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Physiochemical property space distribution among human metabolites, drugs and toxins

BACKGROUND: The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's &q...

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Autores principales: Khanna, Varun, Ranganathan, Shoba
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2788350/
https://www.ncbi.nlm.nih.gov/pubmed/19958509
http://dx.doi.org/10.1186/1471-2105-10-S15-S10
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author Khanna, Varun
Ranganathan, Shoba
author_facet Khanna, Varun
Ranganathan, Shoba
author_sort Khanna, Varun
collection PubMed
description BACKGROUND: The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets. RESULTS: We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets. CONCLUSION: The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.
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spelling pubmed-27883502009-12-04 Physiochemical property space distribution among human metabolites, drugs and toxins Khanna, Varun Ranganathan, Shoba BMC Bioinformatics Proceedings BACKGROUND: The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets. RESULTS: We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets. CONCLUSION: The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications. BioMed Central 2009-12-03 /pmc/articles/PMC2788350/ /pubmed/19958509 http://dx.doi.org/10.1186/1471-2105-10-S15-S10 Text en Copyright ©2009 Khanna and Ranganathan; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Khanna, Varun
Ranganathan, Shoba
Physiochemical property space distribution among human metabolites, drugs and toxins
title Physiochemical property space distribution among human metabolites, drugs and toxins
title_full Physiochemical property space distribution among human metabolites, drugs and toxins
title_fullStr Physiochemical property space distribution among human metabolites, drugs and toxins
title_full_unstemmed Physiochemical property space distribution among human metabolites, drugs and toxins
title_short Physiochemical property space distribution among human metabolites, drugs and toxins
title_sort physiochemical property space distribution among human metabolites, drugs and toxins
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2788350/
https://www.ncbi.nlm.nih.gov/pubmed/19958509
http://dx.doi.org/10.1186/1471-2105-10-S15-S10
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