School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada.
Rocket Innovation Studio, 156 Chatham St W, Windsor, ON, Canada.
BMC Bioinformatics. 2022 Apr 20;23(1):143. doi: 10.1186/s12859-022-04662-6.
'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.
从头开始发现药物既昂贵、缓慢又风险高。将已知药物重新用于治疗其他疾病是一种快速、低成本/风险和高效的方法，可以开发出有效的治疗方法。大规模异质生物分子网络、分子、化学和生物活性数据以及药理学化合物的基因组和表型数据的出现，正在推动药物再利用的新领域的发展，即“计算药物再利用”，即计算药物再利用（CDR）。CDR 的目的是为现有药物发现新的适应症（以药物为中心）或为疾病识别有效的药物（以疾病为中心）。以药物为中心和以疾病为中心的方法都面临着共同的挑战，即评估药物和疾病之间的相似性或联系。然而，由于疾病、基因和药物的潜在复杂药理学和生物学以及它们之间的复杂性，传统的 CDR 面临着许多挑战。因此，大多数现有的 CDR 方法很难捕捉到药物、基因和疾病之间的高度非线性关联。我们提出了一种基于网络的集成方法，可以最好地捕捉药物、基因和疾病数据中包含的知识（和复杂关系）。此后，通过使用提取的知识和关系，应用基于网络的机器学习方法，以识别具有潜在治疗效果的单一和成对已批准或实验性药物，用于不同的乳腺癌亚型。实际上，需要进一步的临床分析来确认所识别药物对每种乳腺癌亚型的治疗效果。
