对药物重定向的计算方法:方法,挑战和机会
Henry C Cousins1, Gowri Nayar1, Russ B Altman2,1
1Department of Biomedical Data Science, Stanford University, Stanford, California, USA;
药物重新定位为现有药物的新用途,加速治疗罕见疾病和公共卫生威胁. 基因组学和机器学习的进步改善了候选人选择,但理解方法的局限性是关键.
科学领域:
- 药理学 药理学是指药理学的学科.
- 药物开发 药物开发
- 计算生物学 计算生物学
背景情况:
- 药物再利用为开发罕见疾病,分层人口和公共卫生紧急情况的治疗方法提供了一种有效的方法.
- 从众多可能性中确定最佳药物候选者仍然是药物开发中的一个重大挑战.
- 最近在基因组分析,数据库策划和机器学习方面的进展提高了识别药物重定向候选人的准确性.
研究的目的:
- 审查和分类用于药物重用的主要方法类.
- 突出临床实践中不同药物重定向方法的优点和局限性.
主要方法:
- 该审查根据四种主要方法对药物重定向方法进行了分类:
- 使用蛋白质结构数据.
- 分析基因组的签名.
- 利用生物网络.
- 结合现实世界的临床数据.
主要成果:
- 药物重定向方法被广泛分为四类:基于蛋白质结构,基于基因组签名,基于生物网络和基于真实世界的临床数据.
- 每种方法在应用于临床场景时都具有明显的优势,并面临特定的局限性.
结论:
- 有效的药物再利用需要全面了解各种方法的优点和弱点.
- 整合来自不同方法的见解对于最大限度地提高药物重定向在临床实践中的影响至关重要.
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