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Physiologically‐Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug–Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations

Physiologically‐based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug–drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time‐dependent inhibition, enzyme indu...

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Detalles Bibliográficos
Autores principales: Taskar, Kunal S., Pilla Reddy, Venkatesh, Burt, Howard, Posada, Maria M., Varma, Manthena, Zheng, Ming, Ullah, Mohammed, Emami Riedmaier, Arian, Umehara, Ken‐ichi, Snoeys, Jan, Nakakariya, Masanori, Chu, Xiaoyan, Beneton, Maud, Chen, Yuan, Huth, Felix, Narayanan, Rangaraj, Mukherjee, Dwaipayan, Dixit, Vaishali, Sugiyama, Yuichi, Neuhoff, Sibylle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7232864/
https://www.ncbi.nlm.nih.gov/pubmed/31628859
http://dx.doi.org/10.1002/cpt.1693
Descripción
Sumario:Physiologically‐based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug–drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time‐dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome‐P450‐mediated DDIs and is routinely used. However, the application of PBPK for transporter‐mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling.