# Adaptive dosing of high-dose busulfan in real-world adult patients undergoing haematopoietic cell transplant conditioning

Dorian Protzenko, Benjamin Bouchacourt, Laure Carriat, Paul Maroselli, Clara Boéri, Raynier Devillier, Joseph Ciccolini

► To cite this version:

Dorian Protzenko, Benjamin Bouchacourt, Laure Carriat, Paul Maroselli, Clara Boéri, et al.. Adaptive dosing of high-dose busulfan in real-world adult patients undergoing haematopoietic cell transplant conditioning. British Journal of Clinical Pharmacology, 2024, 10.1111/bcp.16343. hal-04824850

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Distributed under a Creative Commons Attribution 4.0 International License
ORIGINAL ARTICLE

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BRITISH
PHARMACOLOGICAL
SOCIETY

# Adaptive dosing of high-dose busulfan in real-world adult patients undergoing haematopoietic cell transplant conditioning

Dorian Protzenko<fnref n="1" /> | Benjamin Bouchacourt<fnref n="2" /> | Laure Carriat<fnref n="3" /> | Paul Maroselli<fnref n="3" /> |
Clara Boéri<fnref n="3" /> | Raynier Devillier<fnref n="2" />,<fnref n="4" /> | Joseph Ciccolini<fnref n="1" />,<fnref n="3" /> ID

**Correspondence**

Joseph Ciccolini, COMPO, Inserm U1068 Centre de Recherche en Cancérologie de Marseille, Aix Marseille University, Marseille, France.

Email: ciccolini.joseph@gmail.com

**Funding information**

None.

**Aim:** To evaluate the effectiveness of a Bayesian adaptive dosing strategy in achieving target busulfan exposure in adult patients undergoing haematopoietic cell transplantation (HCT).

**Methods:** This study included 71 adult patients scheduled to receive high-dose busulfan. Busulfan was administered to achieve a cumulative area under the curve (AUC) of 66.0 mg/L/h (16 000 µM/min), 82.60 mg/L/h (20 000 µM/min) or 87.6 mg/L/h (21 200 µM/min) depending on the regimen. Individual pharmacokinetic (PK) parameters of busulfan were estimated from three blood samples using a one-compartment model and Bayesian estimation after the first standard dose. Individual PK parameters were used to adjust subsequent doses to achieve the target exposure.

**Results:** All patients had their dose adjusted after the first dose administration. The final deviation from the target AUC was significantly improved compared to the initial deviation after standard mg/kg dosing (mean absolute deviation 19.5% vs 11.7%, *P* < .01). In addition, the proportion of patients with marked deviation from target exposure (ie, >25%) decreased significantly from 31% after standard dosing to 10% after PK-guided dosing (*P* < .01). Canonical busulfan-related toxicity, specifically veno-occlusive disease, was observed in 5% of patients who achieved successful PK-guided dosing. In contrast, one-third of patients with off-target exposure with poor dosing experienced toxicity.

**Conclusion:** The Bayesian adaptive dosing strategy significantly improves the accuracy of achieving the target busulfan AUC in patients undergoing HCT. This approach not only reduces marked deviations from target exposure, but also reduces the incidence of busulfan-related toxicity, thereby maintaining a favourable toxicity/efficacy ratio.

**KEYWORDS**

adaptive dosing, Bayesian, busulfan, haematopoietic cell transplant, pharmacokinetics

<footnote n="1">¹COMPO, Inserm U1068 Centre de Recherche en Cancérologie de Marseille, Aix Marseille University, Marseille, France</footnote>

<footnote n="2">²HematologyDepartment, Paoli Calmettes Institute, Marseille, France</footnote>

<footnote n="3">³PRISM Platform, Biogénopôle, La Timone University Hospital of Marseille APHM, Marseille, France</footnote>

<footnote n="4">⁴OHIO, Inserm U1068 Centre de Recherche en Cancérologie de Marseille, Aix Marseille University, Marseille, France</footnote>
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# 1 | INTRODUCTION

Haematopoietic cell transplantation (HCT) is used as a potentially curative treatment for leukaemia. Pre-transplant conditioning plays a central role in allogeneic HCT.¹ There are a variety of conditioning regimens that differ in their early morbidity, relapse-related mortality and long-term disease control. Reduced intensity conditioning shows less early relapse-related morbidity compared to more aggressive myeloablative conditioning, but with a higher relapse rate.² Myeloablative conditioning is based on the administration of a purine analogue and an alkylating agent at high doses.³ Busulfan is the cornerstone of most conditioning regimens, and cumulative doses >12.8 mg/kg are considered high-dose (HD) regimens for myeloablative purposes.² Busulfan is an alkylating agent with several safety concerns, including hepatic veno-occlusive disease (VOD) and interstitial pneumonia.⁴

The target exposure levels for busulfan in both adults and children vary depending on the protocols and dosing regimens: in adults, the target cumulative area under the curve (AUC) for a four-times-daily regimen (ie, Bu-4) is approximately 3.7-5.5 mg/h/L per injection (ie, 900-1350 µM/min), whereas for a once-daily regimen (Bu-1), the target AUC is significantly higher, ranging from 19.2 to 24.0 mg/h/L per injection (ie, 4680-5848 µM/min).⁵⁻⁷ A recent study suggests a new target AUC which is associated with lower non-relapse mortality (NRM). This indicates potential benefits in terms of reducing NRM and improving survival by aiming at the bottom of the recommended target AUC.⁸ In these reduced intensity conditioning (RIC) protocols, the new AUC targets are 3.98 mg/h/L (969 µM/min) for the Bu-4 regimen,⁸ with a total target AUC of 16 000 µM/min⁹⁻¹¹ for the entire regimen.

Strong exposure effects have been repeatedly well documented with busulfan.⁷ Gene polymorphism, comorbidities, age or unknown reasons could lead to slight changes in pharmacokinetics (PKs) among patients.¹²⁻¹⁴ Furthermore, it has been suggested that busulfan could have a time-dependent decrease in clearance¹⁵,¹⁶ that could impact on subsequent dose adjustments and favours model-based dosing.¹⁷ Shifting from oral administration to the intravenous route has markedly improved both inter-and intra-individual PK variability with busulfan and thus helped to reduce drug-related toxicities.¹⁸⁻²⁰ However, due to its narrow therapeutic window, patients with higher systemic exposure still have an increased risk of severe toxicity, whereas patients with lower exposure have a higher risk of graft rejection or disease relapse.²¹ To limit the clinical impact of such variability and to improve the toxicity/efficacy ratio of busulfan, several strategies have been proposed over the last two-decades to individualize dosing. Most of those strategies are based on PK-guided dosing, using either non-compartmental analysis or more sophisticated population-pharmacokinetic (pop-PK) approaches.¹⁶,²² Recently, machine learning techniques have been proposed to help predict busulfan exposure.²³ However, PK-guided dosing using dedicated models is usually considered as the best strategy to improve the safety of busulfan, especially when high doses are to be given,¹¹,²⁴ and to increase its efficacy.²⁵,²⁶ PK-guided approaches are usually based on extensive sampling or can rely on sparse sampling provided that pop-PK parameters are

## What is already known about this subject

* Administration of high-dose busulfan is risky in haematopoietic cell transplantation due to its narrow therapeutic window.

* Achieving target busulfan exposure is essential to balance efficacy and toxicity, but standard dosing often results in significant deviations from target in patients.

* There is a need for simple adaptive dosing strategies to optimize busulfan exposure and minimize adverse effects.

## What this study adds

* Only three plasma samples allowed Bayesian identification of individual pharmacokinetic parameters and subsequent dose tailoring.

* Bayesian adaptive dosing significantly improves the accuracy of busulfan exposure to target in patients undergoing haematopoietic cell transplantation.

* This approach reduces the risk of off-target exposure and subsequent busulfan-related toxicities such as veno-occlusive disease.

available.²⁷ Typically, the first administration is given at a standard mg/kg dose with PK monitoring to evaluate the exposure and tailor subsequent courses with respect to the target cumulative AUC to be achieved. Others have also suggested a preliminary suboptimal test dose combined with subsequent therapeutic drug monitoring (TDM) to better tailor the subsequent busulfan dose.²⁸,²⁹ However, no real consensus has emerged yet.³⁰ For example, it is unclear whether longitudinal TDM of busulfan provides a clinical benefit compared with on-off (ie, baseline) TDM.³¹ Here, we present the performances of PK-guided dosing of HD busulfan using a Bayesian approach in real-world adult patients scheduled for HCT.

# 2 | PATIENTS AND METHODS

## 2.1 | Patients

This was a retrospective observational study conducted at the Department of Haematology of the Institut Paoli Calmettes, Marseille, France, and the Biogenopole of the Assistance Publique Hôpitaux de Marseille, Marseille, France. All our real-world data were included, ie, the entire cohort of patients treated with HD busulfan as part of conditioning for haematopoietic stem cell transplantation, without censoring. The retrospective data collection and statistical analysis were
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approved by the Review Board (Cellule d'Evaluation at Direction de la Recherche et de l'Innovation) of the institution and followed the European recommendations regarding the Clinical Trials Regulation (EU) 536/20141 and the General Data Protection Regulation (EU) 2016/6792, hereafter General Data Protection Regulation. This study was granted the French MR004 legal status regarding data analysis in the standard care of patients. Routine clinical and biological data, including busulfan serum concentrations, were retrieved from the institution database. Given the retrospective and observational nature of this study, signed informed consent was waived by our institution review board. As an observational study, we collected data from any patient scheduled for HD busulfan as part of a conditioning regimen, therefore because no hypothesis was to be tested, no appropriate sample size applies here.

## 2.2 | Busulfan dosing and scheduling

Busulfan was administered once a day (BU-1) in four to six consecutive courses as a 3-h continuous infusion (T = 3 h). Total body weight was used to customized the initial theoretical dose (ie, 3 mg/kg), but this standard dose was likely to be modified at the discretion of the clinician based on clinical considerations such as extreme weight, comorbidities, age or performance status. The target exposure was based on the total cumulative AUC (ie, the sum of all intermediate AUCs reached after each separate dose). The target AUC was defined as 66 mg/h/L (16 000 µM/min, ie, RIC), 82.6 mg/h/L (20 000 µM/min) or 87.6 mg/h/L (21 200 µM/min), myelo-ablative conditioning for both,⁹⁻¹¹ depending on the type of donor (ie, genoidentical, phenoidentical, haploidentical), the associated drugs in the conditioning regimen (Fludarabine, Thiotepa) and graft vs host disease prophylaxis. Fludarabine was associated with busulfan in every patient of the cohort (Thiotepa- Busulfan- Fludarabine (TBF) regimen) and was given at 40 mg/m²/d for 4 days following the current guidelines.

## 2.3 | PK analysis and Bayesian adaptive dosing

Busulfan plasma concentration was measured using a bioanalytical method fully validated following ISO-15189 and European Medical Agency guidelines.³² Briefly, after simple liquid-liquid extraction from plasma, busulfan was analysed by triple quadrupole liquid chromatography-mass spectrometry (TQS Waters France) over a 50-3000 ng/ mL range with precision and accuracy both <15%.

All PK analyses were performed using Kinetic Pro software (MIPPS), which runs Bayesian estimation of PK parameters and allows subsequent adaptive dosing.

PK processing of patients' data was based on Bayesian estimation technique. The technique combines a term describing the pop-PKs of busulfan with the individual assessed data (ie, busulfan plasma concentrations) to calculate individual PK parameters.

On the one hand, pop-PKs of busulfan has been established from previously monitored patients with rich sampling, using the standard

two-stage method.³³ In a first stage, individual PK parameters of busulfan were obtained in 16 adult patients undergoing HCT and treated with HD busulfan enrolled in the HGB-207 protocol (EuraCT 2015-004122-33) with the rich-sampling protocol (ie, eight samples per patient collected from 2 to 12 h post administration) for compartmental PK analysis, using the weighted-least-squared estimator and considering a proportional error variance model. The 16 kinetic profiles were best described by a one-compartment model with concentrations c(t) given by

$$c(t) = \frac{D}{T} \times \frac{1}{kV} \times [1 - e^{-kt}] \quad \text{for } 0 \le t < T \text{ and}$$

$$c(t) = \frac{D}{T} \times \frac{1}{kV} \times [e^{-k(t-T)} - e^{-kt}] \quad \text{for } T \le t$$

In the above expressions, D is the dose infused over T, V is the volume of distribution, and k is the elimination rate. In a second stage, from the available PK parameters on the 16 patients and assuming Gaussian distribution for interindividual variability, we determined the mean population parameters:

$$\begin{cases} V^{-1} \\ k \end{cases} = \begin{cases} 2.277 \times 10^{-2} L^{-1} \\ 2.448 \times 10^{-1} h^{-1} \end{cases},$$

and the associated covariance matrix:

$$\begin{bmatrix} 2.018 \times 10^{-5} & 3.104 \times 10^{-5} \\ 3.104 \times 10^{-5} & 5.338 \times 10^{-4} \end{bmatrix},$$

On the other hand, three blood samples were taken from routine patients at 3 h and 15 min, 9 h and 12 h after the start of the infusion. Individual data consisted of the busulfan concentrations measured in these samples.

Finally, population characteristics and the three-sample individual data were combined with the Bayesian estimation to obtain individual PK parameters. All the above data processing was performed using Kinetic Pro software.³⁴

For each individual i, PK parameters $V_i^{-1}$ and $k_i$ were consistently estimated. The other derived parameters, such as clearance $CL_i = V_i k_i$, half life $t_{1/2} = \ln^2 k_i$ and the area under the curve $AUC_i = \frac{D_i}{CL_i}$, were assessed with standard errors below 20%.

Adaptive dosing was performed by considering the remaining total area under the curve to be reached (that is the target cumulative $AUC^* - AUC_i$) and the remaining number of busulfan administrations was n. Briefly, the adjusted dose is:

$$D_i^* = \frac{1}{n} \frac{AUC^* - AUC_i}{CL_i}$$

For all patients, PK sampling was systematically performed during the last injection to assess the latest $CL_i$ and $AUC_i$. This was then used to sum all the intermediate $AUC_i$ values and make a comparison with the target cumulative exposure.
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The performance of the dosing adjustment in terms of reaching the target AUC* was set at ±25% deviation from the target to define a right exposure.

## 2.4 | Clinical endpoints and data analysis

Overall survival (OS) was considered the primary efficacy endpoint. Non-relapse mortality (NRM) and VOD (defined using EBMT 2023 criterias³⁵) were considered as treatment-related toxicity endpoints.

The efficacy of Bayesian dose adjustment was assessed by comparing the absolute deviation (%) from the first target AUC after standard dosing and the absolute deviation (%) from the cumulative target AUC after dose adjustment. The distributions of first and cumulative AUC, clearances and doses before and after adaptive dosing were also evaluated.

Cumulative AUC was computed as:

$$AUC_n = AUC_1 + (n - 2) \times AUC_{last}$$

where AUCn is the cumulative AUC obtained after all the administrations, AUC1 is the first measured AUC (after standard dosing), AUClast is the last measured AUC (after adapted dosing) and n is the total number of administrations.

Relationships between cumulative AUC and clinical endpoints of OS, relapse, NRM and VOD were investigated using Kaplan-Meier analysis.

Statistical analysis of the data was performed using R (version 4.3.2). Graphs were generated using the ggstatsplot package.³⁶ Comparisons between before and after adjustment were made using parametric (Student for paired data, Chi2 Mac Nemar, Chi2 Pearson) and non-parametric (Wilcoxon signed ranks test) tests. Normality of distributions was assessed using the Shapiro-Wilk test and variability was compared using the Fligner-Killeen test.

# 3 | RESULTS

## 3.1 | Patients

Overall, 71 adult patients were treated using the PK-guided HD busulfan protocol between January 2016 and September 2022. The clinical and biological characteristics of the patients at baseline are described in Table 1.

Busulfan was administered repeatedly on a once-daily basis over several consecutive days. Six patients were treated with six consecutive administrations using a retro-planning from the day the HCT is scheduled (ie, 21 days before the HCT (D-21), D-14, D-8, D-6, D-5 and D-4), five patients were treated with five consecutive administrations (ie, D-14, D-8, D-6, D-5 and D-4) and the remaining 60 patients were treated with four consecutive daily administrations (ie, D-6, D-5, D-4 and D-2). Mean busulfan dosing was 2.9 mg/kg (range 1.6-4 mg/kg). Mean absolute busulfan dosing when treatment started was 210 mg (median 211 mg, range 126-294 mg).

<table><caption>TABLE 1 Clinical and biological characteristics at baseline.</caption><thead><tr><th>Characteristic</th><th>N = 71<sup>1</sup></th></tr></thead><tbody><tr><td>Age</td><td>53 (± 14)/19-71</td></tr><tr><td>Sex</td><td></td></tr><tr><td>Female</td><td>35 (49%)</td></tr><tr><td>Male</td><td>36 (51%)</td></tr><tr><td>Weight (kg)</td><td>73 (± 17)/40-135</td></tr><tr><td>Body area (m<sup>2</sup>)</td><td>1.87 (± 0.26)/1.33-2.71</td></tr><tr><td>PS score</td><td></td></tr><tr><td>0</td><td>33 (47%)</td></tr><tr><td>1</td><td>34 (49%)</td></tr><tr><td>2</td><td>3 (4.3%)</td></tr><tr><td>Unknown</td><td>1</td></tr><tr><td>DRI score</td><td></td></tr><tr><td>High</td><td>12 (17%)</td></tr><tr><td>Intermediate</td><td>49 (70%)</td></tr><tr><td>Low</td><td>6 (8.6%)</td></tr><tr><td>Very high</td><td>3 (4.3%)</td></tr><tr><td>Unknown</td><td>1</td></tr><tr><td>Pathology</td><td></td></tr><tr><td>Refractory anaemia with excess blasts</td><td>2 (2.8%)</td></tr><tr><td>Drepanocytosis</td><td>1 (1.4%)</td></tr><tr><td>Acute dendritic leukaemia</td><td>1 (1.4%)</td></tr><tr><td>Acute lymphoblastic leukaemia</td><td>15 (21%)</td></tr><tr><td>Acute myeloid leukaemia</td><td>22 (31%)</td></tr><tr><td>Chronic lymphocytic leukaemia</td><td>1 (1.4%)</td></tr><tr><td>Chronic myeloid leukaemia</td><td>5 (7.0%)</td></tr><tr><td>T lymphoma</td><td>3 (4.2%)</td></tr><tr><td>Myelofibrosis</td><td>10 (14%)</td></tr><tr><td>Multiple myeloma</td><td>2 (2.8%)</td></tr><tr><td>Myeloproliferative syndrome</td><td>5 (7.0%)</td></tr><tr><td>Myelodysplastic syndrome</td><td>3 (4.2%)</td></tr><tr><td>Vaquez disease</td><td>1 (1.4%)</td></tr><tr><td>AUC target (µM/min)</td><td></td></tr><tr><td>16 000</td><td>30 (42%)</td></tr><tr><td>18 000</td><td>1 (1.4%)</td></tr><tr><td>20 000</td><td>12 (17%)</td></tr><tr><td>21 200</td><td>28 (39%)</td></tr><tr><td>AST (UI/L)</td><td>27 (± 16) /5-102</td></tr><tr><td>ALT (UI/L)</td><td>35 (± 28) /6-186</td></tr><tr><td>YGT (UI/L)</td><td>85 (± 100) /10-529</td></tr><tr><td>Bilirubinemia (µmol/L)</td><td>12 (+/- 8) /3-37</td></tr><tr><td>CRP (mg/L)</td><td>22 (+/- 51) /1-343</td></tr><tr><td>Unknown</td><td>5</td></tr><tr><td>Overall dosage (mg/kg)</td><td>12.19 (± 2.35) /6.59-16.95</td></tr><tr><td>Cumulated AUC</td><td>18 930 (± 3403) /11421-28 106</td></tr></tbody></table>

<footnote n="1">¹Mean (± SD)/range.</footnote>
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## 3.2 | Impact of Bayesian dose adjustment on exposure

The mean absolute deviation from the intermediate target after the first standard dosing was 19.5%, with Q1-Q3 quartiles of 8.3% and 27.3% (relative range -47.6% to +60.1%). With respect to an accepted threshold of ±25%, 19 patients (ie, 31%) were outside the acceptable range after standard dosing. After Bayesian dose adjustment, the mean deviation from the overall target significantly decreased from 19.5% to 11.7% (P < .05 using both the paired t-test and Wilcoxon test), with Q1-Q3 quartiles of 3.6% and 17.5% (relative range -41.3% to + 40.5%). The number of patients outside the acceptable range of ±25% decreased from 19 to six (ie, 10%) (P < .05, Chi2 Mac Nemar) (Figures 1 and 2).

## 3.3 | Impact of Bayesian dose adjustment on the AUC distribution

The estimated AUC after the first administration did not follow a normal distribution (P < .05, Shapiro-Wilk) due to an asymmetrical distribution. However, after Bayesian adjustment, the estimated cumulative AUCs showed a normal distribution (P > .05, Shapiro-Wilk)

displaying more homogeneous and tighter distribution, as supported by the fact that the variance of the cumulative AUC decreased after Bayesian adaptation (P < .05, Fligner-Killeen test, both scaled), as well as the observed reduction in the deviation from target (Figure 3). Notably, the dose-weight distribution after Bayesian fitting was wider

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**FIGURE 3** Comparison of the cumulative cumulated area under the curve (AUC) distribution (normalized).</figcaption></figure>

**FIGURE 1** Comparison of the percentage deviation from the intermediate target area under the curve (AUC) (ie, after standard dosing) and the cumulative target AUC (ie, after Bayesian adaptive dosing) (P < .001, n = 71).

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**FIGURE 2** Comparison of the percentage deviation rate from the intermediate target area under the curve (AUC) (first administration using standard dosing) and the cumulative target AUC reached after Bayesian adaptive dosing.

<figure><img src="image_11.png" /></figure>
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(median = 2.6 mg/kg, mean = 2.7, 1st quartile = 2.2, 3rd quartile = 3.3, range = 1.0-4.4) compared to the standard dose distribution (median = 3.0 mg/kg, mean = 2.9, 1st quartile = 2.6, 3rd quartile = 3.3, range = 1.6-4.0), although this difference was not statistically significant (P = .4, Fligner-Killeen test) (Supporting Information Figure S1).

## 3.4 | Intra-patient variability

Overall, there was no statistically significant difference in clearance between the first and last dose (12.0 ± 3.1 vs 11.5 ± 3.42 L/h,

P = .09, paired t-test), but a slightly wider distribution was observed (Q1-Q3: 9.8 and 13.9 L/h vs 9.1 and 13.6 L/h) (Figure 4). Notably, one third of the patients (23, or 33% of our initial cohort) showed marked changes between the first and the last dose, with an absolute change in clearance >20% (Figure 5). In 16 of these 23 patients, a mean -27% decrease in clearance (Q1 23%, Q3 30%, range 21-41%) was observed, whereas conversely in seven other patients a mean +43% increase in clearance was found (Q1 21.4%, Q3 51.3%, range 21-60%). The rest of the cohort (67% of patients) had little variation in plasma clearance and elimination of busulfan remained stable over time. Interestingly, patients with decreased clearance (mean relative deviation rate from target AUC +16%) and

<figure><img src="image_13.png" /><figcaption>**FIGURE 4** Comparison of the estimated clearance of busulfan after the first and last administration (P = .09, n = 71).</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**FIGURE 5** Comparison of the intra-patient variability on busulfan clearance between the first and last administrations.</figcaption></figure>
those with increased clearance (mean relative deviation rate from target AUC –18%) had a higher deviation from the target AUC compared to those with stable clearance (mean relative deviation rate from target AUC 0%, $P < .05$, Chi2 Kruskal-Wallis and Dunn test) (Figure 6). Logically, 29% of patients with increased clearance were next underexposed, compared to 2% of patients with stable clearance. Conversely, 12% of patients with decreased clearance were overexposed, compared to 2% of patients with stable clearance ($P < .05$ for both, Chi2 Pearson).

## 3.5 | Impact of Bayesian adaptive dosing on toxicities and survival

The effect of Bayesian fitting and deviation from the target AUC was assessed on both toxicity and survival endpoints. Regarding toxicities, we compared the incidence VOD in our patients with the sparse literature that could be compared to our cohort.¹,³⁷ A total of 7% of patients (ie, five) in our population had VOD (one mild, two moderate, one severe and one very severe). However, in our study 11 patients (ie, 14%) were considered frail and at risk for VOD and subsequently treated in a time-sequenced manner, unlike most studies in the literature. When focusing on the subset of 60 patients treated with four administrations of busulfan, which is the common regimen found in the literature, the incidence of VOD in our patients was 1.7%, lower than usually reported in this setting,³⁷,³⁸ and close to the incidence reported when busulfan is adjusted on a daily dose basis.¹

Interestingly, the incidence of VOD was significantly higher in the overexposed group (VOD in 50% of overexposed patients) compared to the patients with adequate exposure (5% VOD) and underexposed (0% VOD) patients ($P < .05$, Chi2 Pearson).

Regarding efficacy, OS at 6 months was 82% in the subset with correct exposure, 100% in the underexposed group and only 50% in the overexposed group. NRM at 6 months was 11% in patients with adequate exposure, 0% in the underexposed group and 50% in the overexposed group. Statistical analyses showed no significant difference between those groups, although the difference in NRM was

close to statistical significance ($P = .06$). We also performed a survival analysis using Kaplan-Meier curves, which showed a trend towards a negative impact of marked deviation from target (either in over- or underexposed patients) on both overall survival and NRM (Figure 7A,B). However, this trend was not statistically significant ($P = .27$, $P = .09$, log-rank test).

Finally, the effect of variation in clearance on the probability of survival at 6 months was examined. Patients with decreased clearance had a higher rate of NRM (31%) compared to patients with stable clearance (6%) and patients with increased clearance (0%) ($P < .05$, Chi2 Pearson). Kaplan-Meier curves showed a numerical effect of clearance status on NRM and OS, which was not statistically significant ($P = .4$, $P = .2$, log-rank test).

## 3.6 | Impact of AUC on relapse

In total, only 7% of patients (ie, five) relapsed during follow-up. These five patients were treated with target AUCs of 66.0 mg/L (three patients), 82.5 mg/L (one patient) and 87.6 mg/L (one patient), and all of them were correctly exposed (ie, <25% deviation from the target). No association was thus found between any PK metrics and relapse event, although the small number of patients prevents statistical analysis being performed. Interestingly, the patients with increased clearance appeared to have a higher rate of relapse at 6 months (14%) compared to the patients with stable clearance (8%) and the patients with decreased clearance (0%). Unfortunately, this difference was not statistically significant ($P = .4$, Chi2 Pearson).

## 4 | DISCUSSION

Determining the best way to administrate HD busulfan remains an ongoing issue despite numerous clinical reports over the past two decades. Clear exposure-response relationships have been demonstrated both for clinical efficacy and canonical busulfan-related toxicities such as VOD.³⁹,⁴⁰ The use of a reduced test dose to capture PK parameters for dose adjustment has some limitations due to possible

<figure><img src="image_15.png" /><figcaption>**FIGURE 6** Impact of the clearance status on the exposure target ($P < .05$, $n = 71$).</figcaption></figure>
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>**FIGURE 7** Impact of the deviation from the target area under the curve on overall survival (A) and non-relapse mortality (B).</figcaption></figure>

saturation of clearance and possible overexposure, resulting in a poor correlation between test dose clearance and treatment dose clearance.⁴¹ Other authors have suggested a combined test dose followed by new evaluation of the AUC after the first actual administration to assess whether the dose has been correctly adjusted with respect to the target AUC.²⁹ In a comprehensive study, Shukla et al compared different strategies for achieving target exposure of HD busulfan in children. They showed that Bayesian adaptive dosing performed better than TDM with non-compartmental analysis or conventional dosing.²⁴ In the present study, we have shown that the development of a real-time Bayesian adaptive for HD busulfan is feasible using a limited number of samples (ie, three) and helps to significantly reduce the deviation from the target AUC. Here, we used an extremely simple, one-compartment model for describing busulfan PKs. PK modelling with busulfan relies on one- or two-compartment models, the PKs in children being usually described with a one-compartment model whereas in adults, PKs are preferentially described by two-compartment models. However, one-compartment models have been successfully described in adults too.⁴² In our institution, rich sampling with busulfan (ie, six samples) performed during a previous clinical trial (HGB-207 protocol EuraCT 2015-004122-33) showed that busulfan could be described with a simple, one-compartment model (data not shown). This simplified model allowed us to use three samples only, whereas most PK-guided studies with busulfan require a much higher number of samples to be withdrawn. For instance, in Kangarloo et al,⁴³ Perkins et al,⁴⁴ Salman et al⁴⁵ and Mehta PA et al⁴⁶ five or six samples were necessary to estimate individual PK parameters of busulfan. Remarkably, using only those three blood samples, our adaptive dosing achieved a decrease from 19.5% to 11.7% in the mean deviation from the target, a performance that is similar to that of Yeh et al, which required six blood samples to

successfully target exposure next.⁴⁷ In our study, the number of patients with a large (ie, >25%) deviation from target AUC decreased from 30% after the first standard dose to 10% after PK-guided dosing using three samples. This needs to be compared with other strategies for personalizing HD busulfan that required at least six or seven samples, including in children.²⁹,⁴⁸,⁴⁹

In terms of clinical impact, due to the lack of a control arm and the non-comparative nature of this real-world study, it was not possible to draw direct conclusions regarding the clinical benefit of this approach, such as improving the toxicity/efficacy ratio of busulfan. However, when comparing the incidence of VOD, the canonical toxicity associated with HD busulfan,⁴⁹ we observed that patients treated with four injections had fewer VOD as compared with the incidence reported in the literature in comparable patients. For instance, in Lee et al,⁴⁹ the incidence of VOD ranged from 4% (severe grade) up to 28% (mild grade). In Seong-Gil Ryu et al, the incidence of VOD after the four-infusion protocol was 10%.³⁷ This should be compared with the 1.7% incidence (all grades) we found in the subset of patients treated with four consecutive doses.

We also observed that in our study the incidence of VOD was higher in the subset of patients with a large deviation from the target AUC compared to patients with correct exposure, suggesting that achieving the correct exposure with PK-guided dosing does indeed help to improve the safety of HD busulfan. Regarding efficacy endpoints, we could only observe a trend towards longer OS and less frequent NRM compared to literature data in similar settings. Overall, this real-world study suggests that PK-guided dosing, at least with the current target AUCs, may be more helpful in improving safety than in prolonging survival.

Of note, there are several caveats to this strategy. First, it assumes that individual PK parameters remain the same throughout
<figure><img src="image_18.png" /></figure>

the protocol, which may not be true. Several reports have highlighted that in some unpredictable cases the first-order elimination model failed to accurately describe the elimination of busulfan, ruling out the use of a test dose.⁴⁹ Notably, in this study, one-third of patients had such variability in clearance greater than 20%, ie, between the first and last dose, which affected the accuracy of dose adjustments to reach a target cumulative AUC. In the most extreme cases, this variability in clearance resulted in a large deviation (ie, >25%) from the final target in 10% of our patients, despite PK-guided dosing. Those patients also had poor clinical outcomes, with significantly more VOD and a trend towards poorer NRM. Conversely, in the remaining 90% of patients with correct exposure, we observed a trend towards longer OS and reduced NRM compared to clinical reports in the literature with similar characteristics. The fact that a significant improvement in efficacy could not be demonstrated is due to the real-world nature of this study, in which no patients were censored or excluded due to lack of inclusion criteria. In addition, in this study we mixed different protocols and different levels of target exposure, not to mention the fact that all patients were treated as well with fludarabine, which could act as a major confounding factor on the measured clinical endpoints. This lack of statistical significance could therefore be due to the heterogeneity of the protocols and patients monitored in this study, especially with regard to the relatively small number of patients we followed in our institute.

To improve this performance, and to further reduce the number of patients with a marked deviation from target, one solution would be to perform systematic adaptive dosing with feedback, ie, to perform iterative PK sampling to check that CL values remain the same over time. However, this strategy would require all subsequent courses to be delayed, allowing bioanalysis and pharmacometrics to be performed between two consecutive courses. An alternative strategy would be to develop a pop-PK model of busulfan that incorporates time-varying clearance,²⁴ although in our study, only one-third of patients showed significant variation in clearance values and it was not possible to identify baseline characteristics that could be used to identify such patients. Of note, a pop-PK model including body weight and sex as covariates has been successfully developed to identify busulfan parameters from a single blood sample, but this was done as part of the implementation of test-dose strategies rather than real-time PK-guided dosing as we are doing here.²⁷ However, this suggests that testing and implementing relevant covariates in pop-PK models could help to refine the identification of individual parameters, using an even more limited number of samples. Alternatively, it may be necessary to develop more sophisticated strategies to understand the variability in busulfan CL between events. Unravelling the mechanisms involved in changes in clearance from baseline will likely require sophisticated tools beyond standard pharmacometrics, such as machine-learning (ML) approaches to analyse large data sets.⁵⁰ However, as ML approaches usually require a set of patients that exceeds the set of parameters to be analysed, this means that this type of strategy will only be feasible on a much larger cohort than the one presented in this study.

## 5 | CONCLUSION

HD busulfan is the cornerstone of conditioning protocols for HCT, and the narrow therapeutic window combined with inter-patient variability in PK necessitates personalization of its dosing. Here, we applied a Bayesian procedure using a pop-PK model to identify individual PK parameters from a limited number of samples collected after initial standard dosing. This was then used to personalize the dosing of subsequent administrations to achieve a target cumulative AUC. Our results showed that while 31% of patients were severely off-target on standard dosing, only 10% of patients, a two-thirds reduction, were still off-target after the adaptive dosing strategy. The incidence of VOD in patients with adequate exposure was lower than usually reported after HD busulfan dosing, while a trend towards better OS and reduced NRM was observed in these patients. Conversely, overexposed patients had a 50% incidence of VOD and a trend towards more NRM and shorter OS. Intra-patient variability in CL explains why 10% of patients did not benefit from our strategy. Overall, our data show that the toxicity/efficacy ratio of HD busulfan can be improved in routine practice by PK-guided dosing.

## AUTHOR CONTRIBUTIONS

Dorian Protzenko collected the data, analysed the data, made the figures, made the statistical analysis and wrote the manuscript. Benjamin Bouchacourt collected and analysed the data. Laure Carriat collected and analysed the data and performed pharmacokinetics identification. Paul Maroselli collected and analysed the data. Clara Boéri collected and analysed the data and performed pharmacokinetics identification. Raynier Devillier collected and analysed the data. Joseph Ciccolini designed the study, collected and analysed the data, performed pharmacokinetics identification and wrote the MS.

## ACKNOWLEDGMENTS

We thank Pr Athanassios Iliadis from Aix Marseille University for his help in editing this manuscript.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest related to this work.

## DATA AVAILABILITY STATEMENT

Raw data including PK values, Bayesian model's characteristics and full parameters, and detailed statistical procedures are available upon request to the corresponding author.

## GUIDE TO PHARMACOLOGY

No ligands or targets are disclosed in this article.

## ORCID

Joseph Ciccolini https://orcid.org/0000-0003-1733-3410

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Protzenko D, Bouchacourt B, Carriat L, et al. Adaptive dosing of high-dose busulfan in real-world adult patients undergoing haematopoietic cell transplant conditioning. *Br J Clin Pharmacol*. 2024;1-11. doi:10.1111/bcp.16343