Evaluation of two software using Bayesian methods for monitoring exposure and dosing once-daily intravenous busulfan in paediatric patients receiving haematopoietic stem cell transplantation

Examining Different Methods to Assess Busulfan Exposure in Pediatric Hematopoietic Stem Cell Transplant Recipients

BACKGROUND

Noncompartmental analysis (NCA) and model-based method (MBM) can be used to estimate the cumulative area under the concentration-time curve (AUC cum ) during therapeutic drug monitoring. Understanding predictive differences among these techniques should assist in switching between them and interpreting their results. The aim of this study was to compare busulfan AUC cum prediction based on NCA technique (Kinetica) and MBM (NextDose) applied to the same concentration-time data from pediatric hematopoietic stem cell transplant (HSCT) recipients.

METHODS

Data on busulfan therapy administered once daily through intermittent infusion were obtained from 4 hospitals in Australia and New Zealand. Busulfan concentrations were measured at 3, 3.25, 4, 5, 6, and 8 hours after infusion initiation over 4 treatment days. Information on busulfan dose, pharmacokinetic profile, and patient covariate factors (if required) were supplied sequentially to Kinetica and NextDose to generate NCA-based and MBM-based predictions of busulfan exposure; differences in AUC cum estimates at the end of the treatment course were compared using Bland-Altman plots, box plots, and Wilcoxon signed-rank sum test.

RESULTS

Data from 90 HSCT recipients (2131 busulfan samples) were included. The median patient age and weight were 4.3 years and 17.0 kg, respectively. Median AUC cum estimated based on NCA and MBM were 78.0 mg.h/L [range: 51.7-107.0] and 85.5 mg.h/L [range: 60.6-120.8], respectively, with statistically significant difference in AUC cum values ( P < 0.001). The mean percentage difference in AUC cum values between the 2 different methods suggested that if the AUC cum target using MBMs (NextDose) is 78-101 mg.h/L, then an equivalent target using NCA (Kinetica) would be reduced by 9.1%.

CONCLUSIONS

When switching between NCA and MBMs to estimate busulfan AUC cum in pediatric HSCT recipients, a change in the target AUC cum is likely required to maintain a similar drug exposure during therapeutic drug monitoring.

Leveraging machine learning in limited sampling strategies for efficient estimation of the area under the curve in pharmacokinetic analysis: a review

OBJECTIVE

Limited sampling strategies are widely employed in clinical practice to minimize the number of blood samples required for the accurate area under the curve calculations, as obtaining these samples can be costly and challenging. Traditionally, the maximum a posteriori Bayesian estimation has been the standard method for the area under the curve estimation based on limited samples. However, machine learning is emerging as a promising alternative for this purpose. Here, we review studies that utilize machine learning approaches to develop limited sampling strategies and compare the strengths and weaknesses of these machine learning methods.

METHODS

We searched the literature for studies that used machine learning to estimate the area under the curve using a limited sampling strategy approach.

RESULTS

We identified ten studies that developed machine learning models to estimate the area under the curve for six different drugs. Several of these models demonstrated good accuracy and precision in area under the curve estimation in reference to the traditional Bayesian approach, highlighting the potential of machine learning models in precision dosing.

CONCLUSIONS

Despite these promising early results, the development of machine learning for limited sampling strategies is still in its early stages. Further research might be needed to validate machine learning models with larger, high-quality clinical datasets to ensure their reliability and applicability in clinical settings.

Model-Informed Precision Dosing of Intravenous Busulfan in Thai Pediatrics Undergoing Hematopoietic Stem Cell Transplantation

BACKGROUND

Conditioning bifunctional agent, busulfan, is commonly used on children before hematopoietic stem cell transplantation. Currently, at the Ramathibodi hospital, Bangkok, Thailand, initial dosing is calculated according to age and body surface area, and 7 samples per day are used for therapeutic drug monitoring (TDM). This study aimed to identify the best strategies for individual dosages a priori from patient characteristics and a posteriori based on TDM.

METHODS

The pharmacokinetic data set consisted of 2018 plasma concentrations measured in 135 Thai (n = 135) pediatric patients (median age = 8 years) and were analyzed using a population approach.

RESULTS

Body weight, presence of malignant disease, and genetic polymorphism of Glutathione S-transferase Alpha-1 (GSTA1) were predictors of clearance. The optimum sampling times for TDM concentration measurements were 0.25, 2, and 5 hours after a 3-hour infusion. This was sufficient to obtain a Bayesian estimate of clearance a posteriori. Simulations showed the poor performance of a priori formula-based dose calculations with 90% of patients demonstrating a 69%-151% exposure interval around the target. This interval shrank to 85%-124% if TDM was carried out only at day 1 and to 90%-116% with TDM at days 1 and 3.

CONCLUSIONS

This comprehensive study reinforces the interest of TDM in managing interindividual variability in busulfan exposure. Therapeutic drug monitoring can reliably be implemented from 3 samples using the Bayesian approach, preferably over 2 days. If using the latter is not possible, the formulas developed herein could present an alternative in Thai patients.

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.

Individualizing busulfan dose in specific populations and evaluating the risk of pharmacokinetic drug-drug interactions

INTRODUCTION

Busulfan is an alkylating agent widely used in the conditioning of hematopoietic stem cell transplantation possessing a complex metabolism and a large interindividual and intra-individual variability, especially in children. Combined with the strong rationale of busulfan PK/PD relationships, factors altering its clearance (e.g., weight, age, and GST-A genetic polymorphism mainly) can also affect clinical outcomes.

AREAS COVERED

This review aims to provide an overview of the current knowledge on busulfan pharmacokinetics, its pharmacokinetics variabilities in pediatric populations, drug-drug interactions (DDI), and their consequences regarding dose individualization. This review was based on medical literature up until October 2021.

EXPERT OPINION

To ensure effective busulfan exposure in pediatrics, different weight-based nomograms have been established to determine busulfan dosage and provided improved results (65 - 80% of patients correctly exposed). In addition to nomograms, therapeutic drug monitoring (TDM) of busulfan measuring plasmatic concentrations to estimate busulfan pharmacokinetic parameters can be used. TDM is now widely carried out in routine practices and aims to ensure the targeting of the reported therapeutic windows by individualizing busulfan dosing based on the clearance estimations from a previous dose.

Therapeutic drug monitoring for cytotoxic anticancer drugs: Principles and evidence-based practices

Cytotoxic drugs are highly efficacious and also have low therapeutic index. A great degree of caution needs to be exercised in their usage. To optimize the efficacy these drugs need to be given at maximum tolerated dose which leads to significant amount of toxicity to the patient. The fine balance between efficacy and safety is the key to the success of cytotoxic chemotherapeutics. However, it is possibly more rewarding to obtain that balance for this class drugs as the frequency of drug related toxicities are higher compared to the other therapeutic class and are potentially life threatening and may cause prolonged morbidity. Significant efforts have been invested in last three to four decades in therapeutic drug monitoring (TDM) research to understand the relationship between the drug concentration and the response achieved for therapeutic efficacy as well as drug toxicity for cytotoxic drugs. TDM evolved over this period and the evidence gathered favored its routine use for certain drugs. Since, TDM is an expensive endeavor both from economic and logistic point of view, to justify its use it is necessary to demonstrate that the implementation leads to perceivable improvement in the patient outcomes. It is indeed challenging to prove the utility of TDM in randomized controlled trials and at times may be nearly impossible to generate such data in view of the obvious findings and concern of compromising patient safety. Therefore, good quality data from well-designed observational study do add immense value to the scientific knowledge base, when they are examined in totality, despite the heterogeneity amongst them. This article compiles the summary of the evidence and the best practices for TDM for the three cytotoxic drug, busulfan, 5-FU and methotrexate. Traditional use of TDM or drug concentration data for dose modification has been witnessing a sea change and model informed precision dosing is the future of cytotoxic drug therapeutic management.

Implementation and Cross-Validation of a Pharmacokinetic Model for Precision Dosing of Busulfan in Hematopoietic Stem Cell Transplanted Children

Busulfan, a drug used in conditioning prior to hematopoietic stem cell transplantation (HSCT) in children, has a narrow therapeutic margin. The model-informed precision dosing (MIPD) of busulfan is desirable, but there is a lack of validated tools. The objective of this study was to implement and cross-validate a population pharmacokinetic (PK) model in the Tucuxi software for busulfan MIPD in HSCT children. A search of the literature was performed to identify candidate population PK models. The goodness of fit of three selected models was assessed in a dataset of 178 children by computing the mean error (ME) and root-mean-squared error of prediction (RMSE). The best model was implemented in Tucuxi. The individual predicted concentrations, the area under the concentration-time curve (AUC), and dosage requirements were compared between the Tucuxi model and a reference model available in the BestDose software in a subset of 61 children. The model from Paci et al. best fitted the data in the full dataset. In a subset of 61 patients, the predictive performance of Tucuxi and BestDose models was comparable with ME values of 6.4% and -2.5% and RMSE values of 11.4% and 13.6%, respectively. The agreement between the estimated AUC and the predicted dose was good, with 6.6% and 4.9% of the values being out of the 95% limits of agreement, respectively. To conclude, a PK model for busulfan MIPD was cross-validated and is now available in the Tucuxi software.

Population pharmacokinetic model for once‐daily intravenous busulfan in pediatric subjects describing time‐associated clearance

This study aimed to characterize the population pharmacokinetics (PK) of busulfan focusing on how busulfan clearance (CL) changes over time during once‐daily administration and assess different methods for measuring busulfan exposure and the ability to achieve target cumulative exposure under different dosing adjustment scenarios in pediatric stem cell transplantation recipients. Daily serial blood sampling was performed and concentration‐time data were analyzed using a nonlinear mixed‐effects approach. The developed PK model was used to assess achievement of target exposure under six dose‐adjustment scenarios based on simulations performed in RStudio (RxODE package)®. A total of 2491 busulfan plasma concentration–time measurements were collected from 95 patients characterizing 379 dosing days. A two‐compartment model with time‐associated CL best described the data with a typical CL of 14.5 L/h for an adult male with 62 kg normal fat mass (NFM; equivalent to 70 kg total body weight), typical volume of distribution central compartment (V1) of 40.6 L/59 kg NFM (equivalent to 70 kg total body weight), and typical volume of distribution peripheral compartment of 3.57 L/62 kg NFM. Model interindividual variability in CL and V1 was 14.7% and 34.9%, respectively, and interoccasional variability in CL was 6.6%. Patient size described by NFM, a maturation component, and time since start of treatment significantly influenced CL. Simulations demonstrated that using model‐based exposure estimates with each dose, and either a proportional dose‐adjustment calculation or model‐based calculated individual CL estimates to support dose adjustments, increased proportion of subjects attaining cumulative exposure within 5% of target compared with using noncompartmental analysis (100% vs. 0%). A time‐associated reduction in CL during once‐daily busulfan treatment was described.

Automation in Busulfan Therapeutic Drug Monitoring: Evaluation of an Immunoassay on two Routine Chemistry Analyzers

BACKGROUND

Therapeutic drug monitoring (TDM) of busulfan is recommended for hematopoietic stem cell transplant recipients. Timely reporting of these TDM results is essential given the short administration period and the planned dose adjustments on day 2. The authors evaluated the performance of a new nanoparticle-based competitive immunoassay on two routine clinical chemistry analyzers and compared its performance to that of an in-house high-resolution mass spectrometry (HRMS) method.

METHODS

The MyCare Oncology Busulfan Assay Kit (Saladax Biomedical) was applied on two routine clinical chemistry analyzers (Abbott Architect c8000 and Roche Cobas c502) with a linearity range of 187-2000 ng/mL. The study evaluation measured imprecision and accuracy, sample probe carry-over, and dilution integrity. Method comparison with liquid chromatography (LC)-HRMS was performed using samples from patients undergoing busulfan treatment.

RESULTS

Within- and between-run coefficient of variations for both analyzers were ≤5.23% and ≤8.45%, respectively, across the busulfan concentration range. The obtained biases were ≤10.3%. Both analyzers met the acceptance criteria for sample probe carry-over and dilution integrity. Agreement between the immunoassay and LC-HRMS was high: 92% and 89% of the samples measured on Architect and Cobas, respectively, were within the ±15% limit compared to the corresponding LC-HRMS results.

CONCLUSIONS

Overall, good analytical performance and high agreement with LC-HRMS results were obtained for the immunoassay installed on both routine clinical chemistry analyzers. Therefore, this assay could be implemented as a valid alternative for LC methods in clinical laboratories on different open-channel clinical chemistry analyzers, resulting in shorter turn-around times for reporting busulfan TDM results with subsequent faster dosage adjustments.

Can First-Dose Therapeutic Drug Monitoring Predict the Steady State Area Under the Blood Concentration-Time Curve of Busulfan in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation?

Busulfan has high intra-individual variability and possible time-dependent changes in clearance, which complicates therapeutic drug monitoring (TDM), as first dose sampling may not predict the steady state concentrations. In this study, we aimed to use Bayesian pharmacokinetic parameters estimated from the first dose to predict the steady state AUC for busulfan. This observational study was conducted among pediatric patients at King Abdullah Specialist Children's Hospital. From each patient, we collected six blood samples (2, 2.25, 2.5, 3, 4, and 6 h after the start of IV infusion of the first dose). A subset of patients were also sampled at the steady state. First, we modeled the data using only the first dose. The model was used to estimate the empirical Bayesian estimates of clearance for each individual patient, then we used the empirical Bayesian estimates of clearance to predict the AUC_0-tau at steady state (i.e., predicted AUC_0-tau). Steady state AUC_0-tau was also calculated for patients sampled at steady state using the trapezoidal method using raw time concentration data; this was considered the reference AUC_0-tau.. Then, we compared the AUC_0-tau predicted using the Bayesian approach with the reference AUC_0-tau values. We calculated bias and precision to assess predictability. In total we had 33 patients sampled after first dose and at steady state. Using the Bayesian approach to predict the AUC_0-tau, bias was -2.8% and precision was 33%. This indicates that first dose concentrations cannot accurately predict steady state busulfan concentrations; therefore, follow-up TDM may be required for optimal dosing.

Therapeutic drug monitoring of antimicrobial drugs in neonates. An opinion paper

PURPOSE

Neonatal infections are associated with high morbidity and mortality rates. Optimal treatment of these infections requires knowledge of neonatal pharmacology and integration of neonatal developmental pharmacokinetics of antimicrobial drugs in the design of dosing regimens for use with different gestational and postnatal ages. Population pharmacokinetic (PK) and pharmacodynamic (PD) models are used to personalize the use of these drugs in these fragile patients. The final step to further minimize variability in an individual patient is therapeutic drug monitoring (TDM), where the same population PK/PD models are used in concert with optimally drawn blood samples to further fine-tune therapy. The purpose of this manuscript is to describe the present status and future role of model-based precision dosing and TDM of antimicrobial drugs in neonates.

METHODS

PubMed was searched for clinical trials or clinical studies of TDM in neonates.

RESULTS

A total of 447 papers were retrieved, of which 19 were concerned with antimicrobial drugs. Two papers (one aminoglycoside and one vancomycin) addressed the effects of TDM in neonates. We found that, in addition to aminoglycosides and vancomycin, TDM also plays a role in beta-lactam antibiotics and antifungal drugs.

CONCLUSION

There is a growing awareness that, in addition to aminoglycosides and vancomycin, the use of beta-lactam antibiotics, such as amoxicillin and meropenem, and other classes of antimicrobial drugs, such as antifungal drugs, may benefit from TDM. However, the added value must be shown. New analytical techniques and software development may greatly support these novel developments.

Total Body Irradiation Forever? Optimising Chemotherapeutic Options for Irradiation-Free Conditioning for Paediatric Acute Lymphoblastic Leukaemia

Total-body irradiation (TBI) based conditioning prior to allogeneic hematopoietic stem cell transplantation (HSCT) is generally regarded as the gold-standard for children >4 years of age with acute lymphoblastic leukaemia (ALL). Retrospective studies in the 1990's suggested better survival with irradiation, confirmed in a small randomised, prospective study in the early 2000's. Most recently, this was reconfirmed by the early results of the large, randomised, international, phase III FORUM study published in 2020. But we know survivors will suffer a multitude of long-term sequelae after TBI, including second malignancies, neurocognitive, endocrine and cardiometabolic effects. The drive to avoid TBI directs us to continue optimising irradiation-free, myeloablative conditioning. In chemotherapy-based conditioning, the dominant myeloablative effect is provided by the alkylating agents, most commonly busulfan or treosulfan. Busulfan with cyclophosphamide is a long-established alternative to TBI-based conditioning in ALL patients. Substituting fludarabine for cyclophosphamide reduces toxicity, but may not be as effective, prompting the addition of a third agent, such as thiotepa, melphalan, and now clofarabine. For busulfan, it's wide pharmacokinetic (PK) variability and narrow therapeutic window is well-known, with widespread use of therapeutic drug monitoring (TDM) to individualise dosing and control the cumulative busulfan exposure. The development of first-dose selection algorithms has helped achieve early, accurate busulfan levels within the targeted therapeutic window. In the future, predictive genetic variants, associated with differing busulfan exposures and toxicities, could be employed to further tailor individualised busulfan-based conditioning for ALL patients. Treosulfan-based conditioning leads to comparable outcomes to busulfan-based conditioning in paediatric ALL, without the need for TDM to date. Future PK evaluation and modelling may optimise therapy and improve outcome. More recently, the addition of clofarabine to busulfan/fludarabine has shown encouraging results when compared to TBI-based regimens. The combination shows activity in ALL as well as AML and deserves further evaluation. Like busulfan, optimization of chemotherapy conditioning may be enhanced by understanding not just the PK of clofarabine, fludarabine, treosulfan and other agents, but also the pharmacodynamics and pharmacogenetics, ideally in the context of a single disease such as ALL.