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Predicting liver cytosol stability of small molecules
Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it is possible to overlook the contribution of cytosolic enzymes until much later in the drug...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140498/ https://www.ncbi.nlm.nih.gov/pubmed/33431020 http://dx.doi.org/10.1186/s13321-020-00426-7 |
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author | Shah, Pranav Siramshetty, Vishal B. Zakharov, Alexey V. Southall, Noel T. Xu, Xin Nguyen, Dac-Trung |
author_facet | Shah, Pranav Siramshetty, Vishal B. Zakharov, Alexey V. Southall, Noel T. Xu, Xin Nguyen, Dac-Trung |
author_sort | Shah, Pranav |
collection | PubMed |
description | Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it is possible to overlook the contribution of cytosolic enzymes until much later in the drug discovery process. Few data exist on cytosolic enzyme-mediated metabolism and no reliable tools are available to chemists to help design away from such liabilities. In this study, we screened 1450 compounds for liver cytosol-mediated metabolic stability and extracted transformation rules that might help medicinal chemists in optimizing compounds with these liabilities. In vitro half-life data were collected by performing in-house experiments in mouse (CD-1 male) and human (mixed gender) cytosol fractions. Matched molecular pairs analysis was performed in conjunction with qualitative-structure activity relationship modeling to identify chemical structure transformations affecting cytosolic stability. The transformation rules were prospectively validated on the test set. In addition, selected rules were validated on a diverse chemical library and the resulting pairs were experimentally tested to confirm whether the identified transformations could be generalized. The validation results, comprising nearly 250 library compounds and corresponding half-life data, are made publicly available. The datasets were also used to generate in silico classification models, based on different molecular descriptors and machine learning methods, to predict cytosol-mediated liabilities. To the best of our knowledge, this is the first systematic in silico effort to address cytosolic enzyme-mediated liabilities. [Image: see text] |
format | Online Article Text |
id | pubmed-7140498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-71404982020-04-14 Predicting liver cytosol stability of small molecules Shah, Pranav Siramshetty, Vishal B. Zakharov, Alexey V. Southall, Noel T. Xu, Xin Nguyen, Dac-Trung J Cheminform Research Article Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it is possible to overlook the contribution of cytosolic enzymes until much later in the drug discovery process. Few data exist on cytosolic enzyme-mediated metabolism and no reliable tools are available to chemists to help design away from such liabilities. In this study, we screened 1450 compounds for liver cytosol-mediated metabolic stability and extracted transformation rules that might help medicinal chemists in optimizing compounds with these liabilities. In vitro half-life data were collected by performing in-house experiments in mouse (CD-1 male) and human (mixed gender) cytosol fractions. Matched molecular pairs analysis was performed in conjunction with qualitative-structure activity relationship modeling to identify chemical structure transformations affecting cytosolic stability. The transformation rules were prospectively validated on the test set. In addition, selected rules were validated on a diverse chemical library and the resulting pairs were experimentally tested to confirm whether the identified transformations could be generalized. The validation results, comprising nearly 250 library compounds and corresponding half-life data, are made publicly available. The datasets were also used to generate in silico classification models, based on different molecular descriptors and machine learning methods, to predict cytosol-mediated liabilities. To the best of our knowledge, this is the first systematic in silico effort to address cytosolic enzyme-mediated liabilities. [Image: see text] Springer International Publishing 2020-04-07 /pmc/articles/PMC7140498/ /pubmed/33431020 http://dx.doi.org/10.1186/s13321-020-00426-7 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Shah, Pranav Siramshetty, Vishal B. Zakharov, Alexey V. Southall, Noel T. Xu, Xin Nguyen, Dac-Trung Predicting liver cytosol stability of small molecules |
title | Predicting liver cytosol stability of small molecules |
title_full | Predicting liver cytosol stability of small molecules |
title_fullStr | Predicting liver cytosol stability of small molecules |
title_full_unstemmed | Predicting liver cytosol stability of small molecules |
title_short | Predicting liver cytosol stability of small molecules |
title_sort | predicting liver cytosol stability of small molecules |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140498/ https://www.ncbi.nlm.nih.gov/pubmed/33431020 http://dx.doi.org/10.1186/s13321-020-00426-7 |
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