A REVIEW OF ARTIFICIAL INTELLIGENCE–DRIVEN PREDICTIVE STRATEGIES FOR THE FORMULATION DEVELOPMENT OF ORAL SOLID DOSAGE FORMS
Abstract
Artificial intelligence is continuously developing in the area of Formulation Research & Development (R&D) by increasing predictive and data driven approaches in designing and development of Oral Solid Dosage (OSD) forms . Earlier, formulation development largely was dependent on multiple experimentations which was time consuming and required substantial number of resources or scientists which ultimately ended up in high development costs. AI-driven predictive modelling through machine learning, deep learning, and Quantitative Structure - Property Relationship (QSPR) techniques help in rapid prediction of the Critical Formulation Attributes (CFA's) like solubility, dissolution, stability, and bioavailability. By inserting inputs like the experimental data, API / excipient properties, and all the process parameters these models predict the desired formulation optimization and accelerate to prototype selection.
This review article summarizes the evolving role of AI in OSD formulation R&D, highlighting its application in Drug excipient compatibility prediction, process optimization, Scale up for pilot and commercial scale batches and accelerated stability prediction. Also, few points related to the challenges related to data quality, model intelligibility and regulatory acceptance are discussed. AI-driven predictive approaches hold significant promise to transform formulation R&D by enabling faster, smarter, and more efficient development of oral solid dosage forms.
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