The article explores how artificial intelligence (AI) is transforming transdermal drug delivery systems (TDDS), offering innovative solutions to traditional challenges in drug administration. TDDS provides advantages over conventional oral and injectable methods by bypassing liver metabolism and enabling controlled drug release, but faces limitations due to the skin’s natural barrier properties.
AI and machine learning are revolutionizing TDDS development through multiple applications. These include predictive modeling for skin permeability, optimization of drug formulations, and the development of personalized medicine approaches. Various AI models, such as Deep Neural Networks (DNN), BioSIM, and COMSOL, are being employed to analyze extensive molecular datasets and predict drug-skin interactions.
The paper discusses AI applications in several key areas: screening drug molecules for optimal physicochemical properties, predicting skin permeability across different conditions, designing smart delivery systems including microneedles and patches, and enabling personalized medicine through real-time monitoring. AI-driven simulations help replicate drug interactions with skin layers, reducing dependency on traditional testing methods.
The research highlights specific TDDS applications across various therapeutic areas, including viral infections, central nervous system disorders, cardiovascular diseases, and hormonal imbalances. Tables in the paper detail specific drugs and delivery systems for each therapeutic category, demonstrating the versatility of TDDS applications.
A significant advancement is the development of smart patches integrated with AI-enabled sensors that can monitor drug levels and adjust delivery dynamically. This technology enables personalized treatment approaches by analyzing patient-specific data such as skin type, age, and medical history to optimize drug dosing and delivery schedules.
In clinical trials, AI enhances efficiency by improving patient recruitment, data collection, and analysis processes. The technology helps predict trial outcomes and adapt designs based on interim results, potentially reducing development time and costs. Overall, the integration of AI in TDDS development represents a significant step forward in pharmaceutical research, promising more effective and personalized drug delivery solutions.
Source: www.mdpi.com/1999-4923/17/2/188
