Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev (BGU), P.O.B. 653, Beer-Sheva 8410501, Israel.
Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel.
Brief Bioinform. 2024 Mar 27;25(3). doi: 10.1093/bib/bbae108.
Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leading causes of cancer-related deaths worldwide. The lack of clear treatment directions underscores the urgent need for innovative approaches to address and manage this deadly condition. In this research, we repurpose drugs with potential anti-cancer activity using machine learning (ML).
We tackle the problem by using a neural network trained on drug-target interaction information enriched with drug-drug interaction information, which has not been used for anti-cancer drug repurposing before. We focus on eravacycline, an antibacterial drug, which was selected and evaluated to assess its anti-cancer effects.
Eravacycline significantly inhibited the proliferation and migration of BxPC-3 cells and induced apoptosis.
Our study highlights the potential of drug repurposing for cancer treatment using ML. Eravacycline showed promising results in inhibiting cancer cell proliferation, migration and inducing apoptosis in PDAC. These findings demonstrate that our developed ML drug repurposing models can be applied to a wide range of new oncology therapeutics, to identify potential anti-cancer agents. This highlights the potential and presents a promising approach for identifying new therapeutic options.
胰腺导管腺癌（PDAC）仍然严重威胁着健康，有效的治疗选择有限，特别是由于诊断时已处于晚期，以及其对化疗的固有耐药性，使其成为全球癌症相关死亡的主要原因之一。缺乏明确的治疗方向突出表明需要创新方法来解决和管理这种致命疾病。在这项研究中，我们使用机器学习（ML）重新利用具有潜在抗癌活性的药物。
我们使用经过药物-靶标相互作用信息和药物-药物相互作用信息丰富的神经网络来解决这个问题，以前从未将其用于抗癌药物的再利用。我们专注于依拉环素，一种抗菌药物，我们选择并评估了它的抗癌效果。
依拉环素显著抑制了 BxPC-3 细胞的增殖和迁移，并诱导了细胞凋亡。
我们的研究强调了使用 ML 进行癌症治疗药物再利用的潜力。依拉环素在抑制胰腺导管腺癌中癌细胞增殖、迁移和诱导细胞凋亡方面显示出良好的效果。这些发现表明，我们开发的 ML 药物再利用模型可以应用于广泛的新型肿瘤治疗药物，以识别潜在的抗癌药物。这突显了潜力并提出了一种有前途的方法来确定新的治疗选择。
