Development of a Medication-Related Osteonecrosis of the Jaw Prediction Model Using the FDA Adverse Event Reporting System Database and Machine Learning

Abstract

Background: Medication-related osteonecrosis of the jaw (MRONJ) is a rare but serious adverse event. Herein, we conducted a quantitative structure–activity relationship analysis using the U.S. Food and Drug Administration Adverse Drug Reaction Database System (FAERS) and machine learning to construct a drug prediction model for MRONJ induction based solely on chemical structure information. Methods: A total of 4815 drugs from FAERS were evaluated, including 70 and 139 MRONJ-positive and MRONJ-negative drugs, respectively, identified based on reporting odds ratios, Fisher’s exact tests, and ≥100 total adverse event reports. Then, we calculated 326 chemical structure descriptors for each drug and compared three supervised learning algorithms (random forest, gradient boosting, and artificial neural networks). We also compared the number of chemical structure descriptors (5, 6, 7, 8, 9, 10, 20, and 30 descriptors). Results: We indicated that the MRONJ prediction model using an artificial neural network algorithm and eight descriptors achieved the highest validation receiver operating characteristic curve value of 0.778. Notably, the total polar surface area (ASA_P) was among the top-ranking descriptors, and MRONJ-positive drugs such as bisphosphonates and anticancer drugs showed high values. Our final model demonstrated a balanced accuracy of 0.693 and a specificity of 0.852. Conclusions: In this study, our MRONJ-inducing drug prediction model identified drugs with polar surface area properties as potential causes of MRONJ. This study demonstrates a promising approach for predicting MRONJ risk, which could enhance drug safety assessment and streamline drug screening in clinical and preclinical settings.