An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain management

Abstract

Fentanyl transdermal therapy is a suitable treatment for moderate-to-severe cancer-related pain. Patients show inter-individual drug uptake behavior, which leads to a different response to the therapy. This study aims to determine the effect of different physiological features of the patients on the pain relief achieved with the therapy. Therefore, a set of virtual patients was developed by using Markov Chain Monte Carlo (MCMC), based on actual patient data. The members of this virtual population differ by age, weight, gender, and height. Tailored digital twins were developed using these correlated, individualized parameters to propose a personalized therapy for each patient. It was shown that the patients of different ages, weights, and gender have significantly different fentanyl blood uptake, plasma fentanyl concentration, pain relief, and ventilation rate, which means the same therapy will not reach the same result for the patients. Therefore, we included the virtual patients’ response to the treatment, namely pain relief, in the digital twins. We enabled these digital twins of each patient to adjust in-silico the therapy in real-time to have more efficient pain relief. By implementing digital-twin-assisted therapy, the average pain intensity decreased by 16% compared to conventional therapy. The median of time without pain increased by 23 hours over a 72-hour period. Therefore, the digital twin can be successfully used to assist in individual control of the transdermal therapy to reach higher pain relief and maintain a steady pain relief throughout the therapy.