Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug–disease pharmacological effect between drug–target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.

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.

Osteoporosis is a metabolic bone disease that occurs during aging, characterized by low bone mineral density (BMD) and a high risk of trauma fracture. While current pharmacological interventions provide symptomatic benefits, they are unsatisfactory and have major side effects. In this study, we used multi-omics data and drug similarity to construct osteoporosis driver signaling networks (ODSN) and drug functional networks (DFN), respectively. By integrating ODSN and DFN with treatment transcriptional responses, we observed 8 drugs that demonstrated strong targeting effects on ODSN. Mendelian Randomization analysis determines the causal effect on BMD using cis-eQTLs of the drug targets and BMD GWAS data. The findings suggested Acebutolol and Amiloride may increase BMD, while Acenocoumarol, Aminocaproic acid and Armodafinil may enhance bone loss. Zebrafish experiments experimentally showed Acebutolol hydrochloride and Amiloride hydrochloride had significant protective effects on osteoporosis in zebrafish embryos induced by Dexamethasone. Also, Acenocoumarol reduced bone mineralization compared with the control group. The findings suggest that the hypertension drugs Acebutolol and Amiloride warrant further investigation in functional mechanistic experiments to evaluate their effectiveness for osteoporosis treatments.