A new computational workflow to guide personalized drug therapy

Simone Pernice; Alessandro Maglione; Dora Tortarolo; Roberta Sirovich; Marinella Clerico; Simona Rolla; Marco Beccuti; Francesca Cordero

doi:10.1016/j.jbi.2023.104546

A new computational workflow to guide personalized drug therapy

J Biomed Inform. 2023 Dec:148:104546. doi: 10.1016/j.jbi.2023.104546. Epub 2023 Nov 19.

Authors

Simone Pernice¹, Alessandro Maglione², Dora Tortarolo³, Roberta Sirovich⁴, Marinella Clerico², Simona Rolla⁵, Marco Beccuti¹, Francesca Cordero¹

Affiliations

¹ Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy.
² Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy.
³ Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy.
⁴ Department of Mathematics, University of Turin, Via Carlo Alberto 10, Turin, 10123, Italy.
⁵ Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy. Electronic address: simona.rolla@unito.it.

PMID: 37984546
DOI: 10.1016/j.jbi.2023.104546

Abstract

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.

Keywords: Computational models; Longitudinal data; Multiple Sclerosis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cluster Analysis
Computer Simulation
Humans
Precision Medicine* / methods
Workflow