Using Real-World Data for Drug Repurposing and Pharmacovigilance

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 1725

Special Issue Editors


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Guest Editor
Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 110301, Taiwan
Interests: observational health data research; health data governance; distributed analysis; federated learning; AI for healthcare

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Department of Ophthalmology, Ren-Ai Branch, Taipei City Hospital, Taipei 10341, Taiwan
Interests: health insurance database research; medical informatics; medical ethics; ophthalmology; neuro-ophthalmology

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International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei 110301, Taiwan
Interests: clinical data analysis; precision medicine; biotech and healthcare management
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Special Issue Information

Dear Colleagues,

Drug repurposing is the process of identifying new uses for existing drugs or compounds. Pharmacovigilance is the study of the safety of drugs and their potential side effects. Real-world data can be used for both purposes. There are several sources of real-world data, including electronic health records, claims data, and patient registry data. These data sources can provide information about how drugs are being used in real-world clinical practice, as well as any adverse events or side effects that may be associated with their use. Examples, such as research of how statins could be protective for cancer development other than the initial intention of being protective for cardiovascular diseases, or hydroxycholoroquine in the treatment of autoimmune disease, such as rheumatoid arthritis, could lead to maculopathy, which could cast an impact on quality of life for poor vision. Nowadays, many drugs are being developed, such as monoclonal antibodies, to tackle cancers and autoimmune diseases, which will make this kind of study more imperative and insightful, as we are all learning from the real-world evidence.

By combining real-world data and omics data (i.e., genomics, metabolomics, proteomics, microbiomics, etc.) from biobanks, researchers can gain a more comprehensive understanding of the molecular mechanisms and improve the accuracy and precision of their scientific findings.

Dr. Min-Huei Hsu
Dr. Ju-Chuan Yen
Dr. Jason C. Hsu
Guest Editors

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Keywords

  • real-world data
  • observational study
  • drug repurposing
  • pharmacovigilance
  • electronic health record
  • omics
  • biobank

Published Papers (1 paper)

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Research

26 pages, 4017 KiB  
Article
Genetic and Epigenetic Host–Virus Network to Investigate Pathogenesis and Identify Biomarkers for Drug Repurposing of Human Respiratory Syncytial Virus via Real-World Two-Side RNA-Seq Data: Systems Biology and Deep-Learning Approach
by Bo-Wei Hsu and Bor-Sen Chen
Biomedicines 2023, 11(6), 1531; https://doi.org/10.3390/biomedicines11061531 - 25 May 2023
Viewed by 1342
Abstract
Human respiratory syncytial virus (hRSV) affects more than 33 million people each year, but there are currently no effective drugs or vaccines approved. In this study, we first constructed a candidate host–pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via big-data mining. Then, [...] Read more.
Human respiratory syncytial virus (hRSV) affects more than 33 million people each year, but there are currently no effective drugs or vaccines approved. In this study, we first constructed a candidate host–pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via big-data mining. Then, we employed reversed dynamic methods via two-side host–pathogen RNA-seq time-profile data to prune false positives in candidate HPI-GWGEN to obtain the real HPI-GWGEN. With the aid of principal-network projection and the annotation of KEGG pathways, we can extract core signaling pathways during hRSV infection to investigate the pathogenic mechanism of hRSV infection and select the corresponding significant biomarkers as drug targets, i.e., TRAF6, STAT3, IRF3, TYK2, and MAVS. Finally, in order to discover potential molecular drugs, we trained a DNN-based DTI model by drug–target interaction databases to predict candidate molecular drugs for these drug targets. After screening these candidate molecular drugs by three drug design specifications simultaneously, i.e., regulation ability, sensitivity, and toxicity. We finally selected acitretin, RS-67333, and phenformin to combine as a potential multimolecule drug for the therapeutic treatment of hRSV infection. Full article
(This article belongs to the Special Issue Using Real-World Data for Drug Repurposing and Pharmacovigilance)
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