Pharnext, Paris, France.
Neuro-Sys, Gardanne, France.
J Alzheimers Dis. 2022;88(4):1585-1603. doi: 10.3233/JAD-220120.
Human diseases are multi-factorial biological phenomena resulting from perturbations of numerous functional networks. The complex nature of human diseases explains frequently observed marginal or transitory efficacy of mono-therapeutic interventions. For this reason, combination therapy is being increasingly evaluated as a biologically plausible strategy for reversing disease state, fostering the development of dedicated methodological and experimental approaches. In parallel, genome-wide association studies (GWAS) provide a prominent opportunity for disclosing human-specific therapeutic targets and rational drug repurposing.
In this context, our objective was to elaborate an integrated computational platform to accelerate discovery and experimental validation of synergistic combinations of repurposed drugs for treatment of common human diseases.
The proposed approach combines adapted statistical analysis of GWAS data, pathway-based functional annotation of genetic findings using gene set enrichment technique, computational reconstruction of signaling networks enriched in disease-associated genes, selection of candidate repurposed drugs and proof-of-concept combinational experimental screening.
It enables robust identification of signaling pathways enriched in disease susceptibility loci. Therapeutic targeting of the disease-associated signaling networks provides a reliable way for rational drug repurposing and rapid development of synergistic drug combinations for common human diseases.
Here we demonstrate the feasibility and efficacy of the proposed approach with an experiment application to Alzheimer's disease.
人类疾病是由众多功能网络的扰动引起的多因素生物现象。人类疾病的复杂性质解释了经常观察到的单一治疗干预的边际或短暂疗效。出于这个原因，联合治疗作为一种逆转疾病状态的生物学上合理的策略，越来越受到评估，促进了专门的方法学和实验方法的发展。与此同时，全基因组关联研究（GWAS）为揭示人类特有的治疗靶点和合理的药物再利用提供了一个突出的机会。
在这种情况下，我们的目标是开发一个综合的计算平台，以加速发现和实验验证重新利用药物的协同组合，用于治疗常见的人类疾病。
该方法结合了 GWAS 数据的适应性统计分析、基于途径的基因发现功能注释（使用基因集富集技术）、富含疾病相关基因的信号网络的计算重建、候选再利用药物的选择和概念验证组合实验筛选。
它能够稳健地识别疾病易感性基因座中富集的信号通路。针对与疾病相关的信号网络进行治疗，为合理的药物再利用和快速开发常见人类疾病的协同药物组合提供了可靠的方法。
在这里，我们通过对阿尔茨海默病的实验应用，证明了所提出方法的可行性和有效性。
