Predictive Modeling of Tacrolimus Dose Requirement Based on High-Throughput Genetic Screening

Any biochemical reaction underlying drug metabolism depends on individual gene-drug interactions and on groups of genes interacting together. Based on a high-throughput genetic approach, we sought to identify a set of covariant single-nucleotide polymorphisms predictive of interindividual tacrolimus (Tac) dose requirement variability. Tac blood concentrations (Tac C-0) of 229 kidney transplant recipients were repeatedly monitored after transplantation over 3mo. Given the high dimension of the genomic data in comparison to the low number of observations and the high multicolinearity among the variables (gene variants), we developed an original predictive approach that integrates an ensemble variable-selection strategy to reinforce the stability of the variable-selection process and multivariate modeling. Our predictive models explained up to 70% of total variability in Tac C-0 per dose with a maximum of 44 gene variants (p-value <0.001 with a permutation test). These models included molecular networks of drug metabolism with oxidoreductase activities and the multidrug-resistant ABCC8 transporter, which was found in the most stringent model. Finally, we identified an intronic variant of the gene encoding SLC28A3, a drug transporter, as a key gene involved in Tac metabolism, and we confirmed it in an independent validation cohort.