Pharmaceutical Technology - March 2021

Pharmaceutical Technology - Regulatory Sourcebook - March 2021

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26 Pharmaceutical Technology REGULATORY SOURCEBOOK MARCH 2021 P h a r mTe c h . c o m recommendations to maximize the DDI effect included load- ing dose suggestions, use of solution versus capsule, and length of ITZ dosing after substrate administration (e.g., continuing through 4–5 substrate half-lives). Due to the increasing use of ITZ as a tool in clinical DDI studies across pharmaceutical companies, the development of this mechanistic PBPK model for the accurate prediction of CYP3A4-mediated DDIs was an important collaboration among IQ companies and a benefit for use in clinical research and useful to industry and academia. Another area in which IQ evaluated DDI modeling using PBPK was to assess the prediction of CYP3A and CYP2B6 in- duction compared to other static modeling approaches (9, 14). In these peer-reviewed publications, several DDI prediction models were evaluated for their ability to identify drugs with CYP3A or CYP2B6 induction liability based on in-vitro mRNA data. The drug interaction magnitudes of CYP3A or CYP2B6 substrates were predicted using various static and mechanistic dynamic PBPK models. For CYP3A, the models performed with high fidelity and predicted few false negatives or false positives, with the basic models having a good predictive performance for identifying risk with respect to induction. For CYP2B6, the trials using the substrate efavirenz rather than bupropion were better predicted with the models. In general, it is agreed among IQ companies that PBPK models are useful to optimally design DDI studies, particularly those of complex DDIs including dos- ing regimen, sampling strategy, study duration, and the strat- egy to differentiate the effects from induction and inhibition. Although there was a limited data set used in these studies (e.g., more moderate to strong inducers of CYP3A, for instance), mechanistic models may be a complementary technique when a false positive result is suspected using basic models for induc- tion. These case examples demonstrate the increased impor- tance of establishing the use of PBPK modeling in DDI predic- tions through the IQ TALG over the past few years. Impact of PBPK modeling on assessing the effect of organ impairment on human pharmacokinetics The safe and effective use of drugs requires an understanding of how impaired renal and hepatic drug elimination impacts drug exposure in affected patients. Traditionally, the pharma- ceutical industry has used stand-alone renal and hepatic im- pairment studies sometimes complemented with population PK modeling. These studies are not always available and often recruit small numbers of patients with diverse disease, and are not conducted until later in the drug development process. Predicting the outcome of such studies with PBPK models would be very valuable, but the performance of currently available organ impairment models lacks thorough evalua- tion. The IQ TALG Organ Impairment working group used PBPK models in Simcyp to compare observed and predicted outcomes for renal and hepatic impairment studies for 26 drugs eliminated principally by metabolism (15). For renal impairment, all predictions were considered acceptable using the predefined two-fold criteria. For hepatic impairment the same conclusion was reached with exception of three cases for which a modest over prediction was noted. Moving for- ward, these models are finding utility in rationalizing drug development in these special populations and contributing to a totality of evidence approach to safety assessment. Impact of PBPK modeling on assessing food effect Over the past 10 years, approximately 40% of approved drugs intended for oral administration report some change in phar- macokinetics when dosed with a meal. These changes in phar- macokinetics are commonly referred to as food effects and can be large enough where they warrant special instructions—such as taking the dose on an empty stomach—for physicians and patients to ensure safe and effective dosing. Accordingly, the study of food effect in clinical development is expected by major regulatory agencies around the world and represents an area where virtual-human approaches could have an impact in streamlining new drug development. The physical, chemical, and biological processes involved in drug absorption and food effects have been integrated into such an approach, namely PBPK biopharmaceutics models, and were evaluated by scientists working with the IQ Consor- tium (7). The authors generated de novo mechanistic absorp- tion models for 30 drugs, using a pre-specified modeling ap- proach to prospectively evaluate the predictive performance of the PBPK models and to establish best practices intended to help drive consistency, rigor, and acceptance of the approach. Notably, the authors had human pharmacokinetic data fol- lowing intravenous administration, in addition to oral dosing with and without food, for all drugs in the study, which al- lowed them to better study the changes in PK that arise from changes in absorption, as opposed to any potential effect of food on either elimination or distribution. A thorough analysis of the data revealed no clear trend in prediction accuracy with Biopharmaceutics Classification System (BCS) designation. However, the authors concluded The study of food effect in clinical development is expected by major regulatory agencies around the world and represents an area where virtual-human approaches could have an impact in streamlining new drug development. Quality Collaboration

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