Nguyen Thanh M, Muhammad Syed A, Ibrahim Sara, Ma Lin, Guo Jinlei, Bai Baogang, Zeng Bixin
Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States.
Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan.
Front Pharmacol. 2018 Jun 5;9:583. doi: 10.3389/fphar.2018.00583. eCollection 2018.
In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from "one: disease-gene-drug" to "multi: gene, dru" and from "lazy guilt-by-association" to "systematic model-based pattern matching," mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear-quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.).
在本文中,我们提出了DeCoST(基于控制系统理论的药物重新利用)框架,以将控制系统范式应用于药物重新利用目的。自上一个十年以来,药物重新利用已成为药理学中最活跃的领域之一。与传统药物开发相比,药物重新利用在发现复杂疾病的新疗法方面可能提供更系统且成本显著更低的方法。尽管药物重新利用技术迅速从“单:疾病-基因-药物”发展到“多:基因、药物”,并从“懒惰的关联有罪”发展到“基于系统模型的模式匹配”,但数学系统和控制范式尚未广泛应用于对药物、基因和疾病之间的系统生物学联系进行建模。在这种范式下,我们的DeCoST框架作为最早采用控制理论范式进行药物重新利用的方法之一,应用生物学和药学知识来量化药物、基因和疾病之间丰富的连接数据源,以构建疾病特异性数学模型。我们使用线性二次调节器控制技术来评估药物在疾病特异性治疗中的疗效。DeCoST框架可以区分FDA批准的药物和被拒绝/撤回的药物,这是将DeCoST应用于推荐潜在新疗法的基础。将DeCoST应用于乳腺癌和膀胱癌,我们重新筛选出了8种有前景的乳腺癌ER+候选药物(爱必妥、氟他胺等)、2种乳腺癌ER-药物(柔红霉素和多奈哌齐)以及10种用于膀胱癌重新利用的药物(扎鲁司特、替诺福韦等)。