A new computational workflow to guide personalized drug therapy.

Autor: Pernice S; Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy., Maglione A; Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy., Tortarolo D; Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy., Sirovich R; Department of Mathematics, University of Turin, Via Carlo Alberto 10, Turin, 10123, Italy., Clerico M; Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy., Rolla S; Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy. Electronic address: simona.rolla@unito.it., Beccuti M; Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy., Cordero F; Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy.
Jazyk: angličtina
Zdroj: Journal of biomedical informatics [J Biomed Inform] 2023 Dec; Vol. 148, pp. 104546. Date of Electronic Publication: 2023 Nov 19.
DOI: 10.1016/j.jbi.2023.104546
Abstrakt: Objective: Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics.
Methods: GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations.
Results: To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months.
Conclusion: GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Francesca Cordero reports financial support was provided by Horizon Europe. Marco Beccuti reports financial support was provided by CRT Foundation.
(Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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