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

Abstract

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.

Estimation of the benefit from pre‐emptive genotyping based on the nationwide cohort data in South Korea

“…3 To date, ~400 pharmacogenomic variants have been included in the US Food and Drug Administration (FDA) labels. 4 Identifying these variants is expected to optimize drug outcomes by reducing adverse events (AEs) and maximizing efficacy. 5 However, despite the wealth of gene-drug information available, implementing genotyping in clinical practice is challenging.…”

“…6 Another study highlighted that genes related to drug metabolism and transporters were less actionable (~30%) compared to molecular targets related to oncology (~70%). 4 These issues pose barriers to the implementation of genotyping in clinical settings. 1 Once actionable genes are selected, the implementation of genotyping becomes another concern.…”

“…Another example involves TPMT and NUDT15 variants, where severe myelosuppression can lead to discontinuation of azathioprine in the treatment of inflammatory bowel disease 3 . To date, ~400 pharmacogenomic variants have been included in the US Food and Drug Administration (FDA) labels 4 . Identifying these variants is expected to optimize drug outcomes by reducing adverse events (AEs) and maximizing efficacy 5 …”

“…A study comparing pharmacogenomic guidance from various committees (including the Clinical Pharmacogenomics Implementation Consortium [CPIC], and Dutch Pharmacogenetics Working Group [DPWG]) found that only 18% of the cases agreed 6 . Another study highlighted that genes related to drug metabolism and transporters were less actionable (~30%) compared to molecular targets related to oncology (~70%) 4 . These issues pose barriers to the implementation of genotyping in clinical settings 1 …”

“… 3 To date, ~400 pharmacogenomic variants have been included in the US Food and Drug Administration (FDA) labels. 4 Identifying these variants is expected to optimize drug outcomes by reducing adverse events (AEs) and maximizing efficacy. 5 …”