The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice
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Submitted
21 December 2021
Published
31 May 2022
Keywords:
-
Microbial infections, Molecular docking, Molecular superimposition, 3D-pharmacophore, Drugs, Vaccines
Abstract
In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.
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References
1. Dhama K, Khan S, Tiwari R, Sircar S, Bhat S, Malik YS, et al. Coronavirus Disease 2019-COVID-19. Clin Microbiol Rev. 2020;33(4):e00028-20. doi:10.1128/cmr.00028-20
2. Tabish SA. COVID-19 pandemic: Emerging perspectives and future trends. J Public Health Res. 2020;9(1):1786. doi:10.4081/jphr.2020.1786
3. Zoumpourlis V, Goulielmaki M, Rizos E, Baliou S, Spandidos DA. [Comment] The COVID‑19 pandemic as a scientific and social challenge in the 21st century. Mol Med Rep. 2020;22(4):3035-48. doi:10.3892/mmr.2020.11393
4. Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules. 2020;25(20):4723. doi:10.3390/molecules25204723
5. Anderson AC. The Process of Structure-Based Drug Design. Chem Biol. 2003;10(9):787-97. doi:10.1016/j.chembiol.2003.09.002
6. Takebe T, Imai R, Ono S. The Current Status of Drug Discovery and Development as Originated in United States Academia: The Influence of Industrial and Academic Collaboration on Drug Discovery and Development. Clin Transl Sci. 2018;11(6):597-606. doi:10.1111/cts.12577
7. Ursino M, Zohar S, Lentz F, Alberti C, Friede T, Stallard N, et al. Dose-finding methods for Phase I clinical trials using pharmacokinetics in small populations. Biom J. 2017;59(4):804-25. doi:10.1002/bimj.201600084
8. Van Norman GA. Phase II Trials in Drug Development and Adaptive Trial Design. JACC Basic Transl Sci. 2019;4(3):428-37. doi:10.1016/j.jacbts.2019.02.005
9. Umscheid CA, Margolis DJ, Grossman CE. Key concepts of clinical trials: a narrative review. Postgrad Med. 2011;123(5):194-204. doi:10.3810/pgm.2011.09.2475
10. Zhang X, Zhang Y, Ye X, Guo X, Zhang T, He J. Overview of phase IV clinical trials for postmarket drug safety surveillance: a status report from the ClinicalTrials.gov registry. BMJ Open. 2016;6(11):e010643. doi:10.1136/bmjopen-2015-010643
11. Dara S, Dhamecherla S, Jadav SS, Babu CM, Ahsan MJ. Machine Learning in Drug Discovery: A Review. Artif Intell Rev. 2022;55(3):1947-99. doi:10.1007/s10462-021-10058-4
12. Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today. 2020;25(9):1624-38. doi:10.1016/j.drudis.2020.07.005
13. Pinzi L, Rastelli G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci. 2019;20(18):4331. doi:10.3390/ijms20184331
14. Thomford NE, Senthebane DA, Rowe A, Munro D, Seele P, Maroyi A, et al. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery. Int J Mol Sci. 2018;19(6):1578. doi:10.3390/ijms19061578
15. Marchenko O, Fedorov V, Lee JJ, Nolan C, Pinheiro J. Adaptive Clinical Trials: Overview of Early-Phase Designs and Challenges. Ther Innov Regul Sci. 2014;48(1):20-30. doi:10.1177/2168479013513889
16. Vatansever S, Schlessinger A, Wacker D, Kaniskan HU, Jin J, Zhou MM, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev. 2021;41(3):1427-73. doi:10.1002/med.21764
17. Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V, et al. Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int J Mol Sci. 2021;22(4):1676. doi:10.3390/ijms22041676
18. Insel TR, Voon V, Nye JS, Brown VJ, Altevogt BM, Bullmore ET, et al. Innovative solutions to novel drug development in mental health.Neursci Biobehav Rev. 2013;37(10 Pt 1):2438-44. doi:10.1016/j.neubiorev.2013.03.022
19. Zhou SF, Zhong WZ. Drug Design and Discovery: Principles and Applications. Molecules. 2017;22(2):279. doi:10.3390/molecules22020279
20. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239-49. doi:10.1111/j.1476-5381.2010.01127.x
21. Yu W, MacKerell AD. Computer-Aided Drug Design Methods.Methods Mol Biol. 2017;1520:85-106. doi:10.1007/978-1-4939-6634-9_5
22. Li D, Hu X, Han T, Liao J, Xiao W, Xu S, et al. NO-Releasing Enmein-Type Diterpenoid Derivatives with Selective Antiproliferative Activity and Effects on Apoptosis-Related Proteins. Molecules. 2016; 21(9):1193. doi:10.3390/molecules21091193
23. Radini IAM, Elsheikh TMY, El-Telbani EM, Khidre RE. New Potential Antimalarial Agents: Design, Synthesis and Biological Evaluation of Some Novel Quinoline Derivatives as Antimalarial Agents. Molecules. 2016; 21(7):909. doi:10.3390/molecules21070909
24. Gouda AM, Ali HI, Almalki WH, Azim MA, Abourehab MAS, Abdelazeem AH. Design, Synthesis, and Biological Evaluation of Some Novel Pyrrolizine Derivatives as COX Inhibitors with Anti-Inflammatory/Analgesic Activities and Low Ulcerogenic Liability. Molecules. 2016; 21(2):201. doi:10.3390/molecules21020201
25. Chopra G, Kaushik S, Elkin PL, Samudrala R. Combating Ebola with Repurposed Therapeutics Using the CANDO Platform. Molecules. 2016; 21(12):1537. doi:10.3390/molecules21121537
26. Spyridopoulou K, Fitsiou E, Bouloukosta E, Tiptiri-Kourpeti A, Vamvakias M, Oreopoulou A, et al. Extraction, Chemical Composition, and Anticancer Potential of Origanum onites L. Essential Oil. Molecules. 2019;24(14):2612. doi:10.3390/molecules24142612
27. Shin WH, Zhu X, Bures MG, Kihara D. Three-dimensional compound comparison methods and their application in drug discovery. Molecules. 2015;20(7):12841–62. doi:10.3390/molecules200712841
28. Ekins S, Spektor AC, Clark AM, Dole K, Bunin BA. Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB). Drug Discov Today. 2017;22(3):555–65. doi:10.1016/j.drudis.2016.10.009
29. Salmaso V, Moro S. Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview. Front Pharmacol. 2018;9:923. doi:10.3389/fphar.2018.00923
30. Paggi JM, Belk JA, Hollingsworth SA, Villanueva N, Powers AS, Clark MJ, et al. Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes. Proc Natl Acad Sci USA. 2021;118(51):e2112621118. doi:10.1073/pnas.2112621118
31. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-57. doi:10.2174/157340911795677602
32. Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, et al. Computational/in silico methods in drug target and lead prediction. Brief Bioinform. 2020;21(5):1663-75. doi:10.1093/bib/bbz103
33. de Ruyck J, Brysbaert G, Blossey R, Lensink MF. Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem. 2016;9:1-11. doi:10.2147/aabc.s105289
34. Stark JL, Powers R. Application of NMR and molecular docking in structure-based drug discovery. Top Curr Chem. 2012;326:1-34. doi: https://doi.org/10.1007/128_2011_213
35. Tarasova O, Poroikov V, Veselovsky A. Molecular Docking Studies of HIV-1 Resistance to Reverse Transcriptase Inhibitors: Mini-Review. Molecules. 2018;23(5):1233. doi:10.3390/molecules23051233
36. Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015;20(7):13384-421. doi:10.3390/molecules200713384
37. Fusani L, Palmer DS, Somers DO, Wall ID. Exploring Ligand Stability in Protein Crystal Structures Using Binding Pose Metadynamics. J Chem Inf Model. 2020;60(3):1528-39. doi:10.1021/acs.jcim.9b00843
38. Glaab E. Building a virtual ligand screening pipeline using free software: a survey. Brief Bioinform. 2016;17(2):352-66. doi:10.1093/bib/bbv037
39. Ferreira RS, Simeonov A, Jadhav A, Eidam O, Mott BT, Keiser MJ, et al. Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J Med Chem. 2010;53(13):4891-905. doi:10.1021/jm100488w
40. Aparoy P, Reddy KK, Reddanna P. Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem. 2012;19(22):3763-78. doi:10.2174/092986712801661112
41. Temmi V, Kutil Z. Structure-based molecular modeling in SAR analysis and lead optimization. Comput Struct Biotechnol J. 2021;19:1431-44. doi:10.1016/j.csbj.2021.02.018
42. Kaserer T, Beck KR, Akram M, Odermatt A, Schuster D. Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases. Molecules. 2015;20(12):22799-832. doi:10.3390/molecules201219880
43. Maveyraud L, Mourey L. Protein X-ray Crystallography and Drug Discovery. Molecules;2020:25(5):1030. doi:10.3390/molecules25051030
44. Meshram RJ, Baladhye VB, Gacche RN, Karale BK, Gaikar RB. Pharmacophore Mapping Approach for Drug Target Identification: A Chemical Synthesis and in Silico Study on Novel Thiadiazole Compounds. J Clin Diagn Res. 2017;11(5):KF01–8. doi:10.7860/jcdr/2017/22761.9925
45. Valasani KR, Vangavaragu JR, Day VW, Yan SS. Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. J Chem Inf Model. 2014;54(3):902–12. doi:10.1021/ci5000196
46. Bakkali ME, Ismaili L, Tomassoli I, Nicod L, Pudlo M, Refouvelet B, Pharmacophore Modelling and Synthesis of Quinoline-3-Carbohydrazide as Antioxidants. Int J Med Chem. 2011;2011:592879. doi:10.1155/2011/592879
47. Awadallah FM. Synthesis, Pharmacophore Modeling, and Biological Evaluation of Novel 5H-Thiazolo[3,2-a]pyrimidin-5-one Derivatives as 5-HT2A Receptor Antagonists. Sci Pharm. 2008;76(3):415–38. doi:10.3797/scipharm.0804-20
48. Che J, Wang Z, Sheng H, Huang F, Dong X, Hu Y, et al. Ligand-based pharmacophore model for the discovery of novel CXCR2 antagonists as anti-cancer metastatic agents. R Soc Open Sci. 2018;5(7):180176. doi:10.1098/rsos.180176
49. Lounnas V, Ritschel T, Kelder J, McGuire R, Bywater RP, Foloppe N. Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery. Comput Struct Biotechnol J. 2013;5:e201302011. doi:10.5936/csbj.201302011
50. Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S, et al. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Front Pharmacol. 2018;9:128. doi:10.3389/fphar.2018.00128
51. Reynolds CH, Holloway MK. Thermodynamics of ligand binding and efficiency. ACS Med Chem Lett. 2011;2(6):433–7. doi:10.1021/ml200010k
52. Fox JM, Kang K, Sherman W, Héroux A, Sastry GM, Baghbanzadeh M, et al. Interactions between hofmeister anions and the binding pocket of a protein. J Am Chem Soc, 2015;137(11):3859–66. doi:10.1021/jacs.5b00187
53. Abdel-Hamid MK, McCluskey A. In silico docking, molecular dynamics and binding energy insights into the bolinaquinone-clathrin terminal domain binding site. Molecules. 2014;19(5):6609–22. doi:10.3390/molecules19056609
54. Claveria-Gimeno R, Vega S, Abian O, Velazquez-Campoy A. A look at ligand binding thermodynamics in drug discovery. Expert Opin Drug Discov. 2017;12(4):363-77. doi:17460441.2017.1297418
55. Reygaert WC. An overview of the antimicrobial resistance mechanisms of bacteria. AIMS Microbiol. 2018;4(3):482-501. doi:10.3934/microbiol.2018.3.482
56. Olsson TSG, Williams MA, Pitt WR, Ladbury JE. The thermodynamics of protein–ligand interaction and solvation: insights for ligand design. J Mol Biol. 2008;384(4):1002–17. doi:10.1016/j.jmb.2008.09.073
57. Gurung AB, Ali MA, Lee J, Farah MA, Al-Anazi KM. An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19. Biomed Res Int. 2021;2021:8853056. doi:10.1155/2021/8853056
58. Ziemert N, Jensen PR. Phylogenetic approaches to natural product structure prediction.Methods Enzymol. 2012;517:161-82. doi:10.1016/b978-0-12-404634-4.00008-5
59. Bouback TA, Pokhrel S, Albeshri A, Aljohani AM, Samad A, Alam R, et al. Pharmacophore-Based Virtual Screening, Quantum Mechanics Calculations, and Molecular Dynamics Simulation Approaches Identified Potential Natural Antiviral Drug Candidates against MERS-CoV S1-NTD. Molecules. 2021;26(16):4961. doi:10.3390/molecules26164961
60. Adelusi TI, Oyedele AQK, Boyenle ID, Ogunlana AT, Adeyemi RO, Ukachi CD, et al. Molecular modeling in drug discovery. Inform Med Unlocked. 2022;29:100880. doi:10.1016/j.imu.2022.100880
61. Türkmenoğlu B, Güzel Y. Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor Thiazoles. Comput Biol Chem. 2018;76:327-37. doi:10.1016/j.compbiolchem.2018.07.003
62. Kaur P, Sharma V, Kumar V. Pharmacophore Modelling and 3D-QSAR Studies on N(3)-Phenylpyrazinones as Corticotropin-Releasing Factor 1 Receptor Antagonists. Int J Med Chem. 2012;2012:452325. doi:10.1155/2012/452325
63. Kutlushina A, Khakimova A, Madzhidov T, Polishchuk P. Correction: Kutlushina, A., et al. Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures. Molecules, 2018, 23, 3094. Molecules. 2019;24(6):1052. doi:10.3390/molecules24061052
64. Spitzer GM, Heiss M, Mangold M, Markt P, Kirchmair J, Wolber G, et al. One concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space. J Chem Inf Model. 2010;50(7):1241-7. doi:10.1021/ci100136b
65. Khedkar SA, Malde AK, Coutinho EC, Srivastava S. Pharmacophore modeling in drug discovery and development: an overview. Med Chem. 2007;3(2):187-97. doi:10.2174/157340607780059521
66. Leach AR, Gillet VJ, Lewis RA, Taylor R. Three-dimensional pharmacophore methods in drug discovery. J Med Chem. 2010;53(2):539-58. doi:10.1021/jm900817u
67. Barlow C. Human Subjects Protection and Federal Regulations of Clinical Trials. Semin Oncol Nurs. 2020;36(2):151001. doi:10.1016/j.soncn.2020.151001
68. Flotte TR, Lord BT, Siedman J. Supporting Families Considering Participation in a Clinical Trial: Parent-Provider Perspectives. Pediatrics. 2021;147(5):e2020042044. doi:10.1542/peds.2020-042044
69. Gillies K, Campbell MK. Development and evaluation of decision aids for people considering taking part in a clinical trial: a conceptual framework. Trials. 2019;20(1):401. doi:10.1186/s13063-019-3489-y
70. Hostiuc S, Rusu MC, Negoi I, Drima E. Testing decision-making competency of schizophrenia participants in clinical trials. A meta-analysis and meta-regression. BMC Psychiatry. 2018;18(1):2. doi:10.1186/s12888-017-1580-z
71. Vanseymortier M, Thery J, Penel N. Évolution du cadre réglementaire de la recherche clinique [Evolution of the regulatory framework in clinical research]. Bull Cancer. 2019;106(4):389-94. doi:10.1016/j.bulcan.2019.01.016
72. Guay J, Suresh S, Kopp S, Johnson RL. Postoperative epidural analgesia versus systemic analgesia for thoraco-lumbar spine surgery in children. Cochrane Database Syst Rev. 2019;1(1):CD012819. doi:10.1002/14651858.cd012819.pub2
73. Monteiro TM, Katz L, Bento SF, Amorim MM, Moriel PC, Pacagnella RC. Reasons given by pregnant women for participating in a clinical trial aimed at preventing premature delivery: a qualitative analysis. BMC Pregnancy Childbirth. 2019;19(1):97. doi:10.1186/s12884-019-2240-8
74. Gordon AL, Witham MD, Henderson EJ, Harwood RH, Masud T. Research into ageing and frailty. Future Healthc J. 2021;8(2):e237-42. doi:10.7861/fhj.2021-0088
75. Witham MD, McMurdo ME. How to get older people included in clinical studies. Drugs Aging. 2007;24(3):187-96. doi:10.2165/00002512-200724030-00002
76. Wendler D. When and how to include vulnerable subjects in clinical trials. Clin Trials. 2020;17(6):696-702. doi:10.1177/1740774520945601
77. White MG. Why Human Subjects Research Protection Is Important. Ochsner J. 2020;20(1):16-33. doi:10.31486/toj.20.5012
78. Sanmukhani J, Tripathi CB. Ethics in Clinical Research: The Indian Perspective. Indian J Pharm Sci. 2011; 73(2): 125–30. doi:10.4103/0250-474x.91564
79. Nardini C. The ethics of clinical trials. Ecancermedicalscience. 2014;8:387. doi:10.3332/ecancer.2014.387
80. Emanuel EJ, Wendler D, Grady C. What makes clinical research ethical? JAMA. 2000;283(20):2701-11. doi:10.1001/jama.283.20.2701
81. Wendler D, Rid A. In Defense of a Social Value Requirement for Clinical Research. Bioethics. 2017;31(2):77–86. doi:10.1111/bioe.12325
82. Manti S, Licari A. How to obtain informed consent for research. Breathe. 2018;14(2):145-52. doi:10.1183/20734735.001918
83. Rao KHS. Informed consent: an ethical obligation or legal compulsion? J Cutan Aesthet Surg. 2008;1(1):33–5. doi:10.4103/0974-2077.41159
84. Nijhawan LP, Janodia MD, Muddukrishna BS, Bhat KM, Bairy KL, Udupa N, et al. Informed consent: Issues and challenges. J Adv Pharm Technol Res. 2013;4(3):134–40. doi:10.4103/2231-4040.116779
85. Dickert NW, Eyal N, Goldkind SF, Grady C, Joffe S, Lo B, et al. Reframing Consent for Clinical Research: A Function-Based Approach. Am J Bioeth. 2017;17(12):3-11. doi:10.1080/15265161.2017.1388448
86. Vijayananthan A, Nawawi O. The importance of Good Clinical Practice guidelines and its role in clinical trials. Biomed Imaging Interv J. 2008;4(1):e5. doi:10.2349/biij.4.1.e5
87. Rizk JG, Forthal DN, Kalantar-Zadeh K, Mehra MR, Lavie CJ, Rizki Y, et al. Expanded Access Programs, compassionate drug use, and Emergency Use Authorizations during the COVID-19 pandemic. Drug Discov Today. 2021;26(2):593-603. doi: https://doi.org/10.1016/j.drudis.2020.11.025
88. Devine S, Dagher RN, Weiss KD, Santana VM. Good clinical practice and the conduct of clinical studies in pediatric oncology. Pediatr Clin North Am. 2008;55(1):187-209. doi:10.1016/j.pcl.2007.10.008
89. Rollo D, Machado S, Ceschin M. Design of clinical trials. Semin Nucl Med. 2010;40(5):332-7. doi:10.1053/j.semnuclmed.2010.03.003
90. Holbein MEB. Understanding FDA regulatory requirements for investigational new drug applications for sponsor-investigators. J Investig Med. 2009;57(6):688–94. doi:10.2310/jim.0b013e3181afdb26
91. Fukushima M. [Quality control in clinical trials]. Gan To Kagaku Ryoho. 1996;23(2):172-82.
92. Mehra M, Kurpanek K, Petrizzo M, Brenner S, McCraken Y, Katz T, et al. The Life Cycle and Management of Protocol Deviations. Ther Innov Regul Sci. 2014;48(6):762-77. doi:10.1177/2168479014530119
93. Ramana KV, Kandi S, Boinpally PR. Ethics in medical education, practice, and research: An insight. Ann Trop Med Public Health. 2013;6(6):599-602. doi:10.4103/1755-6783.140200
94. Shamley D, Ezeani A, Okoye I. Oncology Clinical Trials in Africa: Partnering for Quality. JCO Glob Oncol. 2021;7:572-6. doi:10.1200/jgo.19.00315
95. Corneli A, Forrest A, Swezey T, Lin L, Tenaerts P. Stakeholders' recommendations for revising Good Clinical Practice. Contemp Clin Trials Commun. 2021;22:100776. doi:10.1016/j.conctc.2021.100776
2. Tabish SA. COVID-19 pandemic: Emerging perspectives and future trends. J Public Health Res. 2020;9(1):1786. doi:10.4081/jphr.2020.1786
3. Zoumpourlis V, Goulielmaki M, Rizos E, Baliou S, Spandidos DA. [Comment] The COVID‑19 pandemic as a scientific and social challenge in the 21st century. Mol Med Rep. 2020;22(4):3035-48. doi:10.3892/mmr.2020.11393
4. Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules. 2020;25(20):4723. doi:10.3390/molecules25204723
5. Anderson AC. The Process of Structure-Based Drug Design. Chem Biol. 2003;10(9):787-97. doi:10.1016/j.chembiol.2003.09.002
6. Takebe T, Imai R, Ono S. The Current Status of Drug Discovery and Development as Originated in United States Academia: The Influence of Industrial and Academic Collaboration on Drug Discovery and Development. Clin Transl Sci. 2018;11(6):597-606. doi:10.1111/cts.12577
7. Ursino M, Zohar S, Lentz F, Alberti C, Friede T, Stallard N, et al. Dose-finding methods for Phase I clinical trials using pharmacokinetics in small populations. Biom J. 2017;59(4):804-25. doi:10.1002/bimj.201600084
8. Van Norman GA. Phase II Trials in Drug Development and Adaptive Trial Design. JACC Basic Transl Sci. 2019;4(3):428-37. doi:10.1016/j.jacbts.2019.02.005
9. Umscheid CA, Margolis DJ, Grossman CE. Key concepts of clinical trials: a narrative review. Postgrad Med. 2011;123(5):194-204. doi:10.3810/pgm.2011.09.2475
10. Zhang X, Zhang Y, Ye X, Guo X, Zhang T, He J. Overview of phase IV clinical trials for postmarket drug safety surveillance: a status report from the ClinicalTrials.gov registry. BMJ Open. 2016;6(11):e010643. doi:10.1136/bmjopen-2015-010643
11. Dara S, Dhamecherla S, Jadav SS, Babu CM, Ahsan MJ. Machine Learning in Drug Discovery: A Review. Artif Intell Rev. 2022;55(3):1947-99. doi:10.1007/s10462-021-10058-4
12. Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today. 2020;25(9):1624-38. doi:10.1016/j.drudis.2020.07.005
13. Pinzi L, Rastelli G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci. 2019;20(18):4331. doi:10.3390/ijms20184331
14. Thomford NE, Senthebane DA, Rowe A, Munro D, Seele P, Maroyi A, et al. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery. Int J Mol Sci. 2018;19(6):1578. doi:10.3390/ijms19061578
15. Marchenko O, Fedorov V, Lee JJ, Nolan C, Pinheiro J. Adaptive Clinical Trials: Overview of Early-Phase Designs and Challenges. Ther Innov Regul Sci. 2014;48(1):20-30. doi:10.1177/2168479013513889
16. Vatansever S, Schlessinger A, Wacker D, Kaniskan HU, Jin J, Zhou MM, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev. 2021;41(3):1427-73. doi:10.1002/med.21764
17. Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V, et al. Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int J Mol Sci. 2021;22(4):1676. doi:10.3390/ijms22041676
18. Insel TR, Voon V, Nye JS, Brown VJ, Altevogt BM, Bullmore ET, et al. Innovative solutions to novel drug development in mental health.Neursci Biobehav Rev. 2013;37(10 Pt 1):2438-44. doi:10.1016/j.neubiorev.2013.03.022
19. Zhou SF, Zhong WZ. Drug Design and Discovery: Principles and Applications. Molecules. 2017;22(2):279. doi:10.3390/molecules22020279
20. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239-49. doi:10.1111/j.1476-5381.2010.01127.x
21. Yu W, MacKerell AD. Computer-Aided Drug Design Methods.Methods Mol Biol. 2017;1520:85-106. doi:10.1007/978-1-4939-6634-9_5
22. Li D, Hu X, Han T, Liao J, Xiao W, Xu S, et al. NO-Releasing Enmein-Type Diterpenoid Derivatives with Selective Antiproliferative Activity and Effects on Apoptosis-Related Proteins. Molecules. 2016; 21(9):1193. doi:10.3390/molecules21091193
23. Radini IAM, Elsheikh TMY, El-Telbani EM, Khidre RE. New Potential Antimalarial Agents: Design, Synthesis and Biological Evaluation of Some Novel Quinoline Derivatives as Antimalarial Agents. Molecules. 2016; 21(7):909. doi:10.3390/molecules21070909
24. Gouda AM, Ali HI, Almalki WH, Azim MA, Abourehab MAS, Abdelazeem AH. Design, Synthesis, and Biological Evaluation of Some Novel Pyrrolizine Derivatives as COX Inhibitors with Anti-Inflammatory/Analgesic Activities and Low Ulcerogenic Liability. Molecules. 2016; 21(2):201. doi:10.3390/molecules21020201
25. Chopra G, Kaushik S, Elkin PL, Samudrala R. Combating Ebola with Repurposed Therapeutics Using the CANDO Platform. Molecules. 2016; 21(12):1537. doi:10.3390/molecules21121537
26. Spyridopoulou K, Fitsiou E, Bouloukosta E, Tiptiri-Kourpeti A, Vamvakias M, Oreopoulou A, et al. Extraction, Chemical Composition, and Anticancer Potential of Origanum onites L. Essential Oil. Molecules. 2019;24(14):2612. doi:10.3390/molecules24142612
27. Shin WH, Zhu X, Bures MG, Kihara D. Three-dimensional compound comparison methods and their application in drug discovery. Molecules. 2015;20(7):12841–62. doi:10.3390/molecules200712841
28. Ekins S, Spektor AC, Clark AM, Dole K, Bunin BA. Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB). Drug Discov Today. 2017;22(3):555–65. doi:10.1016/j.drudis.2016.10.009
29. Salmaso V, Moro S. Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview. Front Pharmacol. 2018;9:923. doi:10.3389/fphar.2018.00923
30. Paggi JM, Belk JA, Hollingsworth SA, Villanueva N, Powers AS, Clark MJ, et al. Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes. Proc Natl Acad Sci USA. 2021;118(51):e2112621118. doi:10.1073/pnas.2112621118
31. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-57. doi:10.2174/157340911795677602
32. Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, et al. Computational/in silico methods in drug target and lead prediction. Brief Bioinform. 2020;21(5):1663-75. doi:10.1093/bib/bbz103
33. de Ruyck J, Brysbaert G, Blossey R, Lensink MF. Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem. 2016;9:1-11. doi:10.2147/aabc.s105289
34. Stark JL, Powers R. Application of NMR and molecular docking in structure-based drug discovery. Top Curr Chem. 2012;326:1-34. doi: https://doi.org/10.1007/128_2011_213
35. Tarasova O, Poroikov V, Veselovsky A. Molecular Docking Studies of HIV-1 Resistance to Reverse Transcriptase Inhibitors: Mini-Review. Molecules. 2018;23(5):1233. doi:10.3390/molecules23051233
36. Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015;20(7):13384-421. doi:10.3390/molecules200713384
37. Fusani L, Palmer DS, Somers DO, Wall ID. Exploring Ligand Stability in Protein Crystal Structures Using Binding Pose Metadynamics. J Chem Inf Model. 2020;60(3):1528-39. doi:10.1021/acs.jcim.9b00843
38. Glaab E. Building a virtual ligand screening pipeline using free software: a survey. Brief Bioinform. 2016;17(2):352-66. doi:10.1093/bib/bbv037
39. Ferreira RS, Simeonov A, Jadhav A, Eidam O, Mott BT, Keiser MJ, et al. Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J Med Chem. 2010;53(13):4891-905. doi:10.1021/jm100488w
40. Aparoy P, Reddy KK, Reddanna P. Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem. 2012;19(22):3763-78. doi:10.2174/092986712801661112
41. Temmi V, Kutil Z. Structure-based molecular modeling in SAR analysis and lead optimization. Comput Struct Biotechnol J. 2021;19:1431-44. doi:10.1016/j.csbj.2021.02.018
42. Kaserer T, Beck KR, Akram M, Odermatt A, Schuster D. Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases. Molecules. 2015;20(12):22799-832. doi:10.3390/molecules201219880
43. Maveyraud L, Mourey L. Protein X-ray Crystallography and Drug Discovery. Molecules;2020:25(5):1030. doi:10.3390/molecules25051030
44. Meshram RJ, Baladhye VB, Gacche RN, Karale BK, Gaikar RB. Pharmacophore Mapping Approach for Drug Target Identification: A Chemical Synthesis and in Silico Study on Novel Thiadiazole Compounds. J Clin Diagn Res. 2017;11(5):KF01–8. doi:10.7860/jcdr/2017/22761.9925
45. Valasani KR, Vangavaragu JR, Day VW, Yan SS. Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. J Chem Inf Model. 2014;54(3):902–12. doi:10.1021/ci5000196
46. Bakkali ME, Ismaili L, Tomassoli I, Nicod L, Pudlo M, Refouvelet B, Pharmacophore Modelling and Synthesis of Quinoline-3-Carbohydrazide as Antioxidants. Int J Med Chem. 2011;2011:592879. doi:10.1155/2011/592879
47. Awadallah FM. Synthesis, Pharmacophore Modeling, and Biological Evaluation of Novel 5H-Thiazolo[3,2-a]pyrimidin-5-one Derivatives as 5-HT2A Receptor Antagonists. Sci Pharm. 2008;76(3):415–38. doi:10.3797/scipharm.0804-20
48. Che J, Wang Z, Sheng H, Huang F, Dong X, Hu Y, et al. Ligand-based pharmacophore model for the discovery of novel CXCR2 antagonists as anti-cancer metastatic agents. R Soc Open Sci. 2018;5(7):180176. doi:10.1098/rsos.180176
49. Lounnas V, Ritschel T, Kelder J, McGuire R, Bywater RP, Foloppe N. Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery. Comput Struct Biotechnol J. 2013;5:e201302011. doi:10.5936/csbj.201302011
50. Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S, et al. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Front Pharmacol. 2018;9:128. doi:10.3389/fphar.2018.00128
51. Reynolds CH, Holloway MK. Thermodynamics of ligand binding and efficiency. ACS Med Chem Lett. 2011;2(6):433–7. doi:10.1021/ml200010k
52. Fox JM, Kang K, Sherman W, Héroux A, Sastry GM, Baghbanzadeh M, et al. Interactions between hofmeister anions and the binding pocket of a protein. J Am Chem Soc, 2015;137(11):3859–66. doi:10.1021/jacs.5b00187
53. Abdel-Hamid MK, McCluskey A. In silico docking, molecular dynamics and binding energy insights into the bolinaquinone-clathrin terminal domain binding site. Molecules. 2014;19(5):6609–22. doi:10.3390/molecules19056609
54. Claveria-Gimeno R, Vega S, Abian O, Velazquez-Campoy A. A look at ligand binding thermodynamics in drug discovery. Expert Opin Drug Discov. 2017;12(4):363-77. doi:17460441.2017.1297418
55. Reygaert WC. An overview of the antimicrobial resistance mechanisms of bacteria. AIMS Microbiol. 2018;4(3):482-501. doi:10.3934/microbiol.2018.3.482
56. Olsson TSG, Williams MA, Pitt WR, Ladbury JE. The thermodynamics of protein–ligand interaction and solvation: insights for ligand design. J Mol Biol. 2008;384(4):1002–17. doi:10.1016/j.jmb.2008.09.073
57. Gurung AB, Ali MA, Lee J, Farah MA, Al-Anazi KM. An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19. Biomed Res Int. 2021;2021:8853056. doi:10.1155/2021/8853056
58. Ziemert N, Jensen PR. Phylogenetic approaches to natural product structure prediction.Methods Enzymol. 2012;517:161-82. doi:10.1016/b978-0-12-404634-4.00008-5
59. Bouback TA, Pokhrel S, Albeshri A, Aljohani AM, Samad A, Alam R, et al. Pharmacophore-Based Virtual Screening, Quantum Mechanics Calculations, and Molecular Dynamics Simulation Approaches Identified Potential Natural Antiviral Drug Candidates against MERS-CoV S1-NTD. Molecules. 2021;26(16):4961. doi:10.3390/molecules26164961
60. Adelusi TI, Oyedele AQK, Boyenle ID, Ogunlana AT, Adeyemi RO, Ukachi CD, et al. Molecular modeling in drug discovery. Inform Med Unlocked. 2022;29:100880. doi:10.1016/j.imu.2022.100880
61. Türkmenoğlu B, Güzel Y. Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor Thiazoles. Comput Biol Chem. 2018;76:327-37. doi:10.1016/j.compbiolchem.2018.07.003
62. Kaur P, Sharma V, Kumar V. Pharmacophore Modelling and 3D-QSAR Studies on N(3)-Phenylpyrazinones as Corticotropin-Releasing Factor 1 Receptor Antagonists. Int J Med Chem. 2012;2012:452325. doi:10.1155/2012/452325
63. Kutlushina A, Khakimova A, Madzhidov T, Polishchuk P. Correction: Kutlushina, A., et al. Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures. Molecules, 2018, 23, 3094. Molecules. 2019;24(6):1052. doi:10.3390/molecules24061052
64. Spitzer GM, Heiss M, Mangold M, Markt P, Kirchmair J, Wolber G, et al. One concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space. J Chem Inf Model. 2010;50(7):1241-7. doi:10.1021/ci100136b
65. Khedkar SA, Malde AK, Coutinho EC, Srivastava S. Pharmacophore modeling in drug discovery and development: an overview. Med Chem. 2007;3(2):187-97. doi:10.2174/157340607780059521
66. Leach AR, Gillet VJ, Lewis RA, Taylor R. Three-dimensional pharmacophore methods in drug discovery. J Med Chem. 2010;53(2):539-58. doi:10.1021/jm900817u
67. Barlow C. Human Subjects Protection and Federal Regulations of Clinical Trials. Semin Oncol Nurs. 2020;36(2):151001. doi:10.1016/j.soncn.2020.151001
68. Flotte TR, Lord BT, Siedman J. Supporting Families Considering Participation in a Clinical Trial: Parent-Provider Perspectives. Pediatrics. 2021;147(5):e2020042044. doi:10.1542/peds.2020-042044
69. Gillies K, Campbell MK. Development and evaluation of decision aids for people considering taking part in a clinical trial: a conceptual framework. Trials. 2019;20(1):401. doi:10.1186/s13063-019-3489-y
70. Hostiuc S, Rusu MC, Negoi I, Drima E. Testing decision-making competency of schizophrenia participants in clinical trials. A meta-analysis and meta-regression. BMC Psychiatry. 2018;18(1):2. doi:10.1186/s12888-017-1580-z
71. Vanseymortier M, Thery J, Penel N. Évolution du cadre réglementaire de la recherche clinique [Evolution of the regulatory framework in clinical research]. Bull Cancer. 2019;106(4):389-94. doi:10.1016/j.bulcan.2019.01.016
72. Guay J, Suresh S, Kopp S, Johnson RL. Postoperative epidural analgesia versus systemic analgesia for thoraco-lumbar spine surgery in children. Cochrane Database Syst Rev. 2019;1(1):CD012819. doi:10.1002/14651858.cd012819.pub2
73. Monteiro TM, Katz L, Bento SF, Amorim MM, Moriel PC, Pacagnella RC. Reasons given by pregnant women for participating in a clinical trial aimed at preventing premature delivery: a qualitative analysis. BMC Pregnancy Childbirth. 2019;19(1):97. doi:10.1186/s12884-019-2240-8
74. Gordon AL, Witham MD, Henderson EJ, Harwood RH, Masud T. Research into ageing and frailty. Future Healthc J. 2021;8(2):e237-42. doi:10.7861/fhj.2021-0088
75. Witham MD, McMurdo ME. How to get older people included in clinical studies. Drugs Aging. 2007;24(3):187-96. doi:10.2165/00002512-200724030-00002
76. Wendler D. When and how to include vulnerable subjects in clinical trials. Clin Trials. 2020;17(6):696-702. doi:10.1177/1740774520945601
77. White MG. Why Human Subjects Research Protection Is Important. Ochsner J. 2020;20(1):16-33. doi:10.31486/toj.20.5012
78. Sanmukhani J, Tripathi CB. Ethics in Clinical Research: The Indian Perspective. Indian J Pharm Sci. 2011; 73(2): 125–30. doi:10.4103/0250-474x.91564
79. Nardini C. The ethics of clinical trials. Ecancermedicalscience. 2014;8:387. doi:10.3332/ecancer.2014.387
80. Emanuel EJ, Wendler D, Grady C. What makes clinical research ethical? JAMA. 2000;283(20):2701-11. doi:10.1001/jama.283.20.2701
81. Wendler D, Rid A. In Defense of a Social Value Requirement for Clinical Research. Bioethics. 2017;31(2):77–86. doi:10.1111/bioe.12325
82. Manti S, Licari A. How to obtain informed consent for research. Breathe. 2018;14(2):145-52. doi:10.1183/20734735.001918
83. Rao KHS. Informed consent: an ethical obligation or legal compulsion? J Cutan Aesthet Surg. 2008;1(1):33–5. doi:10.4103/0974-2077.41159
84. Nijhawan LP, Janodia MD, Muddukrishna BS, Bhat KM, Bairy KL, Udupa N, et al. Informed consent: Issues and challenges. J Adv Pharm Technol Res. 2013;4(3):134–40. doi:10.4103/2231-4040.116779
85. Dickert NW, Eyal N, Goldkind SF, Grady C, Joffe S, Lo B, et al. Reframing Consent for Clinical Research: A Function-Based Approach. Am J Bioeth. 2017;17(12):3-11. doi:10.1080/15265161.2017.1388448
86. Vijayananthan A, Nawawi O. The importance of Good Clinical Practice guidelines and its role in clinical trials. Biomed Imaging Interv J. 2008;4(1):e5. doi:10.2349/biij.4.1.e5
87. Rizk JG, Forthal DN, Kalantar-Zadeh K, Mehra MR, Lavie CJ, Rizki Y, et al. Expanded Access Programs, compassionate drug use, and Emergency Use Authorizations during the COVID-19 pandemic. Drug Discov Today. 2021;26(2):593-603. doi: https://doi.org/10.1016/j.drudis.2020.11.025
88. Devine S, Dagher RN, Weiss KD, Santana VM. Good clinical practice and the conduct of clinical studies in pediatric oncology. Pediatr Clin North Am. 2008;55(1):187-209. doi:10.1016/j.pcl.2007.10.008
89. Rollo D, Machado S, Ceschin M. Design of clinical trials. Semin Nucl Med. 2010;40(5):332-7. doi:10.1053/j.semnuclmed.2010.03.003
90. Holbein MEB. Understanding FDA regulatory requirements for investigational new drug applications for sponsor-investigators. J Investig Med. 2009;57(6):688–94. doi:10.2310/jim.0b013e3181afdb26
91. Fukushima M. [Quality control in clinical trials]. Gan To Kagaku Ryoho. 1996;23(2):172-82.
92. Mehra M, Kurpanek K, Petrizzo M, Brenner S, McCraken Y, Katz T, et al. The Life Cycle and Management of Protocol Deviations. Ther Innov Regul Sci. 2014;48(6):762-77. doi:10.1177/2168479014530119
93. Ramana KV, Kandi S, Boinpally PR. Ethics in medical education, practice, and research: An insight. Ann Trop Med Public Health. 2013;6(6):599-602. doi:10.4103/1755-6783.140200
94. Shamley D, Ezeani A, Okoye I. Oncology Clinical Trials in Africa: Partnering for Quality. JCO Glob Oncol. 2021;7:572-6. doi:10.1200/jgo.19.00315
95. Corneli A, Forrest A, Swezey T, Lin L, Tenaerts P. Stakeholders' recommendations for revising Good Clinical Practice. Contemp Clin Trials Commun. 2021;22:100776. doi:10.1016/j.conctc.2021.100776
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1.
Kandi V, Vundecode A, Godalwar TR, Dasari S, Vadakedath S, Godishala V. The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice. Borneo J Pharm [Internet]. 2022May31 [cited 2024Nov.15];5(2):161-78. Available from: https://journal.umpr.ac.id/index.php/bjop/article/view/3013
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