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In silico drug repositioning for the treatment of Alzheimer's disease using molecular docking and gene expression data

机译：用分子对接和基因表达数据进行硅药物重新定位用于治疗阿尔茨海默病

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Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder that affects the elderly. Despite the devastating effects of this disease, therapeutic methods for the treatment of AD remain limited, as many factors contribute to its etiology. In recent decades, many small molecules and bioproducts have been developed for the treatment of AD; however, there are still no drugs that produce efficient disease modifying effects. Drug repositioning is an alternative methodology for drug discovery because many effective drugs act via the modulation of multiple targets, and this 'poly-pharmacology' can be a therapeutic requirement for complex diseases. The multifactorial nature of AD strongly supports the rationale for a drug design strategy centered on drug repositioning. In this study, a computational pipeline based on ligand-protein inverse docking and gene expression data mining were adopted to explore potential drugs for the treatment of AD. We performed a virtual screening of 1553 FDA-approved drugs on seven major AD drug targets. Several drugs showed extremely high binding free energies for the seven targets. Furthermore, gene expression data represent a very rich alternative resource for inferring non-obvious relationships between drugs and genes. The gene expression profiles in response to the identified drugs support the docking results and offer a plausible perspective on the pathways associated with protein responses to drug binding in AD patients. The results have led to the discovery of droperidol, glimepiride, risperidone and other FDA-approved drugs as potential multi-target candidates for the treatment of AD.
机译：阿尔茨海默病（AD）是一种影响老年人的多因素神经变性疾病。尽管这种疾病的毁灭性效果，但是治疗广告的治疗方法仍然有限，因为许多因素有助于其病因。近几十年来，已经开发了许多小分子和生物制作用于治疗广告;然而，仍然没有药物产生有效的疾病改性效果。药物重新定位是药物发现的替代方法，因为许多有效的药物通过调节多个靶标的作用，这种“多药理学”可以是复杂疾病的治疗要求。广告的多因素性质强烈支持药物设计策略的理由，该策略以药物重新定位为中心。在该研究中，采用基于配体蛋白逆对接和基因表达数据采矿的计算管线来探索潜在的药物治疗广告。我们在七种主要AD药物目标上进行了虚拟筛选1553 FDA批准的药物。几种药物表现出极高的七个靶标的束缚能量。此外，基因表达数据代表了一种非常丰富的替代资源，用于推断药物和基因之间的非明显关系。基因表达曲线响应于所鉴定的药物支持对接结果，并对与AD患者中药物结合有关的途径提供合理的观点。结果导致了滴注醇，胶合素，蓖麻籽油和其他FDA批准的药物作为潜在的多目标候选者用于治疗AD。

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  • 来源
    《RSC Advances》 |2016年第100期|共11页
  • 作者

    Xie Hongbo; Wen Haixia; Qin Mingze; Xia Jie; Zhang Denan; Liu Lei; Liu Bo; Liu Qiuqi; Jin Qing; Chen Xiujie;

  • 作者单位

    Harbin Med Univ Coll Bioinformat Sci &

    Technol Harbin 150081 Peoples R China;

    Harbin Med Univ Dept Physiol Harbin 150081 Peoples R China;

    Shenyang Pharmaceut Univ Sch Pharmaceut Engn Shenyang 110016 Peoples R China;

    Harbin Med Univ Coll Bioinformat Sci &

    Technol Harbin 150081 Peoples R China;

    Harbin Med Univ Coll Bioinformat Sci &

    Technol Harbin 150081 Peoples R China;

    Harbin Med Univ Coll Bioinformat Sci &

    Technol Harbin 150081 Peoples R China;

    Harbin Med Univ Coll Bioinformat Sci &

    Technol Harbin 150081 Peoples R China;

    Harbin Med Univ Coll Bioinformat Sci &

    Technol Harbin 150081 Peoples R China;

    Harbin Med Univ Coll Bioinformat Sci &

    Technol Harbin 150081 Peoples R China;

    Harbin Med Univ Coll Bioinformat Sci &

    Technol Harbin 150081 Peoples R China;

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  • 中图分类 化学;
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