Pharmacogenes (PGx-genes): Current understanding and future directions

Katara, Pramod; Yadav, Anamika

doi:10.1016/j.gene.2019.144050

Cited by 22 publications

(19 citation statements)

References 89 publications

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“…Pharmacogenomics is the study of how genes dictate individual drug responses [1] , [2] . Applied clinically, it holds great promise for the personalized prescription of medications [3] .…”

Section: Data Descriptionmentioning

confidence: 99%

Dataset of the first report of pharmacogenomics profiling in an outpatient spine setting

et al. 2021

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Here we describe the dataset of the first report of pharmacogenomics profiling in an outpatient spine setting with the primary aims to catalog: 1) the genes, alleles, and associated rs Numbers (accession numbers for specific single-nucleotide polymorphisms) analysed and 2) the genotypes and corresponding phenotypes of the genes involved in metabolizing 37 commonly used analgesic medications. The present description applies to analgesic medication-metabolizing enzymes and may be especially valuable to investigators who are exploring strategies to optimize pharmacologic pain management (e.g., by tailoring analgesic regimens to the genetically identified sensitivities of the patient). Buccal swabs were used to acquire tissue samples of 30 adult patients who presented to an outpatient spine clinic with the chief concern of axial neck and/or back pain. Array-based assays were then used to detect the alleles of genes involved in the metabolism of pain medications, including all common (wild type) and most rare variant alleles with known clinical significance. Both CYP450 isozymes – including CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5 – and the phase II enzyme UDP-glucuronosyltransferase-2B7 (UGT2B7) were examined. Genotypes/phenotypes were then used to evaluate each patient's relative ability to metabolize 37 commonly used analgesic medications. These medications included both non-opioid analgesics (i.e., aspirin, diclofenac, nabumetone, indomethacin, meloxicam, piroxicam, tenoxicam, lornoxicam, celecoxib, ibuprofen, flurbiprofen, ketoprofen, fenoprofen, naproxen, and mefenamic acid) and opioid analgesics (i.e., morphine, codeine, dihydrocodeine, ethylmorphine, hydrocodone, hydromorphone, oxycodone, oxymorphone, alfentanil, fentanyl, sufentanil, meperidine, ketobemidone, dextropropoxyphene, levacetylmethadol, loperamide, methadone, buprenorphine, dextromethorphan, tramadol, tapentadol, and tilidine). The genes, alleles, and associated rs Numbers that were analysed are provided. Also provided are: 1) the genotypes and corresponding phenotypes of the genes involved in metabolizing 37 commonly used analgesic medications and 2) the mechanisms of metabolism of the analgesic medications by primary and ancillary pathways. In supplemental spreadsheets, the raw and analysed pharmacogenomics data for all 30 patients evaluated in the primary research article are additionally provided. Collectively, the presented data offer significant reuse potential in future investigations of pharmacogenomics for pain management.

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“…Pharmacogenomics is the study of how genes dictate individual drug responses [1] , [2] . Applied clinically, it holds great promise for the personalized prescription of medications [3] .…”

Section: Data Descriptionmentioning

confidence: 99%

Dataset of the first report of pharmacogenomics profiling in an outpatient spine setting

et al. 2021

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“…Bảng 1. Các hiệp hội với chức năng nghiên cứu di truyền dược học (Katara, Yadav, 2019). PGRN đã tạo ra một hệ thống và môi trường hỗ trợ nhằm kết nối tất cả các nhóm nghiên cứu về di truyền dược học trên thế giới.…”

Section: Những Nghiên Cứu đáNg Chú ý Của Pgrnunclassified

Genetic variation of pharmacogenes

Nhung¹,

Ton²,

Hải³

et al. 2020

Vietnam J. Biotechnol.

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Patient specific response against a particular drug could be affected by various factors, in which genetic factors are the most crucial contributor. The genetic variability in pharmacogenes might result in variable drug response of individuals, which in turn can lead to unexpected treatment outcomes or even adverse drug reactions. The pharmacogenes include of genes that encode for several proteins which divided into 3 main functional categories: drug metabolizing enzymes, drug transporters and receptor-drug targets. Genetic variants of genes coding for drug metabolizing enzymes phase I (CYP450), phase II (GSTs, UGT, TPMT) as well as drug transporters (ABC, SLCO) of numerous populations in global have been extensively studied. Among these, SNPs are the major contributor behind variants of pharmacogenes along with copy number variants. Furthermore, the clinical impact on drug response of common variants belonging to several important pharmacogenes has been well understood. On the other hand, information on the variant spectrum of genes encoding for receptor-drug targets as well as their physiological effects have remained limited. In recent years, along with computational methods, next generation sequencing technologies had been developed tremendously. These high throughput methods had greatly promoted the field of pharmacogenetic research through providing ability to detect novel and rare genetic variants. The data on genetic variants of pharmacogenes would be valuable for determining the responder and non-responder to medication during treatment. These are also significant basis which play a vital role in development of the field of optimizing drug dose for individuals and personalized medicine in the future.

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“…12 The development of a new drug, historically confined to the "one-size-fits-all" approach, must now tend toward personalized medicine to increase its chances at providing a superior efficacy, a more convenient dosing regimen or route of administration, or a lower risk of adverse effects. 5 Many studies report associations between genotype and efficacy in patients treated with a given drug, ignoring information from untreated patients. 13 The benefits of incorporating pharmacogenetic into clinical practice is now wellestablished, however, high-quality findings are lacking due to unresolved methodological and statistical issues of pharmacogenetic studies.…”

Section: Introductionmentioning

confidence: 99%

“…However, Cardon et al 25 in their simulation study showed that, in the presence of a polymorphism advantageous for the treatment under study, a pharmacogenomic trial requires a smaller sample size than a traditional trial (not adjusting on the polymorphism) to detect the same effect. Further, Katara et al 5 argued that polymorphisms could be responsible for around 30%-40% of the overall functional variability and significantly impacts drug response differences. In this context, one may expect a common polymorphism to have an intermediate-to-strong effect and not necessarily a combination of many common polymorphisms with a small effect.…”

mentioning

confidence: 99%

Impact of study design and statistical model in pharmacogenetic studies with gene‐treatment interaction

Couffignal

Mentré

Bertrand

2021

CPT Pharmacom & Syst Pharma

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Gene‐treatment interactions, just like drug‐drug interactions, can have dramatic effects on a patient response and therefore influence the clinician decision at the patient’s bedside. Crossover designs, although they are known to decrease the number of subjects in drug‐interaction studies, are seldom used in pharmacogenetic studies. We propose to evaluate, via realistic clinical trial simulations, to what extent crossover designs can help quantifying the gene‐treatment interaction effect. We explored different scenarios of crossover and parallel design studies comparing two symptom‐modifying treatments in a chronic and stable disease accounting for the impact of a one gene and one gene‐treatment interaction. We varied the number of subjects, the between and within subject variabilities, the gene polymorphism frequency and the effect sizes of the treatment, gene, and gene‐treatment interaction. Each simulated dataset was analyzed using three models: (i) estimating only the treatment effect, (ii) estimating the treatment and the gene effects, and (iii) estimating the treatment, the gene, and the gene‐treatment interaction effects. We showed how ignoring the gene‐treatment interaction results in the wrong treatment effect estimates. We also highlighted how crossover studies are more powerful to detect a treatment effect in the presence of a gene‐treatment interaction and more often lead to correct treatment attribution.

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Pharmacogenes (PGx-genes): Current understanding and future directions

Cited by 22 publications

References 89 publications

Dataset of the first report of pharmacogenomics profiling in an outpatient spine setting

Dataset of the first report of pharmacogenomics profiling in an outpatient spine setting

Genetic variation of pharmacogenes

Impact of study design and statistical model in pharmacogenetic studies with gene‐treatment interaction

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