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Shameer K, Glicksberg BS, Hodos R, Johnson KW, Badgeley MA, Readhead B, Tomlinson MS, O’Connor T, Miotto R, Kidd BA, Chen R, Ma’ayan A, Dudley JT. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Brief Bioinform 2018; 19:656-678. [PMID: 28200013 PMCID: PMC6192146 DOI: 10.1093/bib/bbw136] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 11/29/2016] [Indexed: 12/22/2022] Open
Abstract
Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.
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Affiliation(s)
- Khader Shameer
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Benjamin S Glicksberg
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Rachel Hodos
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
- New York University, New York, NY, USA
| | - Kipp W Johnson
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Marcus A Badgeley
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Ben Readhead
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Max S Tomlinson
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | | | - Riccardo Miotto
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Brian A Kidd
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Rong Chen
- Clinical Genome Informatics, Icahn Institute of Genetics and Multiscale
Biology, Mount Sinai Health System, New York, NY
| | - Avi Ma’ayan
- Mount Sinai Center for Bioinformatics, Mount Sinai Health System, New York,
NY
| | - Joel T Dudley
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New
York, NY, USA
- Department of Population Health Science and Policy, Mount Sinai Health System,
New York, NY, USA
- Director of Biomedical Informatics, Icahn School of Medicine at Mount Sinai,
Mount Sinai Health System, New York, NY
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Hodos R, Zhang P, Lee HC, Duan Q, Wang Z, Clark NR, Ma'ayan A, Wang F, Kidd B, Hu J, Sontag D, Dudley J. Cell-specific prediction and application of drug-induced gene expression profiles. Pac Symp Biocomput 2018; 23:32-43. [PMID: 29218867 PMCID: PMC5753597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.
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Affiliation(s)
- Rachel Hodos
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, 10065; New York, USA, ²Department of Genetics and Genomic Sciences, ISMMS, New York, NY, 10029; New York, USA, ³Courant Institute of Mathematical Sciences, New York University, New York, NY, 10012; New York, USA
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Duan Q, Reid SP, Clark NR, Wang Z, Fernandez NF, Rouillard AD, Readhead B, Tritsch SR, Hodos R, Hafner M, Niepel M, Sorger PK, Dudley JT, Bavari S, Panchal RG, Ma'ayan A. L1000CDS 2: LINCS L1000 characteristic direction signatures search engine. NPJ Syst Biol Appl 2016; 2. [PMID: 28413689 PMCID: PMC5389891 DOI: 10.1038/npjsba.2016.15] [Citation(s) in RCA: 195] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.
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Affiliation(s)
- Qiaonan Duan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - St Patrick Reid
- US Army Medical Research Institute of Infectious Diseases, Frederick, MD, USA
| | - Neil R Clark
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zichen Wang
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicolas F Fernandez
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew D Rouillard
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben Readhead
- Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah R Tritsch
- US Army Medical Research Institute of Infectious Diseases, Frederick, MD, USA
| | - Rachel Hodos
- Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marc Hafner
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Mario Niepel
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Peter K Sorger
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Joel T Dudley
- Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sina Bavari
- US Army Medical Research Institute of Infectious Diseases, Frederick, MD, USA
| | - Rekha G Panchal
- US Army Medical Research Institute of Infectious Diseases, Frederick, MD, USA
| | - Avi Ma'ayan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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