Dealing with the complex drug–drug interactions: Towards mechanistic models. (10th February 2015)

• Record Type: Journal Article
• Authors: Varma, Manthena V.; Pang, K. Sandy; Isoherranen, Nina; Zhao, Ping
• Is Part Of: Biopharmaceutics & drug disposition. Volume 36:Number 2(2015:Mar.)
• Page Start: 71
• Page End: 92
• Publication Date: 2015-02-10
• Subjects: Biopharmaceutics -- Periodicals; Drugs -- Metabolism -- Periodicals; Pharmacology -- Periodicals; Biopharmaceutics -- Periodicals; Pharmaceutical Preparations -- metabolism -- Periodicals; 615.19
• DOI: 10.1002/bdd.1934
• Languages: English
• ISSNs: 0142-2782

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Unmanageable severe adverse events caused by drug‐drug interactions (DDIs), leading to market withdrawals or restrictions in the clinical usage, are increasingly avoided with the improvement in our ability to predict such DDIs quantitatively early in drug development. However, significant challenges arise in the evaluation and/or prediction of complex DDIs caused by inhibitor drugs and/or metabolites that affect not one but multiple pathways of drug clearance. This review summarizes the discussion topics at the 2013 AAPS symposium on "Dealing with the complex drug‐drug interactions: towards mechanistic models". Physiologically based pharmacokinetic (PBPK) models, in combination with the established <italic>in vitro</italic>‐to‐<italic>in vivo</italic> extrapolations of intestinal and hepatic disposition, have been successfully applied to predict clinical pharmacokinetics and DDIs, especially for drugs with CYP‐mediated metabolism, and to explain transporter‐mediated and complex DDIs. Although continuous developments are being made towards improved mechanistic prediction of the transporter‐enzyme interplay in the hepatic and intestinal disposition and characterizing the metabolites contribution to DDIs, the prediction of DDIs involving them remains difficult. Regulatory guidelines also recommended use of PBPK modeling for the quantitative prediction and evaluation of DDIs involving multiple perpetrators and metabolites. Such mechanistic modeling approaches culminate to the consensus that modeling is helpful in predicting DDIs or quantitatively rationalizing the clinical findings in complex situations. Furthermore, they provide basis for the prediction and/or understanding the pharmacokinetics in populations like patients with renal impairment, pediatrics, or various ethnic groups where the conduct of clinical studies might not be feasible in early drug development stages and yet some guidance on management of dosage is necessary. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>