Modeling Phospholipidosis Induction: Reliability and Warnings

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 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 well 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.

药物诱导磷脂沉积症（Drug-induced phospholipidosis, PLD）以受累组织溶酶体中磷脂、诱导药物及层状包涵体的蓄积为核心特征。此类不良反应需在药物发现阶段尽早纳入考量范畴，目前已有诸多用于预测PLD的虚拟（in silico）模型发表。然而，任何虚拟模型的质量都无法超越其训练所依托的实验数据集的质量。本文概述了已发表PLD模型所依托的数据库构建过程中面临的难点与各类误差。研究从7篇文献来源中构建了包含466种化合物的全新数据库，且仅纳入公开可获取的化合物。对所选数据库中的PLD分类结果进行比对，有助于揭示部分不一致性，并引发了学界对部分化学品此前被标注为PLD+（磷脂沉积症阳性）与PLD–（磷脂沉积症阴性）的分类的质疑。最后，本研究还采用了偏最小二乘判别分析（Partial Least Squares Discriminant Analysis, PLS-DA）方法，进一步揭示了数据异常情况，并明确表明：在构建可准确预测化合物诱导PLD可能性的方法时，必须将代谢过程与数据质量两大关键因素纳入考量。本研究最终提出了一个包含331种化合物的经精心整理的精选数据库。