PBPK Modeling And Simulation In Drug Research And Development
Authors: Xiaomei Zhuanga, Chuang Lub
Abstract:
Physiologically based pharmacokinetic (PBPK) modeling and simulation offer a valuable approach for predicting how drugs behave in the human body based on preclinical data. This method enables exploration of the impact of physiological factors such as age, ethnicity, and disease status on drug pharmacokinetics. Moreover, it assists in determining optimal dosing regimens, as well as assessing the risk of drug-drug interactions. PBPK modeling has rapidly advanced in the past decade, gaining prominence in both academia and the pharmaceutical industry as an indispensable tool in drug discovery and development.
In this concise review, we provide an overview of PBPK modeling, including its fundamental concept and methodology. We discuss several case studies that demonstrate the diverse applications of PBPK modeling and simulation across different stages of drug discovery and development. These case studies, drawn from our own research and the literature, enhance our understanding of drug candidate absorption, distribution, metabolism, and excretion (ADME), while highlighting the potential to enhance efficiency, reduce reliance on animal studies, and potentially replace certain clinical trials. Additionally, we delve into the regulatory acceptance and current industrial practices surrounding PBPK modeling and simulation.
Key words
PBPK; PK prediction; Absorption; Metabolism; Drug–drug interaction; Special population
A schematic of a PBPK model is shown in Fig. 1. The mass balance differential equations used in these models have been described previously8 and follow the principles shown below.
Citation: Xiaomei Zhuanga, Chuang Lub PBPK Modeling And Simulation In Drug Research And Development doi:10.1016/j.apsb.2016.04.004.
Received: 18 March 2016, Revised: 25 April 2016, Accepted: 26 April 2016, Available online: 23 June 2016
Copyright: © 2016 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Conclusions
PBPK modeling is a useful tool for the prediction of human PK profile from preclinical data. Once FIH PK data or human ADME data becomes available, the model can be further fine-tuned as illustrated in Fig. 715. It is a good tool for evaluating and optimizing clinical trial design, for example, to select the dose and dose schedule. It helps to understand the individual variability and parameters that have the most impact on human PK profile through sensitivity analysis. Hence, PBPK modeling provides a practical solution for extrapolating PK profile from healthy population to some ethnical, special age, or disease populations where clinical PK study is the hardest to conduct. In the DDI prediction area, PBPK modeling can help to determine the washout period in a crossover study design to set the minimal but sufficient clinical trial duration. It can also be applied as an alternative to DDI trials in some special populations, such as pediatrics and organ-impairment patients where the actual DDI trial is hard to conduct due to logistical or ethical issues. Thus, it can sometimes provide waiver for conducting unnecessary clinical DDI trials which then speeds up the drug development process and put fewer burdens on patients. Conducting DDI trials with multiple perpetrators in patients is also not ethical and practical, the PBPK modeling, in this case, can provide information about “what if” all of those drugs are co-administered together. On the other hand, as discussed earlier, PBPK modeling is a bottom-up approach, its results dependent on the quality of the input data. Although software are available for the prediction of physicochemical properties of compounds, such as logP and pKa, in authors experience, it is critical to use measured values to get a reliable PBPK prediction, especially when predicting human PK profile, rather than the AUC ratio for DDI purpose. For example, for a set of clinical candidates (about 40 compounds), the number of compounds for which the predicted PK profile within two fold of observed clinical values dropped from about 70% to half of that when in silico predicted logP and pKa were used (unpublished data). Transporter is another emerging area of PBPK modeling, however, most of the data generated are qualitative to answer the question of yes or no of whether a compound is a substrate of a transporter. PBPK modeling relies on kinetic data, such as the clearance of the compound via that transporter. Thus, additional data of transporter clearance are needed for PBPK modeling.
