Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen,Molecular Pharmaceutics

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Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen
Molecular Pharmaceutics ( IF 4.5 ) Pub Date : 2017-11-08 00:00:00 , DOI: 10.1021/acs.molpharmaceut.7b00388
Lucia Fusani ₁ , Martin Brown ₂ , Hongming Chen ₃ , Ernst Ahlberg ₁ , Tobias Noeske ₁

Affiliation

The drug-induced accumulation of phospholipids in lysosomes of various tissues is predominantly observed in regular repeat dose studies, often after prolonged exposure, and further investigated in mechanistic studies prior to candidate nomination. The finding can cause delays in the discovery process inflicting high costs to the affected projects. This article presents an in vitro imaging-based method for early detection of phospholipidosis liability and a hybrid approach for early detection and risk mitigation of phospolipidosis utilizing the in vitro readout with in silico model prediction. A set of reference compounds with phospolipidosis annotation was used as an external validation set yielding accuracies between 77.6% and 85.3% for various in vitro and in silico models, respectively. By means of a small set of chemically diverse known drugs with in vivo phospholipidosis annotation, the advantages of combining different prediction methods to reach an overall improved phospholipidosis prediction will be discussed.

中文翻译：

使用计算机模型和基于图像的体外筛查预测磷脂酰化的风险

在定期重复剂量研究中，通常是在长时间暴露后，主要观察到药物诱导的磷脂在各种组织的溶酶体内的蓄积，并在候选药物提名之前在机理研究中进行了进一步研究。该发现可能会导致发现过程中的延迟，从而给受影响的项目带来高昂的成本。本文介绍了一种基于体外成像的方法，用于早期检测磷脂酰肌病的病因，以及一种混合方法，用于利用计算机模拟模型预测的体外读数来早期检测和减轻磷脂酰肌病的风险。一组具有磷脂酰肌病注释的参考化合物用作外部验证集，对于各种体外模型和计算机模型，其准确度分别在77.6％和85.3％之间。

更新日期：2017-11-08

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