Predicting Transdermal Fentanyl Delivery Using Mechanistic Simulations for Tailored Therapy

Area of Science:

• Pharmacokinetics and Drug Delivery
• Biomedical Engineering
• Computational Modeling

Background:

• Current transdermal drug delivery systems are often "one-size-fits-all", failing to account for significant inter-individual variability in skin permeability.
• Personalization of transdermal devices requires a deeper understanding of drug release and percutaneous uptake kinetics, influenced by factors like age and anatomical site.

Purpose of the Study:

• To quantify variations in transdermal fentanyl uptake based on patient age and anatomical patch placement.
• To investigate the impact of miniaturizing reservoir surface area on drug flux.
• To explore the potential of computer-aided engineering for designing personalized transdermal drug delivery systems.

Main Methods:

• Utilized validated mechanistic modeling to simulate fentanyl diffusion, storage, and partitioning within the epidermis.
• Employed *in silico* methods to achieve superior spatiotemporal resolution for analyzing drug release and uptake kinetics.
• Investigated the effects of age, anatomical location, and reservoir size on drug flux and bioavailability.

Main Results:

• Transdermal fentanyl uptake exhibited a 36% difference between anatomical locations after 72 hours, alongside significant interpatient variability.
• Aging slowed and reduced fentanyl uptake; a 70-year-old patient received 26% less drug compared to an 18-year-old over 72 hours.
• Micron-sized drug reservoirs demonstrated up to a 200-fold increase in local drug flux compared to conventional patches, primarily due to transverse diffusion.

Conclusions:

• Mechanistic modeling provides crucial insights into personalized transdermal drug delivery, accounting for patient-specific factors.
• Novel micron-sized drug reservoirs offer a promising avenue for individualizing transdermal therapy and optimizing drug release.
• Computer-aided engineering and validated models are key for the *in silico* design and precise control of next-generation drug delivery systems.