Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Shah, Pranav, Siramshetty, Vishal B., Zakharov, Alexey V., Southall, Noel T., Xu, Xin, Nguyen, Dac-Trung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
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
_version_ 1783519005361831936
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
work_keys_str_mv AT shahpranav predictinglivercytosolstabilityofsmallmolecules
AT siramshettyvishalb predictinglivercytosolstabilityofsmallmolecules
AT zakharovalexeyv predictinglivercytosolstabilityofsmallmolecules
AT southallnoelt predictinglivercytosolstabilityofsmallmolecules
AT xuxin predictinglivercytosolstabilityofsmallmolecules
AT nguyendactrung predictinglivercytosolstabilityofsmallmolecules