Predicting transdermal fentanyl delivery using physics-based simulations for tailored therapy based on the age

Abstract

Transdermal fentanyl patches are an effective alternative to the sustained release of oral morphine for chronic pain management. Due to the narrow therapeutic range of fentanyl, the concentration of fentanyl in the blood needs to be carefully monitored. Only then can effective pain relief be achieved while avoiding adverse effects such as respiratory depression. This study developed a physics-based digital twin of a patient by implementing drug uptake, pharmacokinetics, and pharmacodynamics models. The twin was employed to predict the in-silico effect of conventional fentanyl transdermal in a 20–80-year-old virtual patient. The results show that, with increasing age, the maximum transdermal fentanyl flux and maximum concentration of fentanyl in the blood decreased by 11.4% and 7.0%, respectively. However, the results also show that as the patient's age increases, the pain relief increases by 45.2%. Furthermore, the digital twin was used to propose a tailored therapy based on the patient's age. This predesigned therapy customized the duration of applying the commercialized fentanyl patches. According to this therapy, a 20-year-old patient needs to change the patch 2.1 times more frequently than conventional therapy, which leads to 30% more pain relief and 315% more time without pain. In addition, the digital twin was updated by the patient's pain intensity feedback. Such therapy increased the patient's breathing rate while providing effective pain relief, so a safer treatment. We quantified the added value of a patient's physics-based digital twin and sketched the future roadmap for implementing such twin-assisted treatment into the clinics.

Keywords

• Tailored therapy
• pharmacology
• in-silico modeling
• pain management
• mechanistic modeling