Application status and development trend of virtual clinical trials_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIU Yixian ^1,2 ,  ZHENG Li ^1,2

1. Clinical Trial Center, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

ZHENG Li, Email: zhengli@wchscu.cn

Keywords：

Virtual clinical trials; modeling and simulation; model-informed drug development

DOI：

10.7507/1007-4848.202311059

Video：

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Abstract Full text Figures/Tables Video References Cited by

Virtual clinical trials are clinical trials conducted through computer simulation technology, which breaks through the limitations of traditional clinical trials and has the advantages of saving time, reducing costs, and reducing the risk of human trials. With the application of new computer technologies such as population pharmacokinetics, physiologically-based pharmacokinetics, quantitative systems pharmacology, and artificial intelligence, the field of virtual clinical trials in healthcare has become an important development direction. This article will give a preliminary review of the connotation, methods and future development trends of virtual clinical trials, aiming to provide reference for the application of new technologies and methods in clinical trials.

Citation： LIU Yixian, ZHENG Li. Application status and development trend of virtual clinical trials. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(2): 216-222. doi: 10.7507/1007-4848.202311059 Copy

1.	Bhatt A. Evolution of clinical research: A history before and beyond james lind. Perspect Clin Res, 2010, 1(1): 6-10.
2.	Desai M. Recruitment and retention of participants in clinical studies: Critical issues and challenges. Perspect Clin Res, 2020, 11(2): 51-53.
3.	Brown DG, Wobst HJ, Kapoor A, et al. Clinical development times for innovative drugs. Nat Rev Drug Discov, 2022, 21(11): 793-794.
4.	Dowden H, Munro J. Trends in clinical success rates and therapeutic focus. Nat Rev Drug Discov, 2019, 18(7): 495-496.
5.	Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics, 2019, 20(2): 273-286.
6.	Pappalardo F, Russo G, Tshinanu FM, et al. In silico clinical trials: Concepts and early adoptions. Brief Bioinform, 2019, 20(5): 1699-1708.
7.	Zhuang Y, Sheets L, Gao X, et al. Development of a blockchain framework for virtual clinical trials. AMIA Annu Symp Proc, 2020, 2020: 1412-1420.
8.	Girard P, Cucherat M, Guez D. Clinical trial simulation in drug development. Therapie, 2004, 59(3): 287-295, 297-304.
9.	郑青山. 模型引导新药研发模式: 原理与案例. 中国新药杂志, 2023, 32(19): 1946-1952.
10.	焦正, 李新刚, 尚德为, 等. 模型引导的精准用药: 中国专家共识(2021版). 中国临床药理学与治疗学, 2021, 26(11): 1215-1228.
11.	Ludden TM. Population pharmacokinetics. J Clin Pharmacol, 1988, 28(12): 1059-1063.
12.	Espie P, Tytgat D, Sargentini-Maier ML, et al. Physiologically based pharmacokinetics (PBPK). Drug Metab Rev, 2009, 41(3): 391-407.
13.	Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol, 2011, 51: 45-73.
14.	Wang X, Chen F, Guo N, et al. Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs. Cancer Chemother Pharmacol, 2023, 92(4): 253-270.
15.	赵宸, 李改玲, 王亚宁. 新药研发中的定量系统药理学(QSP)模型与虚拟临床试验: 发展及前沿应用. 药学学报, 2023, 58(11): 1-29.
16.	Yellepeddi V, Rower J, Liu X, et al. State-of-the-art review on physiologically based pharmacokinetic modeling in pediatric drug development. Clin Pharmacokinet, 2019, 58(1): 1-13.
17.	Angehrn Z, Haldna L, Zandvliet AS, et al. Artificial intelligence and machine learning applied at the point of care. Front Pharmacol, 2020, 11: 759.
18.	贾运涛, 蒋学华. 群体药动学理论及其应用. 中国药房, 2004,15(1): 50-52.
19.	芮建中, 张震, 李金恒. 群体药代动力学/群体药效动力学原理及研究方法. 医学研究生学报, 2005, 3: 246-249.
20.	Darwich AS, Polasek TM, Aronson JK, et al. Model-Informed precision dosing: Background, requirements, validation, implementation, and forward trajectory of individualizing drug therapy. Annu Rev Pharmacol Toxicol, 2021, 61: 225-245.
21.	Dosne AG, Valade E, Stuyckens K, et al. Population Pharmacokinetics of total and free erdafitinib in adult healthy volunteers and cancer patients: Analysis of phase 1 and phase 2 studies. J Clin Pharmacol, 2020, 60(4): 515-527.
22.	王乐, 夏彬彬, 陈世财. 生理药代动力学模型在临床合理用药中的应用. 中国临床药理学杂志, 2023, 39(1): 127-130.
23.	王迪, 张娟, 李睿, 等. 生理药代动力学软件模拟技术在药物研发和评价中的应用. 药学研究, 2022, 41(3): 195-201.
24.	Shebley M, Sandhu P, Emami Riedmaier A, et al. Physiologically based pharmacokinetic model qualification and reporting procedures for regulatory submissions: A consortium perspective. Clin Pharmacol Ther, 2018, 104(1): 88-110.
25.	Sager JE, Yu J, Ragueneau-Majlessi I, et al. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: A systematic review of published models, applications, and model verification. Drug Metab Dispos, 2015, 43(11): 1823-1837.
26.	Zhuang X, Lu C. PBPK modeling and simulation in drug research and development. Acta Pharm Sin B, 2016, 6(5): 430-440.
27.	Ince I, Solodenko J, Frechen S, et al. Predictive pediatric modeling and simulation using ontogeny information. J Clin Pharmacol, 2019, 59(Suppl 1): S95-S103.
28.	Dallmann A, Ince I, Solodenko J, et al. Physiologically based pharmacokinetic modeling of renally cleared drugs in pregnant women. Clin Pharmacokinet, 2017, 56(12): 1525-1541.
29.	Willmann S, Becker C, Burghaus R, et al. Development of a paediatric population-based model of the pharmacokinetics of rivaroxaban. Clin Pharmacokinet, 2014, 53(1): 89-102.
30.	简伟哲, 陈镕, 周田彦. 联合应用PBPK模型和PopPK模型助力儿科用药研发中的剂量选择: 以利伐沙班为例. 药学学报, 2022, 57(10): 3157-3162.
31.	Rogers M, Lyster P, Okita R. NIH Support for the emergence of quantitative and systems pharmacology. CPT Pharmacometrics Syst Pharmacol, 2013, 2(4): e37.
32.	赵宸, 李改玲, 王亚宁. 新药研发中的定量系统药理学(QSP)模型与虚拟临床试验: 发展及前沿应用. 药学学报, 2023, 58(11): 3296-3310.
33.	Bradshaw EL, Spilker ME, Zang R, et al. Applications of quantitative systems pharmacology in model-informed drug discovery: Perspective on impact and opportunities. CPT Pharmacometrics Syst Pharmacol, 2019, 8(11): 777-791.
34.	Chan JR, Allen R, Boras B, et al. Current practices for QSP model assessment: An IQ consortium survey. J Pharmacokinet Pharmacodyn, 2022. [Online ahead of print.
35.	Nijsen M, Wu F, Bansal L, et al. Preclinical QSP modeling in the pharmaceutical industry: An IQ Consortium survey examining the current landscape. CPT Pharmacometrics Syst Pharmacol, 2018, 7(3): 135-146.
36.	Ma H, Wang H, Sove RJ, et al. A quantitative systems pharmacology model of t cell engager applied to solid tumor. AAPS J, 2020, 22(4): 85.
37.	Ma H, Wang H, Sove RJ, et al. Combination therapy with T cell engager and PD-L1 blockade enhances the antitumor potency of T cells as predicted by a QSP model. J Immunother Cancer, 2020, 8(2): e001141.
38.	Wang H, Ma H, Sové RJ, et al. Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer [published correction appears in J Immunother Cancer, 2021, 9(10)].J Immunother Cancer, 2021, 9(2): e005414.
39.	Ming JE, Abrams RE, Bartlett DW, et al. A quantitative systems pharmacology platform to investigate the impact of alirocumab and cholesterol-lowering therapies on lipid profiles and plaque characteristics. Gene Regul Syst Bio, 2017, 11: 819722589.
40.	Rullmann JA, Struemper H, Defranoux NA, et al. Systems biology for battling rheumatoid arthritis: Application of the Entelos PhysioLab platform. Syst Biol (Stevenage), 2005, 152(4): 256-262.
41.	Fediuk DJ, Nucci G, Dawra VK, et al. End-to-end application of model-informed drug development for ertugliflozin, a novel sodium-glucose cotransporter 2 inhibitor. CPT Pharmacometrics Syst Pharmacol, 2021, 10(6): 529-542.
42.	Sove RJ, Verma BK, Wang H, et al. Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model. J Immunother Cancer, 2022, 10(11): e005414.
43.	Howard J. Artificial intelligence: Implications for the future of work. Am J Ind Med, 2019, 62(11): 917-926.
44.	Yang X, Wang Y, Byrne R, et al. Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev, 2019, 119(18): 10520-10594.
45.	张志豪, 周吟玦, 姜慧君, 等. 人工智能药物发现研究体系的构建及实践. 中国新药杂志, 2022, 31(13): 1294-1303.
46.	刘伯炎, 王群, 徐俐颖, 等. 人工智能技术在医药研发中的应用. 中国新药杂志, 2020, 29(17): 1979-1986.
47.	Kohli A, Mahajan V, Seals K, et al. Concepts in U. S. Food and drug administration regulation of artificial intelligence for medical imaging. AJR Am J Roentgenol, 2019, 213(4): 886-888.
48.	Carlier A, Vasilevich A, Marechal M, et al. In silico clinical trials for pediatric orphan diseases. Sci Rep, 2018, 8(1): 2465.
49.	张静, 段勃, 赵宇. “人工智能+医药”产业现状和趋势. 中国食品药品监管, 2023, 4: 112-117.
50.	赵宇, 张静, 张春明, 等. 从全球药品监管科学视角看计算机建模与仿真新方法的发展现状及趋势. 中国食品药品监管, 2023, 7: 6-15.
51.	Ng CE, Bowman S, Ling J, et al. The future of clinical trials-Is it virtual? Br Med Bull, 2023, Online ahead of print.

1. Bhatt A. Evolution of clinical research: A history before and beyond james lind. Perspect Clin Res, 2010, 1(1): 6-10.
2. Desai M. Recruitment and retention of participants in clinical studies: Critical issues and challenges. Perspect Clin Res, 2020, 11(2): 51-53.
3. Brown DG, Wobst HJ, Kapoor A, et al. Clinical development times for innovative drugs. Nat Rev Drug Discov, 2022, 21(11): 793-794.
4. Dowden H, Munro J. Trends in clinical success rates and therapeutic focus. Nat Rev Drug Discov, 2019, 18(7): 495-496.
5. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics, 2019, 20(2): 273-286.
6. Pappalardo F, Russo G, Tshinanu FM, et al. In silico clinical trials: Concepts and early adoptions. Brief Bioinform, 2019, 20(5): 1699-1708.
7. Zhuang Y, Sheets L, Gao X, et al. Development of a blockchain framework for virtual clinical trials. AMIA Annu Symp Proc, 2020, 2020: 1412-1420.
8. Girard P, Cucherat M, Guez D. Clinical trial simulation in drug development. Therapie, 2004, 59(3): 287-295, 297-304.
9. 郑青山. 模型引导新药研发模式: 原理与案例. 中国新药杂志, 2023, 32(19): 1946-1952.
10. 焦正, 李新刚, 尚德为, 等. 模型引导的精准用药: 中国专家共识(2021版). 中国临床药理学与治疗学, 2021, 26(11): 1215-1228.
11. Ludden TM. Population pharmacokinetics. J Clin Pharmacol, 1988, 28(12): 1059-1063.
12. Espie P, Tytgat D, Sargentini-Maier ML, et al. Physiologically based pharmacokinetics (PBPK). Drug Metab Rev, 2009, 41(3): 391-407.
13. Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol, 2011, 51: 45-73.
14. Wang X, Chen F, Guo N, et al. Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs. Cancer Chemother Pharmacol, 2023, 92(4): 253-270.
15. 赵宸, 李改玲, 王亚宁. 新药研发中的定量系统药理学(QSP)模型与虚拟临床试验: 发展及前沿应用. 药学学报, 2023, 58(11): 1-29.
16. Yellepeddi V, Rower J, Liu X, et al. State-of-the-art review on physiologically based pharmacokinetic modeling in pediatric drug development. Clin Pharmacokinet, 2019, 58(1): 1-13.
17. Angehrn Z, Haldna L, Zandvliet AS, et al. Artificial intelligence and machine learning applied at the point of care. Front Pharmacol, 2020, 11: 759.
18. 贾运涛, 蒋学华. 群体药动学理论及其应用. 中国药房, 2004,15(1): 50-52.
19. 芮建中, 张震, 李金恒. 群体药代动力学/群体药效动力学原理及研究方法. 医学研究生学报, 2005, 3: 246-249.
20. Darwich AS, Polasek TM, Aronson JK, et al. Model-Informed precision dosing: Background, requirements, validation, implementation, and forward trajectory of individualizing drug therapy. Annu Rev Pharmacol Toxicol, 2021, 61: 225-245.
21. Dosne AG, Valade E, Stuyckens K, et al. Population Pharmacokinetics of total and free erdafitinib in adult healthy volunteers and cancer patients: Analysis of phase 1 and phase 2 studies. J Clin Pharmacol, 2020, 60(4): 515-527.
22. 王乐, 夏彬彬, 陈世财. 生理药代动力学模型在临床合理用药中的应用. 中国临床药理学杂志, 2023, 39(1): 127-130.
23. 王迪, 张娟, 李睿, 等. 生理药代动力学软件模拟技术在药物研发和评价中的应用. 药学研究, 2022, 41(3): 195-201.
24. Shebley M, Sandhu P, Emami Riedmaier A, et al. Physiologically based pharmacokinetic model qualification and reporting procedures for regulatory submissions: A consortium perspective. Clin Pharmacol Ther, 2018, 104(1): 88-110.
25. Sager JE, Yu J, Ragueneau-Majlessi I, et al. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: A systematic review of published models, applications, and model verification. Drug Metab Dispos, 2015, 43(11): 1823-1837.
26. Zhuang X, Lu C. PBPK modeling and simulation in drug research and development. Acta Pharm Sin B, 2016, 6(5): 430-440.
27. Ince I, Solodenko J, Frechen S, et al. Predictive pediatric modeling and simulation using ontogeny information. J Clin Pharmacol, 2019, 59(Suppl 1): S95-S103.
28. Dallmann A, Ince I, Solodenko J, et al. Physiologically based pharmacokinetic modeling of renally cleared drugs in pregnant women. Clin Pharmacokinet, 2017, 56(12): 1525-1541.
29. Willmann S, Becker C, Burghaus R, et al. Development of a paediatric population-based model of the pharmacokinetics of rivaroxaban. Clin Pharmacokinet, 2014, 53(1): 89-102.
30. 简伟哲, 陈镕, 周田彦. 联合应用PBPK模型和PopPK模型助力儿科用药研发中的剂量选择: 以利伐沙班为例. 药学学报, 2022, 57(10): 3157-3162.
31. Rogers M, Lyster P, Okita R. NIH Support for the emergence of quantitative and systems pharmacology. CPT Pharmacometrics Syst Pharmacol, 2013, 2(4): e37.
32. 赵宸, 李改玲, 王亚宁. 新药研发中的定量系统药理学(QSP)模型与虚拟临床试验: 发展及前沿应用. 药学学报, 2023, 58(11): 3296-3310.
33. Bradshaw EL, Spilker ME, Zang R, et al. Applications of quantitative systems pharmacology in model-informed drug discovery: Perspective on impact and opportunities. CPT Pharmacometrics Syst Pharmacol, 2019, 8(11): 777-791.
34. Chan JR, Allen R, Boras B, et al. Current practices for QSP model assessment: An IQ consortium survey. J Pharmacokinet Pharmacodyn, 2022. [Online ahead of print.
35. Nijsen M, Wu F, Bansal L, et al. Preclinical QSP modeling in the pharmaceutical industry: An IQ Consortium survey examining the current landscape. CPT Pharmacometrics Syst Pharmacol, 2018, 7(3): 135-146.
36. Ma H, Wang H, Sove RJ, et al. A quantitative systems pharmacology model of t cell engager applied to solid tumor. AAPS J, 2020, 22(4): 85.
37. Ma H, Wang H, Sove RJ, et al. Combination therapy with T cell engager and PD-L1 blockade enhances the antitumor potency of T cells as predicted by a QSP model. J Immunother Cancer, 2020, 8(2): e001141.
38. Wang H, Ma H, Sové RJ, et al. Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer [published correction appears in J Immunother Cancer, 2021, 9(10)].J Immunother Cancer, 2021, 9(2): e005414.
39. Ming JE, Abrams RE, Bartlett DW, et al. A quantitative systems pharmacology platform to investigate the impact of alirocumab and cholesterol-lowering therapies on lipid profiles and plaque characteristics. Gene Regul Syst Bio, 2017, 11: 819722589.
40. Rullmann JA, Struemper H, Defranoux NA, et al. Systems biology for battling rheumatoid arthritis: Application of the Entelos PhysioLab platform. Syst Biol (Stevenage), 2005, 152(4): 256-262.
41. Fediuk DJ, Nucci G, Dawra VK, et al. End-to-end application of model-informed drug development for ertugliflozin, a novel sodium-glucose cotransporter 2 inhibitor. CPT Pharmacometrics Syst Pharmacol, 2021, 10(6): 529-542.
42. Sove RJ, Verma BK, Wang H, et al. Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model. J Immunother Cancer, 2022, 10(11): e005414.
43. Howard J. Artificial intelligence: Implications for the future of work. Am J Ind Med, 2019, 62(11): 917-926.
44. Yang X, Wang Y, Byrne R, et al. Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev, 2019, 119(18): 10520-10594.
45. 张志豪, 周吟玦, 姜慧君, 等. 人工智能药物发现研究体系的构建及实践. 中国新药杂志, 2022, 31(13): 1294-1303.
46. 刘伯炎, 王群, 徐俐颖, 等. 人工智能技术在医药研发中的应用. 中国新药杂志, 2020, 29(17): 1979-1986.
47. Kohli A, Mahajan V, Seals K, et al. Concepts in U. S. Food and drug administration regulation of artificial intelligence for medical imaging. AJR Am J Roentgenol, 2019, 213(4): 886-888.
48. Carlier A, Vasilevich A, Marechal M, et al. In silico clinical trials for pediatric orphan diseases. Sci Rep, 2018, 8(1): 2465.
49. 张静, 段勃, 赵宇. “人工智能+医药”产业现状和趋势. 中国食品药品监管, 2023, 4: 112-117.
50. 赵宇, 张静, 张春明, 等. 从全球药品监管科学视角看计算机建模与仿真新方法的发展现状及趋势. 中国食品药品监管, 2023, 7: 6-15.
51. Ng CE, Bowman S, Ling J, et al. The future of clinical trials-Is it virtual? Br Med Bull, 2023, Online ahead of print.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Application status and development trend of virtual clinical trials

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