系统药物再利用以实现精准医疗:癌症的一个案例研究

K. Taylor, Sayoni Das, M. Pearson, J. Kozubek, M. Strivens, Steve Gardner
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引用次数: 9

摘要

背景和目标:精准医疗和药物再利用为缓解药物研发生产力下降、新药成本上升和患者对现有药物反应率低的挑战提供了机会。多因素的“疾病特征”为复杂疾病群体的结构提供了独特的见解,可用于更好地对患者群体进行分层,有助于提供精准医疗。方法:对一种复杂疾病(癌症)人群进行分析,以确定与不同疾病亚群相关的单核苷酸多态性组合。对与这些亚组的疾病风险相关的靶基因进行了检查,然后将现有的活性化学线索鉴定和评估为药物再利用候选者。结果:确定了175个与癌症患者不同亚群相关的疾病相关基因靶点。其中23个基因被列为有前景的新药靶点和重新利用的候选基因。P4HA2和TGM2这两个靶点具有很高的再利用潜力,与癌症有很强的机制联系。结论:这项研究表明,对组合基因组(和其他)特征的详细分析可以用于准确地对患者群体进行分层,并系统地确定所有疾病相关靶点的高度合理的药物再利用候选物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systematic drug repurposing to enable precision medicine: A case study in breast cancer
Background and Objectives: Precision medicine and drug repurposing provide an opportunity to ameliorate the challenges of declining pharmaceutical R&D productivity, rising costs of new drugs, and poor patient response rates to existing medications. Multifactorial “disease signatures” provide unique insights into the architecture of complex disease populations that can be used to better stratify patient groups, aiding the delivery of precision medicine. Methods: Analysis of a complex disease (breast cancer) population was undertaken to identify the combinations of single-nucleotide polymorphisms that are associated with different disease subgroups. Target genes associated with the disease risk of these subgroups were examined, followed by identification and evaluation of existing active chemical leads as drug repurposing candidates. Results: One hundred and seventy-five disease-associated gene targets relevant to different subpopulations of breast cancer patients were identified. Twenty-three of these genes were prioritized as both promising novel drug targets and repurposing candidates. Two targets, P4HA2 and TGM2, have high repurposing potential and a strong mechanistic link to breast cancer. Conclusions: This study showed that detailed analysis of combinatorial genomic (and other) features can be used to accurately stratify patient populations and identify highly plausible drug repurposing candidates systematically across all disease-associated targets.
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