Abstract:
Physiologically based pharmacokinetic (PBPK) modeling and simulation can be used to predict the pharmacokinetic behavior of drugs in humans using preclinical data. It can also explore the effects of various physiologic parameters such as age, ethnicity, or disease status on human pharmacokinetics, as well as guide dose and dose regiment selection and aid drug–drug interaction risk assessment. PBPK modeling has developed rapidly in the last decade within both the field of academia and the pharmaceutical industry, and has become an integral tool in drug discovery and development. In this mini-review, the concept and methodology of PBPK modeling are briefly introduced. Several case studies were discussed on how PBPK modeling and simulation can be utilized through various stages of drug discovery and development. These case studies are from our own work and the literature for better understanding of the absorption, distribution, metabolism and excretion (ADME) of a drug candidate, and the applications to increase efficiency, reduce the need for animal studies, and perhaps to replace clinical trials. The regulatory acceptance and industrial practices around PBPK modeling and simulation is also discussed.
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.
