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Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes
The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing e...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464169/ https://www.ncbi.nlm.nih.gov/pubmed/26124721 http://dx.doi.org/10.3389/fphar.2015.00123 |
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author | Raunio, Hannu Kuusisto, Mira Juvonen, Risto O. Pentikäinen, Olli T. |
author_facet | Raunio, Hannu Kuusisto, Mira Juvonen, Risto O. Pentikäinen, Olli T. |
author_sort | Raunio, Hannu |
collection | PubMed |
description | The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals. |
format | Online Article Text |
id | pubmed-4464169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-44641692015-06-29 Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes Raunio, Hannu Kuusisto, Mira Juvonen, Risto O. Pentikäinen, Olli T. Front Pharmacol Pharmacology The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals. Frontiers Media S.A. 2015-06-12 /pmc/articles/PMC4464169/ /pubmed/26124721 http://dx.doi.org/10.3389/fphar.2015.00123 Text en Copyright © 2015 Raunio, Kuusisto, Juvonen and Pentikäinen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Raunio, Hannu Kuusisto, Mira Juvonen, Risto O. Pentikäinen, Olli T. Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes |
title | Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes |
title_full | Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes |
title_fullStr | Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes |
title_full_unstemmed | Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes |
title_short | Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes |
title_sort | modeling of interactions between xenobiotics and cytochrome p450 (cyp) enzymes |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464169/ https://www.ncbi.nlm.nih.gov/pubmed/26124721 http://dx.doi.org/10.3389/fphar.2015.00123 |
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