Transdermal Drug Delivery Systems Powered by Artificial Intelligence
Farzaneh Sabbagh, Anna Zakrzewska, Daniel Rybak, Julia Król, Asad Abdi, Paweł Nakielski, Filippo Pierini
Abstract
Transdermal drug delivery systems (TDDSs) offer non-invasive therapy but face persistent challenges. Artificial intelligence (AI) transforms TDDSs by leveraging machine learning (ML) and predictive analytics to address these barriers. ML models predict drug entrapment with 93.0% accuracy, streamlining development. AI enhances transdermal patch formulations by forecasting drug release kinetics, skin penetration, and stability, minimizing reliance on costly clinical trials. Through virtual screening, AI identifies novel drug candidates and permeation enhancers, accelerating innovation. In microneedle systems, AI optimizes geometries, materials, and drug loading, improving precision and personalization. AI-integrated biosensors enable real-time monitoring, supporting adaptive dosing tailored to individual physiological profiles. Compared to traditional modeling, AI provides superior accuracy and scalability, handling complex datasets to reveal non-linear relationships. Despite challenges like data quality and privacy concerns, AI's integration with 3-dimensional printing and stimuli-responsive materials drives the development of personalized, efficient transdermal therapies. This perspective highlights AI's critical role in advancing therapeutic efficacy and patient-centric care in TDDSs, uniquely combining predictive modeling with real-time monitoring to envision next-generation personalized transdermal delivery systems.