Pharmaceutical Technology - March 2021

Pharmaceutical Technology - Regulatory Sourcebook - March 2021

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Pharmaceutical Technology REGULATORY SOURCEBOOK MARCH 2021 25 (refinement and simulation) occurs (8) resulting in a top-down, bottom-up, or middle-out approach to model building. Once these models are built and continuously refined and verified, they can have a significant impact in addressing various chal- lenges in pharmaceutical drug development as outlined below. Impact of PBPK modeling on assessing drug-drug interactions As part of the clinical development process, the potential for pharmacokinetic drug-drug interactions (DDIs) is studied and used to determine optimal doses and prescribing information to achieve the safest and most effective therapy possible. Phar- macokinetic DDIs are those that impact the processes of ab- sorption, distribution, or elimination of other medications. It is also important to consider that a DDI arises when one drug, called the 'perpetrator', modulates the underlying physiological or biochemical mechanism of either the PK or pharmacody- namics (PD) of a 'victim' drug. One method to study DDIs is applying mechanistic PBPK approaches where 'virtual humans' are dosed to accurately predict the clinical outcome of poten- tial pharmacokinetic DDIs with co-medications. Such PBPK models mechanistically integrate physiology, pharmacology, and biochemistry to predict how the administration of one drug may affect the absorption, distribution, and elimination processes of another drug. PBPK modeling and simulation has also been recommended by regulatory authorities to inform clinical DDI study design and estimate the magnitude of DDIs. In a review by IQ Consortium (IQ) member companies (8), it was emphasized that inclusion of PBPK modeling and simulation has increased in both regula- tory submissions and approved drug labels with the majority involving DDI predictions. In addition, a number of approved drug labels include guidance to physicians on specific DDIs based on predictive modeling and simulation using verified and validated PBPK models (4). Notably, the substantial impact of IQ is evident by peer-reviewed publications emphasizing the utilization of PBPK modeling across the industry (8) and those focusing on DDI modeling (9–10) being cited in recent regula- tory DDI guidance (11). PBPK modeling is a useful tool to quantitatively rationalize observed drug interactions and understand the underlying mechanisms. It is highly valued for the elucidation of com- plex DDI mechanisms such as the contribution of perpetra- tor metabolites and transporter-enzyme interplay (12). In this IQ-sponsored manuscript, three case examples of DDIs involving inhibitory metabolites using PBPK modeling were investigated, and guidance was provided for its strategic application. PBPK modeling was used to mechanistically understand the quantita- tive contribution of the metabolites to the DDIs. The examples presented emphasized how perpetrator properties of a parent drug alone can sometimes fail to predict the actual clinical DDI without incorporation of metabolite formation and distribution kinetics as well as their perpetrator properties. What is im- portant is the ability to capture the time- and tissue-dependent formation of these inhibitory metabolites to more accurately predict clinical DDIs in PBPK models. For instance, in one of the case examples, the prediction of amiodarone clinical DDIs with enzyme substrates could not be explained by parent drug in-vitro DDI parameters alone. Inclusion of formation of the abundantly circulating metabolite (mono-N-desethylamiodarone) with its perpetrator properties was necessary to simulate the observed DDI. Mechanisms of drug interaction are also important; for instance, the multi- ple interaction mechanisms (CYP2C8 and OATP1B1) of both parent and metabolite of gemfibrozil were simultaneously implemented in PBPK models to predict DDIs of representa- tive victim drugs. While consideration of parent alone led to a significant underprediction of the DDI, the inclusion of gem- fibrozil-glucuronide metabolite in the PBPK model greatly im- proved the accuracy of predicted area under the curve (AUC) ratios and plasma concentration–time profiles of victim drugs. The gemfibrozil case study reinforces the utility of in-vitro data and the modeling approaches that mechanistically integrate the multiple components in the DDI risk assessment. Incorporation of metabolite DDI within PBPK models was also important for the itraconazole (ITZ) PBPK model built in collaboration with IQ member companies in response to the FDA restriction of the CYP3A4 strong inhibitor, ketoconazole, for use in clinical DDI studies (13). The goal of this working group was to develop and verify a mechanistic ITZ PBPK model that could be used to predict the impact of this strong CYP3A4 inhibitor on CYP3A substrates (e.g., new drug candi- dates) with high confidence. Additionally, the model was used to provide recommendations on optimal DDI study design based on PBPK model simulations. Using in-vitro and clinical PK data for ITZ and its metabolites collected from IQ member companies, the PBPK model developed in this study was built mostly 'bottom-up' and was able to describe the PK accumula- tion of both ITZ and its major CYP3A4-inhibitory metabolite, hydroxy-ITZ (OH-ITZ), after repeat dosing of ITZ as a solu- tion and capsule under both fasted and fed states. Overall, the model predicted the DDI of ITZ with seven different CYP3A substrates (24 AUC ratios) accurately, thus verifying the model for prospective use in DDI predictions. This verified model was then used to simulate various clin- ical DDI study scenarios considering formulation, duration of dosing, dose regimen, and food status to recommend the opti- mal design for maximal inhibitory effect by ITZ. For instance, PBPK modeling is a useful tool to quantitatively rationalize observed drug interactions and understand the underlying mechanisms of DDIs.

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