Artificial Intelligence and Machine Learning in GRDDS

Gastroretentive Drug Delivery Systems (GRDDS) are designed to remain in the stomach for extended periods, releasing their drug payload in the gastric environment. These platforms are especially beneficial for drugs with limited absorption in the distal gastrointestinal tract, narrow absorption windows in the proximal small intestine, stability or solubility issues at alkaline intestinal pH, short biological half-lives, or local therapeutic activity in the upper small intestine. Research and development are hindered by the complexity of GRDDS design, which requires the integration of drug kinetics, materials science, patient physiology, and real-time data. To transform GRDDS research, this study proposes a revolutionary approach that leverages AI-augmented data platforms. These systems enable intelligent decision-making, enhanced formulation strategies, and rapid prototyping throughout the drug development pipeline by integrating machine learning, predictive modelling, and real-world data analytics. When it comes to improving crucial GRDDS performance metrics, such as floating lag time, buoyancy duration, and drug release kinetics, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionary. These factors are crucial for ensuring that the formulation releases the medicine in a controlled and efficient manner and maintains its presence in the stomach for an extended period.