A COMPUTATIONAL FRAMEWORK FOR MODERN HERBAL DRUG DEVELOPMENT: "INTEGRATING ARTIFICIAL INTELLIGENCE WITH NANOCARRIER SYSTEMS FOR ENHANCED PROBIOTIC DELIVERY"

Probiotics offer substantial therapeutic potential for gastrointestinal, metabolic, and immune-related disorders; however, their clinical efficacy is severely limited by poor survival in the gastrointestinal tract, with conventional formulations achieving less than 10% viability at the target site. Nanocarrier systems—including polymeric nanoparticles, lipid-based carriers, hydrogels and nanoemulsions—provide physical encapsulation that protects probiotics from gastric acid, bile salts and enzymatic degradation, enabling targeted colonic delivery and enhanced epithelial adhesion. Yet, the multivariate complexity of nanocarrier optimization renders traditional trial-and-error development approaches impractical. This article presents a comprehensive framework for integrating artificial intelligence (AI) with probiotic nanocarrier systems. Machine learning algorithms, including XGBoost, artificial neural networks (ANNs), and reinforcement learning models, are applied across the development pipeline: from strain–carriercompatibility screening and formulation optimization (reducing experimental runs by 60–80%) to long-term stability prediction and pharmacokinetic simulation. Disease-specific AI applications are explored for inflammatory bowel disease, irritable bowel syndrome, metabolic disorders, neurological conditions and dermatological diseases. Advanced applications including digital twin technology, federated learning for personalized probiotics and AI-driven nanosynbiotics design are discussed. Key limitations including data dependency, computational requirements, interpretability challenges and evolving regulatory frameworks are addressed alongside future directions in precision probiotic therapeutics.