral bioavailability of the passed compounds Using the Swiss ADME web tool, the oral bioavailability and other physicochemical properties of the compounds selected were obtained and are presented in Table 10.6. The bioavailability radar (Figure 10.2) presents the bioavailability properties (SIZE, POLAR, LIPO, INSATU, INSOLU, and FLEX) of these selected compounds and standards. The pink part of the radar shows the best area with good bioavailability properties. As shown in the table, all the selected hits and standards meet the recommended SIZE for a good drug candidate according to Lipinski, this says, an effective drug-like compound must possess a molecular weight (MW) ≤ 500 g mol−1. The polarity was determined using the Total Polarity Surface Area (TPSA) whose recommended value ranges between 20 and 130 Å2. From Table 10.6 and Figure 10.2, it can be deduced that the polarity of all the compounds selected and the standards fell within the recommended range except C1 (Hopenone 1) which has a lower value. It should also be noted that all the hit compounds have a lower value of TPSA compared to the two standards. The Lipophilicity (LIPO) and unsaturation (INSATU) were examined using xlogP3 and Csp3 which are expected to range between

−. ± .

Melphalan (standard drug)

Thr, Asp Thr, Glu

−. ± . −. ± .

−. ± .

Procurcumenol

Hopenone 

Tyr

Thr

−. ± .

JDR Receptor amino acids forming H-bond with ligands

Topotecan (Standard drug) Epiprocurcumenol

Binding Affinity (ΔG), kcal/mol

−. ± .

Ligands

Lys, Ser

−. ± .

Topotecan (Standard drug) Epiprocurcumenol

His , Cys , Ile , Phe , Cys , His  Val , Leu , Arg , Lys , Pro , Met , Lys , Met , Ser , Trp , Val , Gln , Ile , Ser , Met , Trp , Ile , Tyr , Ile , Val 

Electrostatic/hydrophobic interactions involved

Val , Thr , Glu , Phe , Met , Glu  Leu , Phe , Thr , Asp , Met , Val , Ala  Thr , Lys , Phe , Glu , Ala , Phe , Leu , Val , Met , Tyr , Tyr , Lys , Tyr , Ala , Pro , Trp 

Electrostatic/Hydrophobic Interactions involved

Protein Kinase B (PKB/Akt) (PDBID: JDR)

Arg

−. ± .

Hopenone 

Cys

DXT Receptor amino acids forming H-bond with ligands

−. ± .

Binding affinity (ΔG), kcal/mol

Hop-()-en--ol

Ligands

Phosphoinositide-kinase (PIK) (PDB ID: DXT)

Table .: Docking scoring and the inhibition constants of the interactions of phytochemicals with the target proteins.

.

.

.

.

Inhibition Constant (Ki), μM

.

.

.

.

.

Inhibition constant (Ki), Μm

250 10 Identification of novel inhibitors of P13K/AKT pathways

Binding Affinity (ΔG), kcal/mol −. ± .

−. ± . −. ± . −. ± . −. ± . −. ± .

Ligands

Curlone

Hop-()-en--ol

bisacurone_B

Curcumenol

Isoprocurcumenol

Melphalan (Standard drug)

Table .: (continued)

Asn

JDR Receptor amino acids forming H-bond with ligands Phe , Lys , Val , Met , Leu , Ala , Phe , Ala , Met  Tyr , Lys , Tyr , Ala , Pro , Trp  Leu , Val , Phe , Met , Asn  Met , Phe , Phe , Phe , Val  Glu , Phe , Lys , Phe , Leu , Met , Ala , Val 

Electrostatic/Hydrophobic Interactions involved

Protein Kinase B (PKB/Akt) (PDBID: JDR)

.

.

.

.

.

.

Inhibition Constant (Ki), μM

10.3 Results and discussion

251

CHO . .  . . −.  .  . .

CHO . .  . . −.  .  . .

Formula Mass TPSA #Rotatable bonds XLOGP WLOGP ESOL Log S Lipinski #violation Bioavailabilty Score PAIN #alerts Fraction Csp Synthetic Accessibility

CHO . .  . . −.  .  . .

C CHO . .  . . −.  .  . .

C CHO . .  . . −.  .  . .

C CHO . .  . . −.  .  . .

C CHO . .  . . −.  .  . .

C CHO . .  . . −.  .  . .

C CHNO . .  . . −.  .  . .

S

CHClNO . .  . . −.  .  . .

S

C = Hopenone , C = Hop-()-en--ol, C = Epiprocurcumenol, C =Procurcumenol, C = Curlone, C = Bisacurone B, C = Curcumenol, C = Isoprocurcumenol, S = Topotecan, S = Mephelan.

C

C

LIGAND

Table .: Oral bioavailability of the selected hit compounds.

252 10 Identification of novel inhibitors of P13K/AKT pathways

10.3 Results and discussion

253

Figure 10.2: The bioavailability radar for the selected hit compounds and standards C1 = Hopenone 1, C2 = Hop-17(21)-en-3-ol, C3 = Epiprocurcumenol, C4 = Procurcumenol, C5 = Curlone, C6 = Bisacurone B, C7 = Curcumenol, C8 = Isoprocurcumenol, S1 = Topotecan, S2 = Mephelan.

(−0.7 and + 5.0) and (0.5 and 1). Table 10.6 thus shows that aside from C1 and C2 (Hopenone and Hop-17(21)-en-3-ol), all the selected hits and standards were within the best range (LIPO) while only S1 (Topotecan) has a lower value for (INSATU). The solubility (INSOLU) and the flexibility (FLEX) properties of the selected compounds were also examined using ESOL (LogS) which ranges between (0 and 6) and the number of rotatable bonds which should not exceed 9 (#Rotatable bond), interestingly, all the hit compounds, as well as the standard, are within the recommended range. In all, these, therefore, is an indication that all the selected hits have good oral bioavailability properties and could be further explored as therapeutic agents targeting ovarian cancer disease.

254

10 Identification of novel inhibitors of P13K/AKT pathways

10.3.6 Prediction of activity spectra for substances (PASS) The PASS online web tool [31] was used in predicting the biological activity spectra of the selected hits and standards. This shows predictions based on the structure-activity relationship (SAR). From Table 10.7, it can be observed that all the selected compounds have antineoplastic (ovarian cancer) activities including the standards. They also possess other anti-carcinogenic activities. Table 10.7 also shows that all the chosen hits exhibit inhibitory activity against ovarian cancer as the probability to be active (Pa) is greater than the probability to be inactive (Pi). Table 10.7: Prediction of activity spectra for the selected hit compounds.

10.3.7 Bioactivity of the selected compounds The bioactivity properties of the selected hits and standards are shown in Table 10.8. The existing relationship between binding energy and inhibition constant is shown in Eq. (10.1). The Ki values of a potential hit compound are expected to range between 0.1 and 1.0 µM and not more than 10Nm for a drug [32]. Of all the eight initially selected compounds, five compounds are thereby selected for their high and better inhibitory value from the two pathways and receptors (P13k/5DXT and AKT/2JDR). The compounds are Epiprocurcumenol, Procurcumenol, Hopenone, Curlone, and Hop-12(21)-en-3-ol. Table .: Prediction of activity spectra for the selected hit compounds. Ligands Hop-()-en--ol Hopenone  Epiprocurcumenol Procurcumenol Curlone Bisacurone_B Curcumenol Isoprocurcumenol Topotecan (standard drug) Melphalan (standard drug)

Probability to be active (Paa)

Probability to be inactive (Pib)

. . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . .

Biological activity Chemopreventive Antineoplastic (ovarian cancer) Apoptosis antagonist Antineoplastic (ovarian cancer) Antimetastatic Antineoplastic (ovarian cancer) Cancer-associated disorders treatment Antineoplastic (ovarian cancer) Anticarcinogenic Chemopreventive Antineoplastic Antineoplastic antibiotic Antineoplastic (ovarian cancer) Anticarcinogenic Antineoplastic (ovarian cancer) Preneoplastic conditions treatment Antineoplastic (ovarian cancer)

255

10.3 Results and discussion

Table .: Bioactivity analysis of the selected hit compounds and standards. DXT Receptor Bioactivity Pyrx docking score (Kcal/mol) Ki (µM) MilogP Ligand efficiency (LE) LE-scale Fit quality (FQ) Ligand-efficiency-dependent lipophilicity (LELP)

C

C

C

S

S

−. . . . . . .

−. . . . . . .

−. . . . . . .

−. . . . . . .

−. . . . . . .

JDR Receptor Bioactivity Pyrx docking score (Kcal/mol) Ki (µM) MilogP Ligand efficiency (LE) LE-scale Fit quality (FQ) Ligand-efficiency-dependent lipophilicity (LELP)

C

C

C

C

C

S

S

−. . . . . . .

−. . . . . . .

−. . . . . . .

−. . . . . . .

−. . . . . . .

−. . . . . . .

−. . . . . . .

C-Hop-()-en--ol, C-Hopenone , C-Epiprocurcumenol, S-Topotecan, S-Melphalan. C-Epiprocurcumenol, C-Procurcumenol, C-Hopenone , C-Curlone, C-Hop-()-en--ol, S-Topotecan, S-Melphalan.

Their inhibition constant (Ki) values range from 0.3 to 0.36 µM for 5DXT receptor and 0.3–0.83 µM for 2JDR receptor. With the Ki values observed from the table, it reveals that all five compounds are fit to be regarded as hit with Hop-12(21)-en-3-ol for the 5DXT receptor and Epiprocurcumenol for the 2JDR receptor being the most potent. Other bioactivity parameters; Ligand Efficiency (LE), Fit Quality (FQ), and Ligandefficiencydependent lipophilicity (LELP) were also calculated using Eq. (10.2)–(10.5). Their recommended values are ≥0.3, ≥0.8, and −10 to 10 respectively. For the 5DXT receptor, S2 has recommended value of LE, and for LELP, S1 has values within the recommended range, while all the hit compounds and standards except S2 have FQ values within the recommended range. LE = –B.E ÷ Heavy atoms (HA)

(10.2)

LEscale = 0.873e–0.026×H.A – 0.064

(10.3)

FQ = LE ÷ LEscale

(10.4)

LELP = LogP ÷ LE

(10.5)

256

10 Identification of novel inhibitors of P13K/AKT pathways

10.3.8 Binding mode and Molecular interactions Evaluation of the binding interactions of the ligands in the active site of the target receptor gives more information on the mode of interaction of the selected compounds with 5DXT and 2JDR receptors. The binding mode and molecular interactions of selected hits with excellent inhibitory potential and good physicochemical and bioactivity properties are discussed. The binding interactions of these compounds and standards with 5DXT and 2JDR receptors are shown in Table 10.9. As observed from the table, in the interaction with 5DXT, C1 formed a conventional hydrogen bond with Cys838, Pi-sigma interaction with Phe666, Alkyl and Pi-alky interaction with Cys257, Ile633, His670, and His759. Likewise, C2 formed a conventional hydrogen bond with Arg852 and alky interaction with Lys271, Met278, Leu279, Pro835, and Val851. While, C3 formed only alkyl and pi-alkyl interaction with Trp780, Tyr836, Ile848, Val850, Met922, and Ile932. Similarly, for 2JDR C1 formed a conventional hydrogen bond with Thr292 and Asp293, Pi-sigma interaction with Phe163, and Alkyl and Pi-alkyl interaction with Leu158, Val166, Ala179, Met282. C2 also formed a conventional hydrogen bond with Glu279 and Thr292, Pi-sigma interaction with Phe163, and Alkyl and Pi-alkyl interaction with Leu158, Table .: Binding mode and molecular interactions of the selected hits and standard against DXT. Ligands C-Hop-()-en--ol

C-Hopenone 

C-Epriprocucumenol

Binding pockets

Interactions

10.3 Results and discussion

257

Val166, Ala179, Lys181, Met282, and Phe439. Likewise, C3 formed a conventional hydrogen bond with Tyr177, Alkyl and Pi-alkyl with Tyr178, Tyr231, Lys285, Ala214, Pro210, Trp479. C4 and C5 was found only to possess Alkyl and Pi-alkyl interaction with Leu158, Phe163 Val166, Ala179, Lys181, Ala232, Met229, Met282, Phe439, and Tyr178, Tyr231, Ala214, Pro210, Lys285, and Trp479 respectively. On this note, examining the amino acid residues obtained in the interaction of C1, C2, and C4 and the amino acids in the active site of 2JDR protein, it should be noted that the amino acids are similar which proved that all of the compounds share the same binding pocket (ATP site) with A-443654 ligand. However, C3 and C5 do not bind with any amino acid among those in the active site and among other compounds and standards. Furthermore, similarities were found among the three compounds and S1 in their interactions, while only C1 shared the same interaction with S2. Therefore, it shows that

Table .: Binding mode and molecular interactions of the selected hits and standard against JDR. Ligands C-Epiprocurcumenol

C-Procurcumenol

C-Curlone

Binding pockets

Interactions

258

10 Identification of novel inhibitors of P13K/AKT pathways

C1, C2, and C4 are potent enough to be therapeutic agent, while C3 and C5 are discarded as it does not bind in the active pocket. Moreso, from the result observed for 5DXT protein, only C2 and C3 interact with active site residues with C3 most potent as most of its amino acids are found in the binding site, though has lower Ki compared to other compounds. C1 is therefore discarded since it has no interaction with the active site (Table 10.10).

10.4 Conclusions The anti-ovarian cancer potential of the Curcuma longa (turmeric) plant was explored via in silico studies. The structure-based screening was employed by using molecular docking simulation, ADMET profiling, Lipinski Rule of 5 (RO5), and other analyses for the target fishing of phytochemicals isolated from Curcuma longa (turmeric) against 2 possible targets (P13K/AKT receptors). This computational analysis reflects that Curcuma longa (turmeric) can serve as excellent anti-ovarian cancer and anti-carcinogenic agents by targeting P13K/AKT receptors. The results obtained revealed that Hopenone 1 (−8.8 kcal mol−1) and Epriprocurcumenol (−7.8 kcal mol−1) are potent inhibitors of the P13K receptor, while Epiprocurcumenol (−9.0 kcal mol−1), Procurcuminol (−8.6 kcal mol−1) and Curlone (−8.3 kcal mol−1) are potential inhibitors of AKT receptor due to their better binding affinities, oral bioavailability, drug-likeness, ADMET properties, PASS properties, bioactivities, binding mechanism, and active site interaction of the target receptors which can serve as promising chemical scaffolds for the development and improvement of inhibitors to treat ovarian cancer compared to the two standard (Melphalan and Topotecan). Acknowledgements: The authors would like to thank the editors XYZ for their guidance and review of this article before its publication.

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Yandri Yandri*, Hendri Ropingi, Tati Suhartati, Bambang Irawan and Sutopo Hadi

11 Immobilization of α-amylase from Aspergillus fumigatus using adsorption method onto zeolite

Abstract: The stability of enzymes which play an important role as biocatalysts in many industrial processes is a persistent challenge with significant impact on production costs. In this study, improvement of the stability of α-amylase obtained from Aspergillus fumigatus was attempted by immobilizing the enzyme onto zeolite using adsorption method. For purification, the isolated enzyme was subsequently subjected to centrifugation, fractionation, and dialysis. The native enzyme was found to have an optimum temperature of 50 °C, while the immobilized enzyme, the optimum temperature of 60 °C was found. The immobilized enzyme was found to have the KM value of 11.685 ± 0.183 mg mL−1 substrate and Vmax of 1.406 ± 0.049 μmol mL−1 min−1, while for the native enzyme, the KM value of 3.478 ± 0.271 mg mL−1 substrate and the Vmax of 2.211 ± 0.096 μmol mL−1 min−1 were obtained. Furthermore, the immobilized enzyme displays the ΔGi of 106.76 ± 0.00 kJ mol−1 and t½ of 90.40 ± 0.00 min, while the native enzyme, the values obtained are ΔGi of 104.35 ± 1.09 kJ mol−1 and t½ of 38.75 ± 1.53 min. As can be seen, the t½ of immobilized enzyme is 2.38 times longer than that of native enzyme, justifying a very significant stability enhancement of the enzyme as a result of. Another important finding is that the immobilized α-amylase enzyme was able to retain its activity as high as 13.80 ± 1.19% activity after five cycles, indicating its potential for industrial use. Keywords: α-amylase; adsorption; Aspergillus fumigatus; immobilization; zeolite.

11.1 Introduction The current rapid expansion in the world’s population leads food consumption to constantly rise. Food must be available to fulfill these demands in order to achieve food security. The food sector must not only generate a large number of food goods, but it must

*Corresponding author: Yandri Yandri, Department of Chemistry, Faculty of Mathematics and Natural Sciences University of Lampung, Bandar Lampung 35145, Indonesia, E-mail: [email protected] Hendri Ropingi, Tati Suhartati and Sutopo Hadi, Department of Chemistry, Faculty of Mathematics and Natural Sciences University of Lampung, Bandar Lampung 35145, Indonesia. https://orcid.org/0000-00016464-7215 (S. Hadi) Bambang Irawan, Department of Biology, Faculty of Mathematics and Natural Sciences University of Lampung, Bandar Lampung 35145, Indonesia As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: Y. Yandri, H. Ropingi, T. Suhartati, B. Irawan and S. Hadi “Immobilization of α-amylase from Aspergillus fumigatus using adsorption method onto zeolite” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0258 | https://doi.org/10.1515/9783111071435-011

262

11 Adsorption method onto zeolite

also focus on ecologically friendly and healthful methods. Starch is the world’s most abundant food raw material. Starch is a carbohydrate that serves as the primary energy source for humans, reaching a market value of USD 97.85 billion in 2020, with expected 7.0% increase in compound annual growth rate (CAGR) between 2020 and 2028 [1]. Rice, wheat, corn, cassava, sweet potatoes, potatoes, sorghum, barley, and other grains are the sources of starch. Starch is a polysaccharide made of glucose monomers with two major chains, amylose, and amylopectin. Amylose has a linear or slightly branched structure, whereas amylopectin has a highly branched structure. The distribution of distinct α-glucans in amylopectin is around 5% of the molecule, resulting in a relatively complicated structure with a double helical helix [2, 3]. Starch has an array of uses in the food industry, including as a raw material for sugar, boosting fiber content, production of corn syrup, gelling agent, emulsion stabilizer, thickening agent, preserving agent, and enhancing the quality of baked products, confectionery, pasta, soups, sauces, sugar, and mayonnaise [4, 5]. Acid and enzymatic techniques can be used to produce sugar from starch. The enzymatic method is recommended because it is environmentally friendly and produces the results that are consistent with expectations. α-Amylase is a frequently utilized enzyme for hydrolyzing starch into glucose. The α-amylase is an enzyme with the ability to break the α-1.4 glycosidic link in the starch molecule to produce glucose. This enzyme has an active site on which two aspartic acid residues and one glutamic acid residue are attached. During the creation of enzyme substrate complexes, glutamic acid works as an acid/base catalyst, whereas aspartic acid acts as a nucleophile. With the aid of α-amylase, many different products were derived from starch, including glucose, maltose, fructose, glucose syrup, high fructose syrup, maltooligomer mixes, G4 syrup alloys (isomaltose, panose, isomaltotriose, and branched oligosaccharide), high molecular weight branching dextrins, bioethanol, starch hydrolyzate as a sizer of paper, and detergent. This enzyme is also used for clinical applications and wastewater treatment of starch processing [6]. The enzymes are chosen as industrial biocatalysts because they provide several advantages over conventional catalysts. An enzyme can accelerate a process 105–1017 times faster than a catalyst-free reaction [7]. Enzymes have biodegradable qualities, making them environmentally benign and producing less waste. Enzymes have the capacity to work in moderate circumstances in the reaction process, therefore they require less energy than conventional catalysts [8]. Because enzymes have less toxicity, they can help to reduce future health issues [9]. Enzymes act specifically and selectively on specified substrates to provide clean and predictable results [10]. However, in the enzyme sector, there are issues with batch solubility, activity declines at high temperatures, and enzymes can only be employed in one operation. The enzyme immobilization approach is a particularly efficient solution to this enzyme issue. Immobilization is the process of binding of the enzyme to the matrix in order to enhance the stability by which the enzyme retains its catalytic activity. The immobilized enzyme works as a heterogeneous biocatalyst, allowing ready separation of the enzyme from the reaction mixture after the reaction is complete, and offering the opportunity to reuse the

11.2 Materials and methods

263

enzyme [11]. Because it is straightforward and simple, enzyme immobilization by adsorption is often employed. In this immobilization technique, several no-covalent interactions between the enzyme and the support material such as hydrogen bonds, hydrophobic interactions, Van der Waals forces, affinity bonds, and enzyme ionic connections are involved [12]. Inorganic materials are used in most adsorption techniques because they are insoluble in water, have mechanical strength, are reactive, and are resistant to thermal, chemical, and microbial degradation [13]. In order to obtain the optimum result with this adsorption technique, it is necessary to consider several factors, including pH, temperature, solvent characteristics, ionic strength, enzyme concentration, and the characteristics of the adsorbent utilized. In this approach, the enzyme is adsorbed on the surface of the solid without removing the unadsorbed enzyme after washing [14]. One class of inorganic material that has been widely utilized as a support material for enzyme immobilization is zeolite. This type of material is acknowledged to offer various advantages, such as selectivity, the availability of a large number of hydroxyl groups to produce strong attachment with the enzyme via hydrogen bonds, and heterogeneity of zeolite surfaces that can support interactions with the enzyme [15]. In this research, a zeolite was used to immobilize α-amylase isolated from A. fumigatus. A. fumigatus is adaptable to a wide range of chemical substances and does not require special nutrition [16]. The study by Talebi et al. demonstrated that immobilization of α-amylase on nano-zeolite can boost the stability the enzyme very significantly [17]. It was reported that the immobilized enzyme was able to retain 80% of its initial activity after 15 days of storage, while the native enzyme was found to completely lose its activity. In another work [18] the laccase enzyme isolated from Polyporus durus ATCC 26726 on immobilized on nanoporous zeolite-x was reported to be able to retain 93.5% of its activity for 30 min at 60 °C, whereas the enzyme without immobilization could only retain 5% [18]. According to Yandri et al. the use of zeolite combined with chitosan as immobilization support can boost the stability of α-amylase isolated from A. fumigatus by 4.65 times when compared to that of the free enzyme [19].

11.2 Materials and methods 11.2.1 Materials Zeolite with the particle size of 5000 mg/kg which confirmed the oil to be safe [51].

14.3.11 Antioxidant studies 14.3.11.1 Qualitative and quantitative free radical scavenging activity using DPPH The qualitative screening showed yellow spot against purple (DPPH) background (Plate 14.3). The qualitative screening of the antioxidant activity showed yellow spots against purple background on the TLC plates. This gave preliminary evidence that the oil contains compounds that can exhibit antioxidant activity [52]. The oxidative stability is a very important quality and safety parameter of oils for their potential commercial applications and utilizations in food and other commercial products [53]. The DPPH stable free

Plate 14.3: TLC plate showing yellow spot against purple background.

318

14 Physicochemical and free radical scavenging activity of A. digitata

Table .: Free radical scavenging activity of Adansonia digitata oil. S/No.

Concentration (mg/mL)

Percentage inhibition (%)

. . . . .

. . . . .

    

radical is commonly used to screen phenolic compounds containing high free radical scavenging ability [54]. Since most vegetable oils contain vitamin E which is a natural antioxidant, the antioxidant activity demonstrated may be due it [55]. The quantitative free radical scavenging activity of Adansonia digitata oil (Table 14.9) showed considerable level of activity. The percentage inhibition was demonstrated was not concentration dependent as expected. This may be due error during the preparation of sample where the exact concentration of the oil might have been measured or machine error. The protection given by an antioxidant depends on the concentration, reactivity towards particular reactive oxygen species, and the status of the antioxidants with which it interacts [56].

14.4 Conclusions The oil contains antioxidant compounds that demonstrated free radical scavenging activity. The oil is not toxic as revealed by the acute toxicity studies with LD50 > 50,000 mg/ kg. The phytochemical screening revealed the presence of steroids. The GC-MS revealed the presence of 13 fatty acids/esters. Acknowledgements: The authors expressed their appreciation to Mr. Aliyu and Mr. Abdullahi of the department of pharmacognosy and ethnomedicine, and pharmacology and toxicology, respectively, for their technical assistance.

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34. Emmanuel HM, Sylvester NM, Millicent LU, Adam BA. Phytochemical and antioxidant evaluation of Cassia sieberiana D.C. stem bark extracts. Proceed Nig Acad Sci 2020;13:97–110. 35. Halilu EM, Abacha YZ, Samagoro C, Bello SS, Abdullahi SJ. Evaluation of physicochemical and antioxidant potential of fixed oil from Curcuma longa linn. Trends Nat Prod Res 2021;2:66–74. 36. Halilu EM, Sani J, Abdullahi S, Umaru ML, Abiodun DJ. Phytochemical screening, free radical scavenging and antibacterial activity of Cassia sieberiana root bark extracts. J Pharm Bioresour 2017;14:75–82. 37. Njoku UO, Umeh CG, Ogugofor MO. Phytochemical profiling and GC-MS analysis of aqueous methanol fraction of Hibiscus asper leaves. Future J Pharm Sci 2021;7:1–5. 38. National Institutes of Health. Guide for the care and use of laboratory animals, 8th ed. Washington DC: Institute for Laboratory Animal Research, National Research Council of the National Academies; 2011. Copyright 2011 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. 11–47 pp. 39. Organization for Economic Development. Principles of good laboratory practice. In: Hand book of good laboratory practice (GLP) TDR, PRD/GLP/, vol 01; 2008:2 p. 40. Kumar A, Ilvarasan R, Jayachandran T, Decaraman M, Aravindhan P, Padmanabha N. Phytochemical investigations on a tropical plant in south India. Pakistan J Nutr 2009;8:83–5. 41. Saxena J, Sahu R. Evaluation of phytochemical constituents in conventional and non conventional species of Adansonia digitata. Int Res J Pharm 2012;3:203–4. 42. Birnin-Yauri U, Garba S. Comparative studies on some physicochemical properties of baobab, vegetable, peanut and palm oils. Nig J Bas Appl Sci 2011;19:64–67. 43. Chindo I, Gushit J, Olotu P, Mugana J, Takbal D. Comparison of the quality parameters of the seed and condiment oil of Adansonia digitata; 2010. 44. Cristina L, Mario C, Valentina D. Pigments in extra-virgin olive oil: authenticity and quality. Rijeka, Croatia: IntechOpen; 2016. Chapter 6. 45. Halilu EM, Muhammad B. Phytochemical and antioxidant studies of Hibiscus Cannabinus seed oil. Phys Sci Rev 2022:1–11. https://doi.org/10.1515/psr-2021-0184. 46. Halilu EM, Ugwah-Oguejiofor CJ, Oduncuoğlu G, Matthias SG. Physicochemical, toxicity and antioxidant activity of Terminalia catappa kernel oil in mice. Pharmacogn Res 2023;15:119–27. 47. Halilu ME, Abiodun DJ, Hassan LG, Umar KJ, Maishanu HM, Warra AA. Proximate and mineral compositions of Ipomoea carnea seeds. J Chem Soc Niger 2017;42:11–4. 48. Ahmed HLE, Mohd SH, Ahemd YSM, Koko WS, Abdelwahab SI. In vitro antimicrobial activities of chloroformic, hexane and ethanolic extracts of Citrullus lanatusvar.citroides (Wild melon). J Med Plants Res 2011;5:1338–44. 49. Mowla G, Sheick NM, Kamal AS. Handbook on edible oils with special reference to Bangladesh, 1st ed. Dhaka, Bangladesh: University of Dhaka; 1990:9–172 pp. 50. Ichu CD, Nwakanma HO. Comparative Study of the physicochemical characterization and quality of edible vegetable oils. Int J Res Inf Sci Appl Tech 2019;3:19321–9. 51. Madan LA, Karampendethu MC, Binu TK. Systematic and comprehensive investigation of curcuminoid essential oil complex: A bioavailable tumeric formulation. Mol Med Rep 2016;13:592–604. 52. Kojima H, Yanai T, Toyota A. Essential oil constituents from Japanese and Indian Curcuma aromatica. Planta Med 1998;64:380–1. 53. Parker TD, Adams D, Zhou K, Harris M, Yu L. Fatty acid composition and oxidative stability of cold-pressed edible seed oils. J Food Sci 2003;68:1240–3. 54. Lee J, Chung H, Chang P-S, Lee J. Development of a method predicting the oxidative stability of edible oils using 2, 2-diphenyl-1- picrylhydrazyl (DPPH). Food Chem 2007;103:662–9. 55. Sibel K, Hüsniye S, Bijen K. α-Tocopherol, flavonoid, and phenol contents and antioxidant activity of Ficus carica leaves. Pharm. Biol. 2005;43:683–6. 56. Vertuani S, Braccioli E, Buzzoni V, Manfredini S. Antioxidant capacity of Adansonia digitata fruit pulp and leaves. Acta Phytother 2002;86:2–7.

Radia Ayad*, Mostefa Lefahal, El Hani Makhloufi and Salah Akkal

15 Photoprotection strategies with antioxidant extracts: a new vision

Abstract: The most harmful to biological compounds is ultraviolet radiation (UVR) from the sun. UVC rays (100–280 nm) are extremely harmful to the skin. Fortunately, it is primarily absorbed in the earth’s troposphere by molecular oxygen and ozone. Although UVB (290–320 nm) accounts for only about 5 % of terrestrial UVR, its effects are typically much stronger than those of UVA (320–400 nm). UVR’s clinical effects on normal-appearing human skin can include erythema, pigmentation, suppression of acquired immunity and enhancement of innate immunity, all caused by UVB, and blood pressure reduction caused by UVA. Long-term effects include photocarcinogenesis and photoaging. All of these effects are supported by molecular or cellular effects such as DNA damage, ROS generation, melanogenesis, and the expression of numerous genes and related proteins. The use of sunscreen and avoiding prolonged sun exposure are the first lines of defense in photoprotection. Sunscreens with the appropriate SPF and protection spectrum are now the mainstay of many studies on UV damage. A variety of novel strategies for developing better sunscreens have been proposed. It has been proposed that incorporating antioxidant phenolic extracts into sunscreens can provide additional photoprotective qualities and provide greater protection by replenishing the skin’s natural reservoirs. Thus, the goal of this study was to look into the use of antioxidant extracts from medicinal plants in sunscreens and cosmetic formulations to boost photoprotection, with a particular emphasis on green extraction of these antioxidants from their complex matrices. Keywords: antioxidant extracts; green extraction; photoprotection; skin; sunscreen; ultraviolet radiation.

15.1 Introduction The skin is really the body’s biggest organ, acting as a strong epithelial barrier to protect the body from the negative impacts of environmental exposures like microbes, pollution,

*Corresponding author: Radia Ayad, Department of Chemistry, Valorization of Natural Resources, Bioactive Molecules and Biological Analysis Unit, University Frères Mentouri Constantine 1 P.O. Box, 325 Ain El Bey Way, Constantine 25017, Algeria; and Department of Chemistry, Laboratory of Phytochemistry and Pharmacology, Faculty of Exact Sciences and Informatics, University Mohammed Seddik Benyahia of Jijel, Jijel 18000, Algeria, E-mail: [email protected]. https://orcid.org/0000-0002-8339-3027 Mostefa Lefahal, El Hani Makhloufi and Salah Akkal, Department of Chemistry, Valorization of Natural ressources, Bioactive Molecules and Biological Analysis Unit, University Frères Mentouri Constantine 1 P.O. Box, 325 Ain El Bey Way, Constantine 25017, Algeria. As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: R. Ayad, M. Lefahal, E. H. Makhloufi and S. Akkal “Photoprotection strategies with antioxidant extracts: a new vision” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0313 | https://doi.org/10.1515/9783111071435-015

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and solar ultraviolet radiation (UVR). The breakdown of the skin barrier is a significant event in the progression of many skin disorders [1]. UVR has the greatest impact on biological compounds. UVC rays (100–280 nm) are extremely harmful to the skin. Fortunately, it is primarily filtered in the earth’s troposphere by molecular oxygen and ozone. Although UVB (290–320 nm) accounts for only about 5 % of terrestrial UVR, its effects are typically far more powerful than UVA (320–400 nm). UVR can have acute or chronic clinical effects on normal-appearing human skin, with the majority of them being aggressive. UVB causes erythema (sunburn), pigmentation (tanning), suppression of acquired immunity and enhancement of innate immunity, and blood pressure reduction, whereas UVA causes photoaging and skin cancer [2–4]. Non-melanoma keratinocyte cancers, such as basal cell carcinomas (BCC), squamous cell carcinomas (SCC), and malignant melanomas, the latter of which has a high mortality rate, are the two main types of skin cancer. Melanoma was responsible for 57,043 deaths and 324,635 new cases in 2020 [5–7]. As a result, because photoprotection was the most important preventive health strategy, a plethora of photoprotection measurements were developed [8–10]. To reduce the toxic effects of prolonged sun exposure, dermatologists strongly recommend the daily application of sunscreens. Many studies now use sunscreens with an effective sun protection factor (SPF) and protection spectrum to reduce UV damage [3, 4, 7, 11]. SPF is a measure of the efficacy of a sunscreen, whether organic or inorganic (mineral). This factor refers to the percentage of UVB rays that the cream, gel, or lotion products block [4, 7]. Recent evidence, on the other hand, has found that sunscreen products have some negative effects. In the worst-case scenario, either human health or natural ecosystems are harmed, or both [4, 11]. Other novel photoprotective strategies for developing better and more ideal sunscreens have been investigated in light of these considerations [4, 11–16]. Right now, there is an undeniable need for safer, more environmentally friendly, and improved sunscreen ingredients. Natural products, in this regard, represent an important opportunity for the pharmaceutical and cosmetic industries to discover compounds with engaging biological activities [14–18]. Natural cosmetics demand has increased over the last decade as a result of increased environmental consciousness and customer education, owing primarily to technologies of the digital age and social media networks. As a result, the industry is under increased pressure to provide more green raw materials from natural sources. The green transition, on the other hand, presents a significant challenge due to the wide range of essential ingredients in sunscreens and cosmetic formulations. In this landscape, bioactive natural products play a unique role. Herbal treatments make up the majority of folk medicines, and beauty routines have always been linked to traditional medicine. The green challenge, however, is not as simple as it appears. Indeed, until recently, the natural origin of a botanical extract was sufficient to qualify it as “natural.” However, expectations have risen, and a commercially available plant extract’s naturalness must now be evaluated holistically, taking into account the extraction method, bio-based solvents, and preservatives. Given the current state of the environment, this appears to be a prudent course of action, but it also necessitates increased business creativity and innovation.

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Working on extraction is, without a doubt, one way to innovate in the plant extraction process [19–23]. Thus, the aim of this paper is to investigate the effectiveness of antioxidant extracts from medicinal plants in sunscreens and cosmetic formulations to boost photoprotection, with a focus on sustainable extraction processes of these antioxidants from their original matrices.

15.2 Material and methods This review’s findings are based on previous bibliographic and scientific works that highlight sunscreen, new photoprotection strategies, natural cosmetics, plant phenolic extracts in photoprotection, green extraction processes, innovative techniques, and their role in promoting sustainable ingredients for the cosmetic industry. This was accomplished by using research articles and reviews from Medline/PubMed, Science Direct, Scopus, and Google Scholar.

15.3 Results and discussions 15.3.1 Photostability Sunscreen stability is critical for photoprotection and safety. UVR is absorbed by chemical UV filters, while energetic levels pass through. The chemical molecule then releases this energy in order to regain its initial energy level. However, this process can cause photoisomerization (E–Z photoisomerization and keto-enol isomerization) and even irreversible bond cleavage (radical fragmentation) of some UV filters, which leads to the photodegradation of many products. As a result, other formulation ingredients can be destabilized and activated, causing adverse skin reactions such as dermatitis or photosensitivity reactions, and the sunscreen’s effectiveness can be reduced [11, 24, 25].

15.3.2 Adverse effects in human beings Numerous research studies on the cytotoxicity of inorganic (mineral) UV filters containing smaller nanoparticles have been carried out. These nanoparticles were discovered to not penetrate the skin and instead remain in the stratum corneum. Organic UV filters, specifically benzophenone and cinammate derivatives, were discovered in biological samples such as blood and urine and thus enter the bloodstream. Furthermore, some UV filters may disrupt the endocrine system, including estrogenic, androgenic, and thyroid activities [11, 26–28]. Other research has revealed and confirmed the adverse impacts of a variety of UV filters on male fertility (10 of 29 UV filters). The far more

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dangerous UV filters are aromatic ketones, such as benzophenone and dibenzoylmethane derivatives, which generally cause toxic and allergic reactions through their reactive photodegradation products, followed by the unsaturated double band systems found in cinnamates and octocrylene, which are known for their ability to suffer Micael addition reactions, causing allergic contact dermatitis. Sulfonated compounds that act as DNA alkylating agents can cause DNA damage [11, 27–29].

15.3.3 Adverse effects on environment and marine organisms UV filters are strongly recommended to mitigate the detrimental UV rays, and thus there is an increase in negative effects associated with their concentrations, particularly in aquatic systems [11]. These compounds have been found to accumulate in sediments near polluted marine waters and to bioaccumulate in terrestrial and marine organisms. Furthermore, these contaminations were caused by wastewater discharges and improper product package disposal. Unfortunately, UV filter contamination in the sea may lead to the persistence of these emerging contaminants in the food system. Several studies have found chemical UV-filters (organic) to be harmful to corals and mussels, as well as algae, brine shrimp, crustaceans, dolphins, and fish. One of the most significant negative effects of organic UV filters was growth inhibition [11, 26, 27, 30–32].

15.3.4 Plant extracts as sustainable ingredients for sunscreens and cosmetic formulations Several strategies have recently been proposed with the goal of developing novel sunscreen formulations in order to produce new UV-filters with improved photoprotection, photostability, and both human and environmental security. For many decades, natural resources have been the primary source of bioactive compound discovery [11–16]. Resende et al. recently reviewed the incorporation of terrestrial and marine natural components in 444 commercial sunscreen formulations. The majority of the ingredients found in the analyzed sunscreen formulations (48 %) are derived from terrestrial organisms, with only 13 % derived from marine organisms. According to this study, the Fabaceae family ranked as the first source of natural ingredients from terrestrial sources in the sunscreens studied, followed by the Asteraceae family, and then the Lamiaceae family [12]. Plant extracts with dual photoprotective and antioxidant properties, for example, have shown promise in sunscreen and cosmetic formulations. Antioxidants from natural sources are frequently used in cosmetic preparations to slow the aging effects of the sun. At the moment, the majority of these formulations contain at least one antioxidant. Plant-derived antioxidant compounds, specifically carotenoids, polyphenols, and flavonoids, are beneficial in sunscreens, according to recent research, because they protect

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against sun damage. Aromatic ring compounds can absorb UV rays, specifically UVA and UVB rays with wavelengths ranging from 200 to 400 nm [12, 33–37]. Topically applied botanical antioxidants with high polyphenol content can improve photoprotection without causing skin sensitization. Furthermore, botanical antioxidants help to repair structural dermal photodamage by boosting collagen production and elastin regeneration while decreasing structural component breakdown, and they help to prevent sunscreen photodegradation by increasing their photostability. As a result of these considerations, phenolic extracts have been proposed as one of the most effective functional raw materials for photoprotective and anti-aging cosmetics [38]. It should also be noted that natural products have become increasingly popular in the pharmaceutical and cosmetic markets for their use as effective, sustainable ingredients due to their involvement in multiple phototoxicity pathways over the years [22, 38, 39]. Recent data show that 25 patents for solar photoprotection were issued in various countries, including the United States of America and Canada, between 1996 and 2014. Among these patents, botanical extracts (17 patents) were discovered to be the most commonly studied natural products [39]. When measured by SPF, 18 plants from different families in the Algerian flora showed promising photoprotective effects. The SPF values for these species are summarized in Table 15.1 [40–57]. Table .: Photoprotective potentials of Algerian plant extracts (listed from  to date). Species

Extract and dose

Family

Capnophyllum peregrinum (L.) Lange Chrysanthemum fontanesii Tamaix galiqua Linaria scariosa Abies numidica Crataegus oxyacantha

Methanolic extract ( mg/mL)

Apiaceae

.

[]

Leaves butanolic extract Methanolic extract ( mg/mL) Methanolic extract ( mg/mL) Ethylacetate extract Fruit ethyl acetate extract ( mg/ mL) Methanolic extract ( mg/mL) Seed methanolic extract ( mg/ mL) Ethyl acetate extract ( mg/mL) Infusion extract ( mg/mL) Butanolic extract ( mg/mL) Butanolic extract ( mg/mL) Methanolic extract ( mg/mL) Seed aqueous extract ( mg/mL) Ethyl acetate extract (. mg/mL) Butanolic extract (. mg/mL) Butanolic extract ( mg/mL) Ethanolic extract

Asteraceae Tamaricaceae Scrophulariaceae Pinaceae Rosaceae

. . . . .

[] [] [] [] []

Liliaceae Lythraceae

. .

[] []

. . . . . . . . . ,

[] [] [] [] [] [] [] [] [] []

Aloe vera Lawsonia inermis Linn Schinus molle L. Thymus serpyllum Centaurea tougourensis Astragalus gombiformis Pomel Artemisia campestris Euphorbia retusa Forssk. Veronica rosea Bunium alpinum Waldst. & Kit Jurinea humilis Ceratonia siliqua L.

Anacardiaceae Lamiaceae Asteraceae Fabaceae Asteraceae Euphorbiaceae Plantaginaceae Apiaceae Asteraceae Fabaceae

SPF References

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15.3.5 Antioxidant polyphenols as photoprotective agents Polyphenols represent the most ubiquitous natural substances in all living species, including plants, fungi, bacteria, and animals. They can be found in nearly every medicinal plant species. The primary groups of phenolic compounds are flavonoids, phenolic acids, tannins, stilbenes, and lignans [12, 36–38]. Phenolic compounds have an aromatic ring and one or more hydroxyl groups in their chemical structure. Polyphenols are a broad class of active phytometabolites with a wide range of biochemical and pharmacological actions, including anti-inflammatory, immunomodulatory, photoprotective, and anti-oxidative features. The majority of phenols’ biological properties may be related to their ability to neutralize free radicals, at least in part. Phenolic acids (Figure 15.1) and flavonoids are the most promising chemical substances for photoprotection. Recent researches indicate that these substances are photoprotective due to their high ultraviolet absorption, neutralizing free radical ability, and anti-aging properties [36, 37]. The flavonoid structure is composed of three rings (C6–C3–C6) that are labeled A, B (two aromatic rings), and C (a three-carbon bridge) that is typically in the form of a heterocyclic ring (Figure 15.2). Due to their molecular structure, flavonoids have three light-protective effects: ultraviolet absorption, direct and indirect antioxidant properties, and modulation of multiple signaling pathways. Flavonols, flavones, flavanones, isoflavones, chalcones, and anthocyanins are some of the flavonoid subgroups. The ability of flavonoids to transport electrons to free radicals is attributed to their biological system-protective effects [36, 37].

Figure 15.1: Some examples of phenolic acids.

Figure 15.2: The base skeleton of flavonoids.

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15.3.6 Application of green extraction processes for the cosmetic industries The cosmetics sector is currently severely engaged in the search for clean and safe sources of active compounds to use as prospective cosmetic ingredients in order to meet changing customer needs for cosmetic ingredients as well as concerns for global environmental protection and sustainable pharmaceuticals, cosmetics, and sunscreen products. One of the most recent advancements in sunscreens is the use of antioxidant extracts to boost antioxidant and photoprotective effects [14, 18, 21, 37, 38]. Extracting those compounds from their complex matrices in parallel necessitates the use of several techniques. Maceration, Soxhlet extraction, distillations, and infusion extractions are examples of classical solvent extraction that have been practiced for many decades. Because of the massive volumes of solvents and energy consumed for their assessment, they are ordinarily not eco-friendly, raising questions about worker safety and health as well as an absence of sustainability and clean extraction guidelines. For these reasons, green extraction technologies of natural products such as supercritical fluid extraction, microwave-assisted extraction, ultrasound-assisted extraction, and other emerging extraction processes may be a recent development to satisfy the issues of the twenty-first century, protecting both the environment and consumers while increasing industry competition to be more ecological, economic, and innovative [58, 59]. Recent advancements in green and sustainable extraction methods that are naturally safe for human health and the environment have primarily concentrated on minimizing the consumption of solvents, energy, and equipment. Novel extraction processes (Figure 15.3) for recovering antioxidant polyphenols are emerging as a result of a desire to develop green technologies [58]. We will concentrate on three critical methods in this work: supercritical fluid extraction (SCO2), microwave assisted extraction (MAE), and ultrasonic assisted extraction (UAE).

Figure 15.3: Innovative processes for green extraction [59].

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15.3.6.1 Supercritical fluid extraction (SCO2) CO2 serves as the most popularly used supercritical fluid solvent since it is non-toxic, non-corrosive, non-flammable, cheap, and also has a lower viscosity and elevated diffusivity. Furthermore, CO2 seems to have a lower critical temperature and pressure (31 °C and 74 bar), which makes it ideal for the extraction of thermosensitive compounds [58, 59]. The supercritical CO2 (SCO2) extraction mechanism includes two key steps: extraction and separation. In the extraction section, the fluid is pressurized to the desired pressure and heated to the suitable temperature before expelling the soluble material in the sample and transferring it from the matrix to the separator section. During the separation step, temperature and/or pressure could be manipulated to decrease the supercritical fluid solubility, likely to result in the precipitation of the extracted sample matrix. SCO2 has demonstrated promise as a green extraction solvent capable of replacing organic toxic solvents [60]. SCO2 extracts are easily incorporated into cosmetic products because of their unique properties, favorable texture, absence of water or solvent residues, chemical stability, and presence of biologically active compounds. The extracted samples could be utilized as popular cosmetic ingredients based on the plant material and its practical and chemical characteristics (thickeners, emollients, waxes, fragrances, and colorants). Furthermore, the final SCO2 extracts have a high level of bioactive substances, which have a variety of health benefits in sunscreens and skin care products, functioning as antioxidants and photoprotectants [61]. 15.3.6.2 Microwave-assisted extraction (MAE) MAE is a low-impact extraction technique that employs electromagnetic radiation to enter the product and produce heat within the matrix, allowing the cell walls to degrade. Uniform heating, which stimulates heat and mass transfer in the same manner, can result in faster extraction and, as a result, a shorter extraction time [57]. A number of factors influence MAE efficiency, the most significant of which are solvent and raw material characteristics, temperature, time, and microwave power. The properties of microwave adsorption through the solvent, the contact of the solvent with the matrix, as well as the dissolution rate of the targeted molecules in the solvent, ought to all be taken into account during the solvent screening process. Temperature is a crucial factor in MAE. The temperature in a closed MAE system can sometimes increase above the solvent boiling point. Although higher temperatures enhance extraction yield, they can be disadvantageous for thermolabile component extraction. At 30 W, the extraction time in MAE is generally below 30 min. In general, increasing microwave power improves extraction efficiency, resulting in significantly shorter extraction times (1–2 min at 150 W) [57, 62, 63]. Many successful antioxidant extractions have been reported [63].

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15.3.6.3 Ultrasound assisted extraction (UAE) Similarly, UAE has emerged as a novel process that can boost mass and heat transfer via plant cell wall cavitation. The effects of high-frequency ultrasound on extraction are related to cavitation bubbles, which themselves are described as the creation, growth, and breakdown of microbubbles within a liquid subjected to ultrasonication (frequency greater than 20 kHz) [64]. Concentrated elevated temperatures and pressures, sound wave diffusion, high shear tension, microjets adjacent solid surfaces, and turbulence all contribute to this breakdown. The ultrasonic bath and probe systems deployed in the UAE are both based on a piezoelectric transducer. Although using an ultrasonic bath is simple and cheap, its scaling and repeatability are limited. A stainless-steel chamber is linked to a transducer inside this system and consists of the solid matrix scattered in the solvent. In contrast, an ultrasonic probe system plunges a probe connected to an ultrasonic transducer in the extraction vessel, reducing the energy failure in the medium. Because of the higher intensity that an ultrasonic probe system can generate, this technique is usually recommended as an effective tool in biologically active extraction because concentrating power in a specific area of the matrix improves the effectiveness of the cavitation effect. The ultrasonic probe system, on the other hand, has a sample volume limitation [57, 58, 64]. On an industrial level, the most essential factor to take into account in the UAE is the product to be investigated. A continuous system with a relatively small reactor volume or ultrasonic baths with a bigger radiation pattern surface can be utilized. Ultrasonic treatment power, temperature, and time are effective parameters in the UAE. These three parameters have a similar impact on the extraction yield process because as each increases, so does the extraction yields. Increasing any of these parameters after they have achieved their peak value will result in a decrease in yield. Ultrasonic frequencies are typically between 20 and 120 kHz. Solvents that are commonly include using acidified water, ethanol, other alcohols, and their solutions [64–67]. When used separately or in conjunction, MAE and UAE extraction procedures can play critical roles in achieving green cosmetic goals, resulting in high active principle yields, selectivity improvement, and high product stability. These techniques can be used on both pilot and industrial scale protocols, and several studies have been carried out to compare their performance to that of traditional procedures [57, 58, 64, 65].

15.3.7 Green extraction, green chemistry, and green cosmetics The concept of “green chemistry” is widely mentioned as a professional guide for making prudent use of biodiversity. It is based on 12 principles that aim to reduce pollution techniques [68]. Naturally, solvents and extraction processes are critical issues in the development of the green chemistry concept. To be considered “green,” a solvent must meet a number of criteria, including security, biodegradability, and recyclability. A large percentage of these concepts apply to methods of plant extraction. Furthermore, all

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botanical extraction procedures begin with a solid–liquid mass transfer, which is the release of phytoconstituents from a solid plant matrix into a solvent system, which is usually a liquid. The greater the affinity of the phytoconstituents for the solvent, the more effective the transfer and diffusion should be. Nonrenewable petrochemical solvents are used extensively in industrial practices; therefore, they are increasingly overlooked. As a result of the increasing demand for sustainability, the percentage of different solvents appropriate for the production of phytocosmetic substances has decreased. Water, glycols, and ethanol (alone or in mixtures with water) are ideal solvents for the recovery of polar compounds such as polyphenols and flavonoids in this framework. The utilization of biocompatible solvents in conjunction with environmentally friendly approaches such as supercritical fluid extraction, microwave-assisted extraction, ultrasound-assisted extraction, and other emerging extraction procedures represents a comprehensive approach to the development of “green cosmetics” [69, 70].

15.4 Conclusions Many natural compounds and plant extracts with antioxidant, antimicrobial, and photoprotective properties could be used as sunscreen agents. Vegetables, fruits, and plants, for example, can provide a diverse range of compounds for use as UV filters and photoprotective cosmetic ingredients. In our opinion, clean extraction techniques were appropriate for preparing those bioactive extracts or isolating those molecules of interest. As society’s demand for environmentally friendly products grows, clean extraction methods become more commercially successful. Sustainable methods of extraction, such as SCO2, MAE, and UAE, have some advantages over conventional extraction techniques, such as reduced solvent and energy utilization and, in some cases, improved matching and extraction yields. Future plant extract photoprotection research should also focus on the implementation of biodegradable solvents and novel extraction processes for bioactive compounds extractions that are commonly consistent with cosmetic legislation. These strategies would have to confront not just the scientific aspects of novelly integrating technology but also price reduction, particularly in terms of financing costs, as well as monitoring and controlling their integration at massive scales, together with the greener valorizing of natural products from an environmentally sustainable and socially friendly standpoint.

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Shayeri Das, Prabhat Ranjan* and Tanmoy Chakraborty*

16 A systematic DFT study of arsenic doped iron cluster AsFen (n = 1–4) Abstract: The research on metallic clusters in relevance to its far-reaching involvement in the high technology sector, solid-state physics and catalysis is an interesting and significant field of study. In this report, the investigation of arsenic doped iron cluster, AsFen (n = 1–4) aided by conceptual density functional theory (CDFT) method has been performed. CDFT based global descriptors-mainly HOMO–LUMO energy gap and other parameters of these clusters are worked out. Obtained data shows that band energy gap varies in the magnitude of 1.451–3.138 eV. Uppermost magnitude of HOMO–LUMO energy gap i.e. 3.138 eV is observed for AsFe while AsFe4 show the smallest energy gap. It is noted that band gap of these systems decreases with increase in the cluster size, ‘n’. Direct association concerning both parameters HOMO–LUMO energy gap and molecular hardness of AsFen clusters have been found. It indicates that among the studied compound AsFe is the most stable system whereas AsFe4 is the least stable. Dipole moment of the clusters is observed in the variation of 2.303 Debye to 3.853 Debye, signifying that the bond within the clusters is ionic in nature. The computed bond length between Fe–Fe in AsFen is in agreement with the experimental data. Keywords: As–Fe; density functional theory; dipole moment; hardness; HOMO–LUMO energy gap.

16.1 Introduction Study of transition metal clusters has generated a lot of interest in the last few years due to its remarkable electronic, optical and magnetic properties [1]. It got established that atomic and electronic structure of restricted size molecular system is different from its bulk counterparts [2]. Similarly, in the case of alkali system stability is controlled by electronic shell structure. It is found that system with number of atoms 2, 8, 20 and 40 are more stable [3]. The open-shell 3d compounds like Fe shows unusual atomic engagements and super-paramagnetism. It is reported that for finite size of Fe clusters

*Corresponding authors: Prabhat Ranjan, Department of Mechatronics Engineering, Manipal University Jaipur, Dehmi Kalan-303007, India, E-mail: [email protected]; and Tanmoy Chakraborty, Department of Chemistry and Biochemistry, School of Basic Sciences and Research, Sharda University, Greater Noida-201310, India, E-mail: [email protected]. https://orcid.org/0000-0002-3374-8125 Shayeri Das, Department of Mechatronics Engineering, Manipal University Jaipur, Dehmi Kalan-303007, India As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: S. Das, P. Ranjan and T. Chakraborty “A systematic DFT study of arsenic doped iron cluster AsFen (n = 1–4)” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0270 | https://doi.org/10.1515/9783111071435-016

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magnetic moments increased between 2.7 and 3.3 μB per atom in comparison to 2.15 μB for bulk [4–6]. Time-of flight mass spectra have shown rare arrangement of atoms in case of iron clusters (Fen) with cluster size 7, 13, 15, 19 and 23 [7]. The correlation among structure, stability and physico-chemical properties of iron clusters possess a challenging task, which encouraged researchers to explore more about it [8–16]. In recent decade, a large number of research is being carried out on bimetallic clusters [17–24], mainly on the Pd–Ag [25, 26], Au–Pd [27, 28], Cu–Ag [24], Ag–Au [29, 30], Au–Fe [31], Ag–Fe [32], Pd–Fe [33], Pd–Al [34] Al–Fe [35, 36] and Cu–Fe [37]. However, there is a limited study done on the arsenic doped iron clusters. Arsenic is an important compound, which is utilized to alter the mechanical characteristics of lead and copperbased alloys and additionally to remove the undesirable colour in glasses [38–40]. It is reported that arsenic has significant applications in high technology, solid-state physics, chemical sciences, surface phenomena and catalysis [38, 39, 41–44]. It is observed that incorporation of impure atom in any metallic cluster heightens the physico-chemical features of the host clusters [24]. Wang et al. [45] reported Mn and Fe-doped GanAsn clusters (n = 7–12) and found that Mn and Fe favour to be placed at the surface of the host cage structure. Mirbt et al. [46] reported first principle study of iron on GaAs and found that bond length amid Fe and As govern the magnetic moment of iron. In this study, we have explored the small clusters of arsenic-doped iron cluster, AsFen where ‘n’ is varied from 1 to 4 with the aid of conceptual density functional theory (CDFT) technique. CDFT based global descriptors of these systems are worked out and analyzed.

16.2 Computational details The density functional theory (DFT) is an important computational method used to understand the various physico-chemical features of metallic clusters. DFT technique has been successfully implemented on metallic clusters for applications in materials science, physics, chemical sciences, fluid mechanics, solid-state physics, nuclear physics, life sciences, earth sciences and surface sciences [19–37, 39, 47–53]. In this work, DFT technique is applied to study the physico-chemical characteristics of AsFen (n = 1–4) clusters. Geometry optimization is performed by using Gaussian 16 and Gauss View 6.0 [54]. Previously, exchange correlation B3LYP [31, 37] with basis set LANL2DZ [31, 32, 37] is successfully applied for the computation of iron based clusters. Based on the literature, B3LYP/LANL2DZ is applied for geometry optimization in this study. Ionization energy (I) along with electron affinity (A) of AsFen have been obtained as under [55]: I = – εHOMO

(16.1)

A = – εLUMO

(16.2)

16.3 Results and discussion

337

By using equations (16.1) and (16.2), CDFT based global descriptors namely molecular hardness (η), molecular softness (S), electronegativity (χ) as well as electrophilicity index (ω) of AsFen are calculated as: χ = −μ =

I +A 2

(16.3)

Here, μ denotes the chemical potential I −A 2

(16.4)

S=

1 2η

(16.5)

ω=

μ2 2η

(16.6)

η=

16.3 Results and discussion 16.3.1 Equilibrium geometry In order to obtain the most stable state configuration of AsFen (n = 1–4) initially lowermost energy structure of Fen+1 is performed. After obtaining the ground state structure of Fen+1, one Fe atom in each cluster is replaced by As atom. The optimum energy structure of AsFen (n = 1–4) is presented in Figure 16.1. For n = 1, linear configuration with symmetry group C∞V and doublet spin multiplicity is found. The bond length of As and Fe bond is observed as 2.370 Å. The most stable configuration of n = 2, AsFe2 is a linear shape structure in which one Fe is located in the middle and other Fe atom and As is placed at opposite ends. This structure has the same symmetry group and spin multiplicity as that of n = 1. The bond distance found for Fe–Fe and As–Fe is 2.320 Å and 2.370 Å respectively. The most stable state configuration of AsFe3 is having Y-shape structure with symmetry group Cs. The structure is obtained at high spin multiplicity

Figure 16.1: Ground state structure of AsFen (n = 1–4) clusters.

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16 DFT study of arsenic doped iron cluster

i.e. dectet. There are two Fe–Fe bonds (2.266 Å and 2.320 Å) associated with this structure. Similarly, two As–Fe bond lengths are observed as 2.365 Å and 2.480 Å. The ground state configuration of AsFe4 is obtained after incorporation of one more Fe atom in AsFe3. The structure resembles a trapezium like shape having symmetry group Cs and spin multiplicity quartet. The As atom in placed in the vertices of the other parallel side and the three Fe atoms are linked with it. The Fe atoms on the same side create As–Fe bonds of the length 2.442 Å and that on the opposite arms have bond lengths 2.487 Å and 2.473 Å. The bond length between Fe–Fe at the non-parallel side is observed as 2.267 Å. Similarly, bond length at one of the parallel side is found as 2.242 Å and 2.224 Å among three Fe atoms. The bond length between Fe–Fe in Fen clusters is reported in the range of 2.13 Å to 2.73 Å [56]. The computed bond lengths between Fe–Fe in the current study are in the same range with the reported data.

16.3.2 CDFT based descriptors In this section, CDFT-based global descriptors namely HOMO–LUMO gap, along with molecular hardness, molecular softness, electro-negativity, electrophilicity index and dipole moment of the arsenic doped iron cluster (n = 1–4) have been calculated and presented in Table 16.1. Frontier orbitals – HOMO and LUMO are a major parameter to comprehend the electronic properties of materials. These are substantial factors to comprehend the charge transfer and bond formation in the donor–acceptor complexes [57–63]. It identifies the minutest energy essential for an electron to shift from an occupied to unoccupied orbital [62–65]. Computed data reveals that band gap of AsFen clusters exhibit magnitudes in the variation of 1.451–3.138 eV. The highest energy gap is observed for AsFe cluster whereas cluster at n = 4 show the minimum gap. Analysis shows that HOMO–LUMO gap of AsFen cluster declines with the rise in cluster size, n. The data demonstrates that these clusters may be suitable for non-linear optical devices. In order to comprehend molecular structure, stability, binding and dynamics of chemical clusters, molecular hardness is vital factor [66]. It is the natural tendency of every molecule to configure themselves with maximum hardness [67]. Molecular Table .: CDFT based global descriptors of AsFen (n = –). Species

AsFe AsFe AsFe AsFe

HOMO–LUMO Gap (eV)

Molecular hardness (eV)

Molecular softness (eV)

Electronegativity (eV)

Electrophilicity index (eV)

Dipole moment (Debye)

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

16.3 Results and discussion

339

hardness is an electronic factor of clusters, depicting the stability and solidity of the molecular system. Movement of a molecule from stable to unstable state reduces its hardness value and vice versa [59]. Molecular hardness of AsFen are observed in the variation of 0.725 eV–1.569 eV. Maximum and minimum value of hardness is found for AsFe and AsFe4 respectively. Molecular hardness of AsFen is directly associated by the HOMO–LUMO energy gap. The relation between molecular hardness and HOMO–LUMO gap is presented in Figure 16.2. It indicates that system with high magnitude of molecular hardness in addition to HOMO–LUMO gap exhibits maximum stability whereas system with low value of hardness and HOMO–LUMO gap shows minimum stability. It reveals that AsFe is the peak stable cluster whereas AsFe4 is the least stable cluster among these. Molecular softness of AsFen are in the span of 0.319 eV–0.689 eV. Softness has an inverse relation with the HOMO–LUMO gap of the cluster. Correspondingly the cluster with the maximum hardness gap exhibits minimum softness and vice versa. AsFe4 displays the maximum whereas AsFe shows the minimum softness value. Electronegativity play significant role to apprehend the transfer of charges from donor to acceptor [68, 69]. Electronegativity of silver doped iron cluster is reported in the range of 4.5–5.2 eV [32]. In this study, electronegativity of AsFen cluster ranges from 4.041 eV to 4.522 eV. Highest and least value of electronegativity is witnessed for AsFe2 and AsFe4 correspondingly. The values exhibited are in overlapping ranges with each other. The electrophilicity index of molecular system specifies the decline in energy because of excess movement of electrons throughout donor–acceptor interface. It is governed by the ionization energy and electron affinity [70]. Electrophilicity index of AsFen clusters ranges from 6.010 eV to 11.255 eV. Clusters AsFe and AsFe4 show the least and highest value of electrophilicity index, respectively.

Figure 16.2: Correlation between HOMO–LUMO gap and molecular hardness of AsFen cluster.

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16 DFT study of arsenic doped iron cluster

Dipole moment is an essential aspect to realize the structural symmetry and electronic properties of clusters. For AsFen clusters, dipole moment is exhibited in span of 2.303 Debye to 3.853 Debye, indicating ionic bonds formation in the cluster. Greatest value of dipole moment is found in the case of AsFe3 cluster where cluster AsFe4 display the least value of dipole moment.

16.4 Conclusions In this article, computational analysis of AsFen (n = 1–4) is achieved by using CDFT approach. Ground state configuration of AsFen cluster is reported. CDFT based descriptors – HOMO–LUMO energy gap, molecular hardness, softness, electronegativity, Electrophilicity index and dipole moment of these clusters are obtained. HOMO–LUMO energy gap of the clusters are observed in variation of 1.451–3.138 eV. Cluster AsFe that has linear structure possessing symmetry group C∞V and doublet spin multiplicity is having highest band gap i.e. 3.138 eV. Minimum HOMO–LUMO gap is detected for cluster AsFe4. It has been perceived that energy band gap of these clusters reduces with escalation in the cluster size, n. Result indicates that HOMO–LUMO gap and molecular hardness of these clusters are having direct association. HOMO–LUMO energy gap and molecular hardness are also related with the stability of clusters, it indicates that among these species AsFe exhibits properties to be the utmost stable cluster whereas AsFe4 is the one with least stability. It indicates that these clusters may be suitable candidate for nonlinear optical devices. Cluster AsFe3 shows maximum dipole moment value whereas cluster AsFe4 display the minimum value of dipole moment. The high magnitude of dipole moment of these clusters designate that the bond within the cluster are ionic in nature. Acknowledgment: Dr. Prabhat Ranjan would like to acknowledge the funding support from Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, under Grant No. [CRG/2022/002539]. Dr Ranjan and Ms Shayeri Das are thankful to Manipal University Jaipur’s research facilities and computational resources. Dr. Tanmoy Chakraborty would like to acknowledge the funding support from Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, under Grant No. [CRG/2020/002951]. Dr. Chakraborty is also thankful to Sharda University for providing the research facility.

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Antony Mugiira Arimba*, David Kuria Wamukuru and Zachary Orato Anditi

17 Effect of case-based learning, team-based learning and regular teaching methods on secondary school students’ self-concept in chemistry in Maara sub-county, Tharaka Nithi county, Kenya Abstract: The use of case-based learning and team-based learning may help increase students’ self-concept in chemistry. The purpose of this study was to fill this gap by finding out the effects of case-based learning, team-based learning and regular teaching methods on secondary school students’ self-concept in chemistry in Maara Sub-County, Kenya. The study employed a 3 × 2 × 2 pre-test, post-test quasi-experimental factorial design. The study targeted 18,611 students in 52 secondary schools. Purposive sampling was used to choose three co-educational secondary schools with similar characteristics in Maara Sub-County. A total of 106 form two chemistry students were selected for the study using simple random sampling method. The experimental groups were taught using case-based learning and team-based learning while the control group was taught by regular teaching methods. The three groups were compared two-by-two to find out groups in which the differences in self-concept in chemistry would occur. Student’s self-concept questionnaire was administered to the students in the three groups. The validity of the instrument was ascertained by experts from Egerton University. Pilot testing was done in Meru South Sub-County in schools with similar characteristics. Reliability of the instruments estimated using Cronbach’s alpha coefficient was 0.81. Descriptive and inferential statistics were used for data analysis at α = 0.05. The mean differences in self-concept in the post-test were statistically significant among the three groups. The results of this study may offer valuable knowledge to policy makers as well as chemistry teachers so as to give greater attention to chemistry self-concept among students. The study findings fill a knowledge gap of effectiveness of methods of teaching chemistry. Keywords: case-based learning; regular teaching methods; team-based learning.

*Corresponding author: Antony Mugiira Arimba, Department of Curriculum Instruction and Educational Management, Egerton University, Njoro, Kenya, E-mail: [email protected] David Kuria Wamukuru and Zachary Orato Anditi, Department of Curriculum Instruction and Educational Management, Egerton University, Njoro, Kenya As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: A. M. Arimba, D. K. Wamukuru and Z. O. Anditi “Effect of case-based learning, team-based learning and regular teaching methods on secondary school students’ self-concept in chemistry in Maara sub-county, Tharaka Nithi county, Kenya” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0287 | https://doi.org/10.1515/9783111071435-017

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17.1 Introduction Education is a social process for society transformation [1]. Stipulated that the primary goal of teaching is to make sure that students attain the objectives of the course for the desired level of learning [2]. Therefore, teachers should use different teaching strategies and materials that are appropriate to the content and subjects of the course. There is need for chemistry teachers to utilize strategies that can help improve the self-concept in chemistry among students in order to serve the increasing societal demands in solving problems that require chemistry knowledge. According to Chinasa et al. [3] instructional approaches can be used by the teacher to attain various learning aims. The teacher should endeavor to apply the most suitable approaches that best suit the learning environment so as to increase the academic self-concept of students. According to Kwaku [4] science comprises of theories and explanations for observed events and that all proposed clarifications are open to interrogations. In view of this it is widely recognized that science education can be enabled when learners articulate their earlier knowledge and clarify their understanding to another which brings transformation in beginning. Several studies of students understanding of science are centered on constructivist learning theories and the belief that existing concepts impact learning outcomes because students connect new information with previous knowledge construction. According to constructivists mental interaction takes place as truth is structured through the mental construction formed by the learner. The cognitive structure changes depending on the learning needs of particular individual and the surroundings. Modification of meanings happens throughout the process of reconstruction [5]. Learners should be allowed to take part actively in all aspects of instruction and education process, particularly in problem solving and constructive thinking as well as work together with one another in the constructivist classroom. Posited that learners should be encouraged to be active, have schemes, assimilate and in the long run accommodate all what they learn [6]. Advised learners to learn in groups to acquire the main ideas and later on practice on their acquired information for continued learning [7]. Case-based learning (CBL) is fundamentally students centered, recognizing the significance of actively engaging students in their particular learning. As the duty of instruction shifts towards students, the role of the instructor also shifts from the conventional authority who bestows the final form knowledge to a skilled leader while encouraging the individual acquisition of learning skills. Cases are not just supplemental illustrations but work centrally as the time for learning. This approach reverberates with the views of the cognitive theorist that our minds reason effectively through the analogy and models as much as through the explanation and use of the broad abstract principles [8]. Cases are stories which contain an educative

17.1 Introduction

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message. A case allows learners to put themselves in the character of the given situation. Argued that while the implementation of CBL has gained popularity in science education, few researches in chemistry education exist in literature [9]. While previous studies have investigated the effects of CBL on students’ motivation, interests and enjoyment in chemistry in biology, researchers have not found the effects of CBL on students’ self-concept in chemistry among secondary school students. It was significant to investigate whether innovative teaching strategies such as CBL would be useful in improving chemistry self-concept in Maara Sub-County, Kenya. Team-based learning (TBL) is an organized method of learning that can be applied in teaching huge classes [10]. TBL represents intense use of small groups by changing the arrangement of the discipline so as to establish and then take benefit of the special abilities of great achievement learning groups [11]. According to Allen et al. [12] TBL depends essentially on creating and keeping up the group, making learners accountable at individual and group activities, giving response and planning team approaches to support learning and improve cooperation. This approach is appropriate in courses that qualify two requirements. First, learners should know significant information. Second, the main aim of the course is to apply content by solving difficulties as well as responding to questions [13]. A pure TBL method requires prior reading by the students with readiness assurance process sessions in form of the handouts [14]. Although TBL improves critical intellect, students’ engagement and content attainment, studies on the use of TBL on its’ distinctive outcome on students is inadequate [15]. Found that constructivist learning strategies such as TBL have potential for increasing progressive lifetime learning skills that have important hands-on use in chemical learning settings although there are few studies comparing TBL to other learning approaches [16]. There are also few studies on the impact of TBL chiefly as measured by performance in examinations [17]. Studies have shown a positive effect of TBL in the areas of teacher–learner experiences and attitudes compared to traditional lectures [18]. Low self-concept scores can provide teachers with information about students who may need an additional support [19]. A number of models have been used by scholars to determine the connection between self-concept and academic achievement [20]. The reciprocal effects model proposes that academic achievement and self-concept reciprocally impact each either positively or negatively. The skill enhancement model proposes that academic self-concept is a determinant of academic achievement [21]. Hesbone et al. [22] reported that science process skills teaching approach had considerable result on student’s chemistry self-concept. They argued that their result of the study may offer an understanding for planning instructional approaches that target to improve student’s chemistry self-concept and add to enhancement of instruction and learning of chemistry.

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17.2 Purpose of the study The purpose of this study was to find out the effects of using the CBL, TBL and regular teaching methods (RTMs) on secondary school students’ chemistry self-concept in Maara Sub-County, Kenya.

17.3 Objective of the study i. To compare the difference in students’ chemistry self-concept among those exposed to CBL, TBL and those taught through RTMs.

17.4 Hypotheses of study The following research hypothesis was formulated and tested at α = 0.05 level of significance. HO1: There is no statistically significant difference in students’ chemistry self-concept among those taught using CBL, TBL and those taught using RTM.

17.5 Research design This study used quasi-experimental factorial design which is applied when the study involves experimental groups and control groups [23]. It is used particularly used when intact groups are involved such as in the case of students learning in various classrooms and therefore cannot be reconstituted for experimental purposes [24]. CBL was applied in the first group; TBL was used in the second group while RTMs were adopted as the control. The three groups were compared together to establish the group that was superior in terms of achievement and self-concept in chemistry. A chemistry selfconcept was initially given to chemistry students at the start of experiment. The treatment was conducted for eight weeks. The Research Design is presented in Figure 17.1. Quasi - Experimental Factorial Design Group

Pre-test

Treatment

1 Experimental

OICBL

XCBL

O2CBL

2nd Experimental

OITBL

XTBL

O2TBL

Control group

OIRTM

st

Figure 17.1: Quasi-experimental factorial design.

Post-test

O2RTM

17.8 Students’ self-concept questionnaire (SSCQ)

349

Key: OICBL represents the pre-test scores for the CBL group O2CBL represents the post-test scores for CBL group XCBL represents the treatment for the CBL group OITBL represents the pre-test scores for the TBL group O2TBL represents the post-test scores for the TBL group XTBL represents the treatment of the TBL group OIRTM represents the pre-test scores for the RTM group O2RTM represents the post-test scores for the RTM group.

17.6 Population of study Mugenda and Mugenda [25] described population as entire group of individuals and objects with common observable characteristics. The population of this study comprised all secondary school students in Maara Sub-County, Kenya. The target population was 18,611 students in all secondary schools in Maara Sub County which had 52 secondary schools with a total of 38 co-educational schools. The schools which had a class size of less than 30 students per class were 6 while 15 schools had between 30 and 40 students per class. There were also 17 co-education schools with more than 40 students per class. In addition, 12 schools had a gender disparity of more than 10 students while 4 schools had more than 60 students per class. The accessible population was 2378 Chemistry students from three mixed secondary schools in Maara Sub-County.

17.7 Instrumentation Students’ self-concept questionnaires (SSCQs) were used as the instruments for the study. The instrument was developed by the researcher to assess self- concept in chemistry. To compare the effects of CBL, TBL and RTM on self-concept a similar content was covered in the three groups. This was done by reviewing instructors’ teaching materials to verify that the students were exposed to similar content in the CBL, TBL and RTM classes.

17.8 Students’ self-concept questionnaire (SSCQ) A questionnaire was used to assess students’ feelings towards chemistry. The items were constructed on a five-point Likert scale. Students were asked to indicate their opinions by ticking, strongly agree (SA = 5), agree (A = 4), undecided (U = 3), disagree (D = 2) or strongly disagree (SD = 1) in front of each item. The items consisted of the academic self-concept only. A modified self-concept scale was adopted from [26].

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17.9 Validity of instrument The instrument was validated with the help of science education specialists of Egerton University, Kenya, in the Department of Curriculum, Instruction and Educational Management. They checked the language used in items, difficulty of test items, ambiguity, test length and arrangement of items. This was done to ensure that the items in the SSCQ items comprise of academic self-concept only.

17.10 Reliability of instrument Pilot testing was done in Meru South Sub-County in mixed secondary schools. The questions in the SSCQ had scores ranging from 1 to 5 on the Likert scale. The reliability coefficient was 0.88 hence was accepted for this instrument.

17.11 Treatment of study Chemistry teachers were trained on the use of CBL and TBL in chemistry instruction for one week. The schemes of works, lesson plans and teaching materials were prepared by both the researcher together with the chemistry teachers. The experimental groups were presented with CBL and TBL separately. Chemistry teachers in CBL and TBL played the role of the researchers. The role of the researcher in the CBL was to guide the students with challenging questions to promote thinking and read aloud the presented cases to students. The students in CBL experimental group read individually the presented cases and answered questions. A total of 5 cases were used to teach a selected topic in chemistry. The role of the teacher was to move from one student to another and assist the students. The role of the chemistry teacher in the TBL was to assign students to heterogeneous teams of between 5 and 7 members. Teachers gave individual and team quizzes based on readings to be taken prior to every class instruction, develop related in-class application exercises, and create individual and team assignments. Students did quizzes individually and as a team. When students completed their individual quizzes, they turned in their answer sheets and joined their teammates to take the same quiz as a team. The teacher was responsible for recording the individual and team quizzes in their team’s folders. Appeals were granted based on the strength of the teams’ written appeals. The students were accorded enough time to apply acquired information in teams’ exercises during the lesson. This provided the teams with opportunity to apply their learning from the pre-class readings. This procedure was repeated in the remaining class sessions.

17.14 Effects of CBL, TBL and RTM on students’ chemistry self-concept

351

17.12 Data collection procedures The authority to undertake research was first sought with the approval by Egerton University Board of Postgraduate Studies. The researcher then sought permit to conduct research in the sampled schools with the National Commission for Science, Technology and Innovation (NACOSTI). A pre-test was administered at the beginning of the experiment to the three groups. The first experimental group was taught using CBL while the second experimental group was taught using TBL. The control group was taught using RTM. Data was collected using self-concept questionnaire (SSCQ). The instrument was administered by the researcher with the assistance from chemistry teachers in the sampled schools. The researcher then used scores of the tests to get quantitative data to use for data analysis.

17.13 Analysis of data Descriptive and inferential statistics were used to examine differences in the three mean scores for self-concept in chemistry. Tukey and LSD post hoc analysis was used to identify the most effective teaching strategy. SPSS Version 25 was used in data analysis.

17.14 Effects of CBL, TBL and RTM on students’ chemistry self-concept The objective of this study was to compare the difference in students’ chemistry selfconcept among those exposed to CBL, TBL with those exposed to RTM. The corresponding hypothesis was that there was no statistically significant difference in students’ chemistry self-concept among those taught using CBL, TBL and RTM. The results are shown in Table 17.1

Table .: Pre-test SSCQ mean scores for students in CBL, TBL and RTM. Group RTM CBL TBL Total

N

Mean

Std. deviation

Std. error

   

. . . .

. . . .

. . . .

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17 Effect of case-based learning

TBL had highest mean scores (68.84 %) while CBL had the lowest mean scores (66.08 %). The standard deviation was 10.88, 9.27 and 8.48 for TBL, RTM and CBL, respectively. The standard error mean for RTM, CBL and TBL were 1.49, 1.41 and 1.79 respectively. Table 17.2 shows results for the comparison for the results in mean scores in SSCQ for the three groups. The results indicate that the mean differences were not statistically significant between groups RTM and TBL, RTM and CBL and between groups CBL and TBL when equal variances were assumed. The ANOVA results are shown in Table 17.3. Data in Table 17.3 show that mean differences in SSCQ was statistically insignificant in the three groups, p > 0.05. The results also indicate that the three groups started the experiment at same levels of self-concept as revealed by SSCQ mean scores. Any significant difference that was observed in the post-test in SSCQ mean scores could be attributed to the effect of interventions. The results for the mean scores in SSCQ attained by the three groups are shown in Table 17.4. Table .: Tukey HSD post hoc comparisons of pre-test SSCQ mean scores for the three groups. Dependent variable: learning type Tukey HSD (I) Group

(J ) Group

RTM

CBL TBL RTM TBL RTM CBL

CBL TBL

Mean difference (I–J)

Std. error

Sig.

. −. −. −. . .

. . . . . .

. . . . . .

Table .: Analysis of variance (ANOVA) of the pre-test SSCQ mean scores. Learning type Between groups Within groups Total

Sum of squares

df

Mean square

F

Sig.

. . .

  

. .

.

.

Table .: Post-test SSCQ mean scores of students in the three groups.

RTM CBL TBL Total

N

Mean

Std. deviation

Std. error

   

. . . .

. . . .

. . . .

353

17.14 Effects of CBL, TBL and RTM on students’ chemistry self-concept

Table .: Analysis of variance (ANOVA) of the post-test SSCQ mean scores. Group

Sum of squares

df

Mean square

F

Sig.

. . .

  

. .

.

.

Between groups Within groups Total

The mean scores show that CBL and TBL attained greater mean scores than RTM. The mean score of the RTM was 64.58 % while that of TBL and CBL were 74.64 % and 72.94, respectively. The standard deviation of CBL, RTM and TBL were 13.88, 9.26 and 12.56. The standard error for RTM, CBL and TBL 1.54, 2.38 and 1.23, respectively. Based on this result, students taught using CBL and TBL attained well in SSCQ. This points out that the use of CBL and TBL improved self-concept of leaners in chemistry. Analysis of variance (ANOVA) was used to analyze the differences in mean scores in the three groups. The outcomes are presented in Table 17.5. The results show that the mean difference among the three groups in post-test mean scores in SSCQ were statistically significant, F(2, 103) = 7.32, P < 0.05. This effect could be attributed to intervention in teaching chemistry since TBL and CBL groups had a higher mean score in SSCQ than RTM. Post hoc multiple comparison of post-test on SSCQ mean scores of students who were taught using CBL, TBL with those taught using RTM was used to determine the differences among the three groups. The outcomes are presented in Table 17.6. The results indicate that the mean differences in post-test SSCQ for groups RTM and CBL, RTM and TBL and groups were significant, P < 0.05. As a result, the null hypothesis stating that there is was statistically significant difference in students’ chemistry selfconcept among those taught using CBL, TBL and those taught through RTM was rejected. The alternative hypothesis was accepted. The findings of this study show that students who were taught through TBL and CBL obtained higher mean scores. Table .: Post Hoc Tukey multiple comparisons of post-test SSCQ mean scores for the three groups. Group (i)

Group (j)

RTM

CBL TBL RTM TBL RTM CBL

CBL TBL

Mean difference (I–J)

Std. error

Sig. p-value

−.* −. .* −. .* .

. . . . . .

. . . . . .

354

17 Effect of case-based learning

Lucas et al. [27] indicated that students perceived the instructor to be autonomous in supporting students throughout the TBL period. This is key since in traditional lecture-based courses, learners are less active and are more prone to accept the role of passive recipients of knowledge. According to Hesbone et al. [22] students associate their own success with that of their class – or school mates which leads them to sense more undesirable about their own capabilities in great achievement atmosphere than in low achieving – atmosphere. The findings of Bryan and Chih [28] showed that knowledge acquirement involves relating information rather than simply attaining high exam scores. Learners may realize enhanced academic performance by understanding their self-learning level. Another study by Siski [13] established that TBL students over all are more fulfilled and more involved and performed well in exam than learners who participated in traditional lecture-based programmes. Chepkorir et al. [29] also recommended from their study that science teachers should encourage development of positive self-concept of ability among students. A study by Priscilla et al. [30] revealed that academic self-concept positively and considerably predicted mathematics attainment. Their study recommended that effective guidance and counselling as well as mentoring interventions can be utilized in schools to aid increase students’ academic self-concept. According to Ommundsen et al. [31] speculative beliefs hold that individuals’ understanding and capability to learn affect students’ acquisition of knowledge. Learning styles can impact accomplishments that may lead to a reinforced self-concept. It would be beneficial to emphasize on improved academic self-concept among students. Learners who score reasonably high on academic self-concept hold a relatively high learning conception of ability. A study by Álvaro et al. [32] evaluated learners’ self-concept development from primary to secondary school and showed a clear decrease in selfconcept [33]. Studied student feelings towards a TBL process and how students’ contentment is primarily formed and later modified due to progressive academic performances. Learners were usually positively disposed toward the TBL method acknowledged its benefits in development of team skills [34]. Proposed from their research that collaboration abilities in pupils with high academic achievement are leading factors in increasing the significant impetuses of collaboration skills for students with exceptional academic attainment.

17.15 Summary of findings The results of this study showed that both CBL and TBL enhanced student’s self-concept in chemistry better than RTM. The results also showed that the mean difference between posttest mean scores in SSCQ were statistically significant. This effect could be attributed to intervention in teaching chemistry since TBL and CBL groups had a higher mean score in SSCQ than RTM. Post hoc multiple comparison of post- test on SSCQ mean scores of students who were taught using CBL, TBL with those taught using RTM was

References

355

carried out to determine whether the mean differences were statistically significant. The results indicated that the mean differences in post-test SSCQ for groups RTM and CBL, RTM and TBL and groups were statistically significant. Based on this study, students taught using CBL and TBL attained higher in SSCQ when compared to those taught using RTM

17.16 Conclusions Based on this study student exposed to TBL acquired higher mean scores in chemistry self-concept than those exposed to CBL. TBL was superior in increasing student’s perceived self-concept in chemistry better than CBL strategy. Student attainment in selfconcept in chemistry was lowest in RTM group. The mean differences were statistically significant.

17.17 Recommendations for improvement The following recommendations were made: i. Curriculum planners should incorporate the use of CBL and TBL methods since both strategies improved students’ chemistry self-concept. Teachers should be encouraged to apply them into chemistry curriculum implementation. ii. TBL is more appropriate in enhancing students’ self-concept in chemistry than CBL and RTM, hence it should be put into greater use where possible in students learning.

17.18 Suggestions for further rlesearch i. A study should be carried out on effects of TBL and CBL on other variables such as gender, attitudes or motivation of students in chemistry. ii. A study should also be carried out to compare effects modified TBL and CBL with other teaching strategies other than RTM on students’ learning on students’ self-concept in chemistry and other subjects.

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3. Chinasa PG, Chukwunazo MO, Angela AA. Impact of generative learning model on academic self concept and achievement of secondary school students in chemistry in Onitisha Education Zone, Anambara State, Nigeria. Int J Res Educ Sustain Dev 2021;1:1–16. 4. Kwaku D. Impact of a constructivist approach to learning on high achieving student’s comprehension of electrochemistry concepts. In: The Eurasia Proceedings of Educational and Social Sciences 2018. pp. 220–31. Available from: https://www.researchgate.net/publication/326543495. 5. Amineh RJ, Asi HD. Review of constructivism and social constructivism. J Soc Sci Lit Lang 2015;1:9–16. Available from: http://jssl.blue.ap.org. 6. Piaget J. To understand is to invent. The future of the education. New York: Penguin Books; 1976. 7. Vygotsky LS. Mind in society the development of higher psychological processes. Cambridge, MA: Harvard University Press; 1978. 8. Douglas A. Problems and case based learning in science. An introduction to distinctions, values and outcomes. CBE-Life Sci Educ 2013;12:364–72. 9. Sendur G The effects of case based learning on freshmen students’ chemistry achievement. Energy Educ Sci Technol Part B. Soc Educ Stud 2012;4:1289–302. 10. Preman R, Jerome IR, Nabil Z, Michael AF, Paula G, Naomi L. Implementation of team based learning on a large scale: factors to keep in mind. Med Teach 2018;40:582–8. 11. Michaelsen LK, Knight AB, Fink LD. Team-based learning: a transformative use of small groups in college teaching. Sterling, VA: Stylus Publishing; 2004. 12. Allen RE, Copeland J, Franks AS, Karimi R, McCollum M, Riese DJ, et al. Team-based learning in US Colleges and schools of pharmacy. Am J Pharmaceut Educ 2013;77:1–9. Article 115. https://commons.pacificu.edu/ phrmfac/54. 13. Siski RJ Team based learning: systematic research review. J Nurs Educ 2011;50:665–9. 14. Paul H, Kimberly JO, Boyd R. An initial experience with team-based learning in medical education. Acad Med 2002;77:40–4. 15. Jeanne W, Sharon V, Shawn CK, Elizabeth AS, Greg R, Martha H, et al. The effects of team-based learning on social studies knowledge acquisition in high school. J Res Educ Effect 2014;7:183–204. 16. Thomas PA, Bowen CW. A controlled trial of team-based learning in an ambulatory medicine clerkship for medical students. Teach Learn Med 211;31–6. https://doi.org/10.1080/1040/334.2011.536888. 17. Paul GK, Adrienne S, Nicole JB, Stuart N. Dean N, XP. The impact of team-based learning on medical students’ academic performance. Acad Med 2010;85:1739–47. 18. Reimschisel T, Herring Al, Huang J, Minor TJ. A systematic review of the published literature on team based learning in health professions education. Med Teach 2017;39:1–10. 19. Karim EMAS, El-Fatih ZE, Jalal AB, Emtinan KH, Omer AE, Muawia EI, et al. Team-based learning and lecturebased learning: comparison of Sudanese medical students’ performance. Adv Med Educ Pract 2021;12: 1513–9. 20. Sara EN, Ellen Y. Exploring the structure and function of chemistry self-concept. Inventory with high school chemistry students. J Chem Educ 2015;92:1782–9. http://acs.org/jchemededucation [Accessed 20 Apr 2020]. 21. Chen SK, Yeh CY, Hwang FM, Lin JS. The relationship between academic self-concept and achievement. A multi-cohort-multi-occasion study. Learn Indiv Differ 2013;23:172–8. 22. Hesbone EA, Samuel WN, Mark OO. Effects of science process skills. Teaching approach on secondary school students self-concept in chemistry in Nyando district, Kenya. J Educ Sci Int Res 2014;4:395–72. 23. Kothari CR. Research methodology. Methods and techniques, 2nd ed. New Delhi, India: New Age International Publishers; 2004. 24. Dinardo J. Natural experiments and quasi-natural experiments. New Palgrave Dict Econ 2008:856–9. https://doi.org/10.1057/9780230226203.1162. ISBN 978-0-333-78676. 25. Mugenda O, Mugenda A. Research methods. Quantitative & qualitative approaches. Nairobi, Kenya: ACTS Press; 2003.

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26. Joyce BYT, Shirley MY. A Rasch analysis of the academic self-concept questionnaire. Int Educ J 2007;8. Available from: http://Iej.Com.Au. 27. Lucas MJA, Sara MK, Kjelle DK, Torstein NH, Mildrid JH, Silje M. The relative effect of team-based learning on motivation and learning: a self determination theory perspective. CBE Life Sci Educ 2017;16:1–12. Available from: https://doi.org/10.1187/cbe.17.03.0055. 28. Bryan HC, Chih CW. The relationship among academic self-concept learning strategies and academic achievement; A case study of national vocation college students in Taiwan via Sem. Asia-Pacific Edu Res 2014;24:419–43. 29. Chepkorir S, Edna MC, Chemutai A. The relationship between related factors and students’ attitudes towards secondary school chemistry subject in Bureti district, Kenya. J Technol Sci Educ 2014;4:228–36. 30. Priscilla GN, Dakota KW, Theresia K, Edward K. Assessing the correlation between academic self-concept and mathematics achievement in secondary schools in Nairobi county, Kenya. J Educ Learn 2019;8:102–11. 31. Ommundsen Y, Lund T, Haugen R. Academic self-concept, implicit theories of ability and self-regulation strategies. Scand J Educ Res 2005;49:461–74. 32. Álvaro P, Rube´ n F, Eduardo F, Covadonga G, Jose´ M. Academic self-concept dramatically declines in secondary school: personal and contextual determinants. Int J Environ Res Publ Health 2022;19:3010. 33. Bruce AR, Ira H, Gerlad EW. The effect of team-based learning on student attitudes and satisfaction. Decis Sci J Innovat Educ 2011;9:1. https://doi.org/10.1111/j.1540-4609.2010.00289.x. 34. Chun-Yen C, Pei-Ling L. The relationship between science achievement and self-concept among gifted students from the Third International Earth Science Olympiad. Eurasia J Math Sci Technol Educ 2017;13: 3993–4007.

Chetana Deoghare*

18 Random and block architectures of N-arylitaconimide monomers with methyl methacrylate Abstract: “Itaconimide” is the members of imide (–CO–NH–CO–) family with reactive exocyclic double bond and it is easily obtained from the renewable resource i.e. D-glucose. The polymerization of various N-arylitaconimide (NAI) monomers with methyl methacrylate (MMA) have been reported to improve the glass transition temperature (Tg) and thermal stability of poly(methyl methacrylate) (PMMA). In literature, these studies have been done mostly using conventional free radical polymerization methods, which restricts the architecture of copolymers to “random” only. The block copolymers of NAI and MMA are an important due to the combination of glassy PMMA and thermally stable poly(NAI), which offers its applications for higher temperature service. The architectural control of polymers in provisions of its topology, composition, and various functionalities is possibly obtained using reversible-deactivation radical polymerizations (RDRPs). In RDRPs, the concentration of free radical is controlled in such a way that the termination reactions are minimized (normally in range of 1–10 mol%), and not allowed to obstruct with the desired architecture. However, this is possible by achieving (or by establishing) a rapid dynamic equilibrium between propagating radical and dormant species (i.e. R–X). Among all RDRPs, the atom transfer radical polymerization (ATRP) is very popular and adaptable method for the synthesis of polymers with specifically controlled architecture. Two different architectures of NAI and MMA copolymers are reported using ATRP process. The effect of various pedant groups on the rate constants of propagation (kp) and thermal properties NAI and MMA copolymers is studied. The poly(NAI-ran-MMA)-b-poly(MMA) are stable up to 200 °C and degraded in three steps. Whereas, the poly(NAI-ran-MMA)-b-poly(NAI) are stable up to 330 °C and degraded in two steps. The density functional theory methods are used for calculation of equilibrium constants (KATRP) for the ATRP process for the series of laboratory synthesized alkyl halides. A good agreement was observed between the experimentally determined and theoretically calculated KATRP values. The mechanistic studies are carried for poly(NAI-ran-MMA) copolymer system using statistical model discrimination method along with 1H decoupled 13C NMR spectroscopy. For studying the mechanism of copolymerization of NAI and MMA via ATRP methods, “trimer model or penultimate model” will be more accurate than “dimer model or terminal model”.

*Corresponding author: Chetana Deoghare, Department of Chemistry, Institute of Sciences, Humanities & Liberal Studies, Indus University, Rancharda, via Shilaj, Ahmedabad 382115, Gujarat, India, E-mail: [email protected] As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: C. Deoghare “Random and block architectures of N-arylitaconimide monomers with methyl methacrylate” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0327 | https://doi.org/10.1515/9783111071435-018

360

18 Random and block architectures of N-arylitaconimide

Keywords: ATRP; glass transition temperature; itaconimide; penultimate model; PMMA; RDRPs.

18.1 Introduction The monomers such as nadimides, maleimides (MIs), citraconimides (CIs) and itaconimides (IIs) having “imide” functionality (–CO–NH–CO–) in there structure. The incorporation of these “imide” functionalized monomer on polymers backbone, results in copolymers with attractive properties i.e. high thermal stability and glass transition temperature (Tg). These monomers can be copolymerized via condensation polymerization methods or free radical polymerization (FRP) methods. Using the condensation polymerization methods such as ring opening metathesis polymerization, the “imide” functionality is incorporated in the polymer backbone and gives “thermosets”. These “imide” monomers polymerized under FRP conditions give “thermoplastics”, in which the “imide” functionality appears as a pendant group in the polymer backbone [1–5]. The various MIs have been copolymerized with methyl methacrylate (MMA) using conventional FRP methods, thus giving copolymers with high Tg and thermal stability [6–13]. An exocyclic double bond present in IIs is more reactive than corresponding MIs and CIs with endocyclic double bond. Hence, the IIs can be integrated into a greater amount as contrast to their analogous MIs and CIs [14, 15]. Also, the MIs are mostly obtained from the non-renewable sources i.e. petrochemicals via maleic acid. However, the monomers from “itaconimide” family are easily generated from the renewable resource i.e. D-Glucose. Most of the vinylic monomers viz. itaconic acids and its derivatives are synthesized from renewable resource (i.e. plants) via fermentation of starch [16–18]. The copolymerizations of various IIs have also been obtained with styrene (St) and MMA monomers, resulting “imide” as the pendant group. The majority of the accessible literature informs about the conventional FRP of N-arylitaconimide (NAI) and MMA monomers and having “random” architecture only [5, 14, 15, 19, 20]. The conventional methods of polymerization offer a poor control on architecture and molecular weight (MW) of copolymers. These limitations can be overcome by using reversible-deactivation radical polymerizations (RDRPs) [21, 22]. There are handful reports available on MMA-NAI copolymer systems reporting architecture other than “random”. The block copolymers (BPCs) of N-phenylitaconimide (N-PII) and its chloro derivatives with MMA were synthesized via RDRP technique [23]. This review describes the various aspects such as mechanism of conventional FRP, the synthesis of various NAI monomers, copolymerization of NAI with MMA, importance of ATRP method, different architectures of NAI with MMA and experimental and computational investigation on kinetics of copolymerization of NAI and MMA monomer under ATRP conditions.

18.3 Conventional FRP of IIs

361

18.2 Conventional FRP FRP is one of the most commercially noteworthy polymerization process utilized in the industries for the manufacture of approximately 60 % of all synthetic polymers. It is a chain polymerization happening via series of initiation, propagation, termination, and chain transfer reactions (Scheme 18.1). As compare to other chain polymerization methods, such as ionic and coordination polymerization, FRP has the benefit of being lenient of easygoing conditions, reagent, solvent impurities and availability of large range of monomer functionalities, making FRP an profit gaining option for many industries. In FRP, low stationary steady-state (SS) concentration of propagating radicals was set upped by harmonizing the rate of termination (kt ≈ 108 M−1 s−1) with a deliberate uninterrupted initiation (ki ≈ 104 M−1 s−1). In order to propagate the chains with an elevated degree of polymerization (>1000) the rate of propagation (kp) must be thousands times more rapidly than termination. Hence, the synthesis of polymers with precise and composite architectures under FRP conditions is difficult, as the polymer chains are slowly initiated and continuously terminate after 1 s life span. The copolymers manufactured using traditional FRP methods are having broad polydispersity with high MWs. However, it is not easy to manufacture the copolymers with different functionalities and architectures that will be having more potential benefits in future [21, 24, 25].

18.3 Conventional FRP of IIs The N-alkyl/NAI monomers have been homo-polymerized and copolymerized with St and acrylic monomers. The copolymerization of N-alkyl/NAI with acrylic monomers were

Scheme 18.1: Mechanism of conventional FRP.

362

18 Random and block architectures of N-arylitaconimide

carried out in THF or toluene solvent and AIBN as initiator in nitrogen atmosphere at 60–65 °C. The copolymers were mostly white in color and soluble in THF, dioxane, DMF and DMSO. The thermal properties of the copolymers (feed composition of monomers = 2:8:NAI:MMA) were measured using differential scanning calorimetry (DSC) and thermogravimetry (TG) techniques and summarized in the Table 18.1. The MWs of the copolymers of N-alkyl/NAI with acrylic monomers were reported in the range of 1 × 103 to 1 × 104 g mol−1 with broad PDI = 2.5–4.5 [5–7, 14, 15, 19, 20, 26–28].

18.4 RDRPs RDRPs have opened the new paths to design the polymers with complex architectures and site specific functionality. In RDRPs, a stationary SS concentration of propagating radicals is obtained by matching the rates of activation-deactivation process. Termination process must be thousands times slower than propagation and initiation should be very fast, preferably faster than propagation, resulting in the simultaneous growth of all propagating chains and enabling the polymers with controlled architectures [29–34]. The RDRP systems that have most often been used are stable free-radical polymerization (SFRP) or nitroxide mediated radical polymerization (NMP), atom transfer radical polymerization (ATRP), and reversible addition fragmentation chain transfer (RAFT) process [35]. The SFRP and ATRP systems obey the persistent radical effect (PRE) [36, 37] and a stationary SS of propagating radicals was obtained via the activation-deactivation reaction relatively than initiation-termination reaction as in the FRP. In SFRP or NMP, Table .: Tg and thermal stability of copolymers of NAI and MMA synthesized via conventional FRP (feed composition of monomers = ::NAI:MMA). where, R =

Tg (°C)

Ti (°C)

Tmax (°C)

Tf (°C)

Char yield (%) at  °C

Reference

         

      – – – –

      – – – –

      – – – –

         

[]

O

N R O

-Chlorophenyl -Chlorophenyl -Chlorophenyl Phenyl -Methylphenyl -Carboxyphenyl -Methoxy--chlorophenyl -Methoxy--chlorophenyl -Methoxyphenyl -Methoxyphenyl

[] [] [] []

here, Ti = initial degradation temperature, Tmax = maximum degradation temperature, Tf = final degradation temperature.

18.5 ATRP

363

alkoxyamines are used as persisting radicals. The thermally wobbly C–O bond in alkoxyamines crumbles upon heating to provide an initiating species for polymerization. The utilization of alkoxyamines in SFRP or NMP is originally limited due to short of suitable synthetic reactions for their synthesis. Most of the reported methods results in low yield of polymers and a large range of by-products [38]. The RAFT process is based on degenerative transfer and does not show PRE [39, 40]. In RAFT process, the dithioesters are the initiating species which may imparts the color and odor to the polymer and can made their purification and storage difficult. In most of the ATRP reactions, alkyl halides are used as persisting radicals. A C–X bond of alkyl halides (R–X) undergoes homolysis in presence of transition metal complex (TMC) to give the initiating alkyl radical [33, 41, 42]. Out of all RDRPs, ATRP is an eye-catching and vastly used technique in the laboratories, due to its simple experimental setups, easy accessible of broad range of monomers and solvents for the polymerization, as well as due to marketable accessibility of many initiators (viz. alkyl halides) and catalytic systems (in terms of TMCs) [43, 44].

18.5 ATRP ATRP was found out in the mid of 1990s by Krzysztof Matyjaszewski group in Carnegie Mellon University. ATRP is having its main basis from ‘atom transfer radical addition’ reaction, which forms the 1:1 adduct of R–X and alkenes, catalyzed by TMCs [45, 46]. ATRP is one of the most attractive techniques to design the polymers with difficult architectures having desired MWs and narrow polydispersity index (PDI) [47]. ATRP has been productively used to design the polymers with linear, stars, cyclic, comb, brushes, networks, dendritic, and hyperbranched topology, composition (such as statistical, stereoblocks, multisegmented BCPs, graft, periodic, alternating, and gradient) and functionalities that can be incorporated into the side chains, end groups as well in many arms of star shaped polymers and hyperbranched polymeric materials [48–50]. The working mechanism of ATRP is given in Scheme 18.2, it involves the homolytic cleavage of R–X bond (mostly used as alkyl halides). This step is generally knows as activation step and governs in the presence of catalyst/activators (mostly TMCs) in its lower oxidation state, MtnY/L. The propagating radical (R•) generates reversibly with rate constant of activation i.e. kact along with the deactivating species (i.e. X–Mtn+1Y/L). This deactivation step is governed by rate constant of deactivation, kdeact, where halide atom (i.e. X) is transferred back from the MtnY/L to the ‘R•’. The activation-deactivation process occurs throughout the polymerization. The deactivation of the generated radicals should be suitably fast to make sure that only a few numbers of monomers are going to add into the R• during each and every step of the reaction [24, 25, 31, 36, 44, 51]. R-X + Mt n Y/ L L = Complexing Ligand

k act k deact

R

.

kp monomer

+

X-Mt n+1Y/ L

kt termination

Scheme 18.2: Mechanism of transition-metal-catalyzed ATRP.

364

18 Random and block architectures of N-arylitaconimide

ATRP is restricted by the formation of dynamic equilibrium between ‘R•’ and dormant species (R–X or macromolecular species) [24, 25, 52]. The ATRP equilibrium constant (KATRP) can be expressed as the combination of the following four equilibrium constants (Scheme 18.3), oxidation of the TMC or electron transfer (KET), R–X bond homolysis (KBH), reduction of a halogen to a halide ion or electron affinity (KEA) and union of the halide ion to the TMC or it is also called “halogenophilicity”. Experimentally, the KATRP values are determined using Fischer-Fukuda equation for the PRE with the support of polymerization kinetics. Deoghare et al. well reported the experimental determination of KATRP values series of bromo succinimides synthesized in the laboratory using UV–Visible spectroscopy with support of Fischer-Fukuda equation. Theoretically KATRP values are calculated by using computational chemistry tools such as density functional theory (DFT) methods. The energetics associated with the homolytic cleavages of R–X is directly correlates with the KATRP [53–60].

18.5.1 Kinetics of ATRP The rate of copper catalyzed ATRP process (Rp) is restricted by maintaining the rapid dynamic equilibrium among activators-deactivators. Several experimental [61–66] as well as theoretical [53, 67–70] studies have been reported on kinetic and thermodynamic factors involves in ATRP. In copper catalyzed ATRP, the Rp depends on the [M] and [P•] as shown in equation (18.1). The value [P•] depends on the position of the equilibrium (shown in Scheme 18.1) and KATRP [71]. Rp = k p [M][P• ] = k p K ATRP [M][[I]0 ] × [CuI Y /L]/[X − CuII Y /L]

(18.1)

Here, kp = rate constant of propagation, [I]0 = initial concentration of initiator R–X and [CuIY/L] = concentration of activator, [M] = concentration of monomer, [X–CuIIY/ L] = concentration of deactivator and [P•] = concentration of propagating radical.

Scheme 18.3: Sub-equilibria in KATRP.

18.5 ATRP

365

However, such type of polymeric reactions obeys 1st order kinetics with regard to the concentration of each monomer, initiator, and CuI complex, under homogeneous circumstances [53]. In ATRP, conversion of monomers with time follows 1st order kinetics. This specifies the steady concentration of active species, [CuIY/L] in polymerization. The accurate kinetics regulation for deactivator, (X–CuIIL/Y) was more intricate due to the impulsive production of CuII by the use of PRE [36, 51, 72]. In ATRP, the synthesis of polymers with predetermined MWs and narrow PDI requires a sufficient concentration of X–CuIIL/Y (equation (18.2)). For the same monomer system, the TMC (lower oxidation state) that deactivates the growing polymers chains more rapidly will generate in polymers having narrow PDI with lesser kp/kdeact ratio. On the other hand, the PDI must be C(O)R > C(O)OR > Ph > Cl > Me, (iii) the R–X bond strength should follow the trend as, R–Cl > R–Br > R–I [33, 43, 44, 78, 109, 110]. The alkyl iodides are light sensitive and use of it in the laboratory requires special precautions. For C–I bond the possibility of degenerative transfer is more prominent due to its heterolytic cleavage leads to difficulties in successive ATRP [111]. An avoiding halogen exchange the similar halogen and metal salt must be used to obtain better polymerization control. Iniferters are successfully utilized as initiators in the ATRP of MMA [112–114]. Most of the literatures have been reported ATRP of MMA using EBiB as initiator due to the structural resemblance with MMA monomer [53, 115–121]. Deoghare et al. well documented the synthesis series of bromo succinimide (BSI) initiators for the ATRP of N-PII with MMA. The published results show that the synthesized BSI has better performance on the kp of copolymerization reaction, MW, Tg and thermal properties of the obtained copolymers as compared to the commercially available ATRP initiator i.e. EBiB [122].

18.6.3 Catalysts The perfect catalysts for ATRP reaction should be very much discerning for atom transfer step and it should neutralize very rapid with considerable diffusion controlled rate. The ATRP catalyst should also have tunable activation rate constant for specific monomer to cope the particular reaction conditions. ATRP has been effectively governed by a range of transition metals, such as, Copper [35, 123], Titanium [124], Molybdenum [125, 126], Rhenium [127], Iron [106, 128–130], Ruthenium [131], Osmenium [132, 133], Rhodium [134], Cobalt [135], Nickel [136, 137] and Palladium [138]. Out of all, the ‘copper’ catalysts are mostly used in ATRP, in terms of its versatility and cost, suitable to broad range of monomers in various solvent systems. ATRP of monomers such as St, acrylate, amides, and acrylonitrile has been successfully carried out using copper complexes. In reaction CuI forms tetrahedral or square planner complexes and CuII forms trigonal bipyramidal complex with tetradentate or with two bidentate ligands [45, 46, 139, 140]. Ligands help to fine-tune the atom transfer equilibrium and to make available suitable catalyst solubility. Ligands such as multidentate alkyl amines [141, 142], bipyridine (Bpy) [143, 144], pyridineimines [145], phosphines [106, 146] or ethers and also organic photoredox compounds [147] have been used in ATRP.

368

18 Random and block architectures of N-arylitaconimide

In ATRP there is an admirable linear correlation of the KATRP and electrochemical redox potentials of the catalyst. The redox properties of the catalyst can be attuned over a very broad range viz. >500 mV, corresponding to a range of KATRP values (on both sides of) eight orders of magnitude. More reducing catalysts are characterized by higher KATRP. However, the KATRP is also relays on the attraction of TMC to halogens viz. halogenophilicity (Scheme 18.3). Therefore, the many transition-metal complexes are more reducing and having weaker attraction towards halogens. Hence, it is easy to decide the suitable complexes for the polymerization of different monomer systems and to evade side reactions arising with the complex redox process of growing free radicals [25, 33–35, 43].

18.6.4 Temperature and Solvents ATRP can be performed over a very wide range of temperatures viz. from subzero to even 150 °C [35]. Similarly, with increase of reaction temperature the kact and KATRP increases. Temperature has pronounced effect on ATRP kinetics, with the slight increase of reaction temperature (50–70 °C) the kp values were increase with the 103 order of magnitude [75]. ATRP reactions can be successfully carried out in bulk, many organic solvents, CO2, water (homogeneous as well as in heterogeneous medium such as emulsion, inverse emulsion, miniemulsion, microemulsion, suspension, precipitation) and even in the gas phase and from the solid surfaces [24, 25, 33–35, 43, 44]. The kact, kdeact and KATRP for the activation-deactivation sequence in copper-catalyzed ATRP are dependent on the nature of the solvent. Increase in polarity of the medium the rate of activation increases and slows down the rate of deactivation, eventually resulting in a higher value of KATRP. In ATRP, it has been found that the kp values were highly depend on the polarity of the solvent viz. the kp values were determined using PLP-SEC for PMMA in THF and DMSO solvent as 2.67 × 103 L mol−1 s−1 and 4.62 × 102 L mol−1 s−1, respectively [53–60].

18.6.5 Modifications on ATRP The CuI complexes are susceptible to oxygen and other oxidative agents [148]. To avoid the oxidation this CuI complexes as well as to reduce the amount of the catalyst in reaction, several modifications have been reported such as, reverse ATRP, simultaneous reverse and normal initiation ATRP, activators generated electron transfer (AGET) ATRP, activators regenerated by electron transfer ATRP, initiators for continuous activator regeneration ATRP, supplemental activators and reducing agents ATRP and electrochemically mediated ATRP [24, 25, 33, 43, 44]. The general mechanism for the AGET-ATRP is shown in Scheme 18.4. In a typical AGET-ATRP reaction, various R–X can be used as initiator, a TMC such as, X–Mtn+1Y/L is used as catalyst in contrast to normal ATRP (n-ATRP) (where MtnY/L is directly used as catalyst). In AGET-ATRP, MtnY/L species is produced in situ by the

18.7 Synthesis of IIs

369

X-Mtn+1Y/ L Sn(EH)2 R-X +

Mtn Y/ L

kact

R.

kdeact

kp

n+1 + X-Mt Y/ L

kt

monomer

termination

Scheme 18.4: Mechanism of AGET-ATRP.

reduction of X-Mtn+1Y/L with reducing agent [Sn(EH)2] [120, 149], D-glucose [150], ascorbic acid [151, 152], phenol [153], thiophenol [154] or triethylamine [155]. After the formation of activator, the subsequent steps of AGET-ATRP follow the same mechanism as n-ATRP (Section 18.5). In AGET-ATRP, a little quantity of catalyst is constantly rejuvenated by the reducing agent to minimize the radical termination. Thus, AGET-ATRP has all benefits as of n-ATRP and includes supplementary profit of simplistic synthesis, storage and handling of catalysts.

18.7 Synthesis of IIs The synthesis of IIs was generally accomplished in two steps. Synthesis of itaconamic acid was done by the reaction of itaconic anhydride with amine subsequent by its cyclization using a dehydrating agent as shown in the reaction Scheme 18.5 [5, 14, 15, 19, 20]. The N-PII can also be obtained from dehydration of their respective itaconamic acid using conc. H2SO4 as catalyst in methanol [156]. The N-alkylitaconimides (N-AIIs) such as N-n-butylitaconimide (N-n-bII) and N-tert-butylitaconimide (N-tert-bII) can be synthesized from their respective itaconamic acid using p-toluene sulphonic acid as catalyst [8]. The N-substituted itaconic acid easily undergoes isomerization in presence of amine to give its corresponding CIs [157–160]. The presence of various substituents on –N– can alter the polarity of imides groups thus affecting the properties of the itaconimide monomers. The effect of substituent ‘R’ on melting point (m.p.) or boiling point (b.p.) and the percentage yield for various itaconimide monomers synthesized in the literature is summarized in Table 18.2.

Scheme 18.5: Synthesis of N-substituted itaconimide.

370

18 Random and block architectures of N-arylitaconimide

Table .: Structures of various N-alkyl/NAI with their physical properties and yield. O

where R =

m.p. (°C)

b.p. (°C) [mmHg]

– – – – 

– – – – –

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – , –

– – – – – – – – –  [.] – – [] – [.] – []  [.]  [.] – [] – [.]  [.] – [.] – [] – [.]  [.] –  [] – [.] – – – – – – – –

Yield (%)

Reference

N R O

Phenyl -Carboxyphenyl -Carboxyphenyl -Carboxyphenyl -(Chloresteroxycarbonyl) phenyl -(Chlorocarbonyl)phenyl -Methoxyphenyl -Methoxyphenyl -Methoxyphenyl -Ethoxyphenyl -Methylphenyl -Ehtylphenyl -Ethoxycarbonylphenyl -Acetoxyphenyl iso-Butyl Methyl Ethyl

n-Propyl iso-Propyl n-Butyl tert-Butyl β-Chloroethyl Benzyl n-Octyl Cyclohexyl -Nitrophenyl -Nitrophenyl -Nitrophenyl -Hydroxyphenyl -Hydroxyphenyl -Hydroxyphenyl -Chlorophenyl

– [, , , , , , –] – [, , , ] – [] – [] . [] . – – – – – –     – – –   – –   – –  –  – – – – – – –  –

[, ] [, , , ] [, ] [] [] [, , , , , ] [, ] [] [] [] [] [] [] [] [] [] [] [] [] [, ] [] [] [] [] [] [] [] [] [] [] [] [] [, ] [, , , , , , , ]

18.8 Mechanism of copolymerization

371

Table .: (continued) O

where R =

m.p. (°C)

b.p. (°C) [mmHg]

Yield (%)

Reference

– , – – – – – – – –   – – –  . . – – –

– –

– –

[, , , , ] [, , , , ]

– – – – – – – – – – – –

– – – – – – – . . – – – –   – – –

[, ] [] [] [, ] [, ] [] [] [] [] [, ] [] [] [] [] [] [] [] []

N R O

-Chlorophenyl -Chlorophenyl -Bromophenyl -Bromophenyl -Bromophenyl -Methylphenyl -Methylphenyl -Isopropylphenyl -Ethylphenyl -Anthryl -Anthryl ,-Dimethylphenyl ,-Diethylphenyl ,-Diisopropylphenyl -Methoxyphenyl -Methoxy--chlorophenyl -Methoxy--chlorophenyl Cyclohexyl Allyl Octadecyl

– – – – –

18.8 Mechanism of copolymerization In literature three main mechanisms proposed for FRP are as follows:

18.8.1 Terminal model According to the terminal model (TM), in copolymerization, the chemical reactivity of P• is completely reliant on the characteristics of the monomer group present at the growing end. Copolymerization of the two monomer (M) generates the possibility of two types of P•, 1st with M1 at the propagating end (M1˙) and 2nd with M2 at the propagating end (M2˙). M1 and M2 can each add either to a propagating chain ending with M1 or to one which is ending with M2, giving four possible propagating reactions as:

372

18 Random and block architectures of N-arylitaconimide

(18.4)

(18.5)

(18.6)

(18.7) Where, k11 is the rate constant for a propagating chain ending in M1˙adding to M1, k12 that for a propagating chain ending in M2˙adding to M2, and so on. The reactivity ratio (RR) is the ratio of the rate constant of reactive propagating monomer adding to M1 to the rate constant of its addition to the M2. The final equation for calculating RRs r1 and r2 values for monomers M1 and M2 using TM is [21, 173], r1 =

k 11 k 22 and r2 = k 12 k 21

(18.8)

18.8.2 Penultimate model The failure of TM is observed for some comonomers indicating that the reactivity of the many P• is affected by the penultimate monomer. This behavior in polymer kinetics is known as second-order Markov or penultimate unit effect (PUE). The stoppage of TM is relatively observed where monomers contain highly bulky or polar substituent and reactivity of both monomers is remarkably different from each other. Penultimate model (PM) consist of eight number of kps and six RRs (viz. r1 , r′1 , r2 , r′2 ; monomer RRs and viz. s1 and s2 ; radical RRs) which are given as below: The radical RR is the ratio of the kp for reaction in which the penultimate monomer differs from the terminal monomer compared to the kp where the penultimate and terminal monomers are the identical [21, 173]. (18.9) (18.10) (18.11) (18.12)

18.8 Mechanism of copolymerization

373

(18.13) (18.14) (18.15) (18.16) r1 =

k 111 k 112

r′1 =

k 211 k 212

r2 =

k 222 k 221

r′2 =

k 122 k 121

s1 =

k 211 k 111

s2 =

k 122 k 222

(18.17)

18.8.3 Complex participation model The complex participation model is also known as “copolymerization with simultaneous depropagation”. The variation from the TM is also examined for the comonomer complex contends with each entity of M1 and M2 in propagation step. The complex participation model depends on copolymer composition, temperature and concentration of M1 and M2. It involves eight kps, the initial four propagation steps of M1 and M2 (equation (18.4)–(18.7)) are similar as explained for TM and the next propagation reactions of the copolymerization as given follows: (18.18) (18.19) (18.20) (18.21) where, M 1 M 2 and M 1 M 2 are complex adding to a propagating center at the M1∙ and M2∙ ends respectively. The equilibrium between uncomplexed and complexed monomers is given in equation (18.22). (18.22) There are total six RRs including two radical RRs.

374

18 Random and block architectures of N-arylitaconimide

r1 =

k 11 k 12

r1c =

k 112 k 121

r2 =

k 222 k 221

r2c =

k 221 k 212

s1c =

k 112 k 11

s2c =

k 221 k 22

(18.23)

This model is useful mostly for studying the mechanism of copolymerization in “bulk” [21].

18.8.4 Discrimination between TM and PM During the effort to search for “best” copolymerization model in favor of definite copolymerization system, researchers have tried to fit these selected models to a variety of copolymer distinctive properties i.e. its composition, polymer chain length and kp. In literature two common approaches are reported to search the “best” mechanistic model for FRP. An exceptionally 1st approach is to execute a designed experiment which is intended to validate a meticulous step (or steps) in the propagation. This approach is well explained by Hill et al. [174] to demonstrate that the PUE in many copolymerization systems of certain monomers. The later approach is ‘to investigate the fit of suggested models for the obtained data’. Though, an analysis is merely valuable as the various experimental results have been composed. The statistical model discrimination (SMD) methods were employed to select experimental conditions that every experiment will enclose the highest probable amount of information as well as potency and failure of the tough models. Similarly, SMD methods illustrate ‘how to scrutinize the data consistently to conclude which model provides “best” description of the collected experimental results. Discovering a model that gives the greatest picture of resulting copolymerization is imperative for designing and controlling the rate of copolymerization mechanism. Additionally, if modeling postulations are accurate, searching the model which affords the finest explanation of experimental results will assist to recognize the mainly possible mechanism of copolymerization system [175]. In 1994, Burke et al. [176] have put forward the methodical purpose of SMD methods to decrease the required figure of experiments to discriminate amid “TM” and “PM” with respective St and acrylonitrile copolymerization to cover the whole series of feed compositions. The authors have used triad fraction data to explain the PUE in the copolymerization of St and acrylonitrile monomer system. In 1997, Andrzej Kaim [177] used SMD methods to find the correct mechanism of copolymerization of St and MMA system. The obtained results show that the copolymerization system is having limited PUE than terminal. Later, in 2003, author used multiresponse maximum likelihood method and statistical tests, to discriminate between TM and PM, for the same system in bulk [178]. Similarly, author also explained that the investigated statistical methods are suitable for kinetic-model discrimination when they are applied to a posteriori data of copolymer composition. In 2005, Pramil Deb [179] used non-linear curve-fitting regression method to calculate the RRs suggesting the consequences of terminal and penultimate monomer in the copolymerization of St and MMA.

18.9 Mechanism and kinetics of copolymerizations of IIs

375

18.8.5 Microstructure analysis of copolymers of NAI and MMA The physico-chemical properties of polymers are affected due to its varied microstructure. Thus the role of monomer allotment and stereochemical arrangement of the various groups in the polymer chain is important [180, 181]. Microstructural studies of polymer having an enormous help in developing the structure-property relationship [182]. Microstructure analysis of the polymers mostly carried out by using 1H and 13C NMR spectroscopy. The 13C NMR has been extensively used to study the microstructure of many vinyl copolymers [183–188]. Anand et al. reported the microstructure analysis of N-(o-/m-/ p-chlorophenyl)itaconimide-MMA using 1H decoupled 13C NMR experiments and theoretically via Harwood program and Monte Carlo simulation method [169]. The ‘>C=O’ carbon signals from the 13C NMR spectra of MMA and NAI of copolymers were used for the evaluation of sequence distribution triads. Chauhan et al. reported the microstructure analysis of the various substituted NAI monomers and MMA copolymers by 1H decoupled 13 C NMR spectra demonstrated that the mechanism of copolymerization follows firstorder Markov model [189]. Deoghare et al. reported a detailed study of microstructure analysis for the N-PII and MMA copolymerization system. The mechanistic study for the N-PII and MMA copolymerization system was carried out experimentally as well as theoretically. Experimentally, ‘>C=O’ regions from the 1H decoupled 13C NMR spectra of the N-PII-MMA copolymers were taken under the observation. Theoretically, the triad fractions of N-PII-MMA copolymer are calculated by means of TM and PM. For this the resulting copolymers of N-PII-MMA are modeled as a dimer (H–M2–M1–X) and trimer (H–M3–M2–M1–X) model. Experimentally obtained triad fractions were matching well with the theoretically calculated triad fractions for this the polymeric species were modeled as trimer. Hence, it is obvious that the mechanism of polymerization of IIs (N-PII) with MMA via ATRP follows PUE/PM [190].

18.9 Mechanism and kinetics of copolymerizations of IIs During the study of FRP of various IIs and MMA system, the higher RRs have been reported in the literature for itaconimide monomers as compared to MMA. The RRs of the monomers can be determined using its feed ratios and copolymer composition. Copolymer composition can be determined using different analytical tools such as IR, 1H NMR, UV–Vis spectroscopy and elemental analysis. The monomer RRs for the copolymerization N-alkyl/NAI with MMA/St based on TM were reported. The monomer feed composition and polymers composition were employed to calculate the RRs with the help of Finemann Ross (FR) method (equation 18.24) or Kelen Tudos (KT) method (equation 18.25) [21].

376

18 Random and block architectures of N-arylitaconimide

FM (fM − fI ) fM FM2 rM = − rI FM FI2 F M fM

(18.24)

where, f = mole fractions of monomer in the copolymer and F = mole fractions of monomer feed. The subscript M and I denotes MMA and N-alkyl/NAI, respectively. rI rI η = (rM + )ξ − α α

(18.25)

where, η and α are the parameters depending on f and F. In literature, mostly TM was employed to the find the RRs of NAI monomers and PM have not been investigated [5, 14, 15, 19–21]. The possibility of PUE was considered particularly for radical terminated with MMA. This is because; the selected copolymerization system contains NAI monomer, which is bulky monomer with electron deficient substituent/s present on its double bond. The RRs of monomers of N-alkyl/NAI with MMA/ St calculated using TM is given in Table 18.3. Deoghare et al. well reported RRs of monomers (i.e. N-PII and MMA) using PM (RRs of monomer and radical RRs are as rM = 0.23, r’M = 1.46, rI = 8.68, r’I = 3.72 and SM = 19.80, SI = 130.0 [190]. Yamazaki et al. [11] determined the kp and kt for polymerization of N-n-bII, N-cyclohexylitaconimide (N-chII), and N-tert-butylitaconimide (N-tert-bII) using electron spin resonance spectroscopy. The kp for poly(N-n-bII), poly(N-chII) and poly(N-tert-bII) in (L mol−1 s−1) were found to be 550, 1370 and 750, respectively. Similarly, the kt for poly(N-n-bII), poly(N-chII) and poly(N-tert-bII) in (L mol−1 s−1) were reported as 1.2 × 106, 1.0 × 106 and 1.2 × 106, respectively. The kp and kt are varying according to the structure of primary, secondary and tertiary alkyl groups. Sato et al. [155] reported activation energy (Ea) for FRP of N-PII to be 51.2 kJ mol−1. Oishi [6] reported the values of rate of polymerization (Rp), kp, Ea and frequency factor of radical polymerizations of various IIs monomers from the time-conversion relationship and it is given in Table 18.4. Hence, the rate of polymerization will be higher for N-(substituted phenyl)IIs than AIIs and the polymerization reactivity these monomers is highly depends on position, and largeness of the alkyl substituent (present within N-phenyl ring). The alkyl group was substituted onto the phenyl ring of NAI, particularly into the meta or para position, the kp was higher compared to unsubstituted phenyl ring of NAI. Similarly, in the polymerization of N-(2,6-di-substituted phenyl)itaconimides, the kp was reduced as the steric hindrance due to alkyl substituent increased. It is obvious that due to the steric effect (of two alkyl substituent) on ortho positions of the aromatic ring of NAI which plays a vital role in the kinetics of reaction [6].

18.10 MIs and their copolymerization via RDRPs The copolymers of MIs with suitable monomers have been prepared with varied architecture and topology via NMP, RAFT, and mostly by ATRP. The copolymerization of

18.10 MIs and their copolymerization via RDRPs

377

Table .: Monomer RRs for copolymerization of N-alkyl/NAI with MMA/St calculated using TM. Copolymerization with MMA

where, R = O

Copolymerization with St

rI

rM

rI

rS

. – . . . . – . – – . . . . . . . – – – – – –

. – . . . . – . – – . . . . . . . – – – – – –

. . – – – – . . . . – – – – – . – . . . . . .

. . – – – – . . . . – – – – – . – . . . . . .

Reference

N R O

Phenyl

-Chlorophenyl -Chlorophenyl -Chlorophenyl -Methylphenyl -Ethoxycarbonylphenyl -Acetoxyphenyl -Methoxy--chlorophenyl -Methoxy-chlorophenyl -Methoxyphenyl -Methoxyphenyl -Carboxyphenyl Methyl Ethyl n-Propyl i-Propyl n-Propyl i-Butyl β-Chloroethyl Benzyl

[] [] [] [] [] [] [] [, ] [] [] [] [] [] [] [] [] [] [] [] [] [] [] []

here, rI = reactivity ratio of N-alkyl/NAI, rM = reactivity ratio of MMA, rS = reactivity ratio of St.

Table .: Rp, kp, Ea, and A for the FRP of various N-PII monomers, at  °C in THF []. Rp = kp[AIBN]m[M]n

where R =

kp (L mol− s−)

Ea (kcal mol−)

A (s−)

. × − . × − . × − . × − . × −

. . . . .

. ×  . ×  . ×  . ×  . × 

O

m

n

. . . . .

. . . . .

N R O

Phenyl -Chlorophenyl -Methylphenyl (-Ethoxycarbonylphenyl) (-Methoxyphenyl)

378

18 Random and block architectures of N-arylitaconimide

St and MIs such as N-phenylmaleimide (N-PMI), N-benzylmaleimide (N-BMI), and N-chMI has been reported via NMP. The obtained poly(St-co-MI) terpolymers are having well controlled MW with PDI = 1.55 [191]. The NMP of monomers St as a donor and the MIs (such as N-PMI, BMI, and N-chMI) as an acceptor is reported by Butz et al. [192]. The reported reactions are having high valued Rp and less induction periods with moderate PDI (1.5–1.6). The one pot copolymerization of 1,6-bismaleimide hexane (BMIH) with too much of St has been done via RAFT, resulting into the star shaped PSt [193]. The random copolymers of 4-vinylbenzyl methoxytetra(oxyethylene) ether and N-alkyl/aryl substituted MIs were prepared by RAFT [194]. Wei et al. [195] reported alternate copolymers of N-PMI with ethyl α-ethylacrylate using RAFT. A double thermo responsive BCPs of St derivatives and N-alkyl-MIs can be conveniently produced in a one-step procedure using RAFT [196]. The controlled radical copolymerizations of N-PMI and N-chMI with naturally occurring (+)-d-limonene has been done via RAFT. The obtained copolymers were chiral and highly sequence-regulated in nature with high Tg (220–250 °C) [197]. The RAFT dispersion copolymerization of St with N-PMI was carried out with use of poly(methacrylic acid) as chain transfer agent [198]. The obtained PMA-b-poly(St-altPMI) are having 2D lamellae morphologies and were stable up to 347 °C with Tg = 219 °C. Poly(triethylene glycol acrylate)-b-poly(tert-butyl acrylates) micelles were obtained with a fluorescent dithiomaleimide group was synthesized by RAFT. The synthesized BCPs and its self-assembly is having the applicability to tissue imaging [199].

18.11 ATRP of MIs The functional periodic copolymers of N-BMI and N-(2-(amino-tBOC)-ethylene)-MI with St via ATRP were reported by Marie-Alix et al. [200]. Jean-Francois et al. [201] reported the ATRP of St and N-propyl-MI, N-BMI, N-methyl-MI. The obtained alternative copolymers are useful in the preparation of functional periodic microstructures due its acceptordonor nature. The double thermoresponsive di-BCPs of 4-vinylbenzylmethoxytetrakis (oxyethylene) ether with N-(3-trimethyl/ethylsilyl) propargyl-MI have been synthesized via ATRP [196]. ATRP of N-(2-acetoxy-ethyl)-MI and N-PMI with St was successfully carried out using 1-phenylethylbromide/CuIBr/Bpy. The obtained copolymers were having predominantly alternating structure with designed MW, viz. 7.4 × 102 g mol−1 and PDI = 1.1 [202]. ATRP was used for synthesis of graft copolymers of MMA using N-methylol-MI-St copolymers backbone. The thermal stability and Tg of the obtained copolymer was enhanced due to the rigidity of MIs monomer and strength of formation of hydrogen bond [203]. The self-condensing ATRP of N-[4-(α-bromoisobutyryloxy)phenyl]MI and excessive of St typically leads to hyperbranched copolymers. The Tg and melt properties of the obtained copolymers have been enhanced due to highly crossed branched architecture [204]. Hyperbranched copolymers were synthesized by the ATRP of N-(4-α-bromobutyryloxy phenyl)MI monomer with St. The increased Tg of the resulting

18.12 Living polymerizations of IIs

379

hyperbranched copolymer was observed with increasing mole fraction of MIs in polymer backbone [205]. Random copolymers of MMA and N-cyclohexyl-MI have been synthesized under n-ATRP condition in anisole, shows the improved thermal stability of copolymers as compared to PMMA [206]. Haddleton et al. [207] have synthesized poly(PEG-MA)-alt-MI and poly(glycerol-MA)-alt-MI copolymers under ATRP condition. The obtained α-functional MA copolymers have been successfully used in coupling reactions with glutathione and protein for the synthesis of a series of conjugates. One-pot preparation of star shaped PSt using ATRP of N-[2-(2-bromoisobutyryloxy)ethyl]MI were reported by Qingchun et al. [193]. The BCPs of N-PMI and 2-hydroxyethyl MA could be suitable for many industrial applications due to their unique morphology and thermal stability [208].

18.12 Living polymerizations of IIs 18.12.1 Anionic polymerization Apart from the free radical copolymerization of itaconimide monomers via conventional methods as mentioned in Section 18.3, IIs have also been polymerized via other methods such as anionic polymerization and via RDRPs. IIs have two electron-withdrawing ‘>C=O’ groups and therefore may give high MW polymers via anionic polymerization method. IIs monomers are easily polymerized with carbide, nitride, alkali metal and oxide anions (such as sec-butyllithium, lithium diethylamide and tert-butoxide) [209]. The polymerization of various N-substituted IIs has been reported using sec-butyllithium and n-butyllithium (n-BuLi) as initiators in protic solvents at −50 °C. The softening temperature of the obtained polymers was found to be in the range of 250–300 °C. The Rp was varies with nature of solvent such as, when toluene was used for the polymerization of N-(p-methoxyphenyl)II at 25 °C, the polymer was obtained with 20 % monomer conversion. However, in THF the conversion increased to 80 % at −70 °C [8]. The polymerization of an optically active [4-N’-(α-methyl-benzyl)aminocarbonylphenyl]II is reported by Oishi et al. [26] in DMF using n-butyllithium at temperature 0 °C. The obtained polymer was a white powder and had negative optical activity. The specific rotations {[α]D} of polymer was about −46.6 to −52.1° at concentration 1.0 g mol−1, l = 10 cm (in THF solvent). The AIIs and N-PIIs are polymerized using sec-butyllithium in toluene or THF solvent at −78 °C. The N-n-butylitaconimide (N-n-bII) monomer generates a high MW polymer (M n = 4.1 × 104 g mol−1) in toluene [8]. The polymerization of an optically active N-[4-(cholesteroxycarbonyl)-phenyl]itaconimide (ChPII) was reported in toluene or THF at 0 °C with n-BuLi as catalyst. The MWs and PDI for poly(ChPII) were obtained 6.3 × 103 g mol−1 to 4.0 × 103 g mol−1 and 4.0–2.3, respectively. Specific rotation for poly(ChPII) was found to be +1.7–3.9° [27]. Matsumoto et al. [167] documented the polymerization of N-n-bII using n-BuLi as catalyst in toluene at −78 °C. The MW of obtained poly(N-n-bII) was 12.3 × 104 gmol−1 with PDI = 3.0. Asymmetric

380

18 Random and block architectures of N-arylitaconimide

homopolymerization of achiral N-diphenylmethylitaconimide were reported by Oishi et al. [210]. An optically active polymers were obtained with specific rotation in the range of +7.5° to −18.4° and MW of poly(N-diphenylmethylitaconimide) was found to be 1.5 × 103 g mol−1 to 3 × 103 g mol−1 and attributed to configurational chirality rather than conformational. For the anionic polymerization, the reactions should be strictly carried out under inert conditions (viz. in dry condition or reaction mixture should be free from electrophiles as high nucleophilicity of the carbanionic chain ends) [24]. The cationic polymerization of IIs is not investigated and it could be due to the presence of two electrons withdrawing ‘>C=O’ group.

18.12.2 RDRPs of IIs Many reports are available on the RDRPs of maleimides but very few reports are available on RDRPs of itaconimide monomers. The very first study on RDRP of itaconimide monomers was reported by Anand et al. [23]. They have synthesized and characterized the block architecture of MMA and NAI monomers (N-PII and N-(4-methylphenyl)itaconimide and its chloro derivatives) using reverse ATRP. The macroinitiator, PMMA-Cl, was prepared with AIBN/FeCl3·6H2O/PPh3 as the initiating system and used to polymerize the selected NAI monomers. However, only oligomeric blocks of NAI monomer have been incorporated after 7 days period having less MW i.e. 1.0 × 103 g mol−1 to 1.8 × 103 g mol−1 of the copolymers. Satoh et al. [211] called the itaconimide monomers as bio-based starting material and reported the BCPs of IIs [such as N-PII and N-(p-tolyl)itaconimide] and itaconic acid esters [such as di-n-butyl itaconate and bis(2-ethylhexyl) itaconate] via RAFT. The obtained tri-BCPs are having soft poly(itaconate) and hard poly(itaconimide) segments. The different tri-BCPs of N-PII/N-(p-tolyl)itaconimide and itaconic acid esters having MW in the range of 8.7 × 103 g mol−1 to 6.7 × 104 g mol−1 with PDI = 1.5–1.1. The copolymers prepared from NAIs exhibited relatively high Tg (240–260 °C) and it is degraded at ∼300 °C. The tri-BCPs are obtained with microphase-separated morphology are the future thermoplastic elastomers and can be utilized for any high temperature service. Deoghare et al. well documented the synthesis of random and BCPs of various NAI and MMA monomers, viz. [poly(NAI-ran-MMA)] and [poly(NAI-ran-MMA)-b-poly(MMA), poly(NAI-ran-MMA)-b-poly(NAI)] using ATRP methodology. The different functional NAI (monomers with different pendant groups) were used for the different architectures and compositions. As electron donating ability of substituent on phenyl ring of the pendant group (–OCH3) increases, an enhancement in the kp, M n and Tg of the copolymers were reported. The poly(NAI-ran-MMA)-b-poly(MMA) and poly(NAI-ran-MMA)-b-poly(NAI) were synthesized using poly(NAI-ran-MMA) as macroinitiator via AGET-ATRP. The IR and 1 H NMR analysis synthesized BPCs shows the incorporation of poly(MMA) and poly(NAI)

18.13 Computational study on FRP

381

block, respectively. These copolymers are having 60–117 % increase in softening temperature and 80–100 % increase in thermal stability as compared to PMMA [212–214].

18.13 Computational study on FRP Apart from experimental methods, in FRP the computational software’s based on quantum mechanics were used for the study of reaction kinetics. The ‘ab initio’ and ‘semi-emperical’ methods are widely used to forecast important reaction rates and equilibrium constants of radical polymerizations including kp, ktc and also for reactions involving the chain control reagents. At the microscopic level, computational chemistry is used to model the interactions between substituents and control agents or catalyst under varying conditions, and to explain reaction mechanisms. This facilitates a balanced assortment or plan of any reagent or any catalyst used for the practical purpose and this can be achieved via structure-activity relation study. In the past decade, increase in development of computing power and highly cost-effective composite procedures such as ‘ab initio’ have allowed to look into the thermodynamic and kinetic properties of variety of FRP reactions within ‘chemical accuracy’ [68, 173, 215]. The quantum mechanics can be considered as an influential and economical means for the kinetic studies of single reaction of FRP. Particularly, the polymerization process which is fully difficult to analyze in the course of experiments, it can be analyzed via quantum mechanics. The recent focus on copolymerization reactions are secondary reactions such as termination and transfer which plays an important role in kinetics of the reaction. The quantum mechanics calculations are god-matched to stand and also direct the experimental analysis of FRP and easy understand the polymerization mechanisms. Cuccato et al. [216] explained the importance and applications of molecular mechanics in FRP. Percec et al. [217, 218] have explored the DFT methods to study the kact and kdeact of ATRP process using alkyl halides as model compounds. These authors have also reported the KATRP values for many alkyl halides (methyl vinyl ketone, vinyl fluoride, vinylidenedifluoride, vinyl chloride/bromide, butadiene, acrylonitrile, N,N-dimethylacrylamide) and their dimers, which act as the dormant species in the polymerization process. Many theoretical reports are available on the kinetic studies of MMA polymerization and its tacticity control in polymerization [219, 220]. The kp of some important monomers such as St [221], MMA [222], methyl acrylate [223], vinyl acetate [224], acetonitrile [225], vinyl chloride [226] has been well reported. Coote et al. [173] have explored the modeling of kinetic parameters for free radical polymerizations of vinylic monomers such as methyl acrylates, MMA, St, vinyl acetate. Author has presented a detail description of failure of TM (to describe simultaneously the monomer composition and kp in FRP) and the importance of PM in FRP. An ‘ab initio’ molecular orbital calculation was used to find out the degree and beginning of the PUE in ATRP of the comonomers such as many substituted acrylates and propylene [227]. The PUEs have important implications for initiator design and also for the synthesis of block, gradient, random architectures.

382

18 Random and block architectures of N-arylitaconimide

Heuts et al. [228] studied the effects of the PUE on the activation energies for the addition of a series of propyl radicals to various alkenes. Coote et al. [229] have broadened Heuts’s work by studying γ-substituent effects in a small radical addition reaction, providing straight evidences for “explicit PUE” in the reaction activation energies, and suggesting that polar as well as straight interactions are very important in determining PUE. Deoghare et al. reported computational study on homolytic bond dissociation of a series of R–X (modeled on the monomer structure i.e. IIs) and tested as potential initiators for the ATRP process. The DFT methods were used for the prediction of free energy values associated with the homolysis of C-X bond of the R–X. The relative KATRP for the radical activation-deactivation process is calculated from the obtained free energy values. The variation of the obtained KATRP values with change in solvent, temperature and substituent is investigated. It results shows that change of KATRP values with the change in polarity of the solvent and temperature. Similarly, the kt value for various R–Br species were calculated theoretically from the modeled compounds and does depend on the diffusion coefficient. The nature of solvent and temperature acts a significant contribution in mechanism of copolymerization of IIs monomer via ATRP [230, 231].

18.13.1 Methods of molecular modeling The methods for molecular modeling may be categorized into four main groups: one of them uses classical physics (viz. mechanics) and the rest use quantum mechanics to model the actions and relations of an atom and molecules. 1. Molecular mechanics: This method is based on conventional classical mechanics. The atoms are considered as spheres that are associated to other atoms by a spring (representing the bond). The energy of the molecule is expressed as a function of bond elongation, bond vibrations and atom cramming/squeezing etc. which uses the expression of force field to find the possible minima in the energy surface [232–234]. 2. Ab initio methods: Here, the Schrödinger equation is used to calculate the broad variety of quantum chemical and physical properties. Important calculation is the wave function determination. From the results of the wave function, other chemical properties and behaviors can be determined. Herein, inputs are usually the fundamental constants; the final step determination is done by using mathematical equations [235]. 3. Semi-empirical methods: In this method, a fraction of the calculation comes from experimental data or from other sources, and remaining comes from mathematics. The most important advantages of this methods are; it is quicker and capable to carry out calculations for macromolecules (with some compromise on accuracy) [236]. 4. DFT methods: DFT methods are the latest computational methods increasing in reputation among computational researchers. A DFT method find outs properties of the molecules from calculating the values of ρ rather than calculating molecular

18.13 Computational study on FRP

383

properties on the basis of values of wave function [237, 238]. It uses “functional” to determine the ‘ρ’ and properties of an any molecular system. A functional is defined as a function of a given function, viz. the energy of the system/molecule is a functional of its ρ.

18.13.2 DFT methods DFT is an all-purpose computational method and applied to many reaction systems. The most important improvement to DFT methods is a noteworthy enhancement in the computational accuracy devoid of the extra increase in the computing analysis time. Now a day, DFT methods are standard in virtually all trendy software packages counting, Gaussian, GAMESS, Hyper Chem, and Spartan [237–239]. DFT try to find out all the characteristics of atom and molecules from its electron density (ρ). The idea of calculating atomic and molecular properties using the ρ originated from the calculations made independently by Fermi and Dirac in 1920. The Thomas-Fermi model [240] is the result of independent work of Fermi and Thomas where atom were modeled as system with positive potential viz. nucleus located in the uniform electron gas. At present, Kohn-Sham approach is used as basis of DFT calculations on any molecules system [241] which is based on two theorems reported by Hohenberg and Kohn in 1964 [240–242]. The 1st theorem says that any ground state property can be calculated from the ρ(x, y, z) alone, and so it is important to search for methods to calculate molecular properties from the ρ. The 2nd theorem states that the variation approach might generate a way to calculate the energy and ρ of the molecule. The ground-state electronic energy, E0, a functional of ρ, is written as a sum of kinetic energy (ET), electronuclear interaction energy (EV), Coulomb energy (EJ), and exchange/correlation energy (Exc). E0 = Eν [ρ] = ET [ρ] + Ev [ρ] + EJ [ρ] + E xc [ρ]

(18.26)

First three terms of (right side) the above expression (equation (18.26)) are easy to evaluate from ρ and contain the main involvement to the ground-state. Although, the 4th term (Exc), is not easy to evaluate accurately, will be a relatively small term. Thus, the idea of presenting the molecular energy as a sum of terms out of it which a relatively small term involves the unknown functional is fulfilled. Thus, the key to accurate calculation of molecular properties in the Kohn-Sham formalism of DFT is to get a good approximation to Exc. various approximate functional Exc [ρ] are used in molecular DFT calculations. Three types of exchange/correlation functionals are reported in literature which is based on; (i) local spin density approximation (LSDA), (ii) generalized gradient approximation (GGA), and (iii) “exact” Hartree–Fock exchange as component.

384

18 Random and block architectures of N-arylitaconimide

When the DFT calculations use exchange-correlations energy functionals (Exc) that involves not only LSDA but both ρ and its gradient, the functionals are known as gradientcorrected or the generalized-gradient approximation (also known as non-local). The exchange-correlation energy functional is represented as, EXC = Ex + Ec. Some examples are the Gill 1996 (G96), the Lee-Yang-Par (LYP), the Perdew 1986 (P86) etc. The so called “hybrid methods” combine Hartree–Fock DFT approximation (which is based on the exchange energy correlation) to include electron correlation. A well-liked hybrid DFT functional is also obey exchange energy functional build up by Becke in 1993, further customized by Stevens in 1994 with the introduction of additional term called LYP 1988 correlation-energy functional. The obtained combined exchange-correlation functional known as the Becke3LYP or B3LYP functional as given below; B88 VWN = (1 − a0 − ax )ELSDA + a0 EHF + ac E LYP E B3LYP xc x x + ax E x + (1 − ac )E c c

(18.27)

where, ELSDA = accurate LSDA non-gradient-corrected exchange functional (pure DFT), x = KS-orbital-based HF exchange energy functional, EB88 = Becke 88 exchange funcEHF x x VWN tional, Ec = the Vosko, Wilk, Nusair function (which is part of accurate function for the = LYP correlation functional, Ex homogeneous electron gas of the LDA and LSDA), EYLP c and Ec are gradient corrected. The parameter a0, ax, and ac give the best fit of the calculated energy to molecular atomization energy. Table 18.5 represents an excellent summary of sample methods by name, acronym and type. The B3LYP functional is painstaking as the most useful in terms of “industry standard” and of practical applications [241–244].

18.13.3 Basis sets Most of the molecular quantum mechanics methods begin the calculations with the selection of a set of basis functions χ r , and are utilized to state the molecular orbitals ϕi as; Table .: Different DFT methods. Name of the method Hartree-Fock Slater functional

Type

Hartree-Fock with local density approximation exchange Vosko, Wilks, and Nusair Local Density Approximation (emphasis onelectron correlation approximation) Becke correlation functional; Lee, Yang, Parre- Gradient-corrected LDA functional lectron exchange functional Becke -term correlation functional; Lee, Yang, Hybrid DFT and Parr exchange functional Perdew  functional Gradient-corrected LDA functional Becke -term correlation functional; Perdew Hybrid DFT correlation term Modified Perdew-Wang one parameter hybrid Hybrid DFT for kinetics

Acronym HFS VWN BLYP BLYP P PP MPWK

18.14 Summary and future directions

ϕi = ∑cri χ r i

385

(18.28)

To make use of sufficient basis sets (BSs) are necessary for achievement of the desired calculations. Now a day’s, BSs are usually coming inbuilt into the modern software’s used in the field computational chemistry. Similarly, a researcher from chemistry can access resources as “Gaussian Basis Set Order Form” to find a required BS for integration purpose in selected calculation [245]. At present, many of BSs poised of Gaussian-type of orbitals. The smallest of are known ‘minimal BS’. As the name suggests, minimal BSs are designed to increase the speed of calculation, and hardly ever used as research point of view. Minimal BSs are 1st and foremost used to acquire a “first look” of existing molecular properties. STO-3G and STO-6G are the examples of minimal of BSs and these basis set observe all electrons present in the system of equal importance. Split-valence BSs are considered as another improvement in terms of effectiveness and importance. Split-valence BSs are considering the valence electrons caught up in bonding of atoms and its chemical reactions, as be in opposition to the inner electrons. Split-valence BS carry out a fast and filthy approximation of the behavior of the inner electrons, and then do a more systematic and vigilant calculation of the outermost electrons (such as 3–21G and 6–31G). Typically, the position of an electron is described in terms of the “electronic configuration” of any atom. Taking an example, for carbon atom the electronic configuration is described as, 1s22s22p2. This electronic configuration of carbon informs us about the exact position of all six electrons present in it. Nevertheless, some of those electrons may sporadically “wander away” into the “d” orbital and responsible for the ‘polarization’ phenomenon. Hence, polarization can give precise portrayal about the location of the electron and probable transitions of electron. The ‘polarized basis set’ is represented by an asterisk “*” in many literature, or as “name of orbital”. For example, 3–21G* or 3–21G(d) BS, both the nomenclature is having same meaning. The possibility of finding the electrons in an atom in the shells and sub shells are more and that the probability of finding electron is highest near to the nucleus. It then holds that as if there is bigger atomic radius, then there is less probability of finding the electron. As larger the distances of electron from the nucleus, one can stop the calculations, because the probability of finding electron is less. For anions, radicals and in case of excited states, the ‘diffuse basis sets’ to extend the distance away from the nucleus that are searching for electrons. This kind of BSs are denoted as a “+” symbol, such as 3–21+G. A ‘BS’ like 6–31+G(d) would be a polarized diffuse split-valence BS [245, 246].

18.14 Summary and future directions The various NAI monomers and bromo derivatives succinimides were synthesized using itaconic anhydride. The copolymerization of various NAIs was done with MMA using

386

18 Random and block architectures of N-arylitaconimide

ATRP ensuing in ‘N-arylimide’ as pendant group. A substituent on phenyl ring of NAI changed as strongly activating group, –OCH3 to weakly deactivating group, –Cl, leads to change in the polarity and rigidity of the selected pendant group. The kp, MW and Tg of poly(NAI-ran-MMA) copolymers were increased with increasing an electron donating tendency of substituent on phenyl ring of the pendant group. The poly(NAI-ran-MMA)-b-poly(MMA) and poly(NAI-ranMMA)-b-poly(NAI) are synthesized using poly(NAI-ran-MMA) via AGET ATRP. These block copolymers are having 60–80 % increase in softening temperature and 80–100 % increase in thermal stability as compared to PMMA. The KATRP of synthesized BSIs were experimentally determined using UV–Vis-NIR spectroscopy and Fischer–Fukuda equation for PRE. The relative values of KATRP were calculated from the free energy associates with homolysis of R–X bond using DFT methods. A good agreement was obtained between experimentally determined and theoretically calculated KATRP values in acetonitrile at 25 °C. The copolymerization of NAI and MMA was successfully carried out using BSI-33 as initiator via AGET-ATRP. Comparisons with the copolymerization of same systems with commercially available initiator, EBiB, shows that laboratory synthesized BSI-33 has better performance over EBiB in terms of control on Rp, % conversion of monomer and PDI of obtained copolymers. For studying the mechanism of copolymerization of NAI-MMA copolymer system, the triad fractions were calculated experimentally as well as theoretically. Experimental triad fractions are agreed well with theoretically calculated triad fractions obtained when the polymeric species are treated as trimer and using the PM. The PEU affects well on the structural and thermodynamic properties, such as C–X bond length, bond enthalpy, free energy and KATRP. The functionalized alkyl bromides (bromo derivatives of succinimides) based on renewable resource will be used as initiators in future for the synthesis of different architectures and compositions of itaconimide copolymers. These study needs to be explored further. The living copolymers of NAI and MMA monomers with an attractive architectures viz. poly(NAI-ran-MMA), poly(NAI-ran-MMA)-b-poly(MMA) and poly(NAI-ran-MMA)-b-poly(NAI) having high Tg and thermal stability as compared to the PMMA are potential candidates as ‘thermoplastic’ and can be used as a substituent for higher temperature service The other architectures of these copolymers need to be explored. The DFT methods can be explored to study the mechanism, kinetics and thermodynamics of copolymerization of various NAI-MMA copolymer systems (the trimer model or PM should be used for the calculation of RRs). Prior to the experiments, the DFT methods can be used to design the several architectures of NAI-MMA system. Acknowledgment: I am thankful to Prof. R. N. Behera, Department of Chemistry, BITS, Pilani – K. K. Birla Goa Campus, Goa, India, for his guidance and support to carry out this work.

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Enitan Omobolanle Adesanya*, Olumide Olatunde Adesanya and Samuel Ayodele Egieyeh

19 Evaluation of phytochemicals and amino acid profiles of four vegetables grown on a glyphosate contaminated soil in Southwestern Nigeria Abstract: Green vegetables are examples of staple plants eating in Nigeria, and are assumed to be a well-off basis of phytochemicals and amino acids that are useful for the management and prevention of infections. However, in the farming of these vegetables, glyphosate-based herbicides (GBH (round up™)) are used as control against pests invasions which has cause reasons to be concern about their effects on the phytoconstituents present in these vegetables. In this study, we evaluated the phytochemicals constituents and amino acid profile of the leaves of Telfairia occidentalis Hook F, Amaranthus viridis Linn, Celosia argentea Linn and Cnidoscolus aconitifolius (Mill.) I. M. Johnst popular edible vegetables in Southwestern Nigeria. The vegetables seeds of these plants mentioned above were acquired from Institute of Agricultural Research and Training (IAR&T) Ibadan. And on a land designed and previously treated with a GBH (round up™) the seeds were planted and allowed to grow. A destructive method of leaves after maturation was achieved by out rightly plucking them out and air-drying under shade. Phytochemical assessments were done on milled plant samples to determine the presence of ten phyto-constituents. Centrifugation of powdered samples (2 g each) with acetonitrile at 1000 rotations per minute’s (rpm) was used to extract amino acids. Thereafter the extracts were individually spotted on a thin layer chromatography (TLC) plate and developed using the mobile phase consisting of methanol: acetic acid: water in ratio 7:2:1 v/v. The visualization for the presence of amino acids was completed by spraying the developed chromatographic plates with 0.5% ninhydrin in 2-propanol solution and observed in both day light and under the ultraviolet lamp and the retention factor (Rf).calculated for the different spots developed to determine the type of amino acids present. The assessment of phytochemicals from the four vegetables reveals the presence of alkaloids, tannins and steroids in all samples while anthraquinone

*Corresponding author: Enitan Omobolanle Adesanya, Department of Biochemistry, Olabisi Onabanjo University, Sagamu, Ogun State Nigeria, E-mail: [email protected]. https://orcid.org/0000-0002-17907579 Olumide Olatunde Adesanya, Plant Science Department, Olabisi Onabanjo University, Agp-Iwoye, Ogun State, Nigeria Samuel Ayodele Egieyeh, University of the Western Cape school of Pharmacy, Robert Sobukwe Road, Bellville, Cape Town, Western Cape ZA 7535, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: E. O. Adesanya, O. O. Adesanya and S. A. Egieyeh “Evaluation of phytochemicals and amino acid profiles of four vegetables grown on a glyphosate contaminated soil in Southwestern Nigeria” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0308 | https://doi.org/10.1515/9783111071435-019

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glycosides, terpenoids and cardiac glycosides were absent and other phytochemicals varying in them. Ssaponins was found absent in Telfairia occidentalis and flavonoids in Cnidoscolus aconitifolius respectively. The observation of the TLC plates in daylight shows that the vegetables likely contained primary, secondary and proline amino acids while the calculation of the Rf values of the TLC spots observed under an ultraviolet (UV) light indicated that amino acids found in the vegetables were methionine, tyrosine, leucine, isoleucine. However, phenylalanine was found only in Celosia argentea. The study evaluated phytochemicals and amino acids parameters in four leafy vegetables grown on contaminated glyphosate based herbicide soil and assessments shows that their medicinal properties were not altered with the minimal exposure to contaminant. Keywords: amino acids; glyphosate-based herbicides soil; medicinal properties; phytochemicals; vegetables.

19.1 Introduction The challenge of food insecurity as a result of the daily increase in human population necessitated for the growing demand for food supply [1]. Many factors affect the inadequate supply of food which includes but not limited to pest invasion, war, drought, natural disasters etc. Among these causes of this inadequacy pest control is the only one that can be controlled. Over the years, agrochemicals such as herbicides, fungicides, insecticides and nematicides have been used for the control of pests and to enhance food production [2]. The benefits associated with use of these agrochemicals cannot be over emphasised, however the risks associated with its exposure in plants and animals cannot also be ignored. Some of the risks involved in its use include, pesticides drift to sensitive crops, pollution of drinking water through surface runoff, the depletion and killing of inhabiting soil animals’ populace. These risks cause alteration in the ecosystem and resistance development in weeds as a result of the continuous procedure with a specific group or family of herbicides [3]. According to Nicolopoulou-Stamati et al. [4], the use of herbicides by farmers may result in diseases of unknown etiology by contaminating food plants. In addition, high exposures to herbicide may deplete nutritional values of plants [5]. The interaction between the applied herbicides, soil and phytonutrients in vegetables is of utmost importance to human health via their consumption [6] One major group of herbicide commonly used worldwide is glyphosate-based herbicides (GBHs) mixture discovered by the John E. Franz of Mosanto company in 1970 [7]. The lethal effects on the environments, humans, animals and plants have recently been reported [8]. The relevance of plant based diets like vegetables and fruits are numerous as such as it lowering blood pressure, risk of eye problems, reducing the risk of heart diseases, preventing some types of cancer and digestive problems, and have a moderating effect on blood sugar. Vegetables are rich in phytonutrients which makes them indispensable part

19.2 Materials and methods

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of a balanced diet and they are the most consumed roughages in the world Therefore it is essential to ensure the safety and integrity of the phytonutrients in these vegetables. There are several vegetables found all over the world and among them are Telfairia occcidentalis Hook F., Amaranthus viridis L., Celosia argentea L. and Cnidoscolus aconitifolius (Mill.) I. M. Johnst. commonly consumed in Nigeria. These vegetables were selected due to their ethnomedicinal advantage. Telfairia occcidentalis Hook F is an important staple vegetable which are rich in minerals, antioxidants, vitamins and phytochemicals [9]. Amaranthus viridis L. ethnomedicinally is used to cure fever, intestinal cramps, diarrhea, dysentery, inflammation and wounds [10]. The combination with other medicinal plants are used to cure liver infection, respiratory and heart troubles, to stop bleeding and as a hair growth [11]. Also it severs as a cholagogue, abortifacient and to treat snake bite and as a galactagogue [12]. It is rich in phytochemicals that include tannins, saponins, flavonoids, alkaloids, phenolic compounds, anthraquinones, steroids and proteins [13]. Celosia argentea L also has high contents of phytochemicals, minerals and vitamins [14]. Its dietetic value was well proven when it was eaten as spinach by prisoners-of-war during the Japanese war in Thailand (1942–45) with good results against beri-beri and pellegra [15]. The Yoruba’s of South-West Nigeria enjoyed eating it and it is believed that it is a Yoruba “Odu”‘incantation for curing cough and lays emphasis on the name sokọyọkọtọ literally meaning make husband fat [16]. Its medicinal values include treatment for infected sores, wounds and skin eruptions, abscesses, for snake-bite, colic, gonorrhea and eczema [17]. It is also used in the treatment of diarrhea, for conditions whose symptoms include discharge of blood, e.g., dysentery, haemoptysis and menstruation, antioxidant, muscular troubles and several other functions [18, 19]. C. aconitifolius (Mill.) I. M. Johnst. is believed to possess activities such as hypoglycemic, antioxidant, analgesic and anti-inflammatory effects [20, 21]. It is also rich in many medicinal constituents [22]. The application of glyphosate-based herbicides (GBH) in Nigeria is on the increase and the potential risk associated with the cumulative use is a major health concern of national interest The investigation of phytochemical components and amino acids profile in four vegetables grown on soil contaminated with glyphosate-based herbicides is being studied.

19.2 Materials and methods 19.2.1 Materials Uproot™ herbicide (liquid) obtained locally from the vendor in Ijebu Ode, Ogun State, Nigeria, and contains 360 g/L glyphosate. Conical flask (Pyrex, 500 mL), absolute ethanol (sigma), water-bath, ferric chloride (FeCl3) reagent, chloroform, 10% hydrochloric acid (HCl), ethylacetate, Dragendorff reagents, concentrated tetraoxosulphate (VI) acid

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(H2SO4), atomic absorption spectrometer (Perkins Elmer AAnalyst 700), refrigerated centrifuge (Mikro 22R Hettich, Germany) and acetic acid (Sigma), methanol (Sigma) and acetonitrile (Sigma), pre-coated thin-layer chromatographic (TLC) plates (Merck, Germany) and chromatographic tank.

19.2.2 Methods 19.2.2.1 Soil preparation An 8 ft × 4 ft land size was measured each for the application of the UprootTM and subsequent seed planting of the research vegetables. Seven cap full cover (14.7 oz.) of UprootTM was dissolved in 5 United States standard gallons of water (20 L) to give 10 mL/ 1000 L v/v concentration and applied on the land using 6 hole-nozzle sprayers. 19.2.2.2 Plant material The seed of the vegetables samples, Telferia occcidentalis Hook F. (Efo Ugwu), Amaranthus viridis (Efo Tete), Celosia argentea (Efo Soko) and C. aconitifolius (Efo Iyana-Ipaja) were gotten from the institute of Agricultural Research and Training (IAR&T), Ibadan and planted on the prepared site previously sprayed with the Uproot™ at Olabisi Onabanjo University, Ago Iwoye after 2 weeks of application. 19.2.2.3 Preparation of vegetable samples The leaves of the vegetables were cleaned by running under water and the water dried with a blotting paper thereafter air dried for 5 days. The dried vegetables were milled into a coarse powder and stored in air-tight containers prior to further analysis. 19.2.2.4 Phytochemical assessment Tests for the presence of the different phytochemicals in the samples were determined using standard protocols according to Sofowora [23] and Trease and Evans [24]. 19.2.2.4.1 Alkaloid Powdered leaves (1 g) samples was extracted for 5 min on a water bath with 10 mL of 10% Hydrochloric acid (HCl). The extract was filtered and allowed to cool for 2 min. The pH of the filtrate was adjusted to reduce the acidity to about 6–7 by adding 10% ammonia and monitored with a pH paper. The filtrate was divided (5 mL) into 2 test tubes and 3 drops of Wagner’s and Dragendorff reagents were added into the separate test tubes and observed. The presence of turbidity or precipitation indicated the presence of alkaloids.

19.2 Materials and methods

403

19.2.2.4.2 Anthraquinone glycoside (The Borntrager’s test) The extraction of 1 g of the sample with 2 mL of 10% HCl was done by boiling for 5 min and filtered while still hot, then allowed to stand for a few minutes. Equal volume (aliquot) of chloroform with the filtrate was partitioned, gently mixed and the chloroform layer (lower layer) transferred to a clean test tube. An aliquot of 10% ammonia solution was added to the chloroform solution and again gently mixed. The presence of a delicate rose– pink layer on the test solution indicated the presence of anthraquinones glycosides. 19.2.2.4.3 Cardiac glycoside (Keller–Kelani test) The sample (1 g) extracted in boiling water for 5 min, cooled and 2 mL of glacial acetic acid- ferric chloride reagent was added into the solution. A 1 mL of concentrated tetraoxosulphate (VI) acid (H2SO4) was gradually added to form an under layer. A brown/ purple/reddish-–brown ring formed at the interface and green colour in the acetic acid layer indicated its present. 19.2.2.4.4 Flavonoids i. The sample (1 g) extracted with ethanol by light heating, cooled. A few [3] drops of ferric chloride (FeCl3) solution was added to the filtrate. Observation of a dark green colour indicated the presence of flavonoid. ii. One gram of the sample was extracted with ethyl acetate and heated for 3 min and filtered. Aliquots of 10% ammonia (NH3) were added to the residue and a yellow colour observed indicated flavonoid presence. iii. A gram of sample was extracted with ionised water, heated for 3 min and allowed to stand at room temperature for 2 min. Concentrated tetraoxosulphate (VI) acid (2 mL) (H2SO4) was added to the filtrate and a yellow colour appearance which disappears on standing observed for positive result. 19.2.2.4.5 Tannins (The Braemer’s test) The sample (1 g) was extracted with 10 mL of distilled water by boiling for 10 min and filtered while hot and allowed to stand for few minutes and 0.1% Ferric chloride solution was added to the filtrate. The presence of tannins is indicated by a blue–black, green or blue–green precipitate. 19.2.2.4.6 Saponins i. Frothing: The sample (1 g) was extracted with 10 mL of ionised water on a water bath 5 min and filtered. The filtrate was shaken vigorously and observed for froth (bubbles) on the surface. The presence of froths indicated the presence of saponins. ii. Emulsification: Three drops of olive oil was added the frothing test tube and shaken vigorously; the formation of emulsions confirms the presence of saponins.

404

19 Evaluation of phytochemicals and amino acid profiles

19.2.2.4.7 Phenols One gram of sample was added to 2 mL of ethanol in a test tube, heated and filtered when cooled. Three drops of phenol solution was added to the filtrate and a dark green colour observed from the solution indicated the presence of phenol. 19.2.2.4.8 Steroids A gram of sample was extracted with ethanol and filtered. Two mills of acetic anhydride were added and H2SO4 (2 mL) was added to the filtrate and a colour changed from violet to blue or green in some samples showed the presence of steroids. 19.2.2.4.9 Terpenoids (Salkowski test) The sample (1 g) was extracted with ethanol by warming for a few minutes on water bath and filtered. Chloroform (2 mL) and concentrated H2SO4 (3 mL) was carefully added to the filtrate to form a layer. A reddish–brown colouration of the inter-phase showed a positive result for the presence of terpenoids. 19.2.2.4.10 Phloba-tannins A gram of sample was measured into a test tube containing 10 mL of distilled water, boiled for 5 min and filtered. 1% HCl (5 mL) was added into the filtrate and re-boiled for 5 min. A positive result is achieved with the presence of a yellow precipitate. 19.2.2.5 Qualitative analysis of amino-acid in the vegetable samples Extraction of amino acid from the vegetable was achieved using acetonitrile according to Adelowo’s et al. method [25] with a slight modification. Into a 10 mL test tube, 5 g of the powdered vegetable leaves each were measured and a mixture of acetonitrile (4 mL) and distilled water (1 mL) were added into the test-tubes to make up to 5 mL solution. The extraction was achieved by centrifuging at 1000 rpm for 15 min, filtered and stored at 4 °C. Thin layer chromatography (TLC) procedure described by Milan et al. [26], was used to determine the presence of amino acid in the samples. Samples were spotted on the TLC silica plate and chromatogram were developed using methanol-acetic-acid-water (70:20:10 v/v) as mobile phase. The visualization of the sample plates was attained by spraying 0.5% ninhydrin in a 2-propanol solution and dried at 80 °C for 5 min in an oven to develop the color and viewed in both daylight and under ultraviolet light.

19.3 Results and discussion Generally, phytochemical assessment of ten phytoconstituents is presented in Table 19.1 and it reveals that the four vegetables grown on contaminated glyphosate soil contained alkaloids, tannins and steroids. The leaves Amaranthus viridis revealed more phytoconstituents than other samples. Also, from the result it shows that anthraquinone

405

19.3 Results and discussion

Table .: Result of phytochemical analysis. Plants test Alkaloids – Wagner – Drangendorff Anthraqunione glycoside Cardiac glycoside Saponins – Frothing – Emulsification – Na2CO3 Phenol Flavonoids – H2O – EtOH – EtOAc Tanins Phylobatanins Terpenoids Steriods

Celosia argentea

Cnidoscolus aconitifolius

Telfairia occcidentalis

Amaranthus viridis

+ ++ – –

– + – –

+ +++ – –

+ ++ – –

++ +++ ++ –

+ + – –

– – _ –

+ ++ – –

++ ++ – ++ – – ++

– – – + – – +

+ ++ – ++ – – ++

– + – + ++ – +

Key: −, absent; +, trace; ++, moderate; +++, abundant; HO, water; EtOH, ethanol; EtOAc, ethylacetate; NaCO, sodium carbonate.

glycosides, terpenoids and cardiac glycosides were absent in all the samples. Whereas saponins were found absent in Telfairia occidentalis and flavonoids in C. aconitifolius while other phytochemicals varies. The amino acid profile result is displayed in Table 19.2 while Figure 19.1 shows the observation on the TLC plates after spraying with ninhydrin-propanol solution under both daylight (A) and the ultraviolet (UV) light (365 nm (B)). Observation from Figure 19.1(A) revealed that the vegetables will likely contained primary, secondary and proline amino acids. However an observation under the UV light ray, calculating the retardation factor (Rf) of the spots on the TLC plates and comparing with standard Table .: Result of amino acid profile. Rf value

. . . . .

Plants Celosia argentea

Cnidoscolus aconitifolius

Telferia occcidentalis

Amaranthus viridis

Methionine Isoleucine Tyrosine Phenylalanine Leucine

Methionine Isoleucine Tyrosine – Leucine

Methionine Isoleucine Tyrosine – Leucine

Methionine Isoleucine Tyrosine – Leucine

406

19 Evaluation of phytochemicals and amino acid profiles

Figure 19.1: TLC plate (A) dayligt (B) ultraviolet after spraying with ninhydrin.

reference methionine, tyrosine, leucine and isoleucine amino acids were found in the vegetables while phenylalanine was found only in Celosia argentea.

19.4 Discussion There are several reports indicating that these vegetables gown on normal soil contained phytochemicals such as alkaloids, tannins, steroids and flavonoids. For instance in an evaluation of Celosia argentea leaves grown on a normal soil it was reported that the leaves contained alkaloids, saponins, phytosterols, tannins, flavonoids and amino acids which was complimentary to the results of our study even though grown a herbicide contaminated soil [27]. Likewise, reports on A. viridis leaves shows tannins, phylobatannins, flavonoids, saponins, steroids, phenolics and amino acids were present in them and most of these constituents were also found from the study [13]. Gain, it has been reported that T. occidentalis leaves extracts contains alkaloids, tannins and flavonoids and from the powdered samples used in the study these compounds where also present [28]. Previous assessment of C. aconitifolius leaves extract phytoconstituents by Oyagbemi et al. [29], revealed that the vegetable is rich in alkaloids, saponins, tannins and flavonoids and results from this research shows also that these compounds were detected in them. Generally, the observation from the research shows that growing the four vegetables on a minimally contaminated glyphosate soil did not have any effects on the phytoconstituents of the plants.

References

407

The results from the phytochemical screening already indicated that the plants under study contained amino acids and proteins so further step was their identification. Celosia argentea, Amaranathus viridis, T. occidentalis and C. aconitifolius leaves grown on a plan soil has been reported to be rich in amino acid especially proline, methione, leucine and lysine [30, 31]. The exposure of these vegetables to low concentrations of GBH in soil shows that the amino acid components were still expressed. The results obtained from the research indicated that the pretreatment of the soil with glyphosate based herbicides (GHB) had minimal effects on the phytoconstituents and amino acid found in the leaves of the vegetable samples. This could have been as a result of glyphosate absorbed in the soil or it might be that the uptake levels from the soil to the samples could be in minute quantities (i.e. less than detection levels). Furthermore, there are studies that indicated that 2, 4-D acid a major component of glyphosate can further decompose in soil by hydroxylation subsequently forming other compounds which are likely to be of small amount and toxicity in the environment [32]. Examples of these compounds converted to include; 1,2,4-benzenetriol, 2,4-dichlorophenol (2,4-DCP), 2,4-dichloroanisole (2,4-DCA), 4-chlorphenol, chlorohydroquinone (CHQ), volatile organics, bound residues and carbon dioxide. Also, there have been several debates on the effects of herbicides on leafy vegetables but by and large it is believed that a minimal application of herbicides is not taken up from the soil by the vegetables and phytocomponents found in the vegetables are preserved.

19.5 Conclusion and recommendation The study reveals that the minimal application of glyphosate based herbicides on soil during cultivation of the four vegetables grown on contaminated glyphosate soil shows relative contents of phytochemicals and amino acids components being retained. Therefore it is recommended that the process of a continuous and indiscriminately application of glyphosate based herbicides by local farmers in agricultural practices should be discouraged and reduced. From the research it will be advisable that the application of glyphosate be done thrice annually on farmlands.

References 1. United Nation UN/DESA Policy Brief #102: Population, food security, nutrition and sustainable development: Department of Economic and Social Affairs Economic Analysis; 2021. 2. Olabode SO, Adesina GO, Olapeju TR. A survey of agricultural chemicals available to farmers in South Western Nigeria. Int J Agric Econ Rural Dev 2011;4:12–8. 3. Soares D, Silva L, Duarte S, Pena A, Pereira A. Glyphosate use, toxicity and occurrence in food. Foods 2021; 10:2785–807.

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19 Evaluation of phytochemicals and amino acid profiles

4. Nicolopoulou-Stamati P, Maipas S, Kotampasi C, Stamatis P, Hens L. Chemical pesticides and human health: the urgent need for a new concept in agriculture. Front Public Health 2016;4:148. 5. Gandhi K, Khan S, Patrikar M, Markad A, Kumar N, Choudhari A, et al. Exposure risk and environmental impacts of glyphosate: highlights on the toxicity of herbicide co-formulants. Environmental Challenges 2021;4:100149. 6. Anhwange BA, Agbaji EB, Gimba CE, Ajibola VO. Chemical analysis of some herbicides contents of most common vegetables and aquatic animals in makurdi metropolis. Int J Nat Sci Res 2013;1:14–9. 7. Baer KN, Marcel BJ. Glyphosate. In: Encyclopedia of toxicology, 3rd ed Academic press, Imprint of Elsevier; 2014, 4:767–9 pp. 8. Abdulkareem M. Nigerian farmers using large amounts of toxic pesticides are banned in EU. Nigeria: Premium Times; 2021. 9. Kayode AAA, Kayode OT. Some medicinal values of Telfairia occidentalis. A Review Am J Biochem Mol Biol 2011;1:30–8. 10. Abbasi AM, Khan MA, Shah MH, Shah MM, Pervez A, Ahmad M. Ethnobotanical appraisal and cultural values of medicinally important wild edible vegetables of Lesser Himalayas-Pakistan. J Ethnobiol Ethnomed 2013; 9:66. 11. Faluyi O. The health benefits of Amaranthus hybridus and Amaranthus virids. Nigeria: Punch.com; 2020. 12. Sher Z, Zaheer UDK, Farrukh H. Ethnobotanical studies of some plants of Chagharzai valley, District Buner, Pakistan. Pakistan J Bot 2011;43:1445–52. 13. Ezekwe AS, Wokocha PG, Woha JB. Phytochemistry and antioxidant activity of Amaranthus viridis L (Green leaf). World J Adv Res Rev 2021;12:306–14. 14. Fayaz M, Bhat M, Kumar A, Jain A. Phytochemical screening and nutritional analysis of some parts of Celosia argentea L. Chem Sci Trans 2019;8:12–9. 15. Otunola GA, Adegbaju OD, Afolayan AJ. Potential of Celosia species in alleviating micronutrient deficiencies and prevention of diet-related chronic diseases: a review. AIMS Agric Food 2019;4:458–84. 16. Malomo A, Kanu C, Owoeye O, Imosemi I. A review of the multifaceted usefulness of Celosia argentea Linn. European J Pharm Med Res 2019;4:72–9. 17. Tang Y, Xin H, Guo M. Review on research of the phytochemistry and pharmacological activities of Celosia argentea. Revista Brasileira de Farmacognosia 2016;26: 787–96. 18. Sharma P, Vidyasagar G, Singh S, Ghule S, Kumar B. Antidiarrhoeal activity of leaf extract of Celosia argentea in experimentally induced diarrhoea in rats. “J Adv Pharm Technol Research” “(JAPTR)” 2010;1:41–8. 19. Molehin OR, Adefegha SA, Oboh G, Saliu JA, Athayde ML, Boligon AA. Comparative study on the phenolic content, antioxidant properties and HPLC fingerprinting of three varieties of Celosia species. J Food Biochem 2014;38:575–83. 20. Miranda-Velasquez L, Oranday-Cardenas A, Lozano-Garza H, Rivas-Morales C, Chamorro-Cevallos G, CruzVega DE. Hypocholesterolemic activity from the leaf extracts of Cnidoscolus chayamansa. Plant Foods Hum Nutr 2010;65:392–5. 21. Onasanwo A, Oyagbemi A, Saba A. Anti-inflammatory and analgesic properties of the ethanolic extract of Cnidoscolus aconitifolius in rats and mice. J Basic Clin Physiol Pharmacol 2011;22:37–41. 22. Panghal A, ShajiNain AK, Garg M, Chhikara N. Cnidoscolus aconitifolius: nutritional, phytochemical composition and health benefits – a review. Bioact Compd Health Dis 2021;4:260. 23. Sofowora A. Medicinal plants and traditional medicine in Africa, 3rd ed. Ibadan, Nigeria: Spectrum Books Ltd; 2008:439 p. 24. Trease GE, Evans WE. Pharmacognosy phytochemistry, 15th ed. London: Saunder Publisher; 2002:363 p. 25. Adelowo FE, Olu-Arotiowa OA, Amuda OS. Biodegradation of glyphosate by fungi species. Adv Biosci Bioeng 2014;2:104–18. 26. Milan CD, Sarmistha B, Amalendu S. Detection of amino acids on TLC plates by a novel spray reagent. Anal Chem Lett 2016;6:886–93.

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27. Preethi J, Saranya VTK. Phytochemical analysis on leaf extract of Celosia argentea land its efficacy of antioxidant and anti -bacterial activity. Int J Pharmtech Res 2015;8:709–12. 28. Usunomena U, Egharevba E. Phytochemical analysis, proximate and mineral composition and in vitro antioxidant activities in Telfairia occidentalis aqueous leaf. Extract 2014;1:74–87. 29. Oyagbemi AA, Odetola AA Hepatoprotective effects of ethanolic extract of Cnidocolus aconitifolius on paracetamol-induced hepatic damage in rats. Pakistan J Biol Sci 2010;13:164–9. 30. Ayodele JT, Olajide OS. Proximate and amino acid composition of Celosia argentea leaves. Nig J Basic Appl Sci 2011;19:162–5. 31. Aja PM, Ale BA, Ekpono EU, Nwite I, Asouzu N, Njoku A. Amino acid profiles of Solanum aethiopicum, Amaranthus hybridus, and Telfairia occidentalis, common leafy vegetables in Nigeria. Sci Prog 2021;104: 00368504211032079. 32. Tyler HL. Impact of 2,4-D and glyphosate on soil enzyme activities in a resistant maize cropping system. Agronomy 2022;1:27–47.

Index 1-phenylethylbromide 378 1st order kinetics 365 2-bromopropionitrile 366 2JDR protein 257 3D structures 208 5DXT protein 258 5-HT2c receptor 103 5-HT2c receptor 89 13C NMR spectroscopy 375 ab initio 381 absorbance 292 acetone extract 75, 77, 79, 80, 82–84 acrid taste 288 acrylic monomers 361 activation-deactivation 362 activation energy 365 active site 104, 141, 170, 187, 258 active species 365 activity spectra for substances (PASS) 210 acute oral toxicity 173 adenocarcinoma 22 adenoma 24, 27 ADME 249 ADMET 87, 88, 96, 116, 118, 144, 161, 172, 210, 231, 258 adsorption 261 AGET-ATRP 368, 386 aglycone 288 agrochemicals 400 AIBN 362 AKT2 249 algerian flora 325 aliphatic carbon 127 alkaloids 93, 97 alkoxyamines 363 alkyl group 125 alkyl halides 363 alkyl iodides 367 alogliptin 35 alternative copolymers 378 amino acid 104, 142, 220, 404, 405 amino acid residue 143, 170, 187 aminopeptidases 16 anionic polymerization 379 antagonist 89, 103, 104 anti-aging 326

https://doi.org/10.1515/9783111071435-020

antibacterial activity 75, 76, 78, 82–84, 121–123, 130, 134–136, 204, 205 antibacterial studies 292 anticancer activity test 121, 199, 201, 205 anticancer drug 158, 233 anti-colorectal cancer 156, 161, 188 antifungi 202 anti-inflammatory 97, 100 antioxidant studies 292 antioxidants 308, 324 antitumor activity 202, 206 antiviral activity 59, 214 apoptosis 25 application 407 approximation 383 architectures 361 aromatic carbon 127 arsenic doped iron 336 aspergillus fumigatus 261 atomic radius 385 atomization 384 ATP 241 ATRP kinetics 368 ATRP method 360 average molecular weight 365 B. abyssinica 75–79, 83 B. subtilis 130 B3LYP 384 bacitracin 32 bacteria 76, 78, 83, 85 basis functions 384 bathochromic shift 125 Benzylic halides 366 bersama abyssinica 75, 76, 84 bestatin 32 bidentate ligands 367 binding affinities 87, 90, 142 binding energy 96, 103, 156, 187, 217 binding mode 188, 215, 256 binding site 180 bioaccumulate 324 bioactive compound 87, 94, 167, 116, 242 bioactivity 192, 214, 254 bioactivity radar 189 bio-based 380 biodegradability 329

412

Index

biodegradable solvents 330 biodiversity 329 biological activity 161, 200, 201, 205 biopolymer 58 block copolymers 386 blood–brain barrier 173 boltzmann constant 365 bond elongation 382 bond length 338 bond vibrations 382 bromo succinimide 367 bulky monomer 376 CADD 153, 168 cancer 1, 166, 231, 289 capsicum annuum L 167 carbon correlation 130 carcinogenesis 19 carcinoma 25, 27 cardiovascular 19 carotenoids 314 (CASTp) 240 catalysts 367 catalytic activity 265 cationic polymerization 380 cavitation effect 329 CD26 15 CD4+ 15 chalcone compounds 121, 124 chemical properties 382 chemoinformatic 195, 219, 231 chloramphenicol 76, 78 chrysobalanaceae 289 classical mechanics 382 clean extraction 327 CO2 368 colon 28 colorectal cancer 138, 166, 195 complex participation model 373 compound 87, 97, 100, 103, 104, 106, 108–110, 116 computational accuracy 383 computational analysis 340 computational chemistry 139 computer-aided drug design 139, 231 contaminated 404, 404 controlled architectures 362 conventional hydrogen bond 256 conventional methods 379 copolymer composition 373 copolymerization mechanism 374

copolymerization model 374 copolymerization 371 copolymers 361 copper catalyzed ATRP 364 copper complexes 367 correlation 383 cost-effective 381 coulomb energy 383 Covid-19 58–61, 69, 70 curcumin 233 curve-fitting regression 374 Cyp450 242 cytokines 11, 19 cytoskeleton 30 cytotoxic activity 121 deactivating species 363 degree of polymerization 365 density functional theory 335, 335, 364 dependence of anticancer activity 204 DFT methods 381 differential scanning calorimetry 362 diffuse basis sets 385 diffusion controlled rate 367 dioscoriaceae 288 dipole moment 340 distribution triads 375 dithioesters 363 (+)-d-limonene 378 DNA 167 docking 240 DPP8 25 DPP9 25 DPPH 292, 317 DPP-IV 15 drug design 153 drug development 139 drug discovery 138, 139, 144, 168 drug-like candidate 168 drug-like properties 249 drug-likeliness 209 drug-likeness analysis 153 dynamic equilibrium 364 EBiB 386 ectopeptidase 3 efficient technology 270 electron affinity 336 electron deficient 376 electron density 383 electron spin resonance 376

Index

electronic energy 383 electrophilicity index 339 electrostatic/hydrophobic interaction 157 electrostatic interactions 187 emetine 157, 169, 190 energy 383 enzyme inhibition 138 equilibrium 373 ethanolic extract 92 ethyl-2-bromoisobutyrate 366 exchange-correlations 384 extraction 310 F. zanthoxyloides 63–65 face-centred cubic 300 fagara 59 FAP 25 Feruloyl-beta-D-glucose 187 finemann ross 375 first principle study 336 first-order markov model 375 fischer-fukuda equation 364 flavoniods 93 fluorescence analysis 291 fluoxetine 87, 103, 105, 108–111, 119, 120 foaming index 290 foaming test 290 food system 324 free energy 382 Friedelan-3beta-Ol 157, 162 friedelin 157, 162 froth 288 FRP 361 FTIR finger printing 291 functional NAI 380 functional periodic copolymers 378 functional raw 325 functionalities 361, 383 gas chromatography–mass spectrometry (GCMS) 275 gaussian-type 385 GC-MS 311 gene expression 171 glass transition temperature 360 gliomas 25 gliptins 26 GLP-1 31 GLP-1R 31 glycone 288 glycosides 75, 75, 80, 83–85, 90, 93

413

glyphosate-based 400 ground state configuration 338 haemolysis test 290 halogenophilicity 364 health 401 heavy metals 87, 89, 94, 99, 119 hepatocarcinoma 26 herbal 87, 89, 90, 95, 96, 99, 104, 116, 118, 119 herbarium 289 hERG 242 HERG inhibitor 173 heterogeneous biocatalyst 262 heterolytic cleavage 367 HF 384 hit 192, 192, 254 homolysis 386 hormone 24 human angiogenesis converting enzymes (ACE2) 208 hybrid methods 384 hydrogen bond donors 210 hydrogen bond interaction 157, 187 hydrophobic and electrostatic interactions 269 hyperbranched copolymers 378 hyperbranched topology 363 hypertension 17 IC50 302 imide 360 immobilization 261, 262, 267 immobilize α-amylase 263 immobilized enzyme 265, 266, 268–270 implementation 330 in silico 87, 118, 158 incretine 11 industrial applications 379 industrial biocatalysts 262 industrial scale usage 270 inert conditions 380 inhibition constant 142, 170, 187, 192, 249 inhibitors 88, 110, 116, 118, 157, 195 inhibitory activity 134 inhibitory efficiency 180 iniferters 366, 367 insecurity 400 interactions 87, 90, 96, 104, 105, 116 intestinal diseases 75, 84 Ionization energy 336 ischemia 19 itaconamic acid 369

414

Index

itaconic acid esters 380 itaconic anhydride 369 itaconimide 360 Adansonia digitata 308 KATRP 364 kelen tudos 375 keratinocyte 20 kernel 309 kinetic and thermodynamic 364 kinetic energy 383 kinetic-model 374 kp/kdeact ratio 365 kt value 382 legislation 330 leukemia 19 leukemia cancer cell 199, 200 lieberman burchard’s 311 liebermann-burchard 80 ligand efficiency 255 ligandefficiency-dependent lipophilicity 255 linagliptin 35 lineages 15 lineweaver-burk equation plot 267 lipinski rule of five 210 LogS 253 LSDA 384 lung 19 lymphoma 19 macroinitiator 380 macromolecules 382 main protease (6LU7) 217 main protease (Mpro) 209 malignancy 20 marine organisms 324 mathematical equations 382 maximal reaction rate 264 mechanism 89, 104, 110, 116, 117, 119 mechanistic model 374 medicinal chemistry 167 medicinal plant 58, 61, 69, 139, 162, 274 medicines 89, 90, 99, 119 melanocyte 20 mesothelioma 26 metabolism 96, 111 metabolites 63 metastasis 19 methacrylates 366 methanol fraction 123 methyl acrylate 381

micelles 378 michaelis-menten constant 264, 267 microphase-separated 380 microscopy 290 microstructural 375 microwave power 328 minimum inhibition concentration 204 MNA (multilevel neighbours of atoms) 214 mole fraction 379 molecular docking 87, 141, 153, 180, 215, 220, 249 molecular energy 383 molecular hardness 339 molecular interaction 188, 217, 256 molecular mechanics 381 Molecular Mechanics/MMFF 239 molecular orbitals 384 molecular properties 383 molecular structure 338 morphology 379 mueller hinton 76, 78 multiwell plate tissue culture 201 NAI monomer 376 N-alkylitaconimides 369 nanoparticles appears 289 native enzyme 269 natural antioxidants 308 natural cosmetics 322 natural plant 283 natural product 139, 168, 195 N-benzylmaleimide 378 n-butyllithium 379 N-cyclohexylitaconimide 376 neoplasms 19 neuropeptides 11 new drugs 283 NF–κB inhibitor 171 n-hexane and methanol fractions 275 NMP 362 non-AMES toxic 242 normal ATRP 368 normal soil 406 N-phenylitaconimide 360 N-phenylmaleimide 378 N-tert-butylitaconimide 376 nuclear factor kappa-B 138, 143, 167 nuts 309 octanol-water partition coefficient 210 OECD 312 oil analyses 316

Index

oligomeric blocks 380 one-pot 379 optically active 380 optimum energy structure 337 oral bioavailability 142, 159, 169, 189, 214, 249 organoleptic 309 organotin(IV) compounds 199, 200, 202 ovarian cancer 232 oxidative stress 307 P13K receptor 258 pancreas 6 paper test 311 PASS analysis 193 PASS prediction 161 PASS properties 232, 258 pathways 231 pendant group 360, 380, 386 pentacyclic ursane 83 penultimate model 372 penultimate monomer 372 penultimate unit effect 372 periodic microstructures 378 persistent radical effect 362 petiveria alliaceae 274 pharmacokinetic property 144 pharmacological activity 96 pharmacological, therapeutic, and microbiological potentials 274 phosphoinositide3-kinase (PI3K) 239 photodegradation 323 photomicrograph 291 photoredox compounds 367 physicochemical property 159 physico-chemical 90, 375 phytoanticipins 288 phytochemical studies 293 phytoconstituents 274, 404 phytonutrients 401 phytoprotectants 288 PI3K inhibitors 233 PI3K/AKT 233 PI3K/AKT/mTOR 232 piperine 100, 110 PKB/AKT 239 planck’s constant 365 PLP-SEC 365 PMMA 366 polarization 385

415

polyacrylates 366 poly(itaconimide) 380 polymer chains 361 polymerization 381 poly(methacrylic acid) 378 polystyrene 367 positive potential 383 potential source 283 prediction of activity spectra for substances 254 premature ejaculation 87, 88, 116, 117 prestoblue reagent 123, 134 proline 11 propagating radicals 361 propagation steps 373 prostate 19 protein kinase B 241 psoriasis 20 PyMol 240 PyRx 210 quantum chemistry 365 quantum mechanics 381 radiotherapy 232 RAFT 362 raltitrexed 157, 169, 190 random architectures 381 random 360 rate constant of activation 363 rate constant 372 rate of polymerization 376 rate of propagation 361 rate of termination 361 RDRP technique 360 reaction kinetics 381 reactivity ratio 372 receptor 88–90, 95, 96, 103, 104, 116, 140, 173 recommended limits 87, 89, 97, 99 recovering 327 redox potentials 368 remdesivir 216 residual activity 268 reuptake 87–89, 104, 111, 116 reverse ATRP 368 risks 400 RO5 242 salkowski’s test 311 sample 91, 92, 94–97, 99, 100 saponins 87, 93, 97, 288 sapotoxins 288

416

Index

SARS-CoV-2 208 saxagliptin 35 scanning electron microscope 292 schrödinger equation 382 SCO2 extracts 328 secondary 405 second-order Markov 372 seed 402 semi-empirical methods 382 serotonin 5-HT2C receptor 88 SFRP 362 shear reagent 124, 125 singlet multiplicity 127 skin cancer 322 Sn(EH)2 369 softening temperature 381 soil 404 solar ultraviolet radiation 322 solubility 290, 310 solvent 382 spartan’14 208 specific gravity 310 specifically and selectively 262 spike glycoprotein 208 spin multiplicity 338 split-valence 385 SSRIs 88, 89, 111 stability 268, 323, 340 standard drug 141, 153, 173 star shaped 378 statistical model discrimination 374 steric effect 376 steroid/triterpenes 311 stiffness 268 stimulants 89 structural features 199 structure-activity 381 subzero 368 sun protection factor 322 sunscreen 322 sustainable methods 330 Swiss-ADME 169, 241 symmetry group 337 synthetic polymers 361 tacticity 381 target receptor 162, 169, 172 temperature 382 terminal model 371

terpenoids 93 the hit compound 144 therapeutic agent 242 thermal inactivation rate constant 265 thermal stability 264, 269, 379, 386 thermodynamic equation 265 thermodynamic 386 thermogravimetry 362 thermoplastic elastomers 380 thermoplastic 386 thin layer chromatography 291, 404 thrice 407 thymidylate synthase 138, 138, 142, 167, 195 thyroid 19, 24 time-conversion 376 TLC 315 topotecan 249 total polarity surface area 249 toxicity profile 173 toxicity study 312 transcriptional factor 138, 166, 167 transition metal complex 363 treatment 117, 118 triad fraction data 374 triterpenoidal saponin 294 triterpenoids 75, 76, 80, 85 tumour 19 turmeric 233 ultrasonic bath 329 ultrasonic probe 329 unsaturaturation 214 UprootTM 402 ursolic acid 75, 75, 80, 82, 84 UV finger 291 UV–Vis spectroscopy 375 vegetables 401, 406 vildagliptin 35 vinyl chloride 366 volatile oil 275 voucher number 289 world health organization (WHO) 99, 119 α-amylase 261, 262, 266 xanthoangelol 121, 130, 134–136 X-CuIIL/Y 366 X-ray diffraction 292 zeolite 261 zone of inhibition 82