External Evaluation of a Single Center Precision Tacrolimus Dosing Model for Pediatric Heart Transplant

Purpose Extensive variability in tacrolimus pharmacokinetics (PK) influences attainment of therapeutic immunosuppression following pediatric heart transplant. Population PK (popPK) model-based personalized dosing of tacrolimus can reduce the need for tacrolimus titration and improve long-term pediatric heart transplant outcomes. The objective of this study was to externally evaluate a previously described single-center popPK model for personalized tacrolimus therapy. Methods A multicenter, retrospective analysis utilized demographics, tacrolimus dosing and concentration data, and laboratory values collected from pediatric inpatients during the first 6 weeks post-transplant, or until discharge, from three sites. PopPK analysis used the modeling software NONMEM v7.2. Observed concentration data were compared to data simulated with our single-center popPK model, using median prediction error (MPE, a measure of bias, <15% acceptable) and median absolute prediction error (MAPE, a measure of accuracy, <30% acceptable). Results A total of 285 children: 115 female, 196 white, 32 black, were included. Median age and creatinine clearance were 9.1 years and 101 mL/min/1.73m2, respectively. Fluconazole use was rare, occurring in only 6 subjects. When utilizing all data the MPE and MAPE (interquartile range) were acceptable, at -3% (-25%, 10%) and 18% (5%, 49%), respectively, and not different by institution. However, when allowed only 3 concentrations (i.e. the minimum number to adequately predict future concentrations in our prior single-center study) the model poorly predicted future concentrations at all sites (MPE=31%; MAPE=47%). MPE and MAPE varied by site, ranging from 7 to 61% and 30 to 63%, respectively. Including site as a covariate in the popPK model improved predictions (MPE=21%; MAPE=37%) and decreased variability in predictive ability between sites (MPE: 11 to 34%; MAPE: 32 to 45%). Conclusion Our single-center popPK model did not adequately predict tacrolimus concentrations from external institutions. Study site appears to drive some of the poor model performance. Future directions include identifying study site differences that influence model performance, as well as identifying additional patient-specific factors affecting tacrolimus dosing requirements. Extensive variability in tacrolimus pharmacokinetics (PK) influences attainment of therapeutic immunosuppression following pediatric heart transplant. Population PK (popPK) model-based personalized dosing of tacrolimus can reduce the need for tacrolimus titration and improve long-term pediatric heart transplant outcomes. The objective of this study was to externally evaluate a previously described single-center popPK model for personalized tacrolimus therapy. A multicenter, retrospective analysis utilized demographics, tacrolimus dosing and concentration data, and laboratory values collected from pediatric inpatients during the first 6 weeks post-transplant, or until discharge, from three sites. PopPK analysis used the modeling software NONMEM v7.2. Observed concentration data were compared to data simulated with our single-center popPK model, using median prediction error (MPE, a measure of bias, <15% acceptable) and median absolute prediction error (MAPE, a measure of accuracy, <30% acceptable). A total of 285 children: 115 female, 196 white, 32 black, were included. Median age and creatinine clearance were 9.1 years and 101 mL/min/1.73m2, respectively. Fluconazole use was rare, occurring in only 6 subjects. When utilizing all data the MPE and MAPE (interquartile range) were acceptable, at -3% (-25%, 10%) and 18% (5%, 49%), respectively, and not different by institution. However, when allowed only 3 concentrations (i.e. the minimum number to adequately predict future concentrations in our prior single-center study) the model poorly predicted future concentrations at all sites (MPE=31%; MAPE=47%). MPE and MAPE varied by site, ranging from 7 to 61% and 30 to 63%, respectively. Including site as a covariate in the popPK model improved predictions (MPE=21%; MAPE=37%) and decreased variability in predictive ability between sites (MPE: 11 to 34%; MAPE: 32 to 45%). Our single-center popPK model did not adequately predict tacrolimus concentrations from external institutions. Study site appears to drive some of the poor model performance. Future directions include identifying study site differences that influence model performance, as well as identifying additional patient-specific factors affecting tacrolimus dosing requirements.