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梦想:一个R包对人类复杂疾病的药用性评估

Antonio Federico^1,2,3, Michele Fratello¹, Alisa Pavel¹

¹Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Tampere 33100, Finland.

Bioinformatics (Oxford, England)

|July 20, 2023

概括

这项研究介绍了DREAM,一个R包,它统一了机械和化学中心的方法,以进行可靠的药物重定向预测. DREAM有助于评估治疗疾病的药物疗效,并优化组合疗法.

科学领域:

计算生物学是一种计算生物学.
药理学药理学是指药理学的学科.
生物信息学是一种生物信息学.

背景情况:

药物重新定位通过重复使用现有药物来加速治疗的发展.
目前的药物重定向方法往往无法将预测转化为临床实践,原因是不同方法的整合不佳.
机械 (基于配置文件和网络) 和化学中心 (基于结构) 方法在单独使用时存在局限性.

研究的目的:

推出DREAM,一个旨在整合机械和化学中心方法用于药物重定向的R包.
为评估疾病的可药性提供统一的计算工作流.
为了能够预测治疗用途的最佳药物组合.

主要方法:

开发DREAM R包,整合机械和化学中心药物重定位策略.
实施统一的计算工作流程,用于药物重定向和组合疗法预测.
关于亚托邦皮肤炎的案例研究应用程序,以证明包装功能.

主要成果:

梦想成功地将多种不同的药物重用方法集成到一个单一的工作流中.
该套件有助于对药物适用性进行可靠的评估,并预测合适的药物组合.
一个关于亚托皮性皮炎的案例研究展示了DREAM的实际应用和实用性.

结论:

梦想提供了一个新的计算框架,以克服当前药物重新定位策略的局限性.
该套件提高了药物重定向预测到临床应用中的可转化性.
DREAM支持对现有药物和优化组合疗法的新疗法用途的识别.

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