MAP THE SMA: a Machine-learning Based Algorithm to Predict THErapeutic Response in Spinal Muscular Atrophy

Recruiting

• Conditions: Spinal Muscular Atrophy
• Interventions: Drug: disease modifying treatments

• Registration Number: NCT05769465
• Lead Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Brief Summary

Spinal Muscular Atrophy (SMA) is caused by the homozygous loss of the Survival Motor Neuron (SMN) 1 gene, which leads to degeneration of spinal alpha-motor neurons and muscle atrophy. Three treatments have been approved for SMA but the available data show interpatient variability in therapy response and, to date, individual factors such as age or SMN2 copies,cannot fully explain this variance.

The aim of this project is:

* collect clinical data and patient-reported outcome measures (PROM) from patients treated with nusinersen, risdiplam, onasemnogene abeparvovec,

* identify novel biomarkers and RNA molecular signature profiling,

* develop a predictive algorithm using artificial intelligence (AI) methodologies based on machine learning (ML), able to integrate clinical outcomes, patients' characteristics, and specific biomarkers.

This effort will help to better stratify the SMA patients and to predict their therapeutic outcome, thus to address patients towards personalized therapies.

Detailed Description

Not available

Study Timeline

• Status Verified: 2023-03
• Study First Submitted: 2023-03-03
• Study First Submitted QC: 2023-03-03
• Study First Posted: 2023-03-15
• Study Start: 2023-04-01
• Last Update Submitted: 2023-09-11
• Last Update Posted: 2023-09-13
• Primary Completion: 2025-11-01
• Study Completion: 2026-04-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 247

Inclusion Criteria

• confirmed genetic diagnosis of SMA (5q)
• clinical phenotype of type I or II or III;
• able to provide (patient/caregiver) written informed consent

Exclusion Criteria

• None

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Patients treated with nusinersen	disease modifying treatments	-
Patients treated with risdiplam	disease modifying treatments	-
Patients treated with onasemnogene abeparvovec	disease modifying treatments	-

Primary Outcome Measures

Name	Time	Method
Collect clinical data and patient-reported outcome measures (PROM) from patients treated with nusinersen, risdiplam, onasemnogene abeparvovec	30 months
Identify novel biomarkers and RNA molecular signature profiling	30 months
Develop a predictive algorithm using artificial intelligence (AI) methodologies based on machine learning (ML), able to integrate clinical outcomes, patients' characteristics, and specific biomarkers	24 months

Trial Locations

Locations (1)

Fondazione Policlinico Universitario Agostino Gemelli IRCCS; 🇮🇹 Roma, Italy