Evaluation of Bayesian Forecasting Methods for Prediction of Tacrolimus Exposure Using Samples Taken on Two Occasions in Adult Kidney Transplant Recipients

Bayesian optimization of tacrolimus exposure in stable kidney transplant patients

STUDY OBJECTIVE

The objective was to compare tacrolimus AUC0-12 determined by Non-Compartmental Analysis (NCA) using intensive sampling to Maximum a Posteriori-Bayesian (MAP-Bayesian) estimates from robust (n = 9 samples/subject) and sparse (n = 2 samples/subject) sampling in 67 stable KTRs and a validation group of similar patients.

DESIGN

This open-label, prospective, single center 12-h PK study included nine serial samples collected in KTRs to determine steady-state NCA tacrolimus AUC0-12 .

SETTING

This study was conducted at a single site within a large, urban hospital in the western New York area.

PATIENTS

This study described tacrolimus pharmacokinetics in stable kidney transplant recipients on maintenance tacrolimus therapy.

INTERVENTION

Robust and sparse AUC0-12 estimates by a MAP-Bayesian approach were obtained using the Advanced Dosing Solutions (AdDS) and ADAPT5 freeware. Limited sampling strategies were evaluated using the original population PK model (n = 67), which was also assessed using a validation group (n = 15). AUC0-12 agreement was tested by paired t-tests with intraclass correlation coefficient (ICC) and Bland Altman analysis.

MEASUREMENTS AND MAIN RESULTS

A total of 35 Black and 32 White stable KTRs (estimated glomerular filtration rate [eGFR] = 55.2 ± 15.7 mL/min/1.73m2 ) received the tacrolimus dose of 3.4 ± 1.7 mg/study with troughs of 6.8 ± 1.8 ng/mL. The NCA-AUC0-12 was 123.8 ± 33.6 μg·h/L compared to MAP-Bayesian estimates for Robust-AUC0-12 of 124.7 ± 33.3 μg·h/L and optimal 2-specimen Sparse-AUC0-12 of 119.7 ± 32.7 μg·h/L for the training group. Comparison of Robust-AUC0-12 to NCA-AUC0-12 had an ICC of 0.96 (p = 0.99) while comparison of Robust-AUC0-12 to Sparse-AUC0-12 using Pre-dose trough [C(t0h )] and 1 h [C(t1h )] resulted in an ICC of 0.93 (p = 0.014). In the validation group, 5 Black and 10 White KTRs (eGFR = 56.4 ± 16.8 mL/min/1.73m2 ) received a mean tacrolimus dose of 1.9 ± 1.2 mg/study with a trough of 6.0 ± 1.7 ng/mL. The validation group's NCA-AUC0-12 (88.4 ± 33.1 μg·h/L) was comparable to Robust-AUC0-12 (85.1 ± 33.8 μg·h/L, ICC = 0.93; p = 0.12) and Sparse-AUC0-12 determined from C(t0h ) and C(t4h ) (86.7 ± 33.9 μg·h/L, ICC = 0.91; p = 0.61).

CONCLUSION

MAP-Bayesian estimation for patient-specific AUC0-12 using sparse, two-specimen sampling is comparable to NCA and may enhance tacrolimus TDM in stable KTRs.

External Evaluation of Population Pharmacokinetic Models of Busulfan in Chinese Adult Hematopoietic Stem Cell Transplantation Recipients

Objective: Busulfan (BU) is a bi-functional DNA-alkylating agent used in patients undergoing hematopoietic stem cell transplantation (HSCT). Over the last decades, several population pharmacokinetic (pop PK) models of BU have been established, but external evaluation has not been performed for almost all models. The purpose of the study was to evaluate the predictive performance of published pop PK models of intravenous BU in adults using an independent dataset from Chinese HSCT patients, and to identify the best model to guide personalized dosing.

Methods: The external evaluation methods included prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting. In prediction-based diagnostics, the relative prediction error (PE%) was calculated by comparing the population predicted concentration (PRED) with the observations. Simulation-based diagnostics included the prediction- and variability-corrected visual predictive check (pvcVPC) and the normalized prediction distribution error (NPDE). Bayesian forecasting was executed by giving prior one to four observations. The factors influencing the model predictability, including the impact of structural models, were assessed.

Results: A total of 440 concentrations (110 patients) were obtained for analysis. Based on prediction-based diagnostics and Bayesian forecasting, preferable predictive performance was observed in the model developed by Huang et al. The median PE% was -1.44% which was closest to 0, and the maximum F₂₀ of 57.27% and F₃₀ of 72.73% were achieved. Bayesian forecasting demonstrated that prior concentrations remarkably improved the prediction precision and accuracy of all models, even with only one prior concentration.

Conclusion: This is the first study to comprehensively evaluate published pop PK models of BU. The model built by Huang et al. had satisfactory predictive performance, which can be used to guide individualized dosage adjustment of BU in Chinese patients.

Can Published Population Pharmacokinetic Models of Busulfan Be Used for Individualized Dosing in Chinese Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation? An External Evaluation

Busulfan is a bifunctional alkylating agent that is widely used before hematopoietic stem cell transplantation (HSCT), in combination with other chemotherapeutic drugs. As of 2020, there is no population pharmacokinetic (popPK) model for busulfan in Chinese pediatric patients. A systemic external evaluation of 11 published popPK models was conducted in Chinese pediatric patients undergoing HSCT. Forty pediatric patients were enrolled in this study, with a total of 183 blood concentrations. The relative prediction error (PE%), median PE%, median absolute PE%, and percentage of PE% within ±20% and ±30% were calculated in prediction-based diagnostics. Simulation-based diagnostics were conducted through a prediction- and variability-corrected visual predictive check and the normalized prediction distribution error. The relative individual prediction error was calculated using Bayesian forecasting with 1 to 3 concentration points. The 1-compartment open linear popPK model, which was built by Su-jin Rhee et al (model H), incorporating the patient's body surface area, age, dosing day, and aspartate aminotransferase as significant covariates had preferable predictability than other popPK models. In prediction-based diagnostics, the median PE%, percentage of PE% within ±20%, and percentage of PE% within ±30% of model H were 8.48%, 45.35%, and 59.56%, respectively. The normalized prediction distribution error of model H showed that it followed the normal distribution. Based on Bayesian forecasting, model H showed good predictive performance. Thus, model H was the most appropriate model that can be used clinically for individualized dosage adjustments in Chinese pediatric HSCT patients.