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Cited by 573Computational Drug Discovery MethodsBioinformatics and Genomic NetworksGenetic Associations and Epidemiology

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Network-based approach to prediction and population-based validation of in silico drug repurposing

Open access
Cheng, Feixiong, Desai, Rishi J., Handy, Diane E., Wang, Ruisheng, Schneeweiss, Sebastian, Barabási, Albert-László, Loscalzo, Joseph·2018·Nature Communications
DOI 10.1038/s41467-018-05116-5PMC PMC6043492
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Abstract

Here we identify hundreds of new drug-disease associations for over 900 FDA-approved drugs by quantifying the network proximity of disease genes and drug targets in the human (protein–protein) interactome. We select four network-predicted associations to test their causal relationship using large healthcare databases with over 220 million patients and state-of-the-art pharmacoepidemiologic analyses. Using propensity score matching, two of four network-based predictions are validated in patient-level data: carbamazepine is associated with an increased risk of coronary artery disease (CAD) [hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.12–2.18], and hydroxychloroquine is associated with a decreased risk of CAD (HR 0.76, 95% CI 0.59–0.97). In vitro experiments show that hydroxychloroquine attenuates pro-inflammatory cytokine-mediated activation in human aortic endothelial cells, supporting mechanistically its potential beneficial effect in CAD. In summary, we demonstrate that a unique integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data complemented by mechanistic in vitro studies can facilitate drug repurposing.

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Fig. 1 The predicted drug-disease network. The high-confidence predicted drug-disease association network connects 22 types of cardiovascular disease (outcomes) (red circles) and 431 FDA-approved non-cardiac drugs. The edges between drugs and diseases are weighted and highlighted by different color representing the calculated z-score (Supplementary Data 2 and Methods section). Four selected drug-disease pairs, including carbamazepine-coronary artery disease (CAD) with z = −2.36, hydroxychloroquine-CAD (z = −3.85), mesalamine-CAD (z = −6.10), and lithium-stroke (z = −5.97), tested in patient data (Figs. 2 and 3), are highlighted. Drugs are colored by the first-level anatomical therapeutic chemical (ATC) classification system codes. The node size scales indicate the degree (connectivity) of nodes in the network
Fig. 1 The predicted drug-disease network. The high-confidence predicted drug-disease association network connects 22 types of cardiovascular disease (outcomes) (red circles) and 431 FDA-approved non-cardiac drugs. The edges between drugs and diseases are weighted and highlighted by different color representing the calculated z-score (Supplementary Data 2 and Methods section). Four selected drug-disease pairs, including carbamazepine-coronary artery disease (CAD) with z = −2.36, hydroxychloroquine-CAD (z = −3.85), mesalamine-CAD (z = −6.10), and lithium-stroke (z = −5.97), tested in patient data (Figs. 2 and 3), are highlighted. Drugs are colored by the first-level anatomical therapeutic chemical (ATC) classification system codes. The node size scales indicate the degree (connectivity) of nodes in the networkCC BY · PubMed Central
Fig. 2 Flow-chart of the pharmacoepidemiologic investigations using Truven MarketScan and Optum Clinformatics patient databases. AZA azathioprine, 6-MP 6-mercaptopurine, CBZ carbamazepine, HCQ hydroxychloroquine, IBD inflammatory bowel disease, LAM lamotrigine, LEF leflunomide, LEV levetiracetam, LIT lithium, MES mesalamine, RA rheumatoid arthritis. The balance achieved in patient characteristics and outcome risk factors between the two treatment groups compared through 1:1 PS-matching are provided in Supplementary Tables 2–5
Fig. 2 Flow-chart of the pharmacoepidemiologic investigations using Truven MarketScan and Optum Clinformatics patient databases. AZA azathioprine, 6-MP 6-mercaptopurine, CBZ carbamazepine, HCQ hydroxychloroquine, IBD inflammatory bowel disease, LAM lamotrigine, LEF leflunomide, LEV levetiracetam, LIT lithium, MES mesalamine, RA rheumatoid arthritis. The balance achieved in patient characteristics and outcome risk factors between the two treatment groups compared through 1:1 PS-matching are provided in Supplementary Tables 2–5CC BY · PubMed Central
Fig. 3 Hazard ratios and 95% confidence intervals for four cohort studies. Four cohort studies were performed using the pooled data from four drug pairs (a-d) Truven MarketScan and Optum Clinformatics databases (Methods section). In the primary analysis approach (as-treated), follow-up was stopped upon discontinuation of the index medication. Follow-up assumptions were varied in three sensitivity analyses to: (1) exclude the first 60 days of follow-up to reduce unmeasured baseline confounding, (2) truncate the follow-up to 1-year to minimize time-varying confounding, and (3) continue the follow-up for 1-year regardless of treatment discontinuation under an intent-to-treat (ITT) principle. Propensity score (PS) matching accounted for >50 relevant patient characteristics; all analyses were conducted separately in two databases and results were pooled using the DerSimonian and Laird random effects model with inverse variance weights. * In the as-treated approach, the follow-up was stopped if patients either filled a prescription for a drug in the other exposure group or discontinued the index exposure. ** In the ITT analysis, patients were followed in their index exposure group regardless of treatment change or discontinuation for up to 365 days
Fig. 3 Hazard ratios and 95% confidence intervals for four cohort studies. Four cohort studies were performed using the pooled data from four drug pairs (a-d) Truven MarketScan and Optum Clinformatics databases (Methods section). In the primary analysis approach (as-treated), follow-up was stopped upon discontinuation of the index medication. Follow-up assumptions were varied in three sensitivity analyses to: (1) exclude the first 60 days of follow-up to reduce unmeasured baseline confounding, (2) truncate the follow-up to 1-year to minimize time-varying confounding, and (3) continue the follow-up for 1-year regardless of treatment discontinuation under an intent-to-treat (ITT) principle. Propensity score (PS) matching accounted for >50 relevant patient characteristics; all analyses were conducted separately in two databases and results were pooled using the DerSimonian and Laird random effects model with inverse variance weights. * In the as-treated approach, the follow-up was stopped if patients either filled a prescription for a drug in the other exposure group or discontinued the index exposure. ** In the ITT analysis, patients were followed in their index exposure group regardless of treatment change or discontinuation for up to 365 daysCC BY · PubMed Central
Fig. 4 Experimental validation of hydroxychloroquine’s likely mechanism-of-action in coronary artery disease (CAD). a A highlighted subnetwork shows the inferred mechanism-of-action for hydroxychloroquine’s protective effect in CAD by network analysis. A network analysis was designed to meet four criteria: (1) the shortest paths from the known drug targets (TLR7 and TLR9) in the human protein–protein interaction network; (2) the blood vessel-specific gene expression level based on RNA-seq data from Genotype-Tissue Expression database; (3) known CAD or cardiovascular disease (CVD) gene products (proteins); and (4) literature-reported in vitro and in vivo evidence. There are three proposed mechanisms: (i) ERK5 (encoded by MAPK7) activation prevents endothelial inflammation via inhibition of cell adhesion molecule expression (VCAM-1 and ICAM-1), (ii) suppression of pro-inflammatory cytokines (TNF-α and IL-1β), and (iii) improvement in endothelial dysfunction via enhanced nitric oxide production by endothelial nitric oxide synthase (NOS3). The node size scales show the blood vessel-specific expression level based on RNA-seq data from Genotype-Tissue Expression database (Methods section). b, d Endothelial cells were pretreated with various concentrations of hydroxychloroquine (HCQ, 10–50 µM) for 1 h prior to 24 h incubation with 5 ng/ml TNF-α. qRT-PCR was used to monitor gene expression of inflammatory genes (b) VCAM1 and IL1B; and (d) NOS3. Expression of the β-actin gene was used as an internal standard. VCAM1: no HCQ, no TNF, n = 8; TNF; n = 8; TNF+ 10 μM HCQ, n = 5; TNF+ 20 μM HCQ, n = 4; TNF+ 30 μM HCQ, n = 3; TNF+ 50 μM HCQ, n = 6. IL-1β and NOS3: no HCQ, no TNF, n = 9; TNF; n = 9; TNF+ 10 μM HCQ, n = 5; TNF+ 20 μM HCQ, n = 5; TNF+ 30 μM HCQ, n = 4; TNF+ 50 μM HCQ, n = 6. Error bars are standard deviations. *significantly different from TNF-α with no HCQ, p < 0.05 as determined by post-hoc testing using the Student's Newman–Keuls test. c Western blot of VCAM-1, IL-1β, and actin. Endothelial cells were pretreated with 10 µM hydroxychloroquine for 1 h prior to 24 h incubation with 5, 10 or 20 ng/ml TNF-α. Each condition was tested six times; shown are representative blots
Fig. 4 Experimental validation of hydroxychloroquine’s likely mechanism-of-action in coronary artery disease (CAD). a A highlighted subnetwork shows the inferred mechanism-of-action for hydroxychloroquine’s protective effect in CAD by network analysis. A network analysis was designed to meet four criteria: (1) the shortest paths from the known drug targets (TLR7 and TLR9) in the human protein–protein interaction network; (2) the blood vessel-specific gene expression level based on RNA-seq data from Genotype-Tissue Expression database; (3) known CAD or cardiovascular disease (CVD) gene products (proteins); and (4) literature-reported in vitro and in vivo evidence. There are three proposed mechanisms: (i) ERK5 (encoded by MAPK7) activation prevents endothelial inflammation via inhibition of cell adhesion molecule expression (VCAM-1 and ICAM-1), (ii) suppression of pro-inflammatory cytokines (TNF-α and IL-1β), and (iii) improvement in endothelial dysfunction via enhanced nitric oxide production by endothelial nitric oxide synthase (NOS3). The node size scales show the blood vessel-specific expression level based on RNA-seq data from Genotype-Tissue Expression database (Methods section). b, d Endothelial cells were pretreated with various concentrations of hydroxychloroquine (HCQ, 10–50 µM) for 1 h prior to 24 h incubation with 5 ng/ml TNF-α. qRT-PCR was used to monitor gene expression of inflammatory genes (b) VCAM1 and IL1B; and (d) NOS3. Expression of the β-actin gene was used as an internal standard. VCAM1: no HCQ, no TNF, n = 8; TNF; n = 8; TNF+ 10 μM HCQ, n = 5; TNF+ 20 μM HCQ, n = 4; TNF+ 30 μM HCQ, n = 3; TNF+ 50 μM HCQ, n = 6. IL-1β and NOS3: no HCQ, no TNF, n = 9; TNF; n = 9; TNF+ 10 μM HCQ, n = 5; TNF+ 20 μM HCQ, n = 5; TNF+ 30 μM HCQ, n = 4; TNF+ 50 μM HCQ, n = 6. Error bars are standard deviations. *significantly different from TNF-α with no HCQ, p < 0.05 as determined by post-hoc testing using the Student's Newman–Keuls test. c Western blot of VCAM-1, IL-1β, and actin. Endothelial cells were pretreated with 10 µM hydroxychloroquine for 1 h prior to 24 h incubation with 5, 10 or 20 ng/ml TNF-α. Each condition was tested six times; shown are representative blotsCC BY · PubMed Central

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  1. Muller-Calleja, N., Manukyan, D., Canisius, A., Strand, D. & Lackner, K. J. Hydroxychloroquine inhibits proinflammatory signalling pathways by targeting endosomal NADPH oxidase. Ann. Rheum. Dis. 76, 891–897 (2016). · DOI

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Metadata

Authors
Cheng, F., Desai, R. J., Handy, D. E., Wang, R., Schneeweiss, S., Barabási, A.-L., & Loscalzo, J.
Affiliations
  • Northeastern University (US)
  • Brigham and Women's Hospital (US)
Corresponding author
Loscalzo, Joseph
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2018-07-12
Volume
9
Issue
1
Pages
2691-2691
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-018-05116-5
PMC
PMC6043492
OpenAlex
W2826750344
Cited by
552
References
90
Citations by year
2026: 47 · 2025: 66 · 2024: 60 · 2023: 58 · 2022: 88 · 2021: 97 · 2020: 86 · 2019: 43 · 2018: 5
FWCI
43.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Open Research (Auckland University of Technology) (submittedVersion, cc-by)
Funders
  • U.S. Department of Health & Human Services | National Institutes of Health (HL106373, HG007690, GM 107618, HL016795, HL108630)
  • Patient-Centered Outcomes Research Institute (ME-1303-5638)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
drug repurposing, protein-protein interaction network, network proximity, drug-disease associations, coronary artery disease, carbamazepine, hydroxychloroquine, pharmacoepidemiology, propensity score matching, endothelial inflammation, inflammatory cytokines, FDA-approved drugs
MeSH terms
Carbamazepine, Computer Simulation, Coronary Artery Disease, Humans, Hydroxychloroquine, Prognosis, Risk Factors, Reproducibility of Results, Proportional Hazards Models, Databases, Factual, Risk Assessment, Propensity Score, Drug Repositioning, Protein Interaction Maps

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