Repurposing Acebutolol for Osteoporosis Treatment: Insights From Multi-Omics and Multi-Modal Data Analysis

Abstract

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.

Figure 1

The experimental procedure for the zebrafish model of osteoporosis. The strategy involved the following steps: (1) Breeding wild zebrafish in pairs; (2) Randomly dividing zebrafish embryos into groups and treating them with drugs from 3 dpf to 9 dpf; (3) Staining bone with alizarin red at 9 dpf; (4) Photographing under a stereomicroscope.

Figure 2

Volcano plots. Differential biomarker analysis includes volcano plots illustrating (a) differential gene expression and (b) differentially methylated positions.

Figure 3

Drug functional module schematic. (a) Heat map of the drug similarity matrix. The color of each grid cell signifies the logarithmic values of the drug similarity matrix. The gradient from deep blue to light blue indicates a decrease in similarity. (b) Drug functional modules. In the STITCH database, PPIs are shown in gray, chemical–protein interactions are shown in green, and drug–drug interactions are shown in red.

Figure 4

Forest plots showing the effects of alleles associated with drug target gene expression on the risk of BMD. Mendelian Randomization analysis revealed a significant association between the expression of candidate drug targets and BMD, providing insights into the impact of drugs on osteoporosis.

Figure 5

Target networks of potential anti‐osteoporosis drugs and their associations with osteoporosis‐related genes. Drug Target Networks of (a) Acebutolol, (b) Alfacalcidol, (c) Doxercalciferol, and (d) Ruxolitinib. The elliptical nodes represent all drug targets, while the red nodes indicate targets that are osteoporosis‐related genes. Dashed lines denote drug‐target relationships predicted using the DT‐Hybrid method.

Figure 6

Alizarin red staining for different concentrations of drug treatment in zebrafish larvae at 9 dpf. The results showed the alizarin red staining area of zebrafish skulls in the model group (a), the control group (b), the Acebutolol HCl groups at the dose of 10/1/0.1/0.01 μg/mL (c/d/e/f), Alfacalcidol groups at the dose of 0.1/0.01 μg/mL (g/h). Mineralization area of drug treatment per group (i). The bars showed the fold change compared with the model/control group at different doses of the different treatments. *Indicates statistical significance compared to the model group.

Figure 7

Beta‐adrenergic blockers and comparators exposure in PS‐matched cohort: impact on osteoporosis risk. The Beta‐Adrenoceptor Blocking Drugs were significantly associated with increased likelihood of BMD compared with Potassium‐Sparing Diuretics, Nitrates, and Cardiac Glycosides. The users exhibit marginally significantly higher BMD with Beta‐Adrenoceptor Blocking Drugs compared to Antiplatelet Drugs. The users of Angiotensin‐Converting Enzyme Inhibitors exhibit higher BMD compared to those using beta‐adrenergic blockers.