Modeling Phospholipidosis Induction: Reliability and Warnings
Modeling Phospholipidosis Induction: Reliability and Warnings
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DOI：
10.1021/ci400113t
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发表时间：
2013-06-01
影响因子：
5.6
通讯作者：
Cruciani, Gabriele
中科院分区：
文献类型：
--
作者：
Goracci, Laura;Ceccarelli, Martina;Cruciani, Gabriele
药物性磷脂沉着症（PLD）的特点是受影响组织溶酶体中磷脂、诱导药物和片层包涵体的积累。在药物发现过程中必须尽早考虑这些副作用，事实上，已经发表了许多用于预测PLD的计算机模型。然而，任何计算机模型的质量都不可能优于用于构建模型的实验数据集的质量。本文概述了在生成用于已发表的PLD模型的数据库时遇到的困难和错误。从七个文献来源构建了一个包含466种化合物的新数据库，其中仅包含公开可用的化合物。对选定数据库中PLD分配的比较证明有助于揭示一些不一致之处，并对先前分配的某些化学物质的PLD+和PLD-分类提出质疑。最后，还应用了偏最小二乘判别分析（PLS-DA）方法，揭示了进一步的异常，并清楚地表明，在生成预测化合物诱导PLD的可能性的准确方法时，必须考虑代谢和数据质量。提出了一个包含331种化合物的新数据库。
Drug-induced phospholipidosis (PLD) is characterized by accumulation of phospholipids, the inducing drugs and lamellar inclusion bodies in the lysosomes of affected tissues. These side effects must be considered as early as possible during drug discovery, and, in fact, numerous in silico models designed to predict PLD have been published. However, the quality of any in silico model cannot be better than the quality of the experimental data set used to build it. The present paper reports an overview of the difficulties and errors encountered in the generation of databases used for the published PLD models. A new database of 466 compounds was constructed from seven literature sources, containing only publicly available compounds. A comparison of the PLD assignations in selected databases proved useful in revealing some inconsistencies and raised doubts about the the previously assigned PLD+ and PLD- classifications for some chemicals. Finally, a Partial Least Squares Discriminant Analysis (PLS-DA) approach was also applied, revealing further anomalies and clearly showing that metabolism as as data quality must be taken into account when generating accurate methods for predicting the likelihood that a compound will induce PLD. A new curated database of 331 compounds is proposed.