An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain management.
Publisher DOI
PubMed ID
36897525
Abstract
Fentanyl transdermal therapy is a suitable treatment for moderate-to-severe cancer-related pain. The inter-individual variability of the patients leads to different therapy responses. This study aims to determine the effect of physiological features on the achieved pain relief. 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 patients of different ages, weights, and gender have significantly different fentanyl blood uptake, plasma fentanyl concentration, pain relief, and ventilation rate. In the digital twins, we included the virtual patients' response to the treatment, namely, pain relief. Therefore, the digital twin was able to adjust the therapy in silico 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 time without pain increased by 23 h over 72 h. Therefore, the digital twin can be successfully used in individual control of transdermal therapy to reach higher pain relief and maintain steady pain relief. (Created with BioRender.com).
Date Issued
2023-09
Publication Type
Article
Subject(s)
Subjects
Cancer-induced pain Pharmacodynamics model Pharmacokinetics model Pharmacology population Physics-based modeling Physiological features
Language(s)
en
Author(s)
Bahrami, Flora | |
Rossi, René Michel | |
De Nys, Katelijne | |
Defraeye, Thijs |
Journal
Drug delivery and translational research
Publisher
Springer
ISSN
2190-3948
Access(Rights)
open.access