Predicting adverse side effects of drugs
<p>Abstract</p> <p>Background</p> <p>Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology com...
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| Format: | Article |
| Language: | English |
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BMC
2011-12-01
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| Series: | BMC Genomics |
| Online Access: | http://www.biomedcentral.com/1471-2164/12/S5/S11 |
| _version_ | 1818119127922900992 |
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| author | Huang Liang-Chin Wu Xiaogang Chen Jake Y |
| author_facet | Huang Liang-Chin Wu Xiaogang Chen Jake Y |
| author_sort | Huang Liang-Chin |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology combines data from clinical observation and molecular biology. This approach is new, however, and there are few examples of how it can practically predict adverse reactions (ADRs) from an experimental drug with acceptable accuracy.</p> <p>Results</p> <p>We have developed a new and practical computational framework to accurately predict ADRs of trial drugs. We combine clinical observation data with drug target data, protein-protein interaction (PPI) networks, and gene ontology (GO) annotations. We use cardiotoxicity, one of the major causes for drug withdrawals, as a case study to demonstrate the power of the framework. Our results show that an <it>in silico </it>model built on this framework can achieve a satisfactory cardiotoxicity ADR prediction performance (median AUC = 0.771, Accuracy = 0.675, Sensitivity = 0.632, and Specificity = 0.789). Our results also demonstrate the significance of incorporating prior knowledge, including gene networks and gene annotations, to improve future ADR assessments.</p> <p>Conclusions</p> <p>Biomolecular network and gene annotation information can significantly improve the predictive accuracy of ADR of drugs under development. The use of PPI networks can increase prediction specificity and the use of GO annotations can increase prediction sensitivity. Using cardiotoxicity as an example, we are able to further identify cardiotoxicity-related proteins among drug target expanding PPI networks. The systems pharmacology approach that we developed in this study can be generally applicable to all future developmental drug ADR assessments and predictions.</p> |
| first_indexed | 2024-12-11T05:05:16Z |
| format | Article |
| id | doaj.art-e2bdba99bf4741b88510b034a0f86eda |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-11T05:05:16Z |
| publishDate | 2011-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-e2bdba99bf4741b88510b034a0f86eda2022-12-22T01:20:03ZengBMCBMC Genomics1471-21642011-12-0112Suppl 5S1110.1186/1471-2164-12-S5-S11Predicting adverse side effects of drugsHuang Liang-ChinWu XiaogangChen Jake Y<p>Abstract</p> <p>Background</p> <p>Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology combines data from clinical observation and molecular biology. This approach is new, however, and there are few examples of how it can practically predict adverse reactions (ADRs) from an experimental drug with acceptable accuracy.</p> <p>Results</p> <p>We have developed a new and practical computational framework to accurately predict ADRs of trial drugs. We combine clinical observation data with drug target data, protein-protein interaction (PPI) networks, and gene ontology (GO) annotations. We use cardiotoxicity, one of the major causes for drug withdrawals, as a case study to demonstrate the power of the framework. Our results show that an <it>in silico </it>model built on this framework can achieve a satisfactory cardiotoxicity ADR prediction performance (median AUC = 0.771, Accuracy = 0.675, Sensitivity = 0.632, and Specificity = 0.789). Our results also demonstrate the significance of incorporating prior knowledge, including gene networks and gene annotations, to improve future ADR assessments.</p> <p>Conclusions</p> <p>Biomolecular network and gene annotation information can significantly improve the predictive accuracy of ADR of drugs under development. The use of PPI networks can increase prediction specificity and the use of GO annotations can increase prediction sensitivity. Using cardiotoxicity as an example, we are able to further identify cardiotoxicity-related proteins among drug target expanding PPI networks. The systems pharmacology approach that we developed in this study can be generally applicable to all future developmental drug ADR assessments and predictions.</p>http://www.biomedcentral.com/1471-2164/12/S5/S11 |
| spellingShingle | Huang Liang-Chin Wu Xiaogang Chen Jake Y Predicting adverse side effects of drugs BMC Genomics |
| title | Predicting adverse side effects of drugs |
| title_full | Predicting adverse side effects of drugs |
| title_fullStr | Predicting adverse side effects of drugs |
| title_full_unstemmed | Predicting adverse side effects of drugs |
| title_short | Predicting adverse side effects of drugs |
| title_sort | predicting adverse side effects of drugs |
| url | http://www.biomedcentral.com/1471-2164/12/S5/S11 |
| work_keys_str_mv | AT huangliangchin predictingadversesideeffectsofdrugs AT wuxiaogang predictingadversesideeffectsofdrugs AT chenjakey predictingadversesideeffectsofdrugs |