Molecular Modelling and Drug Design Lab, Institute for Drug Research and Fraunhofer Project Center for Drug Discovery and Delivery, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
Int J Mol Sci. 2024 Sep 23;25(18):10230. doi: 10.3390/ijms251810230.
Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach and reducing the risk of failure in these efforts. Recognizing the potential for drug repurposing, we employed our Iterative Stochastic Elimination (ISE) algorithm to screen known drugs from the DrugBank database. Repurposing in our hands is based on computer models of the actions of ligands: the ISE algorithm is a machine learning tool that creates ligand-based models by distinguishing between the physicochemical properties of known drugs and those of decoys. The models are large sets of "filters" made out, each, of molecular properties. We screen and score external sets of molecules (in our case- the DrugBank molecules) by our agonism and antagonism models based on published data (i.e., IC, K, or EC) and pick the top-scoring molecules as candidates for experiments. Such agonist and antagonist models for six G-protein coupled receptors (GPCRs) families facilitated the identification of repurposing opportunities. Our screening revealed 5982 new potential molecular actions (agonists, antagonists), which suggest repurposing candidates for the cannabinoid 2 (CB2), histamine (H1, H3, and H4), and dopamine 3 (D3) receptors, which may be useful to treat conditions such as neuroinflammation, obesity, allergic dermatitis, and drug abuse. These sets of best candidates should now be examined by experimentalists: based on previous such experiments, there is a very high chance of discovering novel highly bioactive molecules.
重新定位利用具有已知安全性的现有药物，并通过结合实验和计算方法发现新用途。计算方法的整合极大地推进了药物重新定位，提供了一种合理的方法，并降低了这些努力失败的风险。我们认识到药物重新定位的潜力，因此使用我们的迭代随机消除 (ISE) 算法从 DrugBank 数据库中筛选已知药物。我们手中的重新定位基于配体作用的计算机模型：ISE 算法是一种机器学习工具，通过区分已知药物和诱饵的物理化学性质来创建基于配体的模型。这些模型是由大量“过滤器”组成的，每个过滤器都由分子特性组成。我们根据已发表的数据（即 IC、K 或 EC）使用我们的激动剂和拮抗剂模型对外部分子集（在我们的案例中是 DrugBank 分子）进行筛选和评分，并选择得分最高的分子作为实验候选物。六个 G 蛋白偶联受体 (GPCR) 家族的激动剂和拮抗剂模型有助于确定重新定位机会。我们的筛选揭示了 5982 种新的潜在分子作用（激动剂、拮抗剂），这为大麻素 2 (CB2)、组胺 (H1、H3 和 H4) 和多巴胺 3 (D3) 受体的重新定位候选物提供了线索，这些候选物可能有助于治疗神经炎症、肥胖、过敏性皮炎和药物滥用等疾病。现在应该由实验人员检查这些最佳候选物集：基于以前的此类实验，发现新型高生物活性分子的可能性非常高。
