Evaluation of the role of comedication properties in the severity of drug-induced liver injury using machine learning techniques

Drug-induced liver injury (DILI) is a complex liver pathology modulated by multiple factors, most of which remain unknown. Previous studies have suggested that concomitant medications and patient characteristics play an important role as modulators of this disease. This study aimed to determine the most relevant concomitant medications and patient characteristics that influence the severity of idiosyncratic DILI. Two clinical databases, discovery and validation, were analyzed to evaluate host and drug properties. Predictive algorithms, elastic net regression model and logistic regression model, were implemented using R, both achieving ROC AUC > 0.7. The findings revealed the existence of significant relationship between DILI severity and multiple factors. These factors included: hepatocellular injury, hydrophobic drugs with logP > 3 (octanol-water partition coefficient), and the use of concomitant medications containing halogen compounds or heterorings when taken together with culprit drugs with significant hepatic metabolism. These findings offer valuable insights into predicting the severity of DILI. By identifying the key factors that influence the severity of DILI, it would be possible for healthcare providers to predict the severity of damage in a patient with DILI. This enables early interventions in cases of DILI, thus subsequently reducing its negative effects.