AUTHOR=Abouir Kenza , Samer Caroline F , Gloor Yvonne , Desmeules Jules A , Daali Youssef 

TITLE=Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.708299

DOI=10.3389/fphar.2021.708299

ISSN=1663-9812

ABSTRACT=Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp® software and Full PBPK. Overall, the necessary physiological information needed for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23 % integrated the genetic aspect to the developed models. Marked differences in predicted pharmacokinetic parameters could be explained by the significant difference in the data sources used by the software. In conclusion, PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.