Time-related clinical determinants of long-term tacrolimus pharmacokinetics in combination therapy with mycophenolic acid and corticosteroids: a prospective study in one hundred de novo renal transplant recipients

Clin Pharmacokinet. 2004;43(11):741-62. doi: 10.2165/00003088-200443110-00005.


Background: Tacrolimus is an efficient primary immunosuppressive drug in renal transplantation but its long-term use is associated with calcineurin-inhibitor-related toxicity. The specific characteristics of the inter-relationship between dose, concentration and clinical (side-)effects for tacrolimus have not yet been identified and extensive long-term pharmacokinetic studies are presently lacking.

Objective: To establish the characteristics of the long-term pharmacokinetics of tacrolimus, to determine the time-dependent factors that influence the pharmacokinetics within the first critical post-transplant year and to identify a more appropriate way of monitoring drug exposure in clinical practice.

Study design: A prospective pharmacokinetic study of tacrolimus was conducted in 100 de novo renal allograft recipients during the first year post-transplantation.

Methods: Area under the concentration-time curve (AUC) blood samplings for tacrolimus were performed on days 7, 42, 90, 180 and 360 for all patients. Model-independent pharmacokinetic parameters for tacrolimus were calculated and dose-corrected when appropriate: AUC12, peak plasma concentration (Cmax), pre-dose trough concentration (C0), time to Cmax, average steady-state blood concentration, steady-state total body clearance, terminal half-life, volume of distribution and an estimate for tacrolimus bioavailability was derived from additional steady-state intravenous clearance data. The association between tacrolimus pharmacokinetic parameters and different clinical variables was evaluated on days 7, 42, 90, 180 and 360. The clinical variables were either donor-related (e.g. donor age), transplantation-related (e.g. delayed graft function), recipient-related (e.g. bodyweight), biochemical (e.g. serum albumin), therapeutic variables (e.g. corticosteroid dose) or disease variables (e.g. liver dysfunction).

Results: Long-term tacrolimus dose-corrected exposure (AUC12, C0) is characterised by a late significant increase towards the end of the first year post-transplantation as the result of a significant increase in tacrolimus bioavailability (p < 0.05) and a slow decrease in tacrolimus steady-state clearance. Consequently, tacrolimus dose-requirements corrected for bodyweight decrease significantly in the first postoperative year (p < 0.05), in part because of the simultaneous tapering of the corticosteroid dose which significantly affects tacrolimus bioavailability (p < 0.05). Other clinical variables that significantly influenced tacrolimus administration, exposure and bioavailability in a time-related fashion were identified in this study (renal allograft function [p < 0.05], liver dysfunction [p < 0.05], diarrhoea [p < 0.05]), while the clinical relevance of other variables was considerably moderated by our findings (serum albumin, haematocrit). Time-unrelated variables proved to be of significant continuing clinical importance for tacrolimus dose-exposure pharmacokinetics throughout the first post-transplant year (recipient age [p < 0.05], gender [p < 0.01] and donor-receptor gender mismatch [p < 0.05]), while donor hypotension (p < 0.05) and cold ischaemia time (p < 0.05) also proved significant although at present the reasons for this are unknown. Finally, using multiple stepwise regression analysis we demonstrated that classical assessment of tacrolimus exposure by monitoring pre-dose trough blood concentration (or any other single concentration sampling timepoint) is not the most reliable method and that abbreviated AUC measurements may constitute a more accurate clinical tool for (therapeutic) monitoring of drug exposure.

Conclusion: Tacrolimus pharmacokinetics in the first year after renal transplantation are characterised by a specific time-dependent evolution. The identification of clinical variables that determine tacrolimus pharmacokinetics is an important aid in the development of reliable drug monitoring strategies using abbreviated AUC measurements.

Publication types

  • Clinical Trial

MeSH terms

  • Adrenal Cortex Hormones / adverse effects*
  • Adrenal Cortex Hormones / therapeutic use
  • Adult
  • Algorithms
  • Antibiotics, Antineoplastic / adverse effects*
  • Antibiotics, Antineoplastic / therapeutic use
  • Area Under Curve
  • Biological Availability
  • Dose-Response Relationship, Drug
  • Drug Interactions
  • Drug Monitoring
  • Drug Therapy, Combination
  • Female
  • Graft Rejection / prevention & control
  • Half-Life
  • Humans
  • Immunosuppressive Agents / pharmacokinetics*
  • Immunosuppressive Agents / therapeutic use
  • Kidney Transplantation / immunology*
  • Male
  • Models, Biological
  • Mycophenolic Acid / adverse effects*
  • Mycophenolic Acid / therapeutic use
  • Prospective Studies
  • Tacrolimus / pharmacokinetics*
  • Tacrolimus / therapeutic use
  • Time Factors


  • Adrenal Cortex Hormones
  • Antibiotics, Antineoplastic
  • Immunosuppressive Agents
  • Mycophenolic Acid
  • Tacrolimus