AI-Driven Simulation and Design of Sustained Release Metformin Tablets: Experimental Validation and Predictive Accuracy Assessment

Background/Objectives : The development of extended-release oral dosage forms remains a complicated and time consuming process, particularly during early formulation screening. Recently, artificial intelligence (AI) tools have emerged as supportive tools for formulation development in pharmaceutical research. However, experimental validation of AI-generated formulations remains limited. The present study explores a novel approach in which a large language model (ChatGPT, GPT-4, OpenAI) was used to generate sustained-release metformin hydrochloride matrix tablet compositions under predefined pharmaceutical ingredients, followed by comprehensive experimental evaluation. Methods : Several AI-generated formulations were prepared using the direct compression technique and evaluated for their physical attributes, in vitro dissolution behavior, release kinetics, and statistical similarity to a marketed reference product. Dissolution profiles were analyzed using similarity and difference factors (f₂ and f₁), kinetic modelling, and advanced statistical tools, including correlation analysis and clustering. Results : Among the AI generated formulations, F3 and F4 showed dissolution profiles closest to the reference product, as indicated by f₂ values above 75 together with f₁ values below 15 and comparable release kinetics. The remaining formulations exhibited either slower or faster release behavior, highlighting the importance of experimental validation of AI generated outputs. Conclusions : This study demonstrates that, when provided with pharmaceutical ingredients and formulation considerations, GPT-4 proposed tablet matrix compositions that resulted in manufacturable dosage forms with predictable release behavior upon experimental evaluation. The integration of AI-driven predictions with experimental validation represents a novel and practical strategy to support rational excipient selection, reduce trial and error experimentation, and accelerate early-stage formulation development.