Low Yen S, Caster Ola, Bergvall Tomas, Fourches Denis, Zang Xiaoling, Norén G Niklas, Rusyn Ivan, Edwards Ralph, Tropsha Alexander
Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA Department of Environmental Sciences and Engineering, Gillings School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.
Uppsala Monitoring Centre, Uppsala, Sweden Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
J Am Med Inform Assoc. 2016 Sep;23(5):968-78. doi: 10.1093/jamia/ocv127. Epub 2015 Oct 24.
Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models.
Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan).
We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature.
Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts.
We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.
定量构效关系（QSAR）模型可预测药物不良反应（ADR），从而对潜在危害提供早期预警。及时识别潜在的安全问题可保护患者，并有助于对暴露人群中的ADR进行早期诊断。我们的目的是确定全球自发报告模式是否能识别与史蒂文斯-约翰逊综合征（SJS）相关的化学亚结构，并将其用于QSAR模型进行ADR预测。
利用VigiBase全球个体病例安全报告库（瑞典乌普萨拉监测中心，乌普萨拉）中与SJS有正或负报告相关性的364种药物的参考集，从药物分子结构计算化学描述符。使用随机森林和支持向量机方法开发QSAR模型，并通过外部5折交叉验证进行验证。将模型用于DrugBank的虚拟筛选，以预测SJS活性药物和非活性药物，并使用VigiBase、ChemoText和MicroMedex（Truven Health Analytics公司，密歇根州安阿伯）等知识库进行确证。
我们开发的QSAR模型能够准确预测药物是否与SJS相关（曲线下面积为75%-81%）。我们预测的10种最具活性和非活性的药物在文献中的SJS报告（或无报告）中得到了证实。
根据重要化学描述符对QSAR模型的解释提出了新的SJS结构警报。
我们已经证明QSAR模型可以准确识别SJS活性药物和非活性药物。仅需化学结构，QSAR模型就提供了有效的计算手段，以标记潜在有害药物，用于后续的靶向监测和药物流行病学调查。
