磷脂蓄积症数据库的开发和预测定量构效关系（QSAR）模型。
Development of a phospholipidosis database and predictive quantitative structure-activity relationship (QSAR) models.
机构信息
U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, 10903 New Hampshire Avenue, Silver Spring, MD, 20993-0002.
出版信息
Toxicol Mech Methods. 2008;18(2-3):217-27. doi: 10.1080/15376510701857262.
ABSTRACT Drug-induced phospholipidosis (PL) is a condition characterized by the accumulation of phospholipids and drug in lysosomes, and is found in a variety of tissue types. PL is frequently manifested in preclinical studies and may delay or prevent the development of pharmaceuticals. This report describes the construction of a database of PL findings in a variety of animal species and its use as a training data set for computational toxicology software. PL data and chemical structures were compiled from the published literature, existing pharmaceutical databases, and Food and Drug Administration (FDA) internal reports yielding a total of 583 compounds suitable for modeling. The database contained 190 (33%) positive drugs and 393 (77%) negative drugs, of which 39 were electron microscopy-confirmed negative compounds and 354 were classified as negatives due to the absence of positive reported data. Of the 190 positive findings, 76 were electron microscopy confirmed and 114 were considered positive based on other evidence. Quantitative structure-activity relationship (QSAR) models were constructed using two commercially available software programs, MC4PC and MDL-QSAR, and internal cross-validation (10 x 10%) experiments were performed to assess their predictive performance. Performance parameters for the MC4PC model were specificity 92%, sensitivity 50%, concordance 78%, positive predictivity 76%, and negative predictivity 78%. For MDL-QSAR, predictive performance was similar: specificity 80%, sensitivity 76%, concordance 79%, positive predictivity 65%, and negative predictivity 87%. By combining the output of the two QSAR programs, the overall predictive performance was vastly improved and sensitivity could be optimized to 81% without significant loss of specificity (79%). Many of the structural alerts and significant molecular descriptors obtained from the QSAR software were found to be associated with parts of active molecules known for their cationic amphiphilic drug (CAD) properties supporting the hypothesis that the endpoint of PL is statistically correlated with chemical structure. QSAR models can be useful tools for screening drug candidate molecules for potential PL.
摘要
药物诱导的磷脂病（PL）是一种以溶酶体中磷脂和药物蓄积为特征的病症，存在于多种组织类型中。PL 常在临床前研究中表现出来，并可能延迟或阻止药物的开发。本报告描述了在多种动物物种中构建 PL 发现数据库的方法，并将其用作计算毒理学软件的训练数据集。PL 数据和化学结构是从已发表的文献、现有的药物数据库和美国食品和药物管理局（FDA）内部报告中汇编而成的，共得到 583 种适合建模的化合物。该数据库包含 190 种（33%）阳性药物和 393 种（77%）阴性药物，其中 39 种是电子显微镜确认的阴性化合物，354 种因缺乏阳性报告数据而被归类为阴性。在 190 种阳性发现中，有 76 种是电子显微镜确认的，114 种是基于其他证据被认为是阳性的。使用两个商业上可用的软件程序 MC4PC 和 MDL-QSAR 构建了定量构效关系（QSAR）模型，并进行了内部交叉验证（10×10%）实验来评估它们的预测性能。MC4PC 模型的性能参数为特异性 92%、灵敏度 50%、一致性 78%、阳性预测值 76%和阴性预测值 78%。对于 MDL-QSAR，预测性能相似：特异性 80%、灵敏度 76%、一致性 79%、阳性预测值 65%和阴性预测值 87%。通过结合两个 QSAR 程序的输出，总体预测性能得到了极大的提高，并且可以在不显著降低特异性（79%）的情况下将灵敏度优化到 81%。从 QSAR 软件获得的许多结构警报和显著分子描述符与已知具有阳离子两亲性药物（CAD）特性的活性分子部分相关，这支持了 PL 的终点与化学结构在统计学上相关的假设。QSAR 模型可以成为筛选候选药物分子是否具有潜在 PL 的有用工具。
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