European Journal of Biomedical and Pharmaceutical Sciences

INTEGRATING ARTIFICIAL INTELLIGENCE IN FORMULATION RESEARCH: TOWARD INTELLIGENT AND PERSONALIZED DRUG DELIVERY SYSTEMS

Rup Satya Sagar Chodapindi, Varalakshmi Mummidi*, Vijay Kumar Adapa, Aankshitha Koyaneni

ABSTRACT

This article reviews the revolutionary function of artificial intelligence (AI) and machine learning (ML) in pharmaceutical formulation research, as they outdistance the traditional trial-and-error and empirical approaches. By combining the advanced computational methodologies, like artificial neural networks (ANNs), support vector machines (SVMs), neuro-fuzzy logic, random forest (RF), and genetic algorithms (GAs), we see that scientists are able to digest complex multidimensional data much more rapidly, successfully predict and optimize formulation parameters, like drug release, dissolution, and excipient choice. The review describes how AI models have successfully implemented across a wide variety of dosage forms (solid dispersions, controlled release tablets, immediate release tablets, capsule shells, emulsions, beads, nanoparticles) and have demonstrated improved formulation robustness, stability, and therapeutic effects. Among other caveats, the review acknowledges that implementation cost, such as a lack of a unique and comprehensive datasets, makes it increasingly difficult. Regardless, the review provides evidence of the versatility, predictiveness, and process efficiency that can be achieved with AI as applied to computational pharmaceutics. AI is reestablishing the pace of innovation in drug product design and personalized medicine, implying that the next step in developing a superior pharmaceutical formulation that benefits patients will require the use of both AI-based and AI-derived approaches.

Keywords: Artificial Intelligence, Artificial Neural Networks, Pharmaceutical Formulation Development, Machine Learning, Drug Delivery Systems, Fuzzy Logic.