Emerging strategies to bridge the gap between pharmacogenomic research and its clinical implementation

Abstract

The genomic inter-individual heterogeneity remains a significant challenge for both clinical decision-making and the design of clinical trials. Although next-generation sequencing (NGS) is increasingly implemented in drug development and clinical trials, translation of the obtained genomic information into actionable clinical advice lags behind. Major reasons are the paucity of sufficiently powered trials that can quantify the added value of pharmacogenetic testing, and the considerable pharmacogenetic complexity with millions of rare variants with unclear functional consequences. The resulting uncertainty is reflected in inconsistencies of pharmacogenomic drug labels in Europe and the United States. In this review, we discuss how the knowledge gap for bridging pharmacogenomics into the clinics can be reduced. First, emerging methods that allow the high-throughput experimental characterization of pharmacogenomic variants combined with novel computational tools hold promise to improve the accuracy of drug response predictions. Second, tapping of large biobanks of therapeutic drug monitoring data allows to conduct high-powered retrospective studies that can validate the clinical importance of genetic variants, which are currently incompletely characterized. Combined, we are confident that these methods will improve the accuracy of drug response predictions and will narrow the gap between variant identification and its utilization for clinical decision-support.

Keywords: Genetic markers; Predictive markers.

Fig. 1. Schematic depiction of how deep mutational scanning aspires to improve the translation of pharmacogenomic information into actionable advice.

Cells are transfected with a diversity library of expression plasmids covering many (ideally all possible) amino acid substitutions for a given pharmacogene of interest and a protein-specific selection assay is applied. Deep sequencing of the transfected cells before and after selection, and comparative analysis of the obtained data can provide enrichment scores for each variant, thus enabling the massively parallelized experimental characterization of thousands of variations. Importantly, this data provides a powerful resource for the training and optimization of computational algorithms, including but not restricted to random forests and deep neural networks, which in turn can be applied to the integrated analysis of a patient’s entire pharmacogenome.

Fig. 2. Associations of CYP2D6 genotype with the achievement of guideline-recommended target doses for carvedilol and metoprolol.

Analysis comprises 98 systolic heart failure patients. Only CYP2D6*4 was considered for the definition of CYP2D6 metabolizer status. Patients were 7.7 times more likely to be treated with lower maintenance doses of metoprolol for each CYP2D6*4 allele. For carvedilol, a trend was observed between CYP2D6*4 and higher maintenance dose. Gray shaded box indicates the range of guideline-recommended target doses (≥50 mg). EM extensive metabolizer (no *4 alleles), IM intermediate metabolizer (one *4 allele), OR odds ratio, PM poor metabolizer (two *4 alleles). Figure modified with permission from ref. .

Fig. 3. Effect of CYP2D6 genotype status on risperidone and aripiprazole dose and treatment failure rates.

Plots show the associations between CYP2D6 genotype and daily dose and switching frequency for 725 patients treated with risperidone (a, b) and 890 patients treated with aripiprazole (c, d). Considered genotypes include CYP2D6*3, *4, *5, *6, *9, *10, *41, and functional duplicated alleles *1xN or *2xN. In a and c, the dose reductions needed to compensate for the increase of risperidone exposure in patients who were intermediate metabolizers (IM) and poor metabolizers (PM; 95% CI) are indicated by red dots. Error bars indicate SEM. Statistical tests of subgroups are performed using the normal metabolizers (NM) as reference. IM intermediate metabolizers, PM poor metabolizers, UM ultrarapid metabolizers. Figure modified with permission from ref. .