Exosome-Based Drug Delivery Approaches

Abstract

Exosome-based drug delivery has emerged as a promising strategy for targeted therapeutics because of its intrinsic biocompatibility, low immunogenicity, and ability to cross physiological barriers. Despite rapid advances in exosome engineering, current computational approaches remain largely centralized and rarely address inter-institutional privacy constraints or explainable therapeutic decision-making. This study identifies a research gap in integrating privacy-preserving artificial intelligence with exosome cargo optimization across heterogeneous biomedical datasets. To address this limitation, a novel Explainable Federated Exosome Optimization Framework (E-FEOF) is proposed. The framework combines multimodal biological data with federated deep learning and interpretable machine learning to recommend optimal exosome cargo-loading strategies for individualized therapy. Unlike conventional prediction models, the proposed architecture enables collaborative learning without exchanging sensitive patient information while simultaneously providing biologically meaningful explanations. The study establishes a conceptual experimental pipeline suitable for digital health environments and evaluates predictive performance using multiple learning metrics. The proposed methodology is expected to improve therapeutic precision, facilitate secure multi-center collaboration, and enhance clinical trust in AI-assisted exosome engineering