Pharmacogenomics is a key component of personalized medicine that promises safer and more effective drug treatment by individualizing drug choice and dose based on genetic profiles. In clinical practice, genetic biomarkers are used to categorize patients into *-alleles to predict CYP450 enzyme activity and adjust drug dosages accordingly. However, this approach leaves a large part of variability in drug response unexplained. Here, we present a proof-of-concept approach that uses continuous-scale (instead of categorical) assignments to predict enzyme activity. We used full CYP2D6 gene sequences obtained with long-read amplicon-based sequencing and cytochrome P450 (CYP) 2D6–mediated tamoxifen metabolism data from a prospective study of 561 patients with breast cancer to train a neural network. The model explained 79% of interindividual variability in CYP2D6 activity compared to 54% with the conventional *-allele approach, assigned enzyme activities to known alleles with previously reported effects, and predicted the activity of previously uncharacterized combinations of variants. The results were replicated in an independent cohort of tamoxifen-treated patients (model R2 adjusted = 0.66 versus *-allele R2 adjusted = 0.35) and a cohort of patients treated with the CYP2D6 substrate venlafaxine (model R2 adjusted = 0.64 versus *-allele R2 adjusted = 0.55). Human embryonic kidney cells were used to confirm the effect of five genetic variants on metabolism of the CYP2D6 substrate bufuralol in vitro. These results demonstrate the advantage of a continuous scale and a completely phased genotype for prediction of CYP2D6 enzyme activity and could potentially enable more accurate prediction of individual drug response.
The continuous development of new genotyping technologies requires awareness of their potential advantages and limitations concerning utility for pharmacogenomics (PGx). In this review, we provide an overview of technologies that can be applied in PGx research and clinical practice. Most commonly used are single nucleotide variant (SNV) panels which contain a pre-selected panel of genetic variants. SNV panels offer a short turnaround time and straightforward interpretation, making them suitable for clinical practice. However, they are limited in their ability to assess rare and structural variants. Next-generation sequencing (NGS) and long-read sequencing are promising technologies for the field of PGx research. Both NGS and long-read sequencing often provide more data and more options with regard to deciphering structural and rare variants compared to SNV panels—in particular, in regard to the number of variants that can be identified, as well as the option for haplotype phasing. Nonetheless, while useful for research, not all sequencing data can be applied to clinical practice yet. Ultimately, selecting the right technology is not a matter of fact but a matter of choosing the right technique for the right problem.
This is the first prospective international multicenter RSV study using an expert panel as reference standard to identify children with and without bacterial coinfection. All cases of bacterial coinfections are treated, whereas as many as one-third of all children with RSV LRTI are treated unnecessarily with antibiotics.
For ~ 80 drugs, widely recognized pharmacogenetics dosing guidelines are available. However, the use of these guidelines in clinical practice remains limited as only a fraction of patients is subjected to pharmacogenetic screening. We investigated the feasibility of repurposing whole exome sequencing (WES) data for a panel of 42 variants in 11 pharmacogenes to provide a pharmacogenomic profile. Existing diagnostic WES‐data from child‐parent trios totaling 1,583 individuals were used. Results were successfully extracted for 39 variants. No information could be extracted for three variants, located in CYP2C19, UGT1A1, and CYP3A5, and for CYP2D6 copy number. At least one actionable phenotype was present in 86% of the individuals. Haplotype phasing proved relevant for CYP2B6 assignments as 1.5% of the phenotypes were corrected after phasing. In conclusion, repurposing WES‐data can yield meaningful pharmacogenetic profiles for 7 of 11 important pharmacogenes, which can be used to guide drug treatment.
Genetic variation in the gene encoding CYP2D6 is used to guide drug prescribing in clinical practice. However, genetic variants in CYP2D6 show substrate-specific effects that are currently not accounted for. With a systematic literature, we retrieved 22 original studies describing in vitro experiments focusing on CYP2D6 alleles ( CYP2D6*1, *2, *10 and *17) and substrates. Allele activity (clearance of the allele of interest divided by the clearance of the wildtype) was extracted. The results support the hypothesis of the existence of substrate specificity of the CYP2D6*17-allele (higher debrisoquine clearance), a subtle effect of the CYP2D6*10-allele (lower dextromethorphan clearance) but no substrate-specific effect of the CYP2D6*2-allele. Although our results support substrate specificity, for most substrates data are too sparse and require further studies.
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