Literature DB >> 19878193

Structure-based drug metabolism predictions for drug design.

Hao Sun1, Dennis O Scott.   

Abstract

Significant progress has been made in structure-based drug design by pharmaceutical companies at different stages of drug discovery such as identifying new hits, enhancing molecule binding affinity in hit-to-lead, and reducing toxicities in lead optimization. Drug metabolism is a major consideration for modifying drug clearance and also a primary source for drug metabolite-induced toxicity. With major cytochrome P450 structures identified and characterized recently, structure-based drug metabolism prediction becomes increasingly attractive. In silico methods based on molecular and quantum mechanics such as docking, molecular dynamics and ab initio chemical reactivity calculations bring us closer to understand drug metabolism and predict drug-drug interactions. In this study, we review important progress in drug metabolism and common in silico techniques adopted to predict drug regioselectivity, stereoselectivity, reactive metabolites, induction, inhibition and mechanism-based inactivation, as well as their implementation in hit-to-lead drug discovery.

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Year:  2009        PMID: 19878193     DOI: 10.1111/j.1747-0285.2009.00899.x

Source DB:  PubMed          Journal:  Chem Biol Drug Des        ISSN: 1747-0277            Impact factor:   2.817


  31 in total

1.  QSAR classification of metabolic activation of chemicals into covalently reactive species.

Authors:  Chin Yee Liew; Chuen Pan; Andre Tan; Ke Xin Magneline Ang; Chun Wei Yap
Journal:  Mol Divers       Date:  2012-02-28       Impact factor: 2.943

Review 2.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

Review 3.  Correlating structure and function of drug-metabolizing enzymes: progress and ongoing challenges.

Authors:  Eric F Johnson; J Patrick Connick; James R Reed; Wayne L Backes; Manoj C Desai; Lianhong Xu; D Fernando Estrada; Jennifer S Laurence; Emily E Scott
Journal:  Drug Metab Dispos       Date:  2013-10-15       Impact factor: 3.922

4.  Combination of docking, molecular dynamics and quantum mechanical calculations for metabolism prediction of 3,4-methylenedioxybenzoyl-2-thienylhydrazone.

Authors:  Rodolpho C Braga; Vinícius M Alves; Carlos A M Fraga; Eliezer J Barreiro; Valéria de Oliveira; Carolina H Andrade
Journal:  J Mol Model       Date:  2011-09-08       Impact factor: 1.810

Review 5.  Current Approaches for Investigating and Predicting Cytochrome P450 3A4-Ligand Interactions.

Authors:  Irina F Sevrioukova; Thomas L Poulos
Journal:  Adv Exp Med Biol       Date:  2015       Impact factor: 2.622

6.  Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database.

Authors:  Angelica Mazzolari; Avid M Afzal; Alessandro Pedretti; Bernard Testa; Giulio Vistoli; Andreas Bender
Journal:  ACS Med Chem Lett       Date:  2019-02-12       Impact factor: 4.345

Review 7.  Hypoxia--implications for pharmaceutical developments.

Authors:  Lucas Donovan; Scott M Welford; John Haaga; Joseph LaManna; Kingman P Strohl
Journal:  Sleep Breath       Date:  2010-07-14       Impact factor: 2.816

Review 8.  Structural diversity of eukaryotic membrane cytochrome p450s.

Authors:  Eric F Johnson; C David Stout
Journal:  J Biol Chem       Date:  2013-04-30       Impact factor: 5.157

Review 9.  Studies of metabolite-protein interactions: a review.

Authors:  Ryan Matsuda; Cong Bi; Jeanethe Anguizola; Matthew Sobansky; Elliott Rodriguez; John Vargas Badilla; Xiwei Zheng; Benjamin Hage; David S Hage
Journal:  J Chromatogr B Analyt Technol Biomed Life Sci       Date:  2013-11-25       Impact factor: 3.205

10.  Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing.

Authors:  Rohan Patil; Suranjana Das; Ashley Stanley; Lumbani Yadav; Akulapalli Sudhakar; Ashok K Varma
Journal:  PLoS One       Date:  2010-08-16       Impact factor: 3.240

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