推进罕见疾病建模的前沿:对in silico技术的批判性评估
Francesca Pistollato1, Fabia Furtmann1, Lindsay J Marshall2
1Humane World For Animals, Brussels, Belgium.
NPJ digital medicine
|November 17, 2025
概括
像in silico方法这样的计算方法对于罕见疾病研究至关重要,加速疾病特征和药物发现. 本综述探讨了它们的应用,挑战和促进罕见疾病研究的机会.
科学领域:
- 计算生物学和生物信息学
- 基因组学和个性化医学
- 药物的发现和开发.
背景情况:
- 罕见疾病影响全球超过3亿人,构成重大研究障碍.
- 传统的研究方法在罕见疾病研究中面临着可扩展性和成本挑战.
- In silico方法为罕见疾病研究提供了有前途的解决方案.
研究的目的:
- 对罕见疾病研究的in silico方法进行审查和分类.
- 批判性地评估计算策略的优势和局限性.
- 确定将这些方法转化为临床实践的障碍和机会.
主要方法:
- 机械模型建模 机械模型
- 机器学习算法 机器学习算法
- 计算模拟的计算模拟.
- 系统的文献审查和类别化 in silico 方法.
主要成果:
- 在 silico 方法提供可扩展的工具,用于疾病特征,药物发现和虚拟试验.
- 基于其使用背景的方法分类 (例如,目标识别,临床前测试,临床试验模拟).
- 评估优势 (例如速度,成本效益) 和局限性 (例如数据要求,验证挑战).
结论:
- 计算策略对于推进罕见疾病研究和药物开发至关重要.
- 克服翻译障碍需要跨学科的合作和强大的验证.
- 关键的机遇在于整合多样化的in silico方法,以加速罕见疾病的治疗突破.
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