MAP THE SMA: a Machine-learning Based Algorithm to Predict THErapeutic Response in Spinal Muscular Atrophy
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 247
- 试验地点
- 1
- 主要终点
- Collect clinical data and patient-reported outcome measures (PROM) from patients treated with nusinersen, risdiplam, onasemnogene abeparvovec
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •confirmed genetic diagnosis of SMA (5q)
- •clinical phenotype of type I or II or III;
- •able to provide (patient/caregiver) written informed consent
排除标准
- 未提供
研究组 & 干预措施
Patients treated with nusinersen
干预措施: disease modifying treatments (Drug)
Patients treated with risdiplam
干预措施: disease modifying treatments (Drug)
Patients treated with onasemnogene abeparvovec
干预措施: disease modifying treatments (Drug)
结局指标
主要结局
Collect clinical data and patient-reported outcome measures (PROM) from patients treated with nusinersen, risdiplam, onasemnogene abeparvovec
时间窗: 30 months
Identify novel biomarkers and RNA molecular signature profiling
时间窗: 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
时间窗: 24 months
次要结局
未报告次要终点