PBPK modeling and simulation in drug research and development

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.

Reverse Translation of US Food and Drug Administration Reviews of Oncology New Molecular Entities Approved in 2011–2017: Lessons Learned for Anticancer Drug Development

“…Another trend evident from our survey and also consistent with findings in another recent survey 38 is that PBPK modeling and simulation is increasingly accepted by the FDA to inform clinical DDI risk in product labeling, provided that PBPK models are first qualified with PK data from one or more clinical studies designed to evaluate the worst-case scenario with respect to DDIs or drug-genotype interactions (e.g., DDI study with a strong CYP inhibitor or inducer, or study assessing genotype-PK relationships in extensive vs. poor metabolizers). [39][40][41] In this context, PBPK modeling and simulation can subsequently be used to predict lower-risk scenarios (e.g., the effect of moderate inhibitors or inducers or intermediate metabolizers) and support labeling statements related to DDI risk. This approach was utilized successfully for small molecule drugs, such as cobimetinib and ibrutinib, the details of which have been previously published.…”

Pharmacokinetics of the Hypoxia-Imaging Agent [¹²³I]IAZA in Healthy Adults Following Exercise-Based Cardiac Stress

“…PBPK can be used to assess the exposure in a target organ after dosing, taking into account organ-specific absorption, metabolism and disposition rates in that organ [22]; it does not rely heavily on plasma or serum PK to elucidate all of the physiological compartments. In the current imaging work, attempts to discern whether these regions are separate compartments of a multicompartmental pharmacokinetic model for …”

Population variability in animal health: Influence on dose‐exposure‐response relationships: Part II: Modelling and simulation

“…This is a hybrid of bottom‐up and top‐down approaches where observed clinical data (“top down”) are examined from the perspective of predictions derived through the use of mechanistic models (bottom‐up). The resulting fitted output values are used to refine the PBPK model in accordance to the changes necessary to minimize the difference between observed and fitted values (Tsamandouras et al., ; Zhuang & Lu, ). While this approach to M&S can potentially be a powerful alternative to traditional compartmental or population‐based modelling methods, it is important to recognize that the use of a middle‐out approach is accompanied by a risk of generating model parameter values that while providing a good fit to the observed data, may lack biological relevance (or may lack logical rationalization as to the fitting of that parameter).…”