Machine Learning Analysis of Cytotoxicity Determinants in Nanoparticle-Based Rheumatoid Arthritis Therapies

Nanoparticle-based therapies have gained attention in recent years as promising treatments for rheumatoid arthritis (RA), due to the potential offered for targeted delivery, controlled drug release, and improved biocompatibility. A deep understanding of the factors that drive cytotoxicity is crucial for safer and more effective nanomedicine formulations. To systematically analyze the determinants of cytotoxicity reported in the literature, we constructed a data set comprising 2,060 instances from 56 publications. Each instance was described by 23 features covering nanoparticle characteristics, cellular environment factors, and assay conditions potentially associated with cytotoxicity. Machine learning (ML) approaches were incorporated to gain deeper insight into key cytotoxicity drivers. We combined Boruta for feature selection, Random Forest (RF) for cytotoxicity prediction and feature importance evaluation, and Association Rule Mining (ARM) for rule-based, hidden pattern discovery. Boruta feature selection results identified the drug and nanoparticle concentration, core-shell material, and cell type as major determinants of cytotoxicity. The RF model demonstrated a strong predictive performance, further confirming the significance of these features. Moreover, ARM revealed high-confidence association rules linking specific conditions, such as high drug concentrations and poly(aspartic acid)-based systems, to cytotoxic outcomes. This structured machine learning framework provides a foundation for optimizing nanoparticle formulations that balance therapeutic efficacy with cellular safety in RA therapy.