Drug performance, safety, and therapeutic outcomes are greatly impacted by pharmaceutical formulation design. Historically, development efforts have focused on guaranteeing product stability, bioavailability, and ease of manufacturing, frequently ignoring individual patient requirements. More recently, there has been a growing emphasis on patient-centered formulation strategies that prioritise treatment adherence, acceptability, and ease of administration; factors like age, physiological differences, genetic variability, disease conditions, swallowing capacity, sensory preferences, and lifestyle. By analysing vast and varied datasets to enable well-informed formulation decisions, artificial intelligence (AI) has emerged as a useful tool for addressing these challenges. Drug release behaviour, processing parameters, and formulation components can all be predicted and optimised to meet the needs of individual patients thanks to technologies like deep learning, machine learning, and intelligent modelling systems. This review highlights the potential of AI-driven patient-focused formulation development to change pharmaceutical design into a more accurate and customised field by examining its concepts, methods, practical applications, benefits, drawbacks, and future prospects Pharmaceutical formulation development is undergoing a revolution because to artificial intelligence (AI), which makes data-driven decision-making, predictive modelling, and process optimisation possible. Trial-and-error experimentation, which is time-consuming, expensive, and resource-intensive, is a major component of traditional formulation development. Artificial intelligence (AI) technologies, including as machine learning (ML), deep learning (DL), artificial neural networks (ANNs), and expert systems, provide creative ways to forecast formulation performance, optimise excipient selection, enhance manufacturing procedures, and improve product quality. The uses of AI in pharmaceutical formulation development, recent developments, advantages, difficulties, regulatory issues, and future prospects are all covered in this review. AI-driven strategies have shown great promise in cutting down on experimental workload, speeding up the commercialisation of pharmaceutical goods, and shortening development times.
Anshika Yadav2 Manish Chaturvedi1* (Wed,) studied this question.