Xu Sumei, Mangione William, Van Norden Melissa, Elefteriou Katherine, Hu Yakun, Falls Zackary, Samudrala Ram
Phase I Clinical Trial Center, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China.
bioRxiv. 2025 May 23:2025.05.19.654757. doi: 10.1101/2025.05.19.654757.
Glioma is a highly malignant brain tumor with limited treatment options. We employed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to predict new glioma therapies. We began by computing interaction scores between extensive libraries of drugs/compounds and proteins to generate "interaction signatures" that model compound behavior on a proteomic scale. Compounds with signatures most similar to those of drugs approved for a given indication were considered potential treatments. These compounds were further ranked by degree of consensus in corresponding similarity lists. We benchmarked performance by measuring the recovery of approved drugs in these similarity and consensus lists at various cutoffs, using multiple metrics and compari ng results to random controls and performance across all indications. Compounds ranked highly by consensus but not previously associated with the indication of interest were considered new predictions. Our benchmarking results showed that CANDO improved accuracy in identifying glioma-associated drugs across all cutoffs compared to random controls. Our predictions, supported by literature-based analysis, identified 23 potential glioma treatments, including approved drugs like vitamin D, taxanes, vinca alkaloids, topoisomerase inhibitors, and folic acid, as well as investigational compounds such as ginsenosides, chrysin, resiniferatoxin, and cryptotanshinone. Further functional annotation-based analysis of the top targets with the strongest interactions to these predictions identified Vitamin D3 receptor, thyroid hormone receptor, acetylcholinesterase, cyclin-dependent kinase 2, tubulin alpha chain, dihydrofolate reductase, and thymidylate synthase. These findings indicate that CANDO's multitarget, multiscale framework is effective in identifying glioma drug candidates thereby informing new strategies for improving treatment.
胶质瘤是一种恶性程度很高的脑肿瘤，治疗选择有限。我们采用新型药物机会计算分析（CANDO）平台进行多尺度治疗发现，以预测新的胶质瘤治疗方法。我们首先计算大量药物/化合物库与蛋白质之间的相互作用得分，以生成在蛋白质组学尺度上模拟化合物行为的“相互作用特征”。与已批准用于特定适应症的药物特征最相似的化合物被视为潜在治疗药物。这些化合物在相应的相似性列表中进一步按共识程度排名。我们通过在不同截止值下测量这些相似性和共识列表中批准药物的回收率来评估性能，使用多种指标并将结果与随机对照以及所有适应症的性能进行比较。在共识排名中靠前但之前未与感兴趣的适应症相关联的化合物被视为新的预测结果。我们的基准测试结果表明，与随机对照相比，CANDO在所有截止值下识别胶质瘤相关药物的准确性都有所提高。我们的预测得到基于文献分析的支持，确定了23种潜在的胶质瘤治疗药物，包括已批准的药物如维生素D、紫杉烷、长春花生物碱、拓扑异构酶抑制剂和叶酸，以及研究性化合物如人参皂苷、白杨素、树脂毒素和隐丹参酮。对与这些预测相互作用最强的顶级靶点进行的基于功能注释的进一步分析确定了维生素D3受体、甲状腺激素受体、乙酰胆碱酯酶、细胞周期蛋白依赖性激酶2、微管蛋白α链、二氢叶酸还原酶和胸苷酸合成酶。这些发现表明，CANDO的多靶点、多尺度框架在识别胶质瘤候选药物方面是有效的，从而为改进治疗的新策略提供了依据。
