Authors : Sahil Sanjay Nantekar; Madan D. Pomaje; Avadhut Nitin Hande; Sanika Mahesh Gangan; Ashwini B. Patil

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/4bnke84n

DOI : https://doi.org/10.38124/ijisrt/26Oct553

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The integration of machine learning (ML) and artificial intelligence (AI) into pharmaceutical sciences is reshaping the development of advanced drug delivery systems. Traditional gastroprotective drug delivery systems (GRDDS) are designed to prolong the residence time of oral dosage forms in the stomach to enhance bioavailability and therapeutic efficacy for drugs with narrow absorption windows. However, conventional formulation development often relies on empirical optimization, which is time-consuming and resource-intensive. AI and ML bring a data-driven paradigm to this complex landscape by enabling computational modeling, predictive analytics, and intelligent optimization of formulation parameters. These technologies can analyze multidimensional datasets to identify patterns and relationships between formulation components, physiological variables, and release kinetics that would be challenging to discern using traditional methods. AI-assisted models such as artificial neural networks, support vector machines, and Bayesian optimization have demonstrated significant potential in predicting drug release profiles, selecting optimal polymer systems, and simulating gastric behavior. Moreover, AI can streamline experimental design, reduce trial-anderror iterations, and accelerate the development timeline, thus improving formulation robustness and performance. The convergence of AI with gastroprotective systems also promises personalized approaches that account for patient-specific gastric motility and physiological factors, fostering tailored therapies. Although challenges remain—such as the need for high-quality datasets, interpretability of complex models, and integration with regulatory frameworks—the current advances provide a compelling foundation for future research. Embracing these intelligent tools in GRDDS development is likely to expand the frontiers of oral drug delivery by enhancing design precision, improving therapeutic outcomes, and reducing developmental costs. Continued innovation and interdisciplinary collaboration will be essential to fully harness the transformative potential of AI and ML in pharmaceutics.

Keywords : Artificial Intelligence in Pharmaceutics, Machine Learning–Based Formulation Design, Gastroprotective Drug Delivery Systems, Predictive Modeling of Drug Release, Intelligent Formulation Optimization, Floating and Mucoadhesive Delivery Systems, Data-Driven Pharmaceutical Development.

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