SNPs affecting the clinical outcomes of regularly used immunosuppressants

Recent studies have suggested that genomic diversity may play a key role in different clinical outcomes, and the importance of SNPs is becoming increasingly clear. In this article, we summarize the bioactivity of SNPs that may affect the sensitivity to or possibility of drug reactions that occur amo...

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Bibliographic Details
Published in:Pharmacogenomics 2018-04, Vol.19 (5), p.495-511
Main Authors: Meng, Huan-Yu, Luo, Zhao-Hui, Hu, Bo, Jin, Wan-Lin, Yan, Cheng-Kai, Li, Zhi-Bin, Xue, Yuan-Yuan, Liu, Yu, Luo, Yi-En, Xu, Li-Qun, Yang, Huan
Format: Article
Language:English
Subjects:
Autoimmune diseases
Azathioprine
Bioinformatics
Biological activity
Blood
Clinical outcomes
Cyclophosphamide
Dihydrofolate reductase
Drug dosages
drug reactions
Genes
Glucocorticoids
immunosuppressants
Immunosuppressive agents
Learning algorithms
Leukemia
Lymphoma
Metabolism
Metabolites
Methotrexate
Mycophenolate mofetil
Mycophenolic acid
Rheumatoid arthritis
SNPs
Tacrolimus
Transplantation
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Summary:Recent studies have suggested that genomic diversity may play a key role in different clinical outcomes, and the importance of SNPs is becoming increasingly clear. In this article, we summarize the bioactivity of SNPs that may affect the sensitivity to or possibility of drug reactions that occur among the signaling pathways of regularly used immunosuppressants, such as glucocorticoids, azathioprine, tacrolimus, mycophenolate mofetil, cyclophosphamide and methotrexate. The development of bioinformatics, including machine learning models, has enabled prediction of the proper immunosuppressant dosage with minimal adverse drug reactions for patients after organ transplantation or for those with autoimmune diseases. This article provides a theoretical basis for the personalized use of immunosuppressants in the future.
ISSN:1462-2416
1744-8042
DOI:10.2217/pgs-2017-0182