Computer-aided prediction of xenobiotic metabolism in the human body

Bezhentsev, Vladislav M.; Tarasova, O.; Дмитриев, А. В.; Rudik, Anastasia V.; Lagunin, Alexey; Филимонов, Д. А.; Poroikov, Vladimir

doi:10.1070/rcr4614

Cited by 26 publications

(16 citation statements)

References 27 publications

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“…This review is intended to describe important contributions in the field of CYP metabolism prediction and cover more recent developments in this field but with a focus on methods that are freely available (Table ). More comprehensive and complete lists of publications, software, and databases can be found elsewhere (Bezhentsev et al., ; Kar & Leszczynski, ; Kirchmair et al., ) with other reviews covering topics such as molecular dynamics, and QM modeling (Kirchmair et al., ; Shaik, Chen, Usharani, & Thiel, ; Williamson, ) and pharmacogenetics, pharmacoepigenetics, and clinical significance (Manikandan & Nagini, ) in more detail.…”

Section: Introductionmentioning

confidence: 99%

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

2019

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In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

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Section: Introductionmentioning

confidence: 99%

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

2019

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“…The evaluated set of compounds was subjected to a prediction of substrate/metabolite specificity, using an SMP web-service (Bezhentsev et al 2016) for in silico prediction of the substrate/metabolite specificity. This service provides the possible interaction of the tested group of compounds with 18 cytochrome P450 and UGT isoforms: CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, UG-T1A10, UGT1A1, UGT2B7, UGT1A7, UGT2B15, UG-T1A8, UGT1A4, UGT2B17, UGT2B10, UGT1A3, UG-T1A9, UGT1A6, UGT2B4; defined as the most probable targets.…”

Section: Prediction Of Substrate/metabolite Specificitymentioning

confidence: 99%

Synthesis and preliminary hepatotoxicity evaluation of new caffeine-8-(2-thio)-propanoic hydrazid-hydrazone derivatives

Mitkov¹,

Kondeva-Burdina²,

Zlatkov³

2019

PHAR

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New series of caffeine-8-(2-thio)-propanoic hydrazid-hydrazone derivatives were designed and synthesized. The targed compounds were obtained in yields of 51 to 96% and their structures were elucidated by FTIR, 1H NMR, 13C NMR, MS and microanalyses. All of the compounds were found to be “drug-like” as they fulfill the criteria of drug-likeness, which includes the MDDR-like rule. The tested compounds were subjected to in silico prediction of substrate/metabolite specificity and Drug Induced Liver Injury (DILI). The prediction for indicated that the evaluated compounds would most probably act as CYP1A2 substrates. The performed in vitro studies didn’t reveal statistically significant hepatotoxicity of the tested compounds, probably due to the pro-oxidant effects expressed on sub-cellular (isolated rat liver microsomes) level. The obtained experimental results confirmed the predicted low hepatotoxicity for the tested structures. Based on these results the compounds may be considered as promising structures for design of future molecules with low hepatotoxicity.

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“…Such changes were essential because most pharmaceutical agents interact with multiple molecular targets in the organism, producing desirable and adverse pharmacological effects. Pleiotropic action of pharmaceuticals is caused by both the parent compound and its metabolites that arise during the biotransformation of drug substances in the human body [ 4 ]. Due to the unsuspected toxicity of drug metabolites, many launched pharmaceuticals have been withdrawn from the market; some examples include troglitazone, tienilic acid, ximelagatran, zomepirac, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Detailed consideration of the drug’s effects on the organism must include the complex analysis of chemical–biological interactions, which may be performed using up-to-date chemoinformatics approaches [ 6 ]. Machine learning methods allow for estimating drug–target interactions with reasonable accuracy, which produces probable biological activity profiles [ 7 , 8 , 9 , 10 ] and different characteristics of drug metabolism [ 4 , 11 , 12 , 13 ]. However, to the best of our knowledge, an investigation that combine the in silico estimates of drug metabolism and predictions of the biological activity profiles for both the parent drug and its metabolites, has never been conducted.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Estimation of Biological Activity Profiles of Drug-Like Compounds Taking into Account Their Metabolism in Human Body

Filimonov

Rudik

Дмитриев

et al. 2020

IJMS

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Most pharmaceutical substances interact with several or even many molecular targets in the organism, determining the complex profiles of their biological activity. Moreover, due to biotransformation in the human body, they form one or several metabolites with different biological activity profiles. Therefore, the development and rational use of novel drugs requires the analysis of their biological activity profiles, taking into account metabolism in the human body. In silico methods are currently widely used for estimating new drug-like compounds’ interactions with pharmacological targets and predicting their metabolic transformations. In this study, we consider the estimation of the biological activity profiles of organic compounds, taking into account the action of both the parent molecule and its metabolites in the human body. We used an external dataset that consists of 864 parent compounds with known metabolites. It is shown that the complex assessment of active pharmaceutical ingredients’ interactions with the human organism increases the quality of computer-aided estimates. The toxic and adverse effects showed the most significant difference: reaching 0.16 for recall and 0.14 for precision.

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Computer-aided prediction of xenobiotic metabolism in the human body

Cited by 26 publications

References 27 publications

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

Synthesis and preliminary hepatotoxicity evaluation of new caffeine-8-(2-thio)-propanoic hydrazid-hydrazone derivatives

Computer-Aided Estimation of Biological Activity Profiles of Drug-Like Compounds Taking into Account Their Metabolism in Human Body

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