Liu Dan-Yang, Shen Hui, Greenbaum Jonathan, Yi Qiao-Rong, Liang Shuang, Zhang Yue, Liu Jia-Chen, Qiu Chuan, Zhao Lan-Juan, Tian Qing, Su Kuan-Jui, Luo Zhe, Wu Li, Meng Xiang-He, Xiao Hong-Mei, Deng Yun, Li Yang, Lovre Dragana, Fonseca Vivian, Sanchez Fernando L, Tan Li-Jun, Deng Hong-Wen
Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China.
Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.
Clin Pharmacol Ther. 2025 Jun 6. doi: 10.1002/cpt.3738.
Osteoporosis is a common metabolic bone disease with aging, characterized by low bone mineral density (BMD) and higher fragility fracture risk. Although current pharmacological interventions provide therapeutic benefits, long-term use is limited by side effects and comorbidities. In this study, we employed driver signaling network identification (DSNI) and drug functional networks (DFN) to identify repurposable drugs from the Library of Integrated Network-Based Cellular Signatures. We constructed osteoporosis driver signaling networks (ODSN) using multi-omics data and developed DFN based on drug similarity. By integrating ODSN and DFN with drug-induced transcriptional responses, we screened 10,158 compounds and identified several drugs with strong targeting effects on ODSN. Mendelian randomization assessed potential causal links between cis-eQTLs of drug targets and BMD using genome-wide association study data. Our findings indicate four drugs, including Ruxolitinib, Alfacalcidol, and Doxercalciferol, may exert anti-osteoporosis effects. Notably, Acebutolol, a β-blocker for hypertension, has not previously been implicated in osteoporosis therapy. For validation, zebrafish osteoporosis models were established using Dexamethasone-induced bone loss, followed by treatment with Acebutolol hydrochloride and Alfacalcidol. Both compounds demonstrated significant protective effects against osteoporosis-related bone deterioration. Furthermore, a population-based data set, utilizing propensity score matching and analyzed via a generalized linear model, revealed that individuals taking β-blocker drugs exhibited significantly higher BMD than users of other cardiovascular medications. In summary, this study integrates multi-omics approaches, experimental validation, and real-world population data to propose acebutolol as a novel candidate for osteoporosis treatment. These findings warrant further mechanistic studies and clinical trials to evaluate its efficacy in osteoporosis management.
骨质疏松症是一种随着年龄增长而出现的常见代谢性骨病，其特征是骨矿物质密度（BMD）低且脆性骨折风险较高。尽管目前的药物干预具有治疗益处，但长期使用受到副作用和合并症的限制。在本研究中，我们采用驱动信号网络识别（DSNI）和药物功能网络（DFN）从基于整合网络的细胞特征库中识别可重新利用的药物。我们使用多组学数据构建了骨质疏松症驱动信号网络（ODSN），并基于药物相似性开发了DFN。通过将ODSN和DFN与药物诱导的转录反应相结合，我们筛选了10158种化合物，并鉴定出几种对ODSN具有强靶向作用的药物。孟德尔随机化使用全基因组关联研究数据评估药物靶点的顺式eQTL与BMD之间的潜在因果联系。我们的研究结果表明，包括鲁索替尼、阿法骨化醇和多西骨化醇在内的四种药物可能具有抗骨质疏松作用。值得注意的是，用于治疗高血压的β受体阻滞剂醋丁洛尔此前未被认为与骨质疏松症治疗有关。为了进行验证，我们使用地塞米松诱导的骨质流失建立了斑马鱼骨质疏松模型，然后用盐酸醋丁洛尔和阿法骨化醇进行治疗。两种化合物均对骨质疏松症相关的骨质恶化表现出显著的保护作用。此外，一个基于人群的数据集利用倾向评分匹配并通过广义线性模型进行分析，结果显示服用β受体阻滞剂药物的个体的BMD显著高于使用其他心血管药物的个体。总之，本研究整合了多组学方法、实验验证和真实世界人群数据，提出醋丁洛尔作为骨质疏松症治疗的新候选药物。这些发现值得进一步进行机制研究和临床试验，以评估其在骨质疏松症管理中的疗效。
