Cargando…
Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network
Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning mo...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325315/ https://www.ncbi.nlm.nih.gov/pubmed/31868917 http://dx.doi.org/10.1002/cpt.1750 |
_version_ | 1783552127222677504 |
---|---|
author | Liu, Ke Ding, Ruo‐Fan Xu, Han Qin, Yang‐Mei He, Qiu‐Shun Du, Fei Zhang, Yun Yao, Li‐Xia You, Pan Xiang, Yan‐Ping Ji, Zhi‐Liang |
author_facet | Liu, Ke Ding, Ruo‐Fan Xu, Han Qin, Yang‐Mei He, Qiu‐Shun Du, Fei Zhang, Yun Yao, Li‐Xia You, Pan Xiang, Yan‐Ping Ji, Zhi‐Liang |
author_sort | Liu, Ke |
collection | PubMed |
description | Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage. |
format | Online Article Text |
id | pubmed-7325315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73253152020-07-01 Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network Liu, Ke Ding, Ruo‐Fan Xu, Han Qin, Yang‐Mei He, Qiu‐Shun Du, Fei Zhang, Yun Yao, Li‐Xia You, Pan Xiang, Yan‐Ping Ji, Zhi‐Liang Clin Pharmacol Ther Research Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage. John Wiley and Sons Inc. 2020-02-28 2020-06 /pmc/articles/PMC7325315/ /pubmed/31868917 http://dx.doi.org/10.1002/cpt.1750 Text en © 2019 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Liu, Ke Ding, Ruo‐Fan Xu, Han Qin, Yang‐Mei He, Qiu‐Shun Du, Fei Zhang, Yun Yao, Li‐Xia You, Pan Xiang, Yan‐Ping Ji, Zhi‐Liang Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network |
title | Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network |
title_full | Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network |
title_fullStr | Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network |
title_full_unstemmed | Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network |
title_short | Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network |
title_sort | broad‐spectrum profiling of drug safety via learning complex network |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325315/ https://www.ncbi.nlm.nih.gov/pubmed/31868917 http://dx.doi.org/10.1002/cpt.1750 |
work_keys_str_mv | AT liuke broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT dingruofan broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT xuhan broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT qinyangmei broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT heqiushun broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT dufei broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT zhangyun broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT yaolixia broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT youpan broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT xiangyanping broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork AT jizhiliang broadspectrumprofilingofdrugsafetyvialearningcomplexnetwork |