Nanoemulsion-Based Drug Delivery Platforms

Abstract

Nanoemulsion-based drug delivery platforms have emerged as versatile carriers for improving the solubility, stability, and bioavailability of poorly watersoluble therapeutic agents. Despite substantial advances in formulation science, selecting optimal nanoemulsion compositions remains highly dependent on empirical experimentation, resulting in increased development time and cost. A major research gap exists in integrating explainable artificial intelligence (AI) with physicochemical formulation parameters to predict longterm nanoemulsion stability and individualized therapeutic outcomes. This study proposes an AI-assisted predictive framework that combines machine learning with experimentally derived nanoemulsion descriptors for formulation optimization. The proposed framework is designed to identify stability-sensitive variables while minimizing laboratory screening efforts. Recent developments in digital pharmaceutical manufacturing, AI-driven formulation design, and computational nanomedicine are synthesized to establish the conceptual foundation of the study. The manuscript emphasizes how predictive analytics can accelerate pharmaceutical development while supporting personalized drug delivery strategies. The proposed research contributes toward intelligent pharmaceutical manufacturing and nextgeneration digital health systems