Current trends in the use of Artificial Intelligence in the Development of Pharmaceutical Formulations
Abstract
The development of pharmaceutical formulations is an empirical, resource-consuming and trial-and-error driven process. With the advent of the Fourth Industrial Revolution (Industry 4.0), artificial intelligence (AI) and machine learning (ML) have emerged as disruptive paradigms that can revolutionise formulation science into an explicit, data-driven, and predictive discipline. The comprehensive review discusses the mechanism and technology architectural layers of AI integration such as artificial neural networks, support vector machines, random forests, fuzzy logic and deep generative structures. We group the broad formulation domains under active optimisation, which encompasses immediate and modified release systems, lipid-based architectures, and complex biologics. Moreover, the review details the fundamental optimisation of preparation methodologies such as direct compression, hot-melt extrusion, 3D printing, and microfluidics through predictive workflows. Computational soft sensors are used to systematically evaluate evaluation parameters from critical quality attributes (dissolution profiles and compaction kinetics) to long-term stability. In this paper, recent state-of-the-art advances up to 2026 are summarised, including operational advantages, intrinsic mathematical limitations and regulatory hurdles. Finally, we discuss recent advanceents such as autonomous self-learning laboratories, digital twins, and continuous closed-loop molecular manufacturing, providing a holistic roadmap for modern pharmaceutical scientists.
DOI
https://doi.org/10.5281/zenodo.20406179References
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