The dosages were normalized by BSA (mg/m2) due to the
fact that body size-based dosing is more familiar to pediatricians. As
demonstrated in Figure 5 (B) , the doses normalized by BSA were
shown to be a log-linear function of BSA. Dosages according to this
log-linear curve would be inconvenient for application in clinical, so a
nomogram of BSA categories was derived. Three dosages for each genotype
were then defined according to selected BSA categories in order for
convenient clinical application. Finally,
a new dosing strategy based on BSA
normalization was listed in Table 4 .
BSA-based dosing recommended by the final PPK model was helpful for
targeting the patient AUC. The rate of success in achieving the targeted
AUC window (900-1350 μM·min) was 99.58% in simulated patients. The new
dosage yielded homogeneous AUC values in different BSA categories, and
the CV of 7.57% in AUC was low.
3.5 Limited sampling strategies
Eighteen models with different sampling time points are all listed inTable 5 . The relationship between the predicted and actual
AUC0-6h for these models is shown in Supporting
Information Figure 2 . From two to four-point models, 83.33% LSSs
fitted well with the correlation (r2 ) of more
than 0.85. Prediction precision of LSSs expressed as rRMSE and MAPE is
also given in Table 5 . Model 2 (C2h,
C2.5h, C4h and C6h)
showed not only the best fit to the Bu AUC0-6h, but also
better
prediction
precision (rRMSE = 0.72% and MAPE = 4.55%) than other LSSs. Among the
three-point models, Model 9 (C2h, C2.5hand C4h) and Model 10 (C2h,
C2.5h and C6h) both behaved well, and
with similar prediction precision to Model 2. Within these models, no
patients had an AUC0-6h lower than -15% or higher than
+15% of the reference value. As for these two-sampling schemes, Model
13 (C2h and C4h), Model 14
(C2h and C6h), Model 17
(C2.5h and C4h) and Model 18
(C2.5h and C6h) had relatively low rRMSE
and MAPE. The Bland–Altman plots have verified the excellent capacity
of prediction of the seven models above (Supporting Information
Figure 3) .
3.6 Clinical outcomes
Among the clinical outcomes analyzed, five patients died after HSCT, one
of whom died of SOS and the other four died of severe infection. Graft
failure occurred in two patients with AUC0-6h of 659.9
and 482.7 μM·min. Engraftment was
achieved for 97.10% of patients (median time 12 days, range 10-19 days)
for neutrophils within 30 days after transplantation and 59 patients
achieved engraftment for platelets within a median of 15 days (range
7-30). As shown in Figure 6 , there were significant differences
in
ANC
recovery and survival rate between patients with two GSTA1genotypes (p < 0.05). Seven patients (10.14%)
developed SOS and aGVHD I–IV was
documented in 57 patients (82.61%), of whom seventeen had grade III–IV
disease (24.64%). The clinical outcomes of patients with twoGSTA1 genotypes were compared in Supporting Information
Table 4 .
In a univariate model, type of donor (HLA matched vs. mismatched)
was the single variable that had significant association with the
incidence of aGVHD (p = 0.022) (Figure 7 ). The logistic
regression model was again put into use to investigate the relationship
between the incidence of aGVHD I–IV, aGVHD Ⅲ/Ⅳ, SOS and Bu
AUC0-6h, while Supporting Information Figure 4revealed that no correlation between Bu AUC0-6h and
regimen-related toxicity or mortality was observed.
Discussion
This study developed the first PPK model for busulfan that successfully
incorporated GSTA1 genotypes in Chinese pediatric population and
partly explained the source of large variability of busulfan exposure,
suggesting that GST genotyping would be necessary for optimization of
pediatric Bu treatment.
The literature until now has been controversial regarding the
correlation between Bu clearance with genetic polymorphisms (36). There
have been several researches supporting the positive association betweenGSTs polymorphisms and Bu clearance since 2016. In a pediatric
multicenter study, Ansari et al. (37) reported that the activity
of GSTA1 promoter was significantly descended in the case of*B haplotype compared with*A haplotype, thus the
correlation between GSTA1 genotypes and clearance was
distinguished. In addition, GSTA1 diplotypes with slow
metabolizing capacity were associated with higher incidence of SOS,
aGVHD and combined treatment-related toxicity. Another study succeeded
in incorporating the GST genetic variants (GSTA1 ) into a
PPK model for Bu in a Caucasian pediatric population, and then tailored
the dose according to the individual metabolic capacity (38). For
Chinese adults, Yin et al. (16) concluded that patients withGSTA1 *A/*B genotype possessed a higher
Cmax, higher AUC and lower clearance than the group withGSTA1 *A/*A genotype. Furthermore, GSTA1 expression in
young children has been reported to be higher than adults (19). In a
recent study of Japanese pediatric population (n=20), Nishikawa et
al. (39) stated the correlation between GST polymorphisms and
clearance was distinguished.
In our two cohorts (n=84), the frequency of GSTA1 *Bhaplotype (GSTA1-52T/-69A ) was 11.9% (MAF = 0.119). In other
words, a minority of patients had the *B haplotype, of which
1.19% were homozygous (GSTA1 *B/*B ). Therefore,
the MAF of Chinese patients was
lower than that of global population (MAF = 0.306) taken from 1000
Genomes. Due to the significant discrepancy of the frequency of
haplotype *B between different populations, studies in
Caucasians may not be able to
represent that in the Asian population. Besides, in a study conducted in
patients with acute myeloid leukemia, Yee et al. (40) found that
C allele in the GSTM1 locus (rs3754446) was associated with
decreased Bu AUC of first dose and lower disease-free survival. Thus,GSTA1 and GSTM1 genotypes were taken into consideration in
the present study when exploring the influence of GSTpolymorphisms on Bu PK in Chinese children.
A population pharmacokinetic model of Bu was developed to test the
influence of gene mutation on the pharmacokinetic characteristics. In
the final model, the estimate value of CL was 4.79 L/h and of V was 14.8
L, consistent with those results in previous studies (38). The PPK model
demonstrated that the GSTA1 polymorphisms were associated with Bu
clearance. Patients carrying the GSTA1 *A/*B genotype had a
17.3% lower clearance than those carrying GSTA1 *A/*A , which is
consistent with previous studies reporting that the presence of mutation
allele probably resulted in the decreased activity of GST enzyme (11,
37). However, genetic variation in GSTM1 showed no significant
impact on Bu CL, likely because the function of the GSTM1enzyme involved in Bu metabolism was
less than the GSTA1 enzyme.
As shown in Figure 1 , there was a significant difference in
clearance per body surface area
between the two GSTA1 genotypes, but there was no significant
difference in AUC per body surface area. These results were indeed
confusing. As is well-known, AUC of drug administered intravenously is
affected by the distribution, metabolism and excretion of drug, while CL
is influenced only by metabolism and excretion. There are many complex
physiological factors that affect drug distribution, especially in
children. Thus, AUC may be not completely inversely proportional to CL.
As shown in Supporting Information Figure 1 , the distribution
phase (0-2 hours after dosing) shown large heterogeneity between two
genotypes, which may slightly interfere the statistical analysis of AUC.
Nevertheless, in the eliminate phase, it was obvious that patients withGSTA1 *A*A appeared faster clearance than those with GSTA1
*A*B . Generally, the non-obvious significance of
AUC0-6h seemed reasonable. Moreover, the number of
patients treated with busulfan with an AUC value of more than 900 μM·min
was eight. In other words, 88.4% of children with HSCT could not reach
the range of 900-1350 μM·min
recommended by FDA. The main factor resulting in the low
AUC0-6h may be the fact that, for Chinese patients, the
frequency of GSTA1 *A with high metabolizing capacity was much
higher when compared to GSTA1 *B with slow metabolizing capacity.
Thus, the relatively low level of AUC0-6h in this study
population was rational.
In the final PPK model constructed in this study, AST levels were
negatively correlated with CL of Bu in pediatrics and CL declined
38.34% when AST increased from 12.7 to 127.4 U/L. This is unsurprising
because busulfan is mainly eliminated by the liver as previously
described. This study divided the patients into two subpopulations
(malignant and non-malignant diseases), which had no significant effect
on the pharmacokinetic parameters of Bu. In fact, malignant diseases
could be subdivided into seven types according to pathology, and
non-malignant diseases included six types. Nevertheless, some of the
pathologies, such as osteopetrosis, were rare and present in only a
small number of patients, resulting in an impossible comprehensive
evaluation of variability of Bu PK. In oral busulfan-based pediatric
research, the influence of underlying diseases on busulfan disposition
was significant and CL/F was significantly lower in group with immune
deficiencies than other groups (metabolic diseases, hemoglobinopathies
and hematological malignancies) (25). Hence, the number of different
types of cases needs to be expanded in order to analyze the specific
impact of every primary disease on Bu PK.
There
are theoretical and documented medication interaction with fludarabine
and busulfan (41). Clearance of IV Bu decreased significantly in
patients receiving concomitant fludarabine administration (p =
0.0016) and the average of reduction was 9.7% (42). However, there was
no other studies drawing similar conclusions (43, 44). Not surprisingly,
in this PPK model, the inclusion of fludarabine as a covariate failed to
significantly decline the value of -2LL during the forward selection. In
fact, patients in our cohort received one of the two regimens,
Bu/CTX/FLU and Bu/CTX/Ara-C, as basic conditioning therapies. Due to the
use of Bu/CTX in all patients without exception, the influence of CTX
has already existed in the model. Although we only regarded FLU as a
candidate covariate, in fact the influence of Ara-C has been also
reflected in the model. When FLU=1, the impact of FLU was added to the
model, and when FLU=0, the effect of Ara-C was included.
BSA was the most predictive covariate for CL and V, explaining 25.50%
and 24.17% of the observed IIV, respectively. Weight-based dosing
schedules were calculated with five fixed doses, however the model
established by this study showed that body weight is not a significant
predictor of Bu PK in children. Additionally, in a retrospective study,
SOS and early infectious complications occurred more frequently in the
weight-based dosing group (45). Besides, a PPK model, developed among
patients of all ages, revealed that the maturation of Bu clearance
reaches half of adult values at 6 weeks after birth (46). Also, in
children, Bu concentration did not show an obvious trend of change with
postnatal age, which strongly supported the conclusion drawn from our
cohort that age was not a significant factor affecting Bu clearance.
According to the PPK model established with Chinese pediatric patients,
BSA-based dosing scheme was recommended. When compared with weight-based
dosage, the new dosing scheme not only took the influence ofGSTA1 polymorphism into consideration, but could also be applied
to patients with different liver function. More importantly, the
simulation analysis demonstrated that the three fixed doses given on an
mg/m2 basis enabled almost all of the young patients
(0.2~1.6 m2) to achieve the target
AUC0-6h. Since this new dosing regimen was based on a
retrospective analysis, a prospective study is necessary to confirm the
benefits in terms of efficacy and safety.
Based on the final PPK model, Bu
Bayesian estimation of individual AUC0-6h values were
performed by using various combinations of 2-4 sampling times within 6
hours following busulfan administration. Until this point, several LSS
strategies have been established to predict BU exposure.
Most LSS strategies published were
estimated by using the trapezoidal rule (TR) or multiple linear
regression (MLR), which usually reduced the accuracy of estimation and
lacked professionalism. For example, Vaughan et al. (47)
concluded that 4-5 sampling points (3, 4, 5 and 6 hours or 2, 3, 4, 5
and 6 hours after dosing) could predict well in adult patients receiving
IV Bu four times daily. Teitelbaum et al. (48) developed an LSS
with four sampling points (0, 2, 3 and 4 hours after the start of the
second infusion). To achieve a more accurate estimate of AUC of every
LSS, PPK models, considered to be the gold standard, should be applied.
In this study population, Cmax generally appeared in
2-2.5 hours after dosing according to the concentration-time curve.
Therefore, peak concentration was picked among C2h,
C2.25h or C2.5h, and C4hand C6h were regarded as time points of elimination
phase. Since the metabolism of busulfan conformed to the one-compartment
model, selecting C4h or C6h on behalf of
elimination phase had a similar predictive function.
Model 9 (C2h,
C2.5h and C4h) and Model 10
(C2h, C2.5h and C6h)
elevated the accuracy and precision of prediction while increasing
medical costs and pain for children. Model 13 (C2h and
C4h) not only had a better predictive performance by
rRMSE, MAPE and Bland-Altman analysis, but was also more in line with
the clinical requirement of reducing sampling points for TDM.
Finally, considering the accuracy
of prediction and the feasibility of pediatric clinical practice
synthetically, Model 13 was selected as the optimal LSS.
The relationship between pharmacokinetics and outcome or toxicity of
busulfan reported previously are summarized in Supporting
Information Table 5 . A multicenter retrospective research stated that
graft-failure occurred more frequently in the low AUC group, meanwhile,
acute toxicity and TRM were significantly higher in the high AUC group
(5). Additionally, Andersson et al. (49) found that the
probability of developing aGVHD increased with increasing AUC. Similar
correlations between toxicities and AUC have been verified in both
Korean children and adult groups (14, 50). However, no relationship
between busulfan PK and toxicity was observed. Based on the data of 27
children with sickle cell disease undergoing HSCT, hepatic toxicity
(SOS) was not associated with busulfan AUC (51). Moreover, Jessicaet al . (52) concluded that there was no significant association
between AUC dose1 and death, relapse, or a composite of
the two.
In terms of ANC recovery and survival rate, there were significant
differences between patients with two GSTA1 genotypes (p< 0.05) in our study. Till now, no research has been reported
concerning the relationship between GSTs polymorphisms and
engraftment or mortality. However, Ansari et al. reported that
GSTA1 *B haploid was associated with higher incidence of
treatment-related toxicity (37). Hence, we deduced that the high
incidence of toxicity was likely connected to the postponement and the
low rate of ANC recovery, which further led to higher chance of
infection, the main cause of death in this study. In the next stage, we
will include more abundant samples to verify the above conjecture.
In this study population, type of donor was the major predictor of the
occurrence of aGVHD, whereas neither GSTM1 genotypes nor
pharmacokinetic parameters were found to be significantly correlative to
SOS. These results are difficult to interpret because multiple
pre-transplantation and transplantation-related factors have been
implicated in the rate of engraftment and the incidence of toxic effect.
So far, no single factor has been reported to result in aGVHD or SOS
independently. On one hand, it is worth noting that the homogeneity of
patients’ disease would be beneficial to the correlation analysis. For
example, Srivastava at al. (53) concluded that GSTM1-null genotype was
relevant to the incidence of SOS in 114 patients undergoing HSCT with
uniform disease, β-thalassemia. On the other hand, the influence of
complex combined medicines could not be ignored. Sixty-eight out of 69
patients adopted a combination of three or more myeloablative drugs and
57.97% of pediatric patients used four drugs with myeloablative
activity in succession, which led that the AUC of a single drug, Bu, may
be not the only influencing factor in curative effect or toxic reaction.
It should be also recognized that when Bu was combined with different
agents, relationships between Bu PK and outcome or toxicity would be
varied (54), as has been confirmed in BU/CY/TBI regimens (55).
Consequently, in order to clarify the correlation, analysis should be
conducted according to disease types and conditioning regimens in the
further.