Dosing algorithm revisit for busulfan following IV infusion

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.

Population Pharmacokinetic Model of Intravenous Busulfan in Hematopoietic Cell Transplantation: Systematic Review and Comparative Simulations

BACKGROUND

Busulfan is commonly used in the chemotherapy prior to hematopoietic cell transplantation (HCT). Busulfan has a narrow therapeutic window and a well-established exposure-response relationship with important clinical outcomes. Model-informed precision dosing (MIPD) based on population pharmacokinetic (popPK) models has been implemented in the clinical settings. We aimed to systematically review existing literature on popPK models of intravenous busulfan.

METHODS

We systematically searched Ovid MEDLINE, EMBASE, Cochrane Library, Scopus, and Web of Science databases from inception to December 2022 to identify original popPK models (nonlinear mixed-effect modeling) of intravenous busulfan in HCT population. Model-predicted busulfan clearance (CL) was compared using US population data.

RESULTS

Of the 44 eligible popPK studies published since 2002, 68% were developed predominantly in children, 20% in adults, and 11% in both children and adults. The majority of the models were described using first-order elimination or time-varying CL (69% and 26%, respectively). All but three included a body-size descriptor (e.g., body weight, body surface area). Other commonly included covariates were age (30%) and GSTA1 variant (15%). Median between-subject and between-occasion variabilities of CL were 20% and 11%, respectively. Between-model variabilities in predicted median CL were < 20% in all of the weight tiers (10-110 kg) in the simulation based on US population data.

CONCLUSION

Busulfan PK is commonly described using a first-order elimination or time-varying CL. A simple model with limited covariates were generally sufficient to attain relatively small unexplained variabilities. However, therapeutic drug monitoring may still be necessary to attain a narrow target exposure.

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.

The Correlation Between Busulfan Exposure and Clinical Outcomes in Chinese Pediatric Patients: A Population Pharmacokinetic Study

Aims: The aims of the study were to 1) establish a population pharmacokinetic (Pop-PK) model for busulfan in Chinese pediatric patients undergoing hematopoietic stem cell transplantation (HSCT) and then estimate busulfan exposure and 2) explore the association between busulfan exposure and clinical outcomes.

Methods: A total of 128 patients with 467 busulfan concentrations were obtained for Pop-PK modeling using nonlinear mixed effect model (NONMEM) software. Sixty-three patients who received the 16-dose busulfan conditioning regimen were enrolled to explore the correlations between clinical outcomes and the busulfan area under the concentration–time curve (AUC) using the Cox proportional hazards regression model, Kaplan–Meier method and logistic regression.

Results: The typical values for clearance (CL) and distribution volume (V) of busulfan were 7.71 L h⁻¹ and 42.4 L, respectively. The allometric normal fat mass (NFM) and maturation function (Fmat) can be used to describe the variability in CL, and the fat-free mass (FFM) can be used to describe the variability in V. Patients with AUCs of 950–1,600 µM × min had 83.7% (95% CI: 73.3–95.5) event-free survival (EFS) compared with 55.0% (95% CI: 37.0–81.8) for patients with low or high exposure (p = 0.024). The logistic regression analysis results showed no association between transplant-related toxicities and the busulfan AUC (p > 0.05).

Conclusions: The variability in busulfan CL was related to the NFM and Fmat, while busulfan V was related to the FFM. Preliminary analysis results suggested that a busulfan AUC of 950–1,600 µM × min was associated with better EFS in children receiving the 16-dose busulfan regimen.

Impact of busulfan pharmacokinetics on outcome in adult patients receiving an allogeneic hematopoietic cell transplantation

Busulfan (Bu) is widely used in conditioning regimens before allogeneic hematopoietic cell transplantation, with variable metabolism due to interindividual differences of pharmacokinetics (PK). The purpose of this study was to correlate pharmacokinetics and clinical outcomes. Lower-AUC, in range-AUC and higher-AUC were defined as ±25% of the targeted Bu-AUC. In 2019, we changed Bu dosing from 4×/day (Bu-4) to 1×/day (Bu-1) for ease of application. AUC-target range was reached in 46% of patients; 40% were in low-AUC and 14% in high-AUC. Among all toxicities, viral and fungal infections were significantly more frequent in high-AUC compared with low-AUC (20% vs. 8%; p = 0.01 and 37% vs. 17%; p = 0.03). Bu-1 showed lower PK values (66% vs. 36% of Bu-4 in low-AUC; p < 0.01) and higher incidence of mucositis (p = 0.02). Long-term outcomes at 2 years showed a higher non-relapse mortality (NRM) (p < 0.01) and higher relative risk of death in the high-AUC group compared to the other groups. Cumulative incidence of relapse and acute/chronic GvHD were not significantly different. The optimal cut-off in Bu-AUC associated with low NRM was 969 µmol/l*min (ROC AUC 0.67, sensitivity 0.86 and specificity 0.47) for Bu-4. In conclusion, low-AUC BU-PK seems of benefit regarding NRM and survival.

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.

Efficacy of Pharmacokinetics-Directed Busulfan, Cyclophosphamide, and Etoposide Conditioning and Autologous Stem Cell Transplantation for Lymphoma: Comparison of a Multicenter Phase II Study and CIBMTR Outcomes

Busulfan, cyclophosphamide, and etoposide (BuCyE) is a commonly used conditioning regimen for autologous stem cell transplantation (ASCT). This multicenter, phase II study examined the safety and efficacy of BuCyE with individually adjusted busulfan based on preconditioning pharmacokinetics. The study initially enrolled Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) patients ages 18 to 80 years but was amended due to high early treatment-related mortality (TRM) in patients > 65 years. BuCyE outcomes were compared with contemporaneous recipients of carmustine, etoposide, cytarabine, and melphalan (BEAM) from the Center for International Blood and Marrow Transplant Research. Two hundred seven subjects with HL (n = 66) or NHL (n = 141) were enrolled from 32 centers in North America, and 203 underwent ASCT. Day 100 TRM for all subjects (n = 203), patients > 65 years (n = 17), and patients ≤ 65 years (n = 186) were 4.5%, 23.5%, and 2.7%, respectively. The estimated rates of 2-year progression-free survival (PFS) were 33% for HL and 58%, 77%, and 43% for diffuse large B cell lymphoma (DLBCL; n = 63), mantle cell lymphoma (MCL; n = 29), and follicular lymphoma (FL; n = 23), respectively. The estimated rates of 2-year overall survival (OS) were 76% for HL and 65%, 89%, and 89% for DLBCL, MCL, and FL, respectively. In the matched analysis rates of 2-year TRM were 3.3% for BuCyE and 3.9% for BEAM, and there were no differences in outcomes for NHL. Patients with HL had lower rates of 2-year PFS with BuCyE, 33% (95% CI, 21% to 46%), than with BEAM, 59% (95% CI, 52% to 66%), with no differences in TRM or OS. BuCyE provided adequate disease control and safety in B cell NHL patients ≤ 65 years but produced worse PFS in HL patients when compared with BEAM.