MAP THE SMA: a Machine-learning Based Algorithm to Predict THErapeutic Response in Spinal Muscular Atrophy (MAP_THE_SMA-01)

Institute of Hospitalization and Scientific Care (IRCCS)

Status: Enrolling

Conditions: Spinal Muscular Atrophy

Treatments: Drug: disease modifying treatments

Study type: Observational

Funder types: Other

Identifiers

NCT05769465

5488

GR-2021-12374579 (Other Grant/Funding Number)

Details and patient eligibility

About

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.

Enrollment: 247 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

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

Trial design

247 participants in 4 patient groups

Patients treated with nusinersen

Treatment:

Drug: disease modifying treatments

Patients treated with risdiplam

Treatment:

Drug: disease modifying treatments

Patients treated with onasemnogene abeparvovec

Treatment:

Drug: disease modifying treatments

Patients naive from disease modifying treatments