Evidence map›Paper›PMID 34560862›Full record

ArticleBMC medical informatics and decision making2021

Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases.

Qianlong Wen, Ruoqi Liu, Ping Zhang

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Qianlong Wen *Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Ave, Columbus, Ohio, 43210, USA.
Ruoqi Liu *Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, Ohio, 43210, USA.
Ping ZhangDepartment of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, Ohio, 43210, USA. mail.pingzhang@gmail.com.ORCID 0000-0002-4601-0779

Funding

The OSU Center for Clinical and Translational Science: Advancing Today's Discoveries to Improve HealthUL1TR002733 · NCATS · OHIO STATE UNIVERSITY · PI RINGEL, MATTHEW D · 2018 to 2022
$28.9M
NCATS NIH HHS UL1 TR002733
6 · The paper itself

Abstract

backgroundDrug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials.

resultsIn this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR

conclusionsThe proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.

Indexed as

Diabetes Mellitus, Type 2Pharmaceutical PreparationsDrug RepositioningElectronic Health RecordsHumansLaboratoriesPharmaceutical PreparationsConnectivity mapDrug repurposingElectronic health recordNational Health and Nutrition Examination Survey

Identifiers

PMID34560862
PMCPMC8461864

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.