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.

A new computational workflow to guide personalized drug therapy

Pernice, Simone
Co-first
;
Maglione, Alessandro
Co-first
;
Tortarolo, Dora;Sirovich, Roberta;Clerico, Marinella;Rolla, Simona
;
Beccuti, Marco
Co-last
;
Cordero, Francesca
Co-last
2023-01-01

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.
2023
148
1
8
https://www.sciencedirect.com/science/article/pii/S1532046423002678?via=ihub
Computational models; Longitudinal data; Multiple Sclerosis.
Pernice, Simone; Maglione, Alessandro; Tortarolo, Dora; Sirovich, Roberta; Clerico, Marinella; Rolla, Simona; Beccuti, Marco; Cordero, Francesca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1945847
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