Two-point blood sampling is sufficient and necessary to estimate the area under the concentration-time curve for intravenous busulfan in infants and young children

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.