Establishment of an in silico phospholipidosis prediction method using descriptors related to molecular interactions...

Although phospholipidosis (PLD) often affects drug development, there is no convenient in vitro or in vivo test system for PLD detection. In this study, we developed an in silico PLD prediction method based on the PLD-inducing mechanism. We focused on phospholipid (PL)-compound complex formation, which inhibits PL degradation by phospholipase. Thus, we used some molecular interactions, such as electrostatic interactions, hydrophobic interactions, and intermolecular forces, between PL and compounds as descriptors. First, we performed descriptor screening for intermolecular force and then developed a new in silico PLD prediction using descriptors related to molecular interactions. Based on the screening, we identified molecular refraction (MR) as a descriptor of intermolecular force. It is known that ClogP and most-basic pKa can be used for PLD prediction. Thereby, we developed an in silico prediction method using ClogP, most-basic pKa, and MR, which were related to hydrophobic interactions, electrostatic interactions, and intermolecular forces. In addition, a resampling method was used to determine the cut-off values for each descriptor. We obtained good results for 77 compounds as follows: sensitivity = 95.8%, specificity = 75.9%, and concordance = 88.3%. Although there is a concern regarding false-negative compounds for pKa calculations, this predictive ability will be adequate for PLD screening. In conclusion, the mechanism-based in silico PLD prediction method provided good prediction ability, and this method will be useful for evaluating the potential of drugs to cause PLD, particularly in the early stage of drug development, because this method only requires knowledge of the chemical structure.