Bahrami, Flora; Rossi, René Michel; De Nys, Katelijne; Defraeye, Thijs (2023). An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain management. Drug delivery and translational research, 13(9), pp. 2272-2285. Springer 10.1007/s13346-023-01305-y
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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).
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Computational Bioengineering |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2190-3948 |
Publisher: |
Springer |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
13 Mar 2023 14:29 |
Last Modified: |
29 Jul 2023 00:12 |
Publisher DOI: |
10.1007/s13346-023-01305-y |
PubMed ID: |
36897525 |
Uncontrolled Keywords: |
Cancer-induced pain Pharmacodynamics model Pharmacokinetics model Pharmacology population Physics-based modeling Physiological features |
BORIS DOI: |
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URI: |
https://boris.unibe.ch/id/eprint/179894 |
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