Literature DB >> 24392841

The role of drug metabolizing enzymes in clearance.

Li Di1.   

Abstract

INTRODUCTION: Metabolism is one of the most important clearance pathways representing the major clearance route of 75% drugs. The four most common drug metabolizing enzymes (DME) that contribute significantly to elimination pathways of new chemical entities are cytochrome P450s, UDP-glucuronosyltransferases, aldehyde oxidase and sulfotransferases. Accurate prediction of human in vivo clearance by these enzymes, using both in vitro and in vivo tools, is critical for the success of drug candidates in human translation. AREAS COVERED: Important recent advances of key DME are reviewed and highlighted in the following areas: major isoforms, tissue distribution, generic polymorphism, substrate specificity, species differences, mechanism of catalysis, in vitro-in vivo extrapolation and the importance of using optimal assay conditions and relevant animal models. EXPERT OPINION: Understanding the clearance mechanism of a compound is the first step toward successful prediction of human clearance. It is critical to apply appropriate in vitro and in vivo methodologies and physiologically based models in human translation. While high-confidence prediction for P450-mediated clearance has been achieved, the accuracy of human clearance prediction is significantly lower for other enzyme classes. More accurate predictive methods and models are being developed to address these challenges.

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Year:  2014        PMID: 24392841     DOI: 10.1517/17425255.2014.876006

Source DB:  PubMed          Journal:  Expert Opin Drug Metab Toxicol        ISSN: 1742-5255            Impact factor:   4.481


  28 in total

1.  Investigating the substrate binding mechanism of sulfotransferase 2A1 based on substrate tunnel analysis: a molecular dynamics simulation study.

Authors:  Li Zhao; Pupu Zhang; Shiyang Long; Linlin Wang; Hanyong Jin; Weiwei Han; Pu Tian
Journal:  J Mol Model       Date:  2016-07-08       Impact factor: 1.810

Review 2.  In vitro platforms for evaluating liver toxicity.

Authors:  Shyam Sundhar Bale; Lawrence Vernetti; Nina Senutovitch; Rohit Jindal; Manjunath Hegde; Albert Gough; William J McCarty; Ahmet Bakan; Abhinav Bhushan; Tong Ying Shun; Inna Golberg; Richard DeBiasio; Berk Osman Usta; D Lansing Taylor; Martin L Yarmush
Journal:  Exp Biol Med (Maywood)       Date:  2014-04-24

Review 3.  Predicting drug metabolism: experiment and/or computation?

Authors:  Johannes Kirchmair; Andreas H Göller; Dieter Lang; Jens Kunze; Bernard Testa; Ian D Wilson; Robert C Glen; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2015-04-24       Impact factor: 84.694

4.  The effect of ligands on the thermal stability of sulfotransferases: a molecular dynamics simulation study.

Authors:  Pu-pu Zhang; Li Zhao; Shi-yang Long; Pu Tian
Journal:  J Mol Model       Date:  2015-03-08       Impact factor: 1.810

5.  The impact of ligands on the structure and flexibility of sulfotransferases: a molecular dynamics simulation study.

Authors:  Li Zhao; Pupu Zhang; Shiyang Long; Linlin Wang; Pu Tian
Journal:  J Mol Model       Date:  2015-07-08       Impact factor: 1.810

6.  Structure-metabolism relationships in human-AOX: Chemical insights from a large database of aza-aromatic and amide compounds.

Authors:  Susan Lepri; Martina Ceccarelli; Nicolò Milani; Sara Tortorella; Andrea Cucco; Aurora Valeri; Laura Goracci; Andreas Brink; Gabriele Cruciani
Journal:  Proc Natl Acad Sci U S A       Date:  2017-04-03       Impact factor: 11.205

7.  Metabolic Profiling of Human Long-Term Liver Models and Hepatic Clearance Predictions from In Vitro Data Using Nonlinear Mixed-Effects Modeling.

Authors:  Nicole A Kratochwil; Christophe Meille; Stephen Fowler; Florian Klammers; Aynur Ekiciler; Birgit Molitor; Sandrine Simon; Isabelle Walter; Claudia McGinnis; Johanna Walther; Brian Leonard; Miriam Triyatni; Hassan Javanbakht; Christoph Funk; Franz Schuler; Thierry Lavé; Neil J Parrott
Journal:  AAPS J       Date:  2017-01-03       Impact factor: 4.009

Review 8.  The influence of gut microbiota on drug metabolism and toxicity.

Authors:  Houkai Li; Jiaojiao He; Wei Jia
Journal:  Expert Opin Drug Metab Toxicol       Date:  2015-12-10       Impact factor: 4.481

9.  Random Forest Model Prediction of Compound Oral Exposure in the Mouse.

Authors:  Haseeb Mughal; Han Wang; Matthew Zimmerman; Marc D Paradis; Joel S Freundlich
Journal:  ACS Pharmacol Transl Sci       Date:  2021-01-26

10.  A Validated LC-MS/MS Assay for the Simultaneous Quantification of the FDA-Approved Anticancer Mixture (Encorafenib and Binimetinib): Metabolic Stability Estimation.

Authors:  Mohamed W Attwa; Hany W Darwish; Nasser S Al-Shakliah; Adnan A Kadi
Journal:  Molecules       Date:  2021-05-05       Impact factor: 4.411

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