Machine Learning-Driven Predictive Modeling and Multi-Objective Exploration of Oxaliplatin-Loaded Nanocarriers for Enhanced Loading and Encapsulation Efficiency

Abstract

Oxaliplatin (OXA), a critical third-generation platinum chemotherapeutic, is significantly limited by suboptimal loading capacity and encapsulation efficiency in nanoparticle-based delivery systems. To address this, we developed an integrated machine learning (ML) and multi-objective optimization (MOO) framework for the simultaneous prediction and exploration of loading efficiency (LE) and encapsulation efficiency (EE). Ensemble learning models, trained on a curated dataset of 70 experimentally characterized nanocarrier formulations, demonstrated robust predictive performance under stringent leave-one-paper-out (LOPO) cross-validation (R² = 0.87 for LE, R² = 0.84 for EE). The multi-objective exploration identified a Pareto-optimal design space, with predicted performance reaching up to 45.3% LE and 87.2% EE, and pinpointed a balanced knee-point formulation at 40.2% LE and 83.7% EE. Interpretable ML analysis revealed surface area-to-volume ratio, coordination site availability, and zeta potential as the primary physicochemical drivers of OXA loading and retention. Consequently, an optimized nanocarrier profile, characterized by a particle size of 90–110 nm, a negative surface charge, and a carboxylate-rich composition, was derived. This study establishes a predictive, data-driven computational framework that bridges the gap between single-objective prediction and the holistic design of high-performance nanocarriers, providing a rational blueprint for accelerating the development of more effective OXA-based nanotherapies for colorectal cancer.

Keywords

encapsulation efficiency / loading efficiency / machine-learning / multi-objective optimization / oxaliplatin

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