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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05769465

Acronym

MAP_THE_SMA-01

Enrollment

247

Registered

2023-03-15

Start date

2023-04-01

Completion date

2026-04-01

Last updated

2023-09-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Spinal Muscular Atrophy

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.

Interventions

DRUGdisease modifying treatments

Patients will be enrolled if exposed to nusinersen, risdiplam, onasemnogene abeparvovec

Study design

Observational model

COHORT

Time perspective

OTHER

Eligibility

Sex/Gender

ALL

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

Design outcomes

Primary

Measure	Time frame
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

Countries

Italy

Contacts

Primary ContactComitato Etico

comitato.etico@policlinicogemelli.it0630156124

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026