Application of Big Data Analytics in Minoxidil Drug Development: A Data-Driven Model for Predicting Efficacy and Side Effect Risks

This study uses big data analytics and machine learning, specifically XGBoost models, to predict the efficacy and side effect risks of Minoxidil (MXD) for treating androgenetic alopecia. By analyzing 5,000 anonymized patient records, the researchers developed models that outperform traditional methods in predicting treatment outcomes and identifying side effect risks. Key findings include the SULT1A1 genotype's influence on efficacy and MXD concentration as a risk factor for side effects. The study advocates for personalized treatment strategies involving genetic testing and MXD concentration adjustments. Despite promising results, the study's retrospective nature and regional data limitations call for further validation through prospective trials and broader testing. Future efforts will focus on improving model interpretability and incorporating more biological data.