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. 2023 Oct 31:10:1006743.
doi: 10.3389/fmed.2023.1006743. eCollection 2023.

Estimating the efficacy of pharmacogenomics over a lifetime

Zhan Ye  1 , John Mayer  1 , Emili J Leary  1 , Terrie Kitchner  1 , Richard A Dart  1 , Murray H Brilliant  1 , Scott J Hebbring  1
Affiliations

Estimating the efficacy of pharmacogenomics over a lifetime

Zhan Ye et al. Front Med (Lausanne). .

Abstract

It is well known that common variants in specific genes influence drug metabolism and response, but it is currently unknown what fraction of patients are given prescriptions over a lifetime that could be contraindicated by their pharmacogenomic profiles. To determine the clinical utility of pharmacogenomics over a lifetime in a general patient population, we sequenced the genomes of 300 deceased Marshfield Clinic patients linked to lifelong medical records. Genetic variants in 33 pharmacogenes were evaluated for their lifetime impact on drug prescribing using extensive electronic health records. Results show that 93% of the 300 deceased patients carried clinically relevant variants. Nearly 80% were prescribed approximately three medications on average that may have been impacted by these variants. Longitudinal data suggested that the optimal age for pharmacogenomic testing was prior to age 50, but the optimal age is greatly influenced by the stability of the population in the healthcare system. This study emphasizes the broad clinical impact of pharmacogenomic testing over a lifetime and demonstrates the potential application of genomic medicine in a general patient population for the advancement of precision medicine.

Keywords: Pharmacogenenomics and personalised medicine; drug responce; electronic health record (EHR); individualized medicine; precision medicine.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Impact of pharmacogenomic testing on drug prescribing. (A) Defined in red is the proportion of population with known pharmacogenomic variants in the corresponding pharmacogenes. Blue represents the percent of the population that was given a drug that may have been impacted by the relevant variants. (B) Box plot describing number of relevant drugs given to those who had clinically significant variants in one to four pharmacogenes.
Figure 2
Figure 2
Impact of pharmacogenomic testing over time. (A) Proportion of living patients (solid lines) and proportion of all 300 patients (dashed line) that may benefit from pharmacogenomic testing when considering age at prescription. Results are provided by gene and all genes combined. (B) Proportion of living patients that may benefit from pharmacogenomic testing when considering age of prescription but modeled to reflect a transient population lost to follow-up in 1, 3, and 10 years. Graphed in (A,B) is a survival curve for all 300 patients (grey dotted line).
See this image and copyright information in PMC

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