PBPK modelling has been successfully applied to support all stages of drug development, from drug discovery, through drug approval, and post marketing requests. But how do we measure this success? One objective measure of meaningful impact on drug development is evidence of approved drug label claims supported by PBPK modelling and simulations. The objective of this analysis was to review new drug applications, and approved drug labels, for examples of successful application of PBPK to provide guidance on CYP450 victim DDI liability. Examples where model-based approaches were accepted in lieu of clinical DDI studies, and equally important, examples where these approaches were not accepted, have been investigated and summarized.
Small molecule drug approvals, from 2021 to present, were reviewed to identify examples where PBPK modelling was applied to investigate CYP victim DDI liability. Summary Reviews were filtered for references to PBPK modelling in drug interaction sections, and classified as either having had an impact on drug label indications, or as rejected by the FDA Clinical Pharmacology reviewer. Relevant properties of each drug were collected from the review report and the literature to identify patterns, and to predict when the results of PBPK simulations are most likely to be accepted in lieu of clinical victim drug interaction studies.
16 drug approvals were identified, which included a submitted PBPK model in support of CYP450 victim drug interaction guidance in the drug label. These examples included substrates of CYPs 1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 2J2, and 3A4. Of these 16 examples, 15 drugs included examples of victim interaction label guidance supported by PBPK modeling and simulation. For one of these drugs, no model predictions were accepted. Of the 15 drugs with some accepted predictions, there were also examples of additional PBPK supported drug interaction predictions which were not accepted by the regulatory reviewer.
In conclusion, PBPK model supported CYP victim interaction guidance have been included in the labels of many drug approvals over the past three years. While many CYPs contribute to the metabolism of these drugs, CYP3A4 was most commonly the major CYP responsible for drug metabolism. In general, successful application of PBPK to support label wording relied on at least one clinical drug interaction study; typically, with a strong CYP inhibitor, and in some cases a strong CYP inducer also. In scenarios where PBPK was not accepted, the most common reasons given by the regulatory reviewer were a lack of proper model validation, or uncertainty in the relative fraction metabolized by individual CYP isoforms. This analysis represents a novel and powerful method for evaluating the relative impact of PBPK models on CYP victim drug interaction guidance. Based on these results, drug development teams will have a better understanding of when, and when not, to apply PBPK in lieu of clinical CYP victim DDI studies.
