Predicting Phospholipidosis Using Machine Learning

Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the importance of computational approaches to the problem has been well documented. Previous work on predictive methods for phospholipidosis showed that state of the art machine learning methods produced the best results. Here we extend this work by looking at a larger data set mined from the literature. We find that circular fingerprints lead to better models than either E-Dragon descriptors or a combination of the two. We also observe very similar performance in general between Random Forest and Support Vector Machine models.</p

Simulation-based cheminformatic analysis of organelle-targeted molecules: lysosomotropic monobasic amines

Cell-based molecular transport simulations are being developed to facilitate exploratory cheminformatic analysis of virtual libraries of small drug-like molecules. For this purpose, mathematical models of single cells are built from equations capturing the transport of small molecules across membranes. In turn, physicochemical properties of small molecules can be used as input to simulate intracellular drug distribution, through time. Here, with mathematical equations and biological parameters adjusted so as to mimic a leukocyte in the blood, simulations were performed to analyze steady state, relative accumulation of small molecules in lysosomes, mitochondria, and cytosol of this target cell, in the presence of a homogenous extracellular drug concentration. Similarly, with equations and parameters set to mimic an intestinal epithelial cell, simulations were also performed to analyze steady state, relative distribution and transcellular permeability in this non-target cell, in the presence of an apical-to-basolateral concentration gradient. With a test set of ninety-nine monobasic amines gathered from the scientific literature, simulation results helped analyze relationships between the chemical diversity of these molecules and their intracellular distributions

Urinary thiodiacetic acid. A selective biomarker for the cytochrome P450-catalyzed oxidation of 1,2-dibromoethane in the rat. Drug Metab Dispos

ABSTRACT: 1,2-Dibromoethane (1,2-DBE) is a carcinogenic compound that is metabolized both by cytochrome P450 (P450) and glutathione Stransferase (GST) enzymes, and that has been used by us as a model compound to study interindividual variability in biotransformation reactions. In this study, the excretion of thiodiacetic acid (TDA) and S-(2-hydroxyethyl)-N-acetyl-l-cysteine (2-HEMA) were measured in the urine of rats dosed with 1,2-DBE, and experiments were performed to investigate to what extent P450 and GST enzymes contribute to the formation of TDA. To this end, CYP2E1, the main P450 isoenzyme catalyzing the oxidation of 1,2-DBE, was inhibited using disulfiram and diallylsulfide. Significant inhibition o