Artificial Neural Networks in Pharma: Revolutionizing Drug Development, Optimization and Smart Manufacturing-A comprehensive Review
E. Shiva Bhargav, Singamaneni Premika, Meka Sai Geetha Mandala Rohitha, A Vamsi Krishna, N Rakshith, Kavya Turaga · Oriental Journal Of Chemistry · 2025
Artificial Neural Networks (ANNs) are transforming pharmaceutical research by enhancing drug development, formulation optimization, and pharmacokinetic modeling. ANNs excel at predicting in vitro–in vivo correlations (IVIVC), drug metabolism, and permeability, surpassing traditional linear models. They enable data-driven decision-making, reduce animal testing, and support personalized dosing. In manufacturing, ANNs improve product quality prediction and identify variability sources. Supported by specialized software like MATLAB, STATISTICA, and SNNS, ANNs integrate seamlessly across Pharma 4.0 workflows, offering robust tools for complex data modeling and process optimization throughout the drug lifecycle. This study highlights the application of Artificial Neural Networks (ANNs) in pharmaceutical development. ANNs effectively predict drug release, optimize formulations, and model complex, nonlinear relationships without predefined equations. Their integration enhances process understanding, reduces experimental workload, and accelerates development. In the case of cerasomes-silica-coated bilayered nanohybrids-ANNs assist in optimizing key parameters for cancer-targeted delivery. Overall, ANNs support data-driven decision-making and robust pharmaceutical design, offering superior accuracy and efficiency compared to traditional modeling approaches.