Perturbation-Theory Machine Learning modelling of immunotoxicity for drugs targeting inflammatory cytokines and study of the anti-microbial G1 using cytometric bead arrays.

Abstract

ChEMBL biological activities prediction for 1-5-Bromofur-2-il-2-bromo-2-nitroethene (G1) is a difficult task for cytokine immunotoxicity. The current study is presenting experimental results for G1 interaction with mouse Th1/Th2 and proinflammatory cytokines using cytometry bead array (CBA). In the in vitro test of CBA, the results show no significant differences between the mean values of the Th1/Th2 cytokines for the samples treated with G1 with respect to the negative control, but there are moderate differences for cytokine values between different periods (24/48 hours). The experiments show no significant differences between the mean values of the proinflammatory cytokines for the samples treated with G1, regarding the negative control, except for the values of tumor necrosis factor (TNF) and Interleukin (IL6) between the group treated with G1 and the negative control at 48 hours. Differences occur for these cytokines in the periods (24/48 hours). The study confirmed that the antimicrobial G1 did not alter the Th1/Th2 cytokines concentration in vitro in different periods, but it can alter TNF and IL6. G1 promotes free radicals production and activates damage processes in macrophages culture. In order to predict all ChEMBL activities for drugs in other experimental conditions, a ChEMBL dataset was constructed using 25 biological activity, 1366 assays, 2 assay types, 4 assay organisms, 2 organism, and 12 cytokine targets. Molecular descriptors calculated with Rcpi and 15 Machine Learning methods were used to find the best model able to predict if a drug could be active or not against a specific cytokine, in specific experimental conditions. The best model is based on 120 selected molecular descriptors and a Deep Neural Network with Area Under the curve of the Receiver Operating Characteristic of 0.904 and accuracy of 0.832. This model predicted 1,384 G1 biological activities against cytokines in all ChEMBL dataset experimental condition (see https://github.com/kennethriva/Machine-Learning-for-drugs-cytokines).