Assimilation of ai softwares in the design and development of personalized transdermal drug delivery platforms

Abstract

Personalized Transdermal Drug Delivery Systems (PTDDS) represent a transformative advancement in precision medicine, enabling tailored drug administration based on individual patient characteristics. With the integration of advanced software tools, machine learning (ML), and deep learning (DL) algorithms, PTDDS are evolving from static platforms into intelligent, adaptive drug delivery systems. Software such as COMSOL Multiphysics, MATLAB, and Simcyp facilitate in silico modeling of skin permeation and pharmacokinetics, while AI platforms like TensorFlow, Scikit-learn, and PyTorch are employed to develop predictive models for dose optimization and patient stratification. Wearable biosensors integrated with smart patches can collect real-time physiological data, which can be analyzed using ML models to dynamically modulate drug release. This convergence of pharmaceutical sciences with data-driven technologies not only enhances therapeutic outcomes but also minimizes adverse effects by enabling patient-specific dosing. Despite significant progress, challenges remain in algorithm validation, data standardization, device integration, and regulatory acceptance. This review highlights the synergy between transdermal drug delivery systems and intelligent software technologies, with emphasis on recent advancements, practical applications, and future directions in the development of AI-powered personalized drug delivery platforms.