Hong-Can Ren1, Yang Sai2, Tao Chen3. 1. Department of Clinical Pharmacology and DMPK, Hutchison MediPharma Ltd., Building 4, 720 Cailun Road, Zhang-Jiang Hi-Tech Park, Shanghai, 201203, People's Republic of China. Hongcanr@hmplglobal.com. 2. Department of Clinical Pharmacology and DMPK, Hutchison MediPharma Ltd., Building 4, 720 Cailun Road, Zhang-Jiang Hi-Tech Park, Shanghai, 201203, People's Republic of China. Yangs@hmplglobal.com. 3. Shanghai PharmoGo Co., Ltd, 3F, Block B, Weitai Building, No. 58, Lane 91, Shanghai, 200127, People's Republic of China.
Abstract
BACKGROUND: Requirements for predicting human pharmacokinetics in drug discovery are increasing. Developing different methods of human pharmacokinetic prediction will facilitate lead optimization, candidate nomination, and dosing regimens before clinical trials at various early drug discovery stages. OBJECTIVES: To develop and validate generic methods of human pharmacokinetic prediction to meet the requirements in early drug discovery. METHODS: The physiologically based pharmacokinetic (PBPK) model implemented in Gastroplus™ was used for human pharmacokinetic predictions. The absorption, distribution, metabolism, and excretion properties of drugs in humans predicted from molecular structure and extrapolated from tested preclinical data were used as inputs in the PBPK model. The approaches were validated by comparison of the predicted pharmacokinetic parameters with actual pharmacokinetic parameters of 15 marketed small-molecule compounds approved by the US Food and Drug Administration. Based on the validation and reported approaches, we proposed a strategy for human pharmacokinetic prediction at different drug discovery stages. RESULTS: Obvious underestimation of exposure (< 1/3 of actual exposure) was not observed using in silico prediction as inputs, which may reduce the probability of missing the potential compounds with predicted false low exposure. The simulated human pharmacokinetic results using tested data as inputs were superior to those obtained via in silico prediction. Both methods similarly predicted the multiphasic shape of pharmacokinetic profiles. CONCLUSION: These generic PBPK approaches of full in silico prediction or perdition using a combination of tested in vivo and in vitro data were validated and proved useful for human pharmacokinetic predictions.
BACKGROUND: Requirements for predicting human pharmacokinetics in drug discovery are increasing. Developing different methods of human pharmacokinetic prediction will facilitate lead optimization, candidate nomination, and dosing regimens before clinical trials at various early drug discovery stages. OBJECTIVES: To develop and validate generic methods of human pharmacokinetic prediction to meet the requirements in early drug discovery. METHODS: The physiologically based pharmacokinetic (PBPK) model implemented in Gastroplus™ was used for human pharmacokinetic predictions. The absorption, distribution, metabolism, and excretion properties of drugs in humans predicted from molecular structure and extrapolated from tested preclinical data were used as inputs in the PBPK model. The approaches were validated by comparison of the predicted pharmacokinetic parameters with actual pharmacokinetic parameters of 15 marketed small-molecule compounds approved by the US Food and Drug Administration. Based on the validation and reported approaches, we proposed a strategy for human pharmacokinetic prediction at different drug discovery stages. RESULTS: Obvious underestimation of exposure (< 1/3 of actual exposure) was not observed using in silico prediction as inputs, which may reduce the probability of missing the potential compounds with predicted false low exposure. The simulated human pharmacokinetic results using tested data as inputs were superior to those obtained via in silico prediction. Both methods similarly predicted the multiphasic shape of pharmacokinetic profiles. CONCLUSION: These generic PBPK approaches of full in silico prediction or perdition using a combination of tested in vivo and in vitro data were validated and proved useful for human pharmacokinetic predictions.