Pharmacogenomic information from CPIC and DPWG guidelines and its application on drug labels

Yoon, Deok Yong; Lee, So‐Young; Ban, Mu Seong; Jang, In Jin; Lee, Seung Hwan

doi:10.12793/tcp.2020.28.e18

Cited by 24 publications

(17 citation statements)

References 16 publications

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“…In order to give an accurate and useful interpretation of genotypes, a full evaluation of administration details was performed on neuropsychiatric drugs ( Table 2 ). As well as those of authorities in other countries [ 8 ], labels approved by the Food and Drug Administration (FDA), European Medicines Agency (EMA), and Italian Medicines Agency (AIFA) sometimes reported administration suggestions that are discordant with guideline recommendations ( Table 4 ). Thus, the evaluation of cytochrome genotypes and metabolizing phenotypes in clinical practice should take into consideration suggestions of dosage adjustments in order to quickly identify the right dosage for the patient.…”

Section: Discussionmentioning

confidence: 99%

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Precision Medicine into Clinical Practice: A Web-Based Tool Enables Real-Time Pharmacogenetic Assessment of Tailored Treatments in Psychiatric Disorders

Zampatti

Fabrizio

Reni

et al. 2021

JPM

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The management of neuropsychiatric disorders involves different pharmacological treatments. In order to perform efficacious drug treatments, the metabolism of CYP genes can help to foresee potential drug–drug interactions. The NeuroPGx software is an open-source web-based tool for genotype/diplotype/phenotype interpretation for neuropharmacogenomic purposes. The software provides information about: (i) the genotypes of evaluated SNPs (single nucleotide polymorphisms); (ii) the main diplotypes in CYP genes and corresponding metabolization phenotypes; (iii) the list of neuropsychiatric drugs with recommended dosage adjustment (according to CPIC and DPWG guidelines); (iv) the list of possible (rare) diplotypes and corresponding metabolization phenotypes. The combined application of NeuroPGx software to the OpenArray technology results in an easy, quick, and highly automated device ready to be used in routine clinical practice.

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Section: Discussionmentioning

confidence: 99%

“…Unfortunately, the reported recommendations may be dissimilar in different guidelines [ 7 ]. Furthermore, sometimes administration suggestions included in drug labels are discordant with what is reported in the guidelines [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Precision Medicine into Clinical Practice: A Web-Based Tool Enables Real-Time Pharmacogenetic Assessment of Tailored Treatments in Psychiatric Disorders

Zampatti

Fabrizio

Reni

et al. 2021

JPM

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“…Overall, there appears to be a lack of consensus between the actionable PGx biomarker in the FDA‐PGx table and the CPIC/DPWG guidelines. The lack of consensus between the FDA‐PGx and the CPIC/DPWG guidelines or the drug labels in other countries have also been reported recently 30,34,35 . One of the reasons could be due to the statistical power in clinical studies with a limited number of patients, particularly in clinical trials with patients carrying low‐frequency variants before the drug approvals.…”

Section: Discussionmentioning

confidence: 96%

“…The lack of consensus between the FDA-PGx and the CPIC/DPWG guidelines or the drug labels in other countries have also been reported recently. 30,34,35 One of the reasons could be due to the statistical power in clinical studies with a limited number of patients, particularly in clinical trials with patients carrying low-frequency variants before the drug approvals. Over the last decade with advances in F I G U R E 5 Summary of pharmacogenetic/genomic biomarker classification in the US Food and Drug Administration-pharmacogenetic/genomic (FDA-PGx) table.…”

Section: Discussionmentioning

confidence: 99%

A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels

Yamazaki

2021

Clinical Translational Sci

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The primary goal of precision medicine is to maximize the benefit-risk relationships for individual patients by delivering the right drug to the right patients at the right dose. To achieve this goal, it has become increasingly important to assess gene-drug interactions (GDI) in clinical settings. The U.S. Food and Drug Administration (FDA) periodically updates the table of pharmacogenetic/genomic (PGx) biomarkers in drug labeling on their website. As described herein, an effort was made to categorize various PGx biomarkers covered by the FDA-PGx table into certain groups. There were 2 major groups, oncology molecular targets (OMT) and drug-metabolizing enzymes and transporters (DMET), which constitute ~70% of all biomarkers (~33% and ~35%, respectively). These biomarkers were further classified whether their labeling languages could be actionable in clinical practice. For OMT biomarkers, ~70% of biomarkers are considered actionable in clinical practice as they are critical for the selection of appropriate drugs to individual patients. In contrast, ~30% of DMET biomarkers are considered actionable for the dose adjustments or alternative therapies in specific populations such as CYP2C19 and CYP2D6 poor metabolizers. In addition, the GDI results related to some of the other OMT and DMET biomarkers are considered to provide valuable information to clinicians. However, clinical GDI results on the other DMET biomarkers can possibly be used more effectively for dose recommendation. As the labels of some drugs already recommend the precise doses in specific populations, it will be desirable to have clear language for dose recommendation of other (or new) drugs if appropriate.

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“…The evidence for translating pharmacogenomic (PGx) results to practice is evolving, expanding, and increasingly formalized into clinical guidelines that are periodically updated [1]. Successful implementation of panel-based PGx testing, driving automated clinical decision support (CDS), has now been described at several institutions [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Impact of Updating Pharmacogenetic Results: Lessons Learned from the PREDICT Program

Liu

Driest

Saucier

et al. 2021

JPM

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Pharmacogenomic (PGx) evidence for selective serotonin reuptake inhibitors (SSRIs) continues to evolve. For sites offering testing, maintaining up-to-date interpretations and implementing new clinical decision support (CDS) driven by existing results creates practical and technical challenges. Vanderbilt University Medical Center initiated panel testing in 2010, added CYP2D6 testing in 2017, and released CDS for SSRIs in 2020. We systematically reinterpreted historic CYP2C19 and CYP2D6 genotypes to update phenotypes to current nomenclature and to launch provider CDS and patient-oriented content for SSRIs. Chart review was conducted to identify and recontact providers caring for patients with current SSRI therapy and new actionable recommendations. A total of 15,619 patients’ PGx results were reprocessed. Of the non-deceased patients reprocessed, 21% (n = 3278) resulted in CYP2C19*1/*17 reinterpretations. Among 289 patients with an actionable recommendation and SSRI medication prescription, 31.8% (n = 92) did not necessitate contact of a clinician, while 43.2% (n = 125) resulted in clinician contacted, and for 25% (n = 72) no appropriate clinician was able to be identified. Maintenance of up-to-date interpretations and recommendations for PGx results over the lifetime of a patient requires continuous effort. Reprocessing is a key strategy for maintenance and expansion of PGx content to be periodically considered and implemented.

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Pharmacogenomic information from CPIC and DPWG guidelines and its application on drug labels

Cited by 24 publications

References 16 publications

Precision Medicine into Clinical Practice: A Web-Based Tool Enables Real-Time Pharmacogenetic Assessment of Tailored Treatments in Psychiatric Disorders

Precision Medicine into Clinical Practice: A Web-Based Tool Enables Real-Time Pharmacogenetic Assessment of Tailored Treatments in Psychiatric Disorders

A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels

Impact of Updating Pharmacogenetic Results: Lessons Learned from the PREDICT Program

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