Literature DB >> 25545151

Dealing with the complex drug-drug interactions: towards mechanistic models.

Manthena V Varma1, K Sandy Pang, Nina Isoherranen, Ping Zhao.   

Abstract

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 in vitro-to-in vivo 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.

Entities:  

Keywords:  CYP; drug-drug interactions; metabolites; physiologically based pharmacokinetic model; transporters

Mesh:

Substances:

Year:  2015        PMID: 25545151     DOI: 10.1002/bdd.1934

Source DB:  PubMed          Journal:  Biopharm Drug Dispos        ISSN: 0142-2782            Impact factor:   1.627


  15 in total

Review 1.  Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Authors:  Jennifer E Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2015-08-21       Impact factor: 3.922

Review 2.  Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group.

Authors:  Peter L Bonate; Malidi Ahamadi; Nageshwar Budha; Amparo de la Peña; Justin C Earp; Ying Hong; Mats O Karlsson; Patanjali Ravva; Ana Ruiz-Garcia; Herbert Struemper; Janet R Wade
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-02-02       Impact factor: 2.745

3.  Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System.

Authors:  Ayman F El-Kattan; Manthena V Varma; Stefan J Steyn; Dennis O Scott; Tristan S Maurer; Arthur Bergman
Journal:  Pharm Res       Date:  2016-09-12       Impact factor: 4.200

4.  Isoflavones in Soybean as a Daily Nutrient: The Mechanisms of Action and How They Alter the Pharmacokinetics of Drugs.

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Journal:  Turk J Pharm Sci       Date:  2021-12-31

5.  How Science Is Driving Regulatory Guidances.

Authors:  Xinning Yang; Jianghong Fan; Lei Zhang
Journal:  Methods Mol Biol       Date:  2021

6.  The Necessity of Using Changes in Absorption Time to Implicate Intestinal Transporter Involvement in Oral Drug-Drug Interactions.

Authors:  Jasleen K Sodhi; Leslie Z Benet
Journal:  AAPS J       Date:  2020-08-17       Impact factor: 4.009

7.  Physiologically based and population PK modeling in optimizing drug development: A predict-learn-confirm analysis.

Authors:  A Suri; S Chapel; C Lu; K Venkatakrishnan
Journal:  Clin Pharmacol Ther       Date:  2015-07-14       Impact factor: 6.875

Review 8.  Recent advances in understanding hepatic drug transport.

Authors:  Bruno Stieger; Bruno Hagenbuch
Journal:  F1000Res       Date:  2016-10-06

9.  Effects of Dexmedetomidine on the Pharmacokinetics of Dezocine, Midazolam and Its Metabolite 1-Hydroxymidazolam in Beagles by UPLC-MS/MS.

Authors:  Wei Zhou; Shuang-Long Li; Ti Zhao; Le Li; Wen-Bin Xing; Xiang-Jun Qiu; Wei Zhang
Journal:  Drug Des Devel Ther       Date:  2020-07-03       Impact factor: 4.162

10.  Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model.

Authors:  Ernesto Callegari; Manthena V S Varma; R Scott Obach
Journal:  Clin Transl Sci       Date:  2020-02-04       Impact factor: 4.689

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