title: A published pharmacogenetic algorithm was poorly predictive of tacrolimus clearance in an independent cohort of renal transplant recipients.
abstract: OBJECTIVE
  An algorithm based on the CYP3A5 genotype to predict tacrolimus clearance to inform the optimal initial dose was derived using data from the DeKAF study (Passey et al. Br J Clin Pharmacol 2011; 72: 948-57) but was not tested in an independent cohort of patients. Our aim was to test whether the DeKAF dosing algorithm could predict estimated tacrolimus clearance in renal transplant recipients at our centre.
  
  
  METHODS
  Predicted tacrolimus clearance based on the DeKAF algorithm was compared with dose-normalized trough whole-blood concentrations (estimated clearance) on day 7 after transplantation in a single-centre cohort of 255 renal transplant recipients.
  
  
  RESULTS
  There was a weak correlation (r = 0.431) between clearance based on dose-normalized trough whole-blood concentrations and DeKAF algorithm-predicted clearance. The means of the tacrolimus clearance predicted by the DeKAF algorithm and the estimated tacrolimus clearance based on the dose-normalized trough blood concentrations were plotted against the differences in the clearance as a Bland-Altman plot. Logarithmic transformation was performed owing to the increased difference in tacrolimus clearance as the mean clearance increased. There was a highly significant systematic error (P < 0.0005) characterized by a sloped regression line [gradient, 0.88 (95% confidence interval, 0.75-1.01)] on the Bland-Altman plot.
  
  
  CONCLUSIONS
  The DeKAF algorithm was unable to predict the estimated tacrolimus clearance accurately based on real tacrolimus doses and blood concentrations in our cohort of patients. Other genes are known to influence the clearance of tacrolimus, and a polygenic algorithm may be more predictive than those based on a single genotype.
authors.affiliation: Division of Clinical Sciences: Renal Medicine, St George's, University of London, London, UK.
authors.forename: Oliver
authors.initials: O
authors.lastname: Boughton
authors.forename: Gabor
authors.initials: G
authors.lastname: Borgulya
authors.forename: Maurizio
authors.initials: M
authors.lastname: Cecconi
authors.forename: Salim
authors.initials: S
authors.lastname: Fredericks
authors.forename: Michelle
authors.initials: M
authors.lastname: Moreton-Clack
authors.forename: Iain A M
authors.initials: IA
authors.lastname: MacPhee
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D015331:Cohort Studies; D051544:Cytochrome P-450 CYP3A / Q000235:genetics*; D004305:Dose-Response Relationship, Drug; D005260:Female; D005838:Genotype; D006801:Humans; D007166:Immunosuppressive Agents / Q000097:blood / Q000493:pharmacokinetics*; D016030:Kidney Transplantation; D008297:Male; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D010597:Pharmacogenetics; D011237:Predictive Value of Tests; D016559:Tacrolimus / Q000097:blood / Q000493:pharmacokinetics*; D055815:Young Adult
chemical_list: D007166:Immunosuppressive Agents; C510164:CYP3A5 protein, human; D051544:Cytochrome P-450 CYP3A; D016559:Tacrolimus
keywords: dosing algorithm; immunosuppression; pharmacogenetics; renal transplant; tacrolimus