The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients

Abstract

Objective: Tacrolimus, an immunosuppressive drug widely prescribed in kidney transplantation, requires therapeutic drug monitoring due to its marked interindividual pharmacokinetic variability and narrow therapeutic index. Previous studies have established that CYP3A5 rs776746 is associated with tacrolimus clearance, blood concentration, and dose requirement. The importance of other drug absorption, distribution, metabolism, and elimination (ADME) gene variants has not been well characterized.

Methods: We used novel DNA biobank and electronic medical record resources to identify ADME variants associated with tacrolimus dose requirement. Broad ADME genotyping was performed on 446 kidney transplant recipients, who had been dosed to a steady state with tacrolimus. The cohort was obtained from Vanderbilt's DNA biobank, BioVU, which contains linked deidentified electronic medical record data. Genotyping included Affymetrix drug-metabolizing enzymes and transporters Plus (1936 polymorphisms), custom Sequenom Massarray iPLEX Gold assay (95 polymorphisms), and ancestry-informative markers. The primary outcome was tacrolimus dose requirement defined as blood concentration to dose ratio.

Results: In analyses, which adjusted for race and other clinical factors, we replicated the association of tacrolimus blood concentration to dose ratio with CYP3A5 rs776746 (P=7.15×10), and identified associations with nine variants in linkage disequilibrium with rs776746, including eight CYP3A4 variants. No NR1I2 variants were significantly associated. Age, weight, and hemoglobin were also significantly associated with the outcome. In final models, rs776746 explained 39% of variability in dose requirement and 46% was explained by the model containing clinical covariates.

Conclusion: This study highlights the utility of DNA biobanks and electronic medical records for tacrolimus pharmacogenomic research.

Figure 1. Derivation of the study cohort

After applying inclusion criteria to the Synthetic Derivative, 527 subject samples were eligible for genotyping. Of these, 13 were removed for sex misclassification, 28 after DMET Plus quality control (QC), and 31 after iPlex Gold QC.

Figure 2. Tacrolimus dose-adjusted trough concentrations over time

(A) entire study period; (B) first year post transplant. Thick black lines represent smoothing lines fitted by the locally-weighted polynomial regression of the raw tacrolimus C/D ratio measurements (black dots) on time since transplant. Thin gray lines represent data from 15 randomly chosen subjects.

Figure 3. Principal component analysis for race

Using EIGENSTRAT to generate principal components, the cohort split into 2 clusters that correspond to black and white based on third party report. The x and y values correspond to values of the first and second principal component vectors, respectively, as derived by EIGENSTRAT's smartPCA program.

Figure 4. Genotype association with tacrolimus dose-adjusted trough concentrations by multiple linear regression analysis

(A) Manhattan plot of all evaluable ADME SNPs. The – log₁₀ of the p value is shown for genotyped SNPs (circles); (B) Synthesis-View Plot [51]: Schematic of chromosome 7 region of interest. Linkage disequilibrium structure is shown.

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