Shared Learning from a Physiologically-Based Pharmacokinetic Modeling Strategy for Human Pharmacokinetics Prediction through Retrospective Analysis of Genentech Compounds
Jialin Mao, Fang Ma, Jesse Yu, Tom De Bruyn, Miaoran Ning, Christine Bowman, Yuan Chen
Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
Introduction: Quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and establishment of a range of doses to enable phase II clinical dose selection. There have been enormous efforts in human PK prediction using many different methodologies in recent decades1. Several companies and institutions have published their evaluations of human PK prediction strategy or performance with their own chemical matter or published compounds 2-7.
In recent years, approaches using physiologically-based pharmacokinetic (PBPK) modeling for human PK prediction have become increasingly adopted in the pharmaceutical industry8. This approach, combining PBPK with in vitro to in vivo extrapolation (IVIVE), enables broad application of PBPK in drug discovery and development 9-10.While this strategy appears promising, scientists often encounter challenges with direct translation of in vitro data for IVIVE of PK parameters independent of the software used. In this regard, reported examples use modeling platforms spanning commercial PBPK platforms such as Gastroplus4 and Simcyp11, to custom models6, to a hybrid of static calculations combined with partial PBPK models3. Notably, publications describing best practices or strategies, especially on the scaling of human PK prediction based upon preclinical IVIVE for human PK prediction prior to FIH, are sparse12.
Objective
The current study focuses on evaluating the performance of the described PBPK strategy for pre-FIH human PK prediction by performing prospective predictions for 18 Genentech small molecules that entered human clinical trials. The diversity of the compound properties reflects current chemical space.
- The goal of this work was to fill gaps in the existing research and to further increase awareness of prediction confidence in human PK estimation using PBPK-IVIVE methodology.
- Address challenges in mechanistic-based human PK prediction when disposition mechanisms are not completely understood in the preclinical stage, and to provide practical solutions to increase confidence in human PK prediction when only in vitro and preclinical in vivo data are available.
- Present a comprehensive analysis of the predicted human intravenous (IV) and oral PK profiles along with exposure and PK parameters, using IVIVE from preclinical PBPK with compound-specific scaling factors.
- Provide insights on resolving challenges for early human PK prediction and identifies an appropriate strategy for human PK prediction in drug discovery.
Methods: Physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Preclinical PBPK model development, using a bottom-up approach, for CL and Vss prediction was evaluated to understand cross-species IVIVE and to gain knowledge regarding how to bridge the IVIVE gap. This information was subsequently applied to prospective human PK predictions using human in vitro data. Simcyp was used as the PBPK platform for the current investigation. The predictive performance of the proposed PBPK approach was evaluated by comparing predicted versus observed clinical data. For the current evaluation, three aspects were examined including the human PK profile, human PK exposure (Cmax and AUC), and human PK parameters (CL, Vss, CL/F, and V/F).
Results: Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. In addition, the predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule.
Conclusion: The retrospective analysis of 18 Genentech molecules using the recommended PBPK strategy demonstrated the possibility of increasing confidence in human PK prediction in the presence of IVIVE disconnect due to insufficient mechanistic understanding. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.
References:
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