Next-Generation Drug Delivery: Smart Nanomaterials for Precision Healthcare(focusing oral cancer)

The convergence of artificial intelligence and nanotechnology has revolutionized the development of precision therapeutics for oral squamous cell carcinoma (OSCC), addressing critical challenges in drug delivery, tumor ablation, and early detection. This review systematically examines how AI-driven approaches—including machine learning (ML), generative adversarial networks (GANs), and reinforcement learning—optimize nanomaterial design for OSCC applications. ML algorithms predict critical nanocarrier properties (size, shape, surface charge) to enhance tumor targeting, while GANs explore novel nanostructures with stimuli-responsive drug release tailored to the acidic OSCC microenvironment. Reinforcement learning and genetic algorithms further refine surface functionalization and release kinetics, achieving unprecedented tumor-to-normal tissue ratios (۱۸:۱) and sustained therapeutic delivery. Clinically, AI-designed nanotherapeutics demonstrate remarkable advances: (۱) polymeric nanoparticles with optimized mucoadhesion for localized delivery, (۲) photothermal agents with ۸۵% energy conversion efficiency for tumor ablation, and (۳) nanosensors detecting salivary biomarkers at ۰.۱ pg/mL for early diagnosis. Despite these breakthroughs, challenges persist in manufacturing scalability and regulatory adaptation of AI-generated designs. Future directions highlight closed-loop systems integrating real-time patient data and multi-objective optimization for personalized nanomedicine. By bridging computational innovation with biological validation, AI-enabled nanomaterial design promises to transform OSCC management, offering targeted, adaptive, and minimally invasive therapeutic strategies. The convergence of artificial intelligence and nanotechnology has revolutionized the development of precision therapeutics for oral squamous cell carcinoma (OSCC), addressing critical challenges in drug delivery, tumor ablation, and early detection. This review systematically examines how AI-driven approaches—including machine learning (ML), generative adversarial networks (GANs), and reinforcement learning—optimize nanomaterial design for OSCC applications. ML algorithms predict critical nanocarrier properties (size, shape, surface charge) to enhance tumor targeting, while GANs explore novel nanostructures with stimuli-responsive drug release tailored to the acidic OSCC microenvironment. Reinforcement learning and genetic algorithms further refine surface functionalization and release kinetics, achieving unprecedented tumor-to-normal tissue ratios (۱۸:۱) and sustained therapeutic delivery. Clinically, AI-designed nanotherapeutics demonstrate remarkable advances: (۱) polymeric nanoparticles with optimized mucoadhesion for localized delivery, (۲) photothermal agents with ۸۵% energy conversion efficiency for tumor ablation, and (۳) nanosensors detecting salivary biomarkers at ۰.۱ pg/mL for early diagnosis. Despite these breakthroughs, challenges persist in manufacturing scalability and regulatory adaptation of AI-generated designs. Future directions highlight closed-loop systems integrating real-time patient data and multi-objective optimization for personalized nanomedicine. By bridging computational innovation with biological validation, AI-enabled nanomaterial design promises to transform OSCC management, offering targeted, adaptive, and minimally invasive therapeutic strategies.

کلیدواژه ها:

Smart materials ، oral cancer ، nanomaterials ، targeted drug delivery ، photothermal therapy ، precision medicine