BioPharm International - March 2021

BioPharm International - Regulatory Sourcebook - March 2021

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28 BioPharm International eBook March 2021 www.biopharminternational.com "well-stirred" model (6), with clear- ance attributed to the liver and kidney. In advanced PBPK models, gut absorption is sub-compartmen- talized into different regions of the gastrointestinal tract. Patients tak- ing drugs with food can impact drug absorption and thus PK prop- erties of the drugs, which can also be simulated (7). PBPK modeling may require a variable degree of pa ra meter izat ion using in-vit ro and in-vivo data depending on the question the model is intended to address. Essential drug-specific pa ra meters required for model execution will vary with the drug absorption, distribution, metab- ol i sm, a nd e xc r e t ion (A DM E) properties and the formulation in which it is administered. Typically, sensitivity analysis is employed to assess the model uncertainty and identify and investigate parame- ters with the greatest impact on the simulation out put (3), and clinical data are used to refine the mechanistic PBPK models fur- ther. In practice, a combination of approaches (ref inement and simulation) occurs (8) resulting in a top-down, bottom-up, or mid- dle-out approach to model build- ing. Once these models are built a nd cont i nuously ref i ned a nd verified, they can have a signifi- cant impact in addressing various challenges 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 pharma- cokinetic drug-drug interactions (DDIs) is studied and used to deter- mine optimal doses and prescribing information to achieve the safest and most effective therapy possible. Pharmacokinetic DDIs are those that impact the processes of absorp- tion, distribution, or elimination of other medications. It is also import- ant to consider that a DDI arises when one drug, called the 'perpe- trator', modulates the underlying physiological or biochemical mech- anism of either the PK or pharma- codynamics (PD) of a 'victim' drug. One method to study DDIs is apply- ing mechanistic PBPK approaches where 'virtual humans' are dosed to accurately predict the clinical outcome of potential pharmaco- kinetic DDIs with co-medications. Such PBPK models mechanistically integrate physiology, pharmacol- ogy, and biochemistry to predict how the administration of one drug may affect the absorption, distribu- tion, and elimination processes of another drug. PBPK modeling and simulation has also been recommended by regulatory authorities to inform clinical DDI study design and esti- mate the magnitude of DDIs. In a review by IQ Consor tium (IQ) member companies (8), it was emphasized that inclusion of PBPK modeling and simulation has increased in both regulatory submissions and approved dr ug labels w ith 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 simu- lation using verified and validated PBPK models (4). Notably, the sub- stantial impact of IQ is evident by peer-reviewed publications empha- sizing the utilization of PBPK mod- eling across the industry (8) and those focusing on DDI modeling (9–10) being cited in recent regulatory DDI guidance (11). P B P K mo d e l i n g i s a u s e f u l tool to quantitatively rationalize obser ved dr ug interactions and understand the underlying mech- anisms of DDI. It is highly valued for the elucidation of complex DDI mechanisms such as the contri- bution of perpetrator metabolites a nd t ra nspor ter- en z y me i nter- play (12). In this IQ-sponsored manu- script, three case examples of DDIs involving inhibitory metabolites using PBPK modeling were investi- gated, and guidance was provided for its strategic application. PBPK modeling was used to mechanisti- cally understand the quantitative contribution of the metabolites to the DDIs. The examples presented emphasized how perpetrator prop- erties of a parent drug alone can sometimes fail to predict the actual clinical DDI without incorpora- tion of metabolite formation and distribution k inetics as well as their perpetrator properties. What is important is the ability to cap- ture the time- and tissue-depen- dent formation of these inhibitory metabolites to more accurately pre- dict clinical DDIs in PBPK models. For i nsta nce, i n one of t he case examples, the prediction of amiodarone clinical DDIs w ith enzyme substrates could not be explained by parent drug in-vitro DDI parameters alone. Inclusion of formation of the abundantly circu- lating metabolite (mono-N-deseth- ylamiodarone) with its perpetrator properties was necessary to simulate Regulatory Sourcebook Quality Collaboration PBPK modeling is a useful tool to quantitatively rationalize observed drug interactions and understand the underlying mechanisms of DDIs.

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