ARTIFICIAL INTELLIGENCE POWERED DESIGN OF EXPERIMENTS: OPTIMIZING ABIRATERONE ACETATE LOADED GELATIN NANOPARTICLES FOR ENHANCED ORAL BIOAVAILABILITY OF ABIRATERONE ACETATE - M., NALLAMUTHU; S., UMADEVI; R., ANANDAN (International Journal of Applied Pharmaceutics, 2025)
Objective: The current probing aims to investigate the Artificial Intelligence (AI) powered Design of Experiments (DoE) to optimize Abiraterone acetate loaded Gelatin Nanoparticles (AGNPs) with the desired Critical Quality Attributes (CQA) for increasing oral bioavailability of abiraterone acetate. Methods: AGNPs were formulated using the desolvation method, guided by a quality-by-design (QbD) approach, to identify CQA. The proposed DoE was a Central Composite Design (CCD) that was done to determine the influence of gelatin, tween 80, and genipin on particle size and Drug Entrapment Efficiency (DEE). Further optimization was performed with Artificial Neural Networks (ANN) to refine the predictive models. Results: The ANN model was developed using data from the CCD. K-fold cross-validation was implemented for training and validating the model. Both training and validation sets' R-squared values were closer to 1, confirming that the algorithms correctly characterized the predictive models. Characterization studies confirmed that optimized AGNPs had particle size (123.4±0.06 nm), poly dispersibility index (0.047±0.07), DEE (88.23±0.26 %), zeta potential (+35.78±0.24 mV), and controlled drug release (96.32±0.78 % over 12 h). Conclusion: The results show that nanoparticles developed according to the criteria set by CQA to enhance oral bioavailability, and an AI-integrated CCD approach optimally worked for AGNPs, which can deliver abiraterone acetate orally. The infusion of advanced technologies ANN undoubtedly holds excellent potential for exciting discoveries in nanomedicine and enables future innovations.