Eugenio Mercuri, Gianpaolo Cicala, Marianna Villa, Maria Carmela Pera
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引用次数: 0
摘要
脊髓性肌肉萎缩症(SMA)是一种进行性神经肌肉疾病,由 SMN1 基因突变引起,导致不可避免的运动神经元退化。疾病修饰疗法的引入极大地改变了该病的自然病史,使治疗方法从姑息治疗转变为积极治疗。新的表型以及治疗反应和疗效方面的差异,都有助于重塑我们对该疾病本身的认识。本文旨在分析从近期治疗进展中得出的经验教训,重点关注治疗窗口、早期治疗的影响以及疾病进展和疗效调节因素等关键方面。我们的经验以及对临床试验和真实世界数据的全面回顾证实,早期治疗能最大限度地提高运动效果,尤其是在疾病的临床前期开始治疗时。有症状的 I 型婴儿在不同年龄段接受治疗后临床症状明显改善,这证明 "治疗窗口期 "扩大了,而之前的报道称,根据神经生理学的研究结果,"治疗窗口期 "仅限于婴儿出生后的头几个月。现有数据还证明,基线功能、SMN2 拷贝数和治疗年龄似乎都是决定反应的关键因素。长期数据的可用性正越来越多地用于试验新的预测模型,以支持临床决策,并根据患者的特定变量调整治疗目标。
What did we learn from new treatments in SMA? A narrative review.
Spinal Muscular Atrophy (SMA) is a progressive neuromuscular disorder caused by SMN1 gene mutations, leading to inevitable motoneuronal degeneration. The introduction of disease modifying therapies has dramatically altered its natural history, shifting management from palliative to proactive approach. The new phenotypes and differences in treatment response and efficacy, are all contributing to reshape our understanding of the disease itself. This paper aims to analyze the lessons derived from the recent therapeutic advances, focusing on key aspects such as therapeutic windows, impact of early treatment and both disease progression and treatment efficacy modifiers. Ultimately, we also aim to give insights on new models of data analysis being explored to optimize patient trajectories and individualize treatment strategies.
Our experience and the overall review of clinical trials and real-world data confirm that early treatment maximizes motor outcomes, especially when started in the pre-clinical phase of the disease. The significant clinical improvements in symptomatic type I infants treated at different ages has provided evidence of an expanded 'therapeutic window', previously reported as limited to the first few months after birth on the basis of neurophysiological findings. The available data also provide evidence that function at baseline, SMN2 copy number, and age at treatment all appear to represent critical determinants of response. The availability of long-term data is increasingly used to pilot new predictive models to support clinical decision-making and to adapt therapeutic goals based on patient-specific variables.