Intelligent pharmaceutical design: Advanced computational and AI-assisted approaches for formulation, optimization, and characterization of naproxen gel for pain management.

Authors

Nimra Zahoor, Adeel Zain, Muhammad Ali, Muhammad Kamran

Published in

Biomaterials advances. Volume 189. Pages 215111. Aug 11, 2026. Epub Aug 11, 2026.

Abstract

Artificial intelligence (AI) and computational modeling have transformed modern pharmaceutical development by replacing empirical paradigms with data-driven workflows. Naproxen is a potent nonsteroidal anti-inflammatory drug (NSAID) that has serious gastrointestinal side effects when taken orally. Topical gel preparations are an ideal medium for localized, non-invasive pain management. However, conventional topical gel production is limited by resource-intensive, trial-and-error optimization over a multidimensional space of ingredient combinations. In this study, a closed-loop intelligent pharmaceutical design framework was established by coupling an Artificial Neural Network (ANN) with hybrid metaheuristic engines (particle swarm optimization (PSO) and Genetic Algorithms (GA)) to systematically design high-performance naproxen gels. A systematic literature screening protocol across the major indexing databases for the period of 2015-2025 was performed to determine the initial boundary variables. Predictive ANNs were built from data variations that mapped gelling agents, penetration enhancers, and surfactants with Critical Quality Attributes (CQAs) such as thixotropic viscosity, transdermal flux, and extended stability. The optimized formulation showed rapid convergence of the algorithm, leading to optimal spreadability profiles and a high cumulative in vitro drug release, which was strongly correlated with the simulated skin permeation dynamics. Furthermore, the platform has high biomedical importance as it provides a robust template for the facile incorporation of biocompatible green nanomaterials and plant-derived bioactive complexes in the design of next-generation drug delivery systems. Overall, this study provides a highly scalable and translational blueprint that successfully bridges molecular modeling, artificial intelligence, and experimental pharmaceutics to accelerate advanced topical therapeutic discovery.

PMID:
42603451
Bibliographic data and abstract were imported from PubMed on 16 Aug 2026.

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