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

AbstractThe 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 Matt hews 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 agreem ent of 0.583 for internal validation and a perfect agreement of 0.893 for external validation. Be...
Source: Journal of Applied Toxicology - Category: Toxicology Authors: Tags: RESEARCH ARTICLE Source Type: research