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Modeling and insights into the structural characteristics of drug-induced autoimmune diseases

The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD p...

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Autores principales: Guo, Huizhu, Zhang, Peitao, Zhang, Ruiqiu, Hua, Yuqing, Zhang, Pei, Cui, Xueyan, Huang, Xin, Li, Xiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637949/
https://www.ncbi.nlm.nih.gov/pubmed/36353637
http://dx.doi.org/10.3389/fimmu.2022.1015409
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author Guo, Huizhu
Zhang, Peitao
Zhang, Ruiqiu
Hua, Yuqing
Zhang, Pei
Cui, Xueyan
Huang, Xin
Li, Xiao
author_facet Guo, Huizhu
Zhang, Peitao
Zhang, Ruiqiu
Hua, Yuqing
Zhang, Pei
Cui, Xueyan
Huang, Xin
Li, Xiao
author_sort Guo, Huizhu
collection PubMed
description The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity.
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spelling pubmed-96379492022-11-08 Modeling and insights into the structural characteristics of drug-induced autoimmune diseases Guo, Huizhu Zhang, Peitao Zhang, Ruiqiu Hua, Yuqing Zhang, Pei Cui, Xueyan Huang, Xin Li, Xiao Front Immunol Immunology The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity. Frontiers Media S.A. 2022-10-24 /pmc/articles/PMC9637949/ /pubmed/36353637 http://dx.doi.org/10.3389/fimmu.2022.1015409 Text en Copyright © 2022 Guo, Zhang, Zhang, Hua, Zhang, Cui, Huang and Li https://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) and the copyright owner(s) 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 Immunology
Guo, Huizhu
Zhang, Peitao
Zhang, Ruiqiu
Hua, Yuqing
Zhang, Pei
Cui, Xueyan
Huang, Xin
Li, Xiao
Modeling and insights into the structural characteristics of drug-induced autoimmune diseases
title Modeling and insights into the structural characteristics of drug-induced autoimmune diseases
title_full Modeling and insights into the structural characteristics of drug-induced autoimmune diseases
title_fullStr Modeling and insights into the structural characteristics of drug-induced autoimmune diseases
title_full_unstemmed Modeling and insights into the structural characteristics of drug-induced autoimmune diseases
title_short Modeling and insights into the structural characteristics of drug-induced autoimmune diseases
title_sort modeling and insights into the structural characteristics of drug-induced autoimmune diseases
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637949/
https://www.ncbi.nlm.nih.gov/pubmed/36353637
http://dx.doi.org/10.3389/fimmu.2022.1015409
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