Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data

Abstract

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.

Study of Regions of Homozygosity (ROH) Patterns to Evaluate the Use of Dogs’ Genome in Human Drug Development

“…CYP2D6, another gene with allelic variants and encoding enzymes with variable degrees of activity, is associated with the development of hepatotoxicity after use of certain pharmaceutical agents. The CYP2D6 gene is polymorphic with over 150 allelic variants (https://www.pharmvar.org/gene/CYP2D6) [20][21][22][23]. The existing variation in the frequencies of alleles of polymorphic pharmacogenes among different ethnic groups may be responsible for severe adverse reactions to, or altered efficacy of, a wide variety of drugs.…”

Cas9 targeted nanopore sequencing with enhanced variant calling improves CYP2D6-CYP2D7 hybrid allele genotyping

“…However, the current classification of CYP2D6 enzyme activities based on the star-allele gene definitions has proven to be a suboptimal predictor for enzyme activity (44). More recent research by Van der Lee et al (45) supported this by confirming that building a predictive model based on the complete CYP2D6 gene sequence gives better predictive values for the gene function than a model built solely based on the star-alleles. By generating complete consensus sequence, CoLoRGen can phase additional mutations, thereby allowing a more accurate gene function predictions.…”

Why We Need to Take a Closer Look at Genetic Contributions to CYP3A Activity

“…This information can also be used to optimize models to predict drug response by taking phasing and full gene sequencing into account. Indeed, recently a continuous scale model based on full gene variants data explored by long-read sequencing and neural network improved the explanation of CYP2D6 activity variability from 54% to 79% compared with the conventional phenotype classification ( van der Lee et al, 2021 ).…”

“…As a result, the conventional variant to metabolizer phenotype interpretation which only includes a limited number of well-known variants with fixed effects inevitably leads to missing information. Previous studies have shown that, at least for CYP2D6, a continuous model is able to better explain the enzyme activity ( McInnes et al, 2020 ; van der Lee et al, 2021 ). It can be expected that the same principle holds true for CYP3A as well.…”