The Impact of Tacrolimus Exposure on Extrarenal Adverse Effects in Adult Renal Transplant Recipients

Abstract

Aims

Tacrolimus has been associated with notable extrarenal adverse effects (AEs), which are unpredictable and impact patient morbidity. The association between model‐predicted tacrolimus exposure metrics and standardized extrarenal AEs in stable renal transplant recipients was investigated and a limited sampling strategy (LSS) was developed to predict steady‐state tacrolimus area under the curve over 12‐hour dosing period (AUC_ss,0‐12hr).

Methods

All recipients receiving tacrolimus and mycophenolic acid ≥6 months completed a 12‐hour cross‐sectional observational pharmacokinetic‐pharmacodynamic study. Patients were evaluated for the presence of individual and composite gastrointestinal, neurological, and aesthetic AEs during the study visit. The associations between AEs and tacrolimus exposure metrics generated from a published population pharmacokinetic model were investigated using a logistic regression analysis in NONMEM 7.3. A LSS was determined using a Bayesian estimation method with the same patients.

Results

Dose‐normalized tacrolimus AUC_ss,0‐12hr and apparent clearance (CL/F) were independently associated with diarrhea, dyspepsia, insomnia, and neurologic AE ratio. Dose‐normalized tacrolimus maximum concentration (C_MAX) was significantly correlated with skin changes and acne. No AE associations were found with trough concentrations. Samples collected at 0‐2, 0‐1‐4 and 0‐1‐2‐4 hours provided a precise and unbiased prediction of tacrolimus AUC (root mean squared prediction error [RMSE] < 10%), which was not well characterized using trough concentrations only (RMSE > 15%).

Conclusions

Several AEs (i.e., diarrhea, dyspepsia, insomnia, and neurologic AE ratio) were associated with tacrolimus dose normalized AUC_ss,0‐12hr, and clearance. Skin changes and acne were associated with dose‐normalized C_MAX concentrations. To facilitate clinical implementation, a LSS was developed to predict AUC_ss,0‐12hr values using sparse patient data to efficiently assess projected immunosuppressive exposure and potentially minimize AE manifestations.

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