Literature DB >> 24050245

Exposition and reactivity optimization to predict sites of metabolism in chemicals.

Gabriele Cruciani, Massimo Baroni, Paolo Benedetti, Laura Goracci, Cosimo Gianluca Fortuna.   

Abstract

Chemical modifications of drugs induced by phase I biotransformations significantly affect their pharmacokinetic properties. Because the metabolites produced can themselves have a pharmacological effect and an intrinsic toxicity, medicinal chemists need to accurately predict the sites of metabolism (SoM) of drugs as early as possible. However, site of metabolism prediction is rarely accompanied by a prediction of the relative abundance of the various metabolites. Such a prediction would be a great help in the study of drug– drug interactions and in the process of reducing the toxicity of potential drug candidates. The aim of this paper is to present recent developments in the prediction of xenobiotic metabolism and to use concrete examples to explain the computational mechanism employed.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 24050245     DOI: 10.1016/j.ddtec.2012.11.001

Source DB:  PubMed          Journal:  Drug Discov Today Technol        ISSN: 1740-6749


  11 in total

1.  Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism.

Authors:  Tyler B Hughes; S Joshua Swamidass
Journal:  Chem Res Toxicol       Date:  2017-02-02       Impact factor: 3.739

2.  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

3.  Model-based estimates of the effects of efavirenz on bedaquiline pharmacokinetics and suggested dose adjustments for patients coinfected with HIV and tuberculosis.

Authors:  Elin M Svensson; Francesca Aweeka; Jeong-Gun Park; Florence Marzan; Kelly E Dooley; Mats O Karlsson
Journal:  Antimicrob Agents Chemother       Date:  2013-04-09       Impact factor: 5.191

4.  Modeling the Bioactivation and Subsequent Reactivity of Drugs.

Authors:  Tyler B Hughes; Noah Flynn; Na Le Dang; S Joshua Swamidass
Journal:  Chem Res Toxicol       Date:  2021-01-26       Impact factor: 3.739

Review 5.  Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes.

Authors:  Hannu Raunio; Mira Kuusisto; Risto O Juvonen; Olli T Pentikäinen
Journal:  Front Pharmacol       Date:  2015-06-12       Impact factor: 5.810

6.  Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network.

Authors:  Tyler B Hughes; Grover P Miller; S Joshua Swamidass
Journal:  ACS Cent Sci       Date:  2015-06-09       Impact factor: 14.553

7.  Plant organ cultures as masked mycotoxin biofactories: Deciphering the fate of zearalenone in micropropagated durum wheat roots and leaves.

Authors:  Laura Righetti; Enrico Rolli; Gianni Galaverna; Michele Suman; Renato Bruni; Chiara Dall'Asta
Journal:  PLoS One       Date:  2017-11-16       Impact factor: 3.240

8.  TPT sulfonate, a single, oral dose schistosomicidal prodrug: In vivo efficacy, disposition and metabolic profiling.

Authors:  Alan R Wolfe; R Jeffrey Neitz; Mark Burlingame; Brian M Suzuki; K C Lim; Mark Scheideler; David L Nelson; Leslie Z Benet; Conor R Caffrey
Journal:  Int J Parasitol Drugs Drug Resist       Date:  2018-11-20       Impact factor: 4.077

9.  Computational Insight Into Vitamin K1 ω-Hydroxylation by Cytochrome P450 4F2.

Authors:  Junhao Li; Hongxiao Zhang; Guixia Liu; Yun Tang; Yaoquan Tu; Weihua Li
Journal:  Front Pharmacol       Date:  2018-09-25       Impact factor: 5.810

10.  Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network.

Authors:  Tyler B Hughes; Na Le Dang; Grover P Miller; S Joshua Swamidass
Journal:  ACS Cent Sci       Date:  2016-07-29       Impact factor: 14.553

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.