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Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions

Authors Tong H, Phan NVT, Nguyen TT, Nguyen DV, Vo NS, Le L

Received 6 November 2020

Accepted for publication 26 December 2020

Published 13 January 2021 Volume 2021:14 Pages 61—75


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Martin Bluth

Hang Tong,1,2 Nga VT Phan,1,2 Thanh T Nguyen,3 Dinh V Nguyen,4,5 Nam S Vo,3 Ly Le1– 3

1School of Biotechnology, International University, Ho Chi Minh City, Vietnam; 2Vietnam National University, Ho Chi Minh City, Vietnam; 3Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam; 4Department of Respiratory, Allergy and Clinical Immunology, Vinmec International Hospital, Hanoi, Vietnam; 5College of Health Sciences, VinUniversity, Hanoi, Vietnam

Correspondence: Ly Le; Nam S Vo Email;

Abstract: Pharmacogenomics has been used effectively in studying adverse drug reactions by determining the person-specific genetic factors associated with individual response to a drug. Current approaches have revealed the significant importance of sequencing technologies and sequence analysis strategies for interpreting the contribution of genetic variation in developing adverse reactions. Advance in next generation sequencing and platform brings new opportunities in validating the genetic candidates in certain reactions, and could be used to develop the preemptive tests to predict the outcome of the variation in a personal response to a drug. With the highly accumulated available data recently, the in silico approach with data analysis and modeling plays as other important alternatives which significantly support the final decisions in the transformation from research to clinical applications such as diagnosis and treatments for various types of adverse responses.

Keywords: pharmacogenomics, adverse drug reactions, next generation sequencing, genome-wide association study, candidate gene approach

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