Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules

• Published in: Pharmaceuticals (Basel, Switzerland) Switzerland), 2024-11, Vol.17 (11), p.1550
• Main Authors: Rao, Mohan, Nassiri, Vahid, Srivastava, Sanjay, Yang, Amy, Brar, Satjit, McDuffie, Eric, Sachs, Clifford
• Format: Article
• Language: English
• Subjects: Accuracy; Acute renal failure; Adverse and side effects; Artificial intelligence; Biomarkers; cheminformatics; computational toxicology; Creatinine; Drug development; Drug dosages; drug induced kidney injury; Drugs; Feature selection; Kidneys; Machine learning; off-target interactions; Permeability; Risk factors
• ISSN: 1424-8247; 1424-8247
• DOI: 10.3390/ph17111550

Drug-Induced Kidney Injury (DIKI) presents a significant challenge in drug development, often leading to clinical-stage failures. The early prediction of DIKI risk can improve drug safety and development efficiency. Existing models tend to focus on physicochemical properties alone, often overlooking drug-target interactions crucial for DIKI. This study introduces an AI/ML (artificial intelligence/machine learning) model that integrates both physicochemical properties and off-target interactions to enhance DIKI prediction. We compiled a dataset of 360 FDA-classified compounds (231 non-nephrotoxic and 129 nephrotoxic) and predicted 6064 off-target interactions, 59% of which were validated in vitro. We also calculated 55 physicochemical properties for these compounds. Machine learning (ML) models were developed using four algorithms: Ridge Logistic Regression (RLR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). These models were then combined into an ensemble model for enhanced performance. The ensemble model achieved an ROC-AUC of 0.86, with a sensitivity and specificity of 0.79 and 0.78, respectively. The key predictive features included 38 off-target interactions and physicochemical properties such as the number of metabolites, polar surface area (PSA), pKa, and fraction of Sp3-hybridized carbons (fsp3). These features effectively distinguished DIKI from non-DIKI compounds. The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development.