Frontiers in pharmacology · 2026
Combined donor-recipient CYP3A functional allele count as an independent predictor of tacrolimus pharmacokinetics after liver transplantation.
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Ramudo-Cela Luis, Muñiz-Castro Brais, Suárez-López Francisco, Otero-Ferreiro Alejandra, Pérez Gilberto, Santos José, Cabalar Pedro, Margusino-Framiñán Luis
Аннотация
BACKGROUND: Tacrolimus (TAC) exhibits high pharmacokinetic variability post-liver transplantation (LT). Traditional models overlook the metabolic chimerism between recipient (intestinal) and donor (hepatic) genotypes. We aimed to evaluate a combined donor-recipient CYP3A functional allele count predicting TAC exposure.
METHODS: This retrospective study analyzed 1,350 TAC trough measurements from 99 LT recipients. CYP3A4 and CYP3A5 functional alleles were summed into a combined pair-level framework (0-8 alleles). The primary outcome was the log-transformed concentration-to-dose ratio (log-CDR). Linear mixed-effects models (LMM) accounted for repeated measurements, adjusting for clinical and pharmacological covariates.
RESULTS: The combined CYP3A functional allele count was the strongest genetic predictor of TAC exposure. Using the most prevalent group (4 alleles) as reference in multivariable LMMs, dose-normalized exposure significantly decreased by 43% and 48% in pairs with 5 and 6 functional alleles, respectively (p < 0.001). This gene-dose relationship remained stable across ICU, ward, and outpatient phases. The combined metric captured 4.2% of baseline variance (marginal R 2 = 0.042), indicating that most TAC exposure variability is driven by non-genetic clinical factors. Nevertheless, this approach doubled the explanatory power achieved with recipient genotype alone and remained an independent predictor after adjustment for major time-varying clinical confounders.
CONCLUSION: The combined donor-recipient CYP3A functional allele count provides a biologically coherent, temporally stable metric of TAC metabolism. Outperforming single-genome models, it offers a stratification tool to guide early post-operative initial dose strategies.
METHODS: This retrospective study analyzed 1,350 TAC trough measurements from 99 LT recipients. CYP3A4 and CYP3A5 functional alleles were summed into a combined pair-level framework (0-8 alleles). The primary outcome was the log-transformed concentration-to-dose ratio (log-CDR). Linear mixed-effects models (LMM) accounted for repeated measurements, adjusting for clinical and pharmacological covariates.
RESULTS: The combined CYP3A functional allele count was the strongest genetic predictor of TAC exposure. Using the most prevalent group (4 alleles) as reference in multivariable LMMs, dose-normalized exposure significantly decreased by 43% and 48% in pairs with 5 and 6 functional alleles, respectively (p < 0.001). This gene-dose relationship remained stable across ICU, ward, and outpatient phases. The combined metric captured 4.2% of baseline variance (marginal R 2 = 0.042), indicating that most TAC exposure variability is driven by non-genetic clinical factors. Nevertheless, this approach doubled the explanatory power achieved with recipient genotype alone and remained an independent predictor after adjustment for major time-varying clinical confounders.
CONCLUSION: The combined donor-recipient CYP3A functional allele count provides a biologically coherent, temporally stable metric of TAC metabolism. Outperforming single-genome models, it offers a stratification tool to guide early post-operative initial dose strategies.