Identification of novel drug-drug interactions: towards precision medicine in women's reproductive health

Abstract

Drug-drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women healthcare. For this purpose, a DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein-protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2,991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in-vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25% respectively). Some were expected to improve current therapies (n=23), while others would cause harmful effects (n=11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Overall, these results show the importance of DDIs studies to personalize female reproductive therapies.