2D‐Quantitative structure–activity relationship modeling for risk assessment of pharmacotherapy applied during pregnancy

Abstract

The risk evaluation for pharmacological therapy during pregnancy is critical for maternal and fetal health. The initial risk assessment stage, the risk measurement, begins with pregnancy‐labeling categories (A, B, C, D, and X) for pharmaceuticals defined by the US Food and Drug Administration (FDA). Recently, in silico methods have been preferred in toxicology studies to eliminate ethical issues before conducting clinical toxicology studies and animal experiments. Quantitative structure–activity relationship (QSAR) modeling is one of the in silico methodologies. The research focuses on creating a QSAR model that predicts the five FDA pregnancy categories of medications. Our dataset included 868 pharmaceuticals, containing nearly every pharmacological group collected from the FDA. 2D‐molecular descriptors were calculated using PaDEL software. Twenty‐four QSAR models were developed, and the best four models were discussed. The results of the models were compared according to sensitivity, accuracy, F‐score, specificity, receiver operating characteristic (ROC) values, and Matthews correlation coefficient. Considering the statistical results, random forest is the best model for determining the pregnancy risk category of drugs. The accuracy of the model was 76.49% for internal and 93.58% for external validation. According to the kappa statistics, there is an average agreement of 0.583 for internal validation and a perfect agreement of 0.893 for external validation. Because the error rates of the model are very close to 0, the model is highly accurate. Consequently, our novel QSAR model gives guidance on the safe use of pharmaceuticals during pregnancy without requiring animal tests or clinical trials on pregnant women.

Machine Learning-Based Prediction of Drug-Induced Hepatotoxicity: An OvA-QSTR Approach

“…8 QSTR modeling has become a popular computational approach in pharmaceutical toxicology. 10 Especially in recent years, it has been discussed that target organ toxicity can be predicted by using in silico models such as QSTR methods. 11 These mathematical models correlate the potential toxicity of pharmaceuticals with structural properties.…”

“…QSTR modeling has become a popular computational approach in pharmaceutical toxicology . Especially in recent years, it has been discussed that target organ toxicity can be predicted by using in silico models such as QSTR methods .…”

Predictionof Environmental FateandToxicityofInsecticidesUsing Multi‐Target QSAR Approach

“…[11][12] Many physiochemical parameters, ecotoxicology, and environmental fate studies are frequently used for this purpose. [13][14] Water solubility, adsorption to soil and sediments, volatilization, biotic and abiotic degradation, and bioaccumulation are all factors that affect a chemical's environmental fate and behavior. [15] Knowing these parameters quantitatively allows one to model the amounts of a certain chemical substance in various environmental compartments.…”

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A novel non-linear approach for establishing a QSAR model of a class of 2-Phenyl-3-(pyridin-2-yl) thiazolidin-4-one derivatives

“…Quantitative structure-activity relationship (QSAR) ( Feng et al, 2023 ; Karaduman and Kelleci Celik, 2023 ) is a method of drug research that uses mathematical models to describe the relationship between molecular structure and certain biological activities of molecules. This method has been widely used in drug activity prediction and the development of new drugs.…”