Darwinian Nanomedicine: Artificial Intelligence-Driven Evolutionary Drug Delivery for Adaptive and Precision Therapeutics

OBJECTIVE: This review presents Darwinian nanomedicine as an artificial intelligence (AI)-enabled drug delivery strategy that applies Darwinian evolutionary principles as a computational and engineering analogy. Rather than implying biological evolution of nanoparticles, the framework employs iterative cycles of variation, selection, adaptation, and computational inheritance to optimize nanoparticle design through experimental feedback and AI-driven optimization. SIGNIFICANCE OF REVIEW: Conventional nanocarrier-based drug delivery systems have improved bioavailability, targeting, and therapeutic efficacy but remain static in design and limited in their ability to accommodate disease heterogeneity and variability. Darwinian nanomedicine represents a framework that integrates artificial intelligence with evolutionary optimization principles to iteratively refine nanoparticle formulations based on experimental performance rather than biological evolution. KEY FINDINGS: Recent studies demonstrate that diverse nanoparticle libraries can be iteratively screened and computationally optimized according to performance. Advances in artificial intelligence, machine learning, high-throughput screening, Bayesian optimization, and evolutionary algorithms have accelerated the identification of nanoparticle formulations with improved targeting efficiency, drug release, stability, and performance through closed-loop design-test-learn workflows. CONCLUSIONS: Darwinian nanomedicine represents a paradigm shift from static nanoparticle design toward AI-guided adaptive optimization of drug delivery systems. Importantly, the evolutionary processes described in the framework are computationally inspired rather than biological, relying on iterative engineering and experimental optimization instead of nanoparticle self-replication or genetic inheritance. Although significant challenges remain regarding scalability, reproducibility, data standardization, manufacturing, and regulatory approval, continued advances in nanotechnology and artificial intelligence may facilitate the future translation of this framework into precision medicine applications.