Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs
Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs
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DOI：
10.1093/toxsci/kft189
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发表时间：
2013-11-01
影响因子：
3.8
通讯作者：
Tong, Weida
中科院分区：
文献类型：
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作者：
Chen, Minjun;Hong, Huixiao;Tong, Weida
药物性肝损伤（DILI）是导致药物开发项目终止的主要原因之一。因此，在开发过程的早期阶段确定候选药物在人体中的DILI风险将大大降低制药工业的药物损耗率，但需要实施新的研究和开发战略。在这方面，已经提出了几个计算机模型作为优先考虑候选药物的替代手段。由于预测模型的准确性和实用性在很大程度上取决于如何以可靠和一致的方式注释药物引起DILI的可能性，因此食品和药物管理局批准的药物标签得到了重视。在387种标注的药物中，197种药物被用于建立定量构效关系（QSAR）模型，随后该模型被左侧药物作为外部验证集挑战，整体预测准确率为68.9%。通过使用另外2个独立验证集进一步评估模型的性能，这3个验证数据集共有483种独特药物。我们观察到，不同治疗用途的药物的QSAR模型表现不同；然而，当只关注这些具有高预测置信度的治疗类别时，它获得了更好的估计准确性（73.6%）和负预测值（77.0%）。从而确定了模型的适用范围。总的来说，所开发的QSAR模型具有潜在的实用性，可以优先考虑化合物对人类DILI的风险，特别是对于镇痛药、抗菌药物和抗组胺药等高可信度的治疗亚组。
Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug candidates. Because the accuracy and utility of a predictive model rests largely on how to annotate the potential of a drug to cause DILI in a reliable and consistent way, the Food and Drug Administrationapproved drug labeling was given prominence. Out of 387 drugs annotated, 197 drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of drugs serving as an external validation set with an overall prediction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent validation sets, and the 3 validation data sets have a total of 483 unique drugs. We observed that the QSAR models performance varied for drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic categories with high prediction confidence. Thus, the models applicability domain was defined. Taken collectively, the developed QSAR model has the potential utility to prioritize compounds risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.