K P Arunraj, K M Haritha, M T Khulood, P Ayisha Sana, K P Khadeeja Thanha, K Pramod
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引用次数: 0
Abstract
Artificial Intelligence (AI) is emerging as a valuable tool in pharmaceutical formulations, including the development of effervescent tablets (ETs). This review highlights how AI techniques are being explored to support ET formulation designs, optimize component ratios, predict disintegration and dissolution behavior, and control reactions through artificial neural networks, support vector machines, and machine learning. These techniques have been applied in recent studies to enhance stability, improve disintegration times, and flavor masking. Computational fluid dynamics simulations of effervescence and dissolution are underexplored for ETs. Data-driven approaches, like response surface modeling, require ingredient concentrations, tablet properties, consumer preferences, and predictive analytics for optimization. However, limited comprehensive datasets, complex reactions, environmental sensitivities, and ethical/regulatory considerations pose challenges. Overcoming these obstacles, as identified in the current literature, could enable AI to innovate ET development and personalization.
期刊介绍:
The aim of Mini-Reviews in Medicinal Chemistry is to publish short reviews on the important recent developments in medicinal chemistry and allied disciplines.
Mini-Reviews in Medicinal Chemistry covers all areas of medicinal chemistry including developments in rational drug design, synthetic chemistry, bioorganic chemistry, high-throughput screening, combinatorial chemistry, drug targets, and natural product research and structure-activity relationship studies.
Mini-Reviews in Medicinal Chemistry is an essential journal for every medicinal and pharmaceutical chemist who wishes to be kept informed and up-to-date with the latest and most important developments.