使用集成基因组策略的药物重新定位肿瘤抗迁移的计算机方法

Yong Mao, K. Cui, W. Lulu, Hong Zhao, F. Nie, Miriam Brandl, D. Beck, Liang Gao, Stephen T C Wong
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引用次数: 0

摘要

细胞迁移是许多原位或转移性恶性肿瘤恶化的关键步骤。肿瘤抗迁移是一种很有前途的治疗癌症的策略,但在这种策略下开发的相应药物仍然处于极度贫困状态,部分原因是药物试验和美国食品和药物管理局(FDA)要求的批准过程漫长。鉴于市场上有数千种FDA批准的药物,我们相信药物重新定位可能提供一种快速且具有成本效益的方法来识别潜在的抗迁移药物。为此,本文提出了一种基于基因组策略的计算机药物筛选方法,采用基因组特征识别与支持向量机建模相结合的方法对药物功效进行估计。高通量、高灵敏度、三维生物荧光定量成像入侵实验证明了该方法在体外疾病模型上的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An in-silico approach for drug repositioning to tumour anti-migration using an integrated genomic strategy
Cell migration is a key step for deterioration of many in situ or metastasis malignant tumours. Tumour anti-migration is a promising strategy to treat cancer, but corresponding drugs developed under such a strategy are still in dire poverty, partly due to the lengthly process of drug trials and approval required by the US Food and Drug Administration (FDA). Given there are thousands of FDA approved drugs in the market, we believe that drug repositioning may provide a fast and cost-effective way to identify potential anti-migration drugs. In this paper, an in-silico drug screening method using a genomic strategy is proposed for the goal, in which genomic signature identification combined with support vector machine modelling is adopted to estimate drug efficacy. And a high-throughput, sensitive, 3-dimensional invasion assay by quantitative bioluminescence imaging proved the performance of proposed method on in vitro disease models.
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