Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial 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 presents experimental results for G1 interaction with mouse Th1/Th2 and pro-inflammatory cytokines using a 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 h). The experiments show no significant differences between the mean values of the pro-inflammatory 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 h. Differences occur for these cytokines in the periods (24/48 h). 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 data set was constructed using 25 biological activities, 1366 assays, 2 assay types, 4 assay organisms, 2 organisms, 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 1384 G1 biological activities against cytokines in all ChEMBL data set experimental conditions.

PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

“…To consider both the structure of any case/chemical and the experimental condition, cj, under which that case/chemical was tested, we applied a two-step approach, known as Box–Jenkins, which is the key aspect accounting for the great success of the PTML models [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 57 , 58 , 59 , 60 , 61 , 62 ]: …”

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

“…Therefore, we calculated a series of topological indices that fused chemical and biological information. To do so, we employed an adaptation of the Box-Jenkins approach, which is the key of the PTML modeling philosophy and for which great successful applications in different research areas have been reported in the scientific literature [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 55 , 56 , 57 ]. In the first step of this approach, we used the following mathematical formula: …”

“…Particularly, as depicted in Table 1 , the purpose here was to build an mtc-QSAR-MLP model to predict the inhibitory activity of a molecule under eight different experimental conditions. As mentioned in the previous section, such a capability of simultaneously predicting complex biological endpoints under dissimilar experimental conditions is an intrinsic characteristic of any PTML model [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 55 , 56 , 57 ].…”

“…The 1476 cases/molecules present in our dataset appear ordered according to their increasing IC 50 values. We randomly split them into training and test series [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 55 , 56 , 57 ] in such a way that the first three out of four cases/molecules were annotated to belong to the training set while the fourth was considered to belong to the test set. This procedure was repeated for the entire dataset.…”

Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles

“…Because of the significance of computational drug discovery, over the last 10 years, several research groups have emphasized the development and application of the methodology known as Perturbation Theory and Machine Learning (PTML) through which it has been possible to overcome all the drawbacks of the in silico approaches mentioned above. Thus, PTML models have been able to integrate chemical and biological data at different levels of complexity in scientific areas as diverse as antimicrobial research, − oncology, − neurosciences, immunology, and peptide/protein science. − In doing so, PTML models have been able to predict multiple activities, toxicities, and/or pharmacokinetic end points, while considering dissimilar biological targets (e.g., proteins, microorganisms, cells, rodents, etc.) and many assay protocols.…”