Application of in silico methods in clinical research and development of drugs and their formulation: A scoping review

Luciana Ferreira Mattos Colli; Lúcio Mendes Cabral; Guacira Correa Matos; Carlos Rangel Rodrigues; Valeria Pereira de Sousa

doi:10.7324/JAPS.2023.87792

Abstract

The drug regularization process involves many steps that are complex and time-consuming. The demand for new drugs has prompted researchers and regulatory authorities to search for predictive methods that can streamline the development process. Current studies point to innovative computational techniques in a drug’s study phases. This study aims to carry out a scoping review of research involving the application of computational methods and in silico studies in the clinical research and development of new drugs. A scoping review was conducted according to the eferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Online databases from 2001 to 2021 in English were used and the trial registration was 10.17605/OSF.IO/USXCM. The development of protocols and the application of a computational method for researching new drugs and their formulation, published in a peer-reviewed journal, were included. The data extraction and analysis were performed by two independent reviewers. In this study, 312 articles were retrieved, of which 6 were duplicates. After the title was read, only 101 remained for analysis. After the abstracts were read, 34 papers were considered for the scoping review. The use of in silico methodologies has been expanding in terms of research into the development of new drugs and the improvement of existing products.

Keyword: Drug discovery in silico clinical trial computational methods drug development virtual patients

Citation:

Colli LFM, Cabral LM, Matos GC, Rodrigues CR, de Sousa VP. Application of in silico methods in clinical research and development of drugs and their formulation: A scoping review. J Appl Pharm Sci, 2023; 13(04):001–010. https://doi.org/10.7324/JAPS.2023.87792

Copyright: © The Author(s). This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reference

Albers S, Meibohm B, Mir TS, Laer S. Population pharmacokinetics and dose simulation of carvedilol in pediatric patients with congestive heart failure. Br J Clin Pharmacol, 2008; 65(4):511-22. https://doi.org/10.1111/j.1365-2125.2007.03046.x

Amin A, Keshishian A, Trocio J, Dina O, Le H, Rosenblatt L, Liu X, Mardekian J, Zhang Q, Baser O, Vo L. Risk of stroke/systemic embolism, major bleeding and associated costs in non-valvular atrial fibrillation patients who initiated apixaban, dabigatran or rivaroxaban compared with warfarin in the United States Medicare population. Curr Med Res Opin, 2017; 33(9):1595-604. ANMAT. Nuevos aranceles para mantenimiento de registros de productos médicos y especialidades medicinales [Online]. Available via https://www.argentina.gob.ar/noticias/ nuevos-aranceles-para-mantenimiento-de-registros-de-productos-medicos-y-especialidades (Accessed 13 February 2022). https://doi.org/10.1080/03007995.2017.1345729

ANVISA. Protocolo de segurança e eficácia de medicamentos inovadores. 2021 [Online]. Available via https://www.gov.br/anvisa/pt-br/setorregulado/regularizacao/ medicamentos/informes/medicamentos-sinteticos/protocolo-de-seguranca-e-eficacia-de-medicamentos-inovadores (Accessed 13 February 2022).

Berndt ER, Nass D, Kleinrock M, Aitken M. Decline in economic returns from new drugs raises questions about sustaining innovations. Health Affairs, 2015; 34(2):245-52. https://doi.org/10.1377/hlthaff.2014.1029

Camm AJ, Amarenco P, Haas S, Hess S, Kirchhof, Kuhls S, van Eickels M, Turpie AG, XANTUS Investigators. XANTUS: a real-world, prospective, observational study of patients treated with rivaroxaban for stroke prevention in atrial fibrillation. Eur Heart J, 2016; 37(14):1145-53. https://doi.org/10.1093/eurheartj/ehv466

Cardozo L, Khullar V, El-Tahtawy A, Guan Z, Malhotra B, Staskin D. Modeling dose-response relationships of the effects of fesoterodine in patients with overactive bladder. BMC Urol, 2010; 10(1):1-11. https://doi.org/10.1186/1471-2490-10-14

Carvalho ARD. Método Monte Carlos e suas aplicações. 122 f. Dissertação (Mestrado em Matemática), Universidade Federal de Roraima, Boa Vista, Brazil, 2017.

Certara. SIMCYP PBPK Simulator. Versão 21. Phoenix: Certara, 2022 [Online]. Available via https://www.certara.com/ software/simcyp-pbpk/ (Accessed 4 April 2022).

Clermont G, Bartels SJ, Kumar R, Constantine G, Vodovotz Y, Chow C. In silico design of clinical trials: a method coming of age. Crit Care Med, 2004; 32(10):2061-70. https://doi.org/10.1097/01.CCM.0000142394.28791.C3

Cuadros DF, Abu-Raddad LJ, Awad SF, García-Ramos G. Use of agent-based simulations to design and interpret HIV clinical trials. Comput Biol Med, 2014; 50:1-8. https://doi.org/10.1016/j.compbiomed.2014.03.008

Dimasi JA, Hansen RW, Grabwski HC, Lasagna L. Research and development costs for new drugs by therapeutic category. Pharm Econ, 1995; 7(2):152-69. https://doi.org/10.2165/00019053-199507020-00007

EMA. Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation, 2016 [Online]. Available via http://www.ema.europa. eu/docs/en_GB/document_library/Scientific_guideline/2016/07/ WC500211315.pdf (Accessed 27 May 2022).

EMA. Research and development [Online]. Available via https://www.ema.europa.eu/en/human-regulatory/research-development (Accessed 13 February 2022).

FDA. Guidance for industry: physiologically based pharmacokinetic analyses-format and content, 2016 [Online]. Available via https://www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/ UCM531207.pdf (Accessed 27 May 2022).

FDA. Learn about clinical studies. 2019. [Online]. Available via https://clinicaltrials.gov/ct2/about-studies/learn (Accessed 13 February 2022).

FDA. The drug development process. Step 3: Clinical research. FDA, 2018. [Online]. Available via https://www.fda. gov/patients/drug-development-process/step-3-clinical-research (Accessed 13 February 2022).

Federico MP, Sakata RAP, Pinto PFC, Furtado GHC. Noções sobre parâmetros farmacocinéticos/farmacodinâmicos e sua utilização na prática médica. Rev Soc Brasil Clín Méd, 2017; 15(3):201-5.

Fransson M, Gréen H. Comparison of two types of population pharmacokinetic model structures of paclitaxel. Eur J Pharm Sci, 2008; 33(2):128-37. https://doi.org/10.1016/j.ejps.2007.10.005

Friberg LE, De Greef R, Kerbusch T, Karlsson MO. Modeling and simulation of the time course of asenapine exposure response and dropout patterns in acute schizophrenia. Clin Pharm Ther, 2009; 86(1):84-91. https://doi.org/10.1038/clpt.2009.44

Gidal BE, Maganti R, Laurenza A, Yang H, Verbel DA, Schuck E, Ferry J. Effect of enzyme inhibition on perampanel pharmacokinetics: why study design matters. Epilepsy Res, 2017; 134:41-8. https://doi.org/10.1016/j.eplepsyres.2017.04.018

Graham DJ, Reichman ME, Wernecke M, Zhang R, Southworth MR, Levenson M, ... Kelman JA. Cardiovascular, bleeding, and mortality risks in elderly medicare patients treated with dabigatran or warfarin for nonvalvular atrial fibrillation. Circulation, 2015; 131(2):157-64. https://doi.org/10.1161/CIRCULATIONAHA.114.012061

ICH. M4 Organisation of the common technical document for the registration of pharmaceuticals for human use guidance for industry. ICH, Geneva, Switzerland, 2004.

Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp® population-based ADME simulator. Expert Opin Drug Metab Toxicol, 2009; 5(2):211-23. https://doi.org/10.1517/17425250802691074

Jensen EJ. Research expenditures and the discovery of new drugs. J Indus Econ, 1987; 36:83-95. https://doi.org/10.2307/2098598

Jereb R, Opara J, Bajc A, Petek B. Evaluating the impact of physiological properties of the gastrointestinal tract on drug in vivo performance using physiologically based biopharmaceutics modeling and virtual clinical trials. J Pharm Sci, 2021; 110(8):3069-81. https://doi.org/10.1016/j.xphs.2021.04.007

Ji Z, Yan K, Li W, Hu H, Zhu X. Mathematical and computational modeling in complex biological systems. BioMed Res Int, 2017; 2017:1-16. https://doi.org/10.1155/2017/5958321

Kar S, Leszczynski J. Recent advances of computational modeling for predicting drug metabolism: a perspective. Curr Drug Metab, 2017; 18(12):1106-22. https://doi.org/10.2174/1389200218666170607102104

Kato T, Nakagawa H, Mikkaichi T, Miyano T, Matsumoto Y, Ando S. Establishment of a clinically relevant specification for dissolution testing using physiologically based pharmacokinetic (PBPK) modeling approaches. Eur J Pharm Sci, 2020; 151:45-52. https://doi.org/10.1016/j.ejpb.2020.03.012

Kowalski KG, Olson S, Remmers AE, Hutmacher MM. Modeling and simulation to support dose selection and clinical development of SC-75416, a selective COX-2 inhibitor for the treatment of acute and chronic pain. Clin Pharmacol Ther, 2008; 83(6):857-66. https://doi.org/10.1038/sj.clpt.6100374

Laliberté F, Cloutier M, Nelson WW, Coleman CI, Pilon D, Olson. Real-world comparative effectiveness and safety of rivaroxaban and warfarin in nonvalvular atrial fibrillation patients. Curr Med Res Opin, 2014; 30(7):1317-25. https://doi.org/10.1185/03007995.2014.907140

Li CH, Sherer EA, Lewis LD, Bies RR. Clinical trial simulation to evaluate population pharmacokinetics and food effect: capturing abiraterone and nilotinib exposures. J Clin Pharmacol, 2015; 55(5):556-62. https://doi.org/10.1002/jcph.449

Litou C, Patel N, Turner DB, Kostewicz E, Kuentz M, Box KJ, Dressman J. Combining biorelevant in vitro and in silico tools to simulate and better understand the in vivo performance of a nano-sized formulation of aprepitant in the fasted and fed states. Eur J Pharm Sci, 2019; 138:105031. Ma L, Zhao L, Xu Y, Yim S, Doddapaneni S, Sahajwalla CG, Wang Y, Ji P. Clinical end point sensitivity in rheumatoid arthritis: modeling and simulation. J Pharmacokinet Pharmacodyn, 2014; 41(5):537-43. https://doi.org/10.1016/j.ejps.2019.105031

Mancini T, Mari F, Massini A, Melatti I, Salvo I, Sinisi S, Tronci E, Ehrig R, Röblitz S, Leeners B. Computing personalised treatments through in silico clinical trials. A case study on down regulation in assisted reproduction. Intell Artif, 2018; 2271:1-16. https://doi.org/10.29007/g864

Mollmann H, Wagner M, Krishnaswami S, Dimova H, Tang Y, Falcoz C, Daley-Yates PT, Krieg M, Stöckmann R, Barth J, Lawlor C, Möllmann AC, Derendorf H, Hochhaus G. Single-dose and steady-state pharmacokinetic and pharmacodynamic evaluation of therapeutically clinically equivalent doses of inhaled fluticasone propionate and budesonide, given as Diskus or Turbo haler dry-powder inhalers to healthy subjects. J Clin Pharmacol, 2001; 52(5):487-95. https://doi.org/10.1177/00912700122012913

Murthy BP, Skee DM, Danyluk AP, Brett V, Vorsanger GJ, Moskovitz BL. Pharmacokinetic model and simulations of dose conversion from immediate-to extended-release tramadol. Curr Med Res Opin, 2007; 23(2):275-84. https://doi.org/10.1185/030079906X162773

Nathan RA, Berger W, Yang W, Cheema A, Silvey M, Wu W, Philpot E. Effect of once-daily fluticasone furoate nasal spray on nasal symptoms in adults and adolescents with perennial allergic rhinitis. Ann Allerg Asth Immunol, 2008; 100(5):497-505. https://doi.org/10.1016/S1081-1206(10)60477-2

Naimi B, Voinov A. Stella R: a software to translate Stella models into R open-source environment. Environ Modell Software, 2012; 38:117-8. https://doi.org/10.1016/j.envsoft.2012.05.012

Najafzadeh M, Schneeweiss S, Choudhry NK, Wang SV, Gagne JJ. Simulation for predicting effectiveness and safety of new cardiovascular drugs in routine care populations. Clin Pharmacol Ther, 2018; 104(5):1008-15. https://doi.org/10.1002/cpt.1045

Neil N, Merchant S, Provenzano D, Ogden K, Mody SH. Clinical simulation model of long-acting opioids for treatment of chronic non-cancer pain in the United States. J Med Econ, 2013; 16(2):307-17. https://doi.org/10.3111/13696998.2012.756401

Page MJ, Moher D. Extensions in development: Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Syst Rev, 2018; 6(1):1-14. https://doi.org/10.1186/s13643-017-0663-8

Pappalardo F, Russo G, Tshinanu FM, Viceconti M. In silico clinical trials: concepts and early adoptions. Brief Bioinform, 2019; 20(5):1699-708. https://doi.org/10.1093/bib/bby043

Parrott NJ, Yu LJ, Takano R, Nakamura M, Morcos PN. Physiologically based absorption modeling to explore the impact of food and gastric pH changes on the pharmacokinetics of alectinib. AAPS J, 2016; 18(6):1464-74. https://doi.org/10.1208/s12248-016-9957-3

Patel A, Wilson R, Harrell AW, Taskar KS, Taylor M, Tracey H, Riddell K, Georgiou A, Cahn AP, Marotti M, Hessel EM. Drug interactions for low-dose inhaled nemiralisib: a case study integrating modeling, in vitro, and clinical investigations. Drug Metab Dispos, 2020; 48(4):307-16. https://doi.org/10.1124/dmd.119.089003

Patel MR, Mahaffey KW, Garg J, Pan G, Singer De, Hacke W. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. N Engl J Med, 2011; 365(10):883-91. https://doi.org/10.1056/NEJMoa1009638

Patsalos PN. The clinical pharmacology profile of the new antiepileptic drug perampanel: a novel noncompetitive AMPA receptor antagonist. Epilepsia, 2015; 56(1):12-27. https://doi.org/10.1111/epi.12865

Peters MD, Godfrey C, McInerney P, Khalil H, Larsen P, Marnie C, Pollock D, Tricco AC, Munn Z. Best practice guidance and reporting items for the development of scoping review protocols. JBI Evid Synth, 2022; 20(4), 953-968. https://doi.org/10.11124/JBIES-21-00242

Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil H. Chapter 11: Scoping reviews. In: Aromataris E, Munn Z (Eds.). JBI Manual for evidence synthesis. JBI, 2020. [Online] Available via https://synthesismanual.jbi.global (Accessed 20 February 2022). https://doi.org/10.46658/JBIRM-20-01

PMDA. History [Online]. Available via https://www. pmda.go.jp/english/about-pmda/outline/0002.html (Accessed 13 February 2022).

Rebeka J, Jerneja O, Igor L, Boštjan P, Aleksander B, Simon Ž, Albin K. PBPK absorption modeling of food effect and bioequivalence in Fed State for two formulations with crystalline and amorphous forms of BCS 2 class drug in generic drug development. AAPS PharmSciTech, 2019; 20(2):1-10. https://doi.org/10.1208/s12249-018-1285-8

Ryan CJ, Smith MR, Fong L, Rosenberg JE, Kantoff P, Raynaud F, Martins V, Lee G, Kheoh T, Kim J, Molina A, Small EJ. Phase I clinical trial of the CYP17 inhibitor abiraterone acetate demonstrating clinical activity in patients with castration-resistant prostate cancer who received prior ketoconazole therapy. J Clin Oncol, 2010; 28(9):1481. https://doi.org/10.1200/JCO.2009.24.1281

Samant TS, Dhuria S, Lu Y, Laisney M, Yang S, Grandeury A, Mueller-Zsigmondy M, Umehara K, Huth F, Miller M, Germa C, Elmeliegy M. Ribociclib bioavailability is not affected by gastric ph changes or food intake: in silico and clinical evaluations. Clin Pharmacol Ther, 2018; 104(2):374-83. https://doi.org/10.1002/cpt.940

Shono Y, Jantratid E, Kesisoglou F, Reppas C, Dressaman, JB. Forecasting in vivo oral absorption and food effect of micronized and nanosized aprepitant formulations in humans. Eur J Pharm Biopharm, 2010; 76(1):95-104. https://doi.org/10.1016/j.ejpb.2010.05.009

Shono Y, Jantraid E, Janssen N, Kesisoglou F, Mao Y, Vertzoni M, Reppas C, Dressman JB. Prediction of food effects on the absorption of celecoxib based on biorelevant dissolution testing coupled with physiologically based pharmacokinetic modeling. Eur J Pharm Biopharm, 2009; 73(1):107-14. https://doi.org/10.1016/j.ejpb.2009.05.009

Shono Y, Jantratid E, Dressman JB. Precipitation in the small intestine may play a more important role in the in vivo performance of poorly soluble weak bases in the fasted state: case example nelfinavir. Eur J Pharm Biopharm, 2011; 79(2):349-56. https://doi.org/10.1016/j.ejpb.2011.04.005

Sinisi S, Alimguzhin V, Mancini T, Tronci E, Leeners B. Complete populations of virtual patients for in silico clinical trials. Bioinformatics, 2020; 36(22-3):5465-5472. https://doi.org/10.1093/bioinformatics/btaa1026

Tanaka C, Yin OQ, Sethuraman V. Clinical pharmacokinetics of the BCR-ABL tyrosine kinase inhibitor nilotinib. Clin Pharmacol Ther, 2010; 87(2):197-203. https://doi.org/10.1038/clpt.2009.208

Van Hest R, Mathot R, Vulto A, Weimar W, Van Gelder T. Predicting the usefulness of therapeutic drug monitoring of mycophenolic acid: a computer simulation. Ther Drug Monit, 2005; 27(2):163-7. https://doi.org/10.1097/01.ftd.0000158083.45954.97

Wang B, Higgs BW, Chang L, Vainshtein I, Liu Z, Streicher K, Liang M, White WI, Yoo S, Richman L, Jallal B, Roskos L, Yao Y. Pharmacogenomics and translational simulations to bridge indications for an anti-interferon-α receptor antibody. Clin Pharmacol Ther, 2013; 93(6):483-92. https://doi.org/10.1038/clpt.2013.35

Xia B, Heimbach T, Lin TH, Li S, Zhang H, Sheng J, He H. Utility of physiologically based modeling and preclinical in vitro/in vivo data to mitigate positive food effect in a BCS class 2 compound. AAPS PharmSciTech, 2013; 14(3):1255-66. Xiao Q, Tang L, Xu R, Qian W, Yang J. Physiologically based pharmacokinetics model predicts the lack of inhibition by repaglinide on the metabolism of pioglitazone. Biopharm Drug Dispos, 2015; 36(9):603-12. https://doi.org/10.1002/bdd.1987

Yu Y, Loi CM, Hoffman J, Wang D. Physiologically based pharmacokinetic modeling of palbociclib. J Clin Pharmacol, 2017; 57(2):173-84. https://doi.org/10.1002/jcph.792

Zhang C, Wang WW, Pan MM, Gu ZC. Simulation of anticoagulation in atrial fibrillation patients with rivaroxaban- from trial to target population. Rev Cardiovasc Med, 2011; 22(3):1019-27. https://doi.org/10.31083/j.rcm2203111

Zytiga™. [Bula]. Toronto: Patheon Inc. 2013. [Online]. Available via http://www.accessdata.fda.gov/drugsatfda_docs/ label/2011/202379lbl.pdf (Accessed: 02 March 2022).

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