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

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

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

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

Ligand-Based Target Prediction with Signature Fingerprints

When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power