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

Birdwell KA, Grady B, Choi L, Xu H, Bian A, Denny JC, Jiang M, Vranic G, Basford M, Cowan JD, Richardson DM, Robinson MP, Ikizler TA, Ritchie MD, Stein CM, Haas DW
Pharmacogenet Genomics. 2012 22 (1): 32-42

PMID: 22108237 · PMCID: PMC3237759 · DOI:10.1097/FPC.0b013e32834e1641

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.

MeSH Terms (24)

Adult Age Factors ATP Binding Cassette Transporter, Subfamily B ATP Binding Cassette Transporter, Subfamily B, Member 1 Body Weight Cytochrome P-450 CYP3A Databases, Nucleic Acid Dose-Response Relationship, Drug Drug Monitoring Electronic Health Records Female Genetic Association Studies Genotype Hemoglobins Humans Immunosuppressive Agents Kidney Transplantation Linkage Disequilibrium Male Middle Aged Polymorphism, Single Nucleotide Pregnane X Receptor Receptors, Steroid Tacrolimus

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