Limited sampling strategy for predicting busulfan exposure in hematopoietic stem cell transplantation recipients

Limited Sampling Strategies for Estimating Busulfan Area Under the Concentration-Time Curve: Based on Peak and Trough Concentrations in Saliva

Therapeutic drug monitoring for busulfan is currently performed by multiple plasma sampling. Saliva is considered a noninvasive therapeutic drug monitoring matrix. This study aimed to investigate intravenous busulfan pharmacokinetics (PK) in plasma and saliva, and establish a limited sampling strategy (LSS) for predicting the area under the concentration-time curve from time zero to infinity in plasma (AUC0-∞,p) by using saliva samples. Therefore, the PK of busulfan was studied in 37 Chinese patients. Pearson correlation analysis was used to evaluate the correlation between the AUC of busulfan in plasma and saliva. LSS models were established by the multiple linear regression analysis. The prediction error, the mean prediction error, and the root mean square error were used to evaluate the predictive accuracy. The agreement between the predicted and observed AUC0-∞ in saliva was investigated by the intraclass correlation coefficient and Bland-Altman analysis. The accuracy and robustness of the models were evaluated by using the bootstrap procedure. The result of PK analysis 62.2% of patients (23/37) was within the target range of AUC0-∞,p . A good correlation between saliva and plasma busulfan AUC0-∞ was observed (r = 0.63, p < .01). The bias and precision of the models 7 and 13 were less than 15%. The intraclass correlation coefficient exceeded 0.9, and the limits of agreement were within ±15%. The 2-point LSS model in saliva is a convenient and desirable approach to predict the AUC0-∞ of 4 times daily intravenous busulfan in plasma, which can be used to design personalized dosing for busulfan.

Limited Sampling Strategy Using End of Infusion and Six-Hour Concentrations Overestimates Intravenous Busulfan Clearance Compared With Standard Six-Point Sampling in Hematopoietic Stem Cell Transplant Patients

BACKGROUND

Therapeutic drug monitoring for busulfan (Bu) is important to improve outcomes of hematopoietic stem cell transplantation. However, standard therapeutic drug monitoring requires multiple samples and is inconvenient, labor-intensive, and costly. Accordingly, a limited sampling strategy (LSS) was evaluated, using 2-point sampling at end of infusion and at 6 hours, and the area-under-the-curve and Bu clearances (CLs) were compared with the results obtained from the standard sampling strategy (SSS) using 5-6 samples.

METHOD

The analysis was based on retrospective clinical data from 202 patients receiving intravenous Bu before hematopoietic stem cell transplantation for malignant or nonmalignant conditions. Bu plasma concentrations were measured via liquid chromatography tandem-mass spectrometry, and pharmacokinetic parameters were calculated using the PKCNA package in R program.

RESULT

A total of 502 doses were analyzed by applying SSS and LSS. Using the modified Bland-Altman plot, the mean percentage difference in CL between the SSS and LSS estimates of Bu 6-hourly regimen was -41% (Limits: -53% and -30%). In the once daily regimen, the mean difference in CL between the 2 strategies on the modified Bland-Altman plot was -22% (Limits: -66% and +22%).

CONCLUSIONS

The Bu CL values estimated based on the BU concentration at end of infusion and at 6 hours postinfusion were significantly higher than the values obtained via the SSS.

Predicting busulfan exposure in patients undergoing hematopoietic stem cell transplantation using machine learning techniques

PURPOSE

This study aimed to establish an optimal model to predict the busulfan (BU) area under the curve at steady state (AUCss) by using machine learning (ML).

PATIENTS AND METHODS

Seventy-nine adult patients (age ≥18 years) who received BU intravenously and underwent therapeutic drug monitoring from 2013 to 2021 at Fujian Medical University Union Hospital were enrolled in this retrospective study. The whole dataset was divided into a training group and test group at the ratio of 8:2. BU AUCss were considered as the target variable. Nine different ML algorithms and one population pharmacokinetic (pop PK) model were developed and validated, and their predictive performance was compared.

RESULTS

All ML models were superior to the pop PK model (R2 = 0.751, MSE = 0.722, 14 and RMSE = 0.830) in model fitting and had better predictive accuracy. The ML model of BU AUCss established through support vector regression (SVR) and gradient boosted regression trees (GBRT) had the best predictive ability (R2 = 0.953 and 0.953, MSE = 0.323 and 0.326, and RMSE = 0.423 and 0.425).

CONCLUSION

All the ML models can potentially be used to estimate BU AUCss with the aim of facilitating rational use of BU on the individualized level, especially models built by SVR and GBRT algorithms.

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.

Estimation of the area under concentration-time curve of polymyxin B based on limited sampling concentrations in Chinese patients with severe pneumonia

AIMS

The efficacy and toxicity of polymyxin B (PB) are closely related to its pharmacokinetic/pharmacodynamic (PK/PD) index area under the concentration-time curve (AUC) to minimum inhibitory concentration (MIC) ratio. The purpose of this study was to obtain PK data for PB in Chinese severe pneumonia patients and establish appropriate blood sampling time points for the PB therapeutic drug monitoring (TDM).

SUBJECT AND METHOD

After treatment with at least four doses of PB (50 IU, q12h), the blood samples were collected immediately after the end of infusion (C₀) and 1.5, 2, 4, 6, 8, and 12 h (C_1.5, C₂, C₄, C₆, C₈, C₁₂) after PB administration. The PB blood plasma concentrations were determined using an ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). All 42 patients were randomly divided into modeling (n = 24) and validation (n = 18) groups. The relationship between AUC_ss,24h and PB plasma concentration at each time point in modeling group was analyzed using limited sampling strategy and a PK method based on one-compartment with correction model.

RESULTS

C₆ scheme was found to provide the most accurate prediction of AUC_ss,24h values (r² = 0.984) with the target value of 1.9-4.2 μg/ml at steady state to reach the 50-100 μg h/ml criteria of AUC_ss,24h. C₀ with target value of 1.0-2.8 μg/ml can be considered an alternative sampling scheme (r² = 0.900) but prediction deviation may exist. C₀ and C_max sampling scheme also demonstrated good predicting ability of AUC values using PK model.

CONCLUSION

This study provides a clear plan for the implementation of TDM of PB, which is useful for optimizing the dosing regimen and individualizing treatment in severe pneumonia patients.

Pharmacokinetic analysis for model-supported therapeutic drug monitoring of busulfan in Japanese pediatric hematopoietic stem cell transplantation recipients

This prospective observational study analyzed the pharmacokinetics of busulfan in Japanese children and evaluated the predicting accuracy of previous pediatric PPK models of busulfan. This study enrolled five patients (aged 2-12 years, BW 14-48 kg) receiving a busulfan-based conditioning regimen for hematopoietic stem cell transplantation at our hospital between January 2017 and December 2018. All patients received a 2-hour intravenous busulfan infusion four times daily for a total of 16 doses. After the infusions, 51 plasma samples were collected with the plasma busulfan concentration measured by liquid chromatography-tandem mass spectrometry. PPK model fitting was analyzed using the (%MPE) and the (%MAPE). Limited sampling strategies for estimating busulfan AUC were also evaluated. High interpatient variability was observed in the PK parameters. The most suitable PPK model that reflected our data was McCune's two-compartment model (%MPE -8.7, %MAPE 19.3). A combination sampling method using the busulfan concentration at 2 and 6 hours after the start of the first busulfan dose was found to be able to estimate AUC_4 day . These results provide useful information on busulfan therapeutic drug monitoring in the Japanese pediatric population.

Accurate Prediction of Initial Busulfan Exposure Using a Test Dose With 2- and 6-Hour Blood Sampling in Adult Patients Receiving a Twice-Daily Intravenous Busulfan-Based Conditioning Regimen

This study aimed to predict the area under the curve (AUC) of the initial busulfan dose using a test dose with the sparse sampling scheme in adult patients who underwent hematopoietic cell transplant. A test dose of 0.8 mg/kg busulfan was used 2 days before twice-daily intravenous busulfan-based conditioning regimens were administered. The AUC and the clearance (CL) were calculated for both the test dose and the first dose (AUC_T , CL_T , AUC_1, and CL₁ ) by noncompartmental analysis. The sparse sampling schemes of the test dose were developed by Bayesian method based on the population pharmacokinetic model. The optimal sparse sampling schemes were determined by evaluating the mean prediction error, the root mean square error, the absolute mean prediction error, and Bland-Altman plot. The mean AUC₁ was 7.20 ± 1.48 mg • h/L, which ranged from 4.70 to 9.46 mg • h/L. The AUC₁ was below the therapeutic concentration of 7.38 mg • h/L in 45% (9 of 20) of the patients. The CL_T of 3.05 ± 0.56 mL/min/kg was not significantly different with the CL₁ of 3.03 ± 0.69 mL/min/kg (P = .901). A sampling scheme at 2 and 6 hours after the test dose was developed to predict the AUC_T (mean prediction error of 1.64%, root mean square error of 6.17%, and absolute mean prediction error of 4.94%). Additionally, the Bland-Altman plot showed that the 2-sampling scheme provided an acceptably accurate prediction of the AUC₁ . A test dose with a 2-sampling scheme was sufficient to personalize the initial busulfan dosing in hematopoietic cell transplant recipients.