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

BACKGROUND: Drug 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...

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Autores principales: Wen, Qianlong, Liu, Ruoqi, Zhang, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8461864/
https://www.ncbi.nlm.nih.gov/pubmed/34560862
http://dx.doi.org/10.1186/s12911-021-01617-4
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author Wen, Qianlong
Liu, Ruoqi
Zhang, Ping
author_facet Wen, Qianlong
Liu, Ruoqi
Zhang, Ping
author_sort Wen, Qianlong
collection PubMed
description BACKGROUND: Drug 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. RESULTS: In 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 CONCLUSIONS: The 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.
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spelling pubmed-84618642021-09-24 Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases Wen, Qianlong Liu, Ruoqi Zhang, Ping BMC Med Inform Decis Mak Research BACKGROUND: Drug 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. RESULTS: In 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 CONCLUSIONS: The 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. BioMed Central 2021-09-24 /pmc/articles/PMC8461864/ /pubmed/34560862 http://dx.doi.org/10.1186/s12911-021-01617-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wen, Qianlong
Liu, Ruoqi
Zhang, Ping
Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
title Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
title_full Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
title_fullStr Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
title_full_unstemmed Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
title_short Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
title_sort clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8461864/
https://www.ncbi.nlm.nih.gov/pubmed/34560862
http://dx.doi.org/10.1186/s12911-021-01617-4
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