Population Pharmacokinetics of Pegasparaginase in pediatric ALL patients: Age as a significant covariate

doi:10.21203/rs.3.rs-3785346/v1

Purpose

Pegasparaginase is essential in treating acute lymphoblastic leukemia (ALL). However, there is considerable variability in its pharmacokinetics, affecting the safety and efficacy of this drug. We aimed to develop a population pharmacokinetic (PopPK) model of pegasparaginase to identify covariates that might help to explain this variability. Quantitative assessments of the pharmacokinetics, efficacy, and safety of pegasparaginase in this patient population would help to optimize the dosing regimen.

Methods

PopPK analysis of pharmacokinetic data obtained from 29 pediatric ALL patients was accomplished using Phoenix® NLME software. The effect of various covariates (age, BSA, liver function parameters, etc.) on the pharmacokinetics of pegasparaginase was evaluated. The impact of the identified covariates on the safety and efficacy of pegasparaginase parameters was also assessed.

Results

A one-compartment model with first-order absorption and elimination adequately described the pegasparaginase pharmacokinetics. The patient's age was a clinically significant covariate for pegasparaginase disposition. C_max in older age group children (> 120 months) was significantly low, resulting in overall lower exposure (AUC) when compared with the younger age group (≤ 120 months). Modifying the dose according to the formula (4* age in months + 715) IU/m² BSA, predicted to achieve similar exposure across all the age groups in pediatric ALL patients.

Conclusions

The developed population pharmacokinetic model helped to establish age as a significant covariate for pegasparaginase pharmacokinetics, suggesting age-based dosing over and above the current BSA-based dosing practices could achieve uniform exposure in all age groups.

Population Pharmacokinetics

Pegasparaginase

Acute lymphoblastic leukemia

Acute Lymphoblastic leukemia (ALL) is blood and bone marrow cancer affecting lymphoid cells. Malignant leukemic cells differ from non-cancer cells in that they lack asparagine synthase and depend on external sources of asparagine for survival. Hence, depleting external sources of asparagine leads to the death of leukemic cells [1]. L-asparaginase (ASNase) is commercially prepared in native and pegylated formulations. It is used as a part of ALL treatment protocols unless the patients are hypersensitive to it.

However, native ASNase has variable pharmacokinetics, a short half-life, a higher incidence of hypersensitivity reactions, and a risk of silent inactivation [2]. Most of these limitations can be overcome using pegylated asparaginase instead of the native form. Despite wide interindividual variability, a consensus exists that a trough ASNase concentration should be ≥ 100 IU/L to ensure adequate antileukemic activity [3]. Currently, the exposure-response relationship for the pegasparaginase is not adequately established [4]. Hence, understanding the pharmacokinetic and pharmacodynamic parameters of pegasparaginase helps us rationally use pegapsparaginase.

Many scientists have tried to identify the variability in the pharmacokinetics of pegasparaginase [5–7].The patient's body surface area (BSA) was recognized as the key among the covariates affecting pegasparaginase Clearance, leading to its dosing practice based on the Patient's BSA. Despite this, variability in the achieved drug levels persists. To understand this, we studied the population pharmacokinetics (PopPK) of pegasparaginase when administered to relapsed pediatric ALL patients. With the help of the developed model, we arrived at a novel dosing regimen to personalize pegasparaginase therapy to minimize this inter-individual variability.

Data required for the population pharmacokinetic analysis was obtained from a pegasparaginase bioequivalence study conducted at our institute [8]. The institutional ethics committee of our institute approved the study; consent and assent were obtained from the patients enrolled in the study. Patients with relapsed ALL, aged 6 to 18 years, were enrolled and administered pegasparaginase from either of the two manufacturers when randomly assigned in a 1:1 ratio. Pegasparaginase was administered intramuscularly at a 1000 IU/m2 BSA dose at the 2-weekly interval. The pharmacokinetics data obtained after the administration of pegasparaginase was used for PopPK analysis. Briefly, blood samples of 3 ml each were collected at 1 hr, 6 hr, 24 hr, 48 hr, 120 hr, 168 hr, and 312 hr after the administration of the first pegasparaginase dose, in addition to a pre-dose sample. Plasma asparaginase activity was estimated using a spectrophotometric method, and the two pegasparaginase formulations were bioequivalent [9].

2.1. Pharmacokinetic data analysis:

Non-compartmental analysis was performed using WINNONLIN (Phoenix32, 8.3.2.166) to obtain asparaginase pharmacokinetic parameters. Population pharmacokinetic analysis was conducted using a non-linear mixed-effects modeling program (NLME, Phoenix32, 8.3.2.166). Different structural models along with other residual error models, were tested to develop a base model. Additive, proportional, and combination error models were evaluated to address the residual error. The exponential model was employed to assess the inter-patient variability. The data was analyzed using the first-order conditional estimation method (FOCE).

2.2. Covariate analysis:

Multiple covariates were tested to account for the observed variability in pharmacokinetic parameters. The covariates applied on the base model included the patient's age, body surface area (BSA), baseline anti-asparaginase antibody status, serum fibrinogen concentration, serum Antithrombin III (AT) levels, serum alanine transaminase (ALT), serum aspartate transaminase (AST), serum alkaline phosphatase (ALP), serum bilirubin and baseline serum creatinine. The influence of each covariate was evaluated by the difference in objective function value (OFV) between the base model and the covariate model by stepwise forward inclusion and backward elimination methods. A p-value less than 0.05 (ΔOFV > 3.841 with one degree of freedom assuming a chi-squared distribution) in the forward inclusion method and 0.05 (ΔOFV > 7.88) in the backward elimination were considered statistically significant.

2.3. Model validation:

The final model was evaluated using the nonparametric bootstrap and visual predictive check options in Phoenix NLME. The precision of the parameter estimates was assessed using a bootstrap analysis with 1000 data sets, randomly sampled from the original data set with replacement. We compared the model parameters' – mean, standard error (SE), and 95% confidence intervals (CIs) with the final parameter estimates and SEs generated in Phoenix NLME. A visual predictive check (VPC) was also performed to evaluate the predictive properties of the model (n = 1000 data sets).

2.4. Simulation and dosing regimen optimization

The final PopPK model was used to predict the concentration-time profiles of asparaginase in the same pediatric patients in the study. The simulation was done for non-modified dose (NMD), i.e., as per pegasparaginase current dosing practice of 1000 IU/m² BSA, and for the modified dose (MD) derived according to the formula - (Age in months*4 + 715) IU/m² BSA.

2.5. Pegasparaginase toxicity and efficacy evaluation

Asparaginase is known to cause hepatotoxicity and also pancreatic toxicity. Hence, liver and pancreatic functions were monitored by monitoring ALT, AST, serum bilirubin, ALP, blood glucose, AT III, and serum fibrinogen during treatment with pegasparaginase. Moreover, the effect of pegasparaginase on these parameters when patients were stratified by age was evaluated.

Data related to treatment response was evaluated by multi-parametric flow cytometry to assess post-induction Minimal Residual Disease (MRD) status, with values < 0.01% considered as responders. Children were stratified by age (≤ 120 months vs. >120 months). The correlation between Cmax, AUC, and MRD status was assessed.

3.1. Patient Characteristics:

Twenty-nine patients enrolled in the study, and their baseline characteristics are in Table 1. Eight blood samples were collected from each patient for PK assessment. As some patients expired before all the blood samples could be collected, 227 pegasparaginase blood levels were available for pharmacokinetic analysis.

Table 1: Baseline demographics of patients

Baseline characteristics	All patients (N=29)
Gender (M:F)	19:10
Age (years)	9.7 ± 2.7
Weight (kg)	30.1 ± 12.4
Height (cm)	135.3 ±17.6
BMI (kg/m²)	16.28 ± 3.98
BSA (m²)	1.06 ± 0.26
Type of ALL (n) B-ALL T-ALL	25 4
Fibrinogen (mg/dL)	381.43 ± 170.38
Antithrombin III (%)	118.28 ± 12.77
Serum ALT (U/L)	47.28 ± 49.42
Serum AST (U/L)	40.38 ± 33.43
Serum ALP (U/L)	171.10 ± 70.78
Haemoglobin (g/dL)	10.02 ± 2.3
Platelet count (x10⁹/L)	54688.89 ± 91711.44
S. Creatinine (mg/dL)	0.56 ± 0.10
S. Bilirubin (mg/dL)	0.54 ± 0.25

ALL: Acute Lymphoblastic Leukemia; ALP: serum alkaline phosphatase;

ALT: alanine transaminase, AST: serum aspartate transaminase;

All continuous data are shown as Mean±SD

3.2. Population Pharmacokinetic Model of PEG-Asparaginase:

A one-compartment model with first-order absorption and elimination adequately described pegasparaginase pharmacokinetics. The model was parameterized as absorption rate constant (k_a), apparent Clearance (CL), and apparent Volume of distribution (V). The additive error model was superior to other error models. Between-subject variability was quantified using an exponential model.

Figure 1 shows the diagnostic plots for the goodness of fit for the final model. Observed (DV) vs. Individual predicted concentrations (IPRED) showed good agreement, which shows the appropriateness of the structural model (Fig.1-A). The lack of any trend in the conditional weighted residuals vs. the population-predicted concentration plot suggests the adequacy of the residual error model (Fig.1-B). Inter-individual variability was observed in the population-predicted plot (Fig.1-C).

3.3. Relationship between predicted pharmacokinetic parameters and covariates:

The inclusion of age as a covariate on the Volume of distribution (Age on V) in the base model resulted in a significant decrease in the OFV (ΔOFV = -34) (Table 2). Shrinkage was evident in the visual inspection of the observed concentration vs. predicted concentration plot (Figure 1d). Moreover, inter-individual variability (% CV) for the Volume of distribution decreased from 30% in the base model to less than 1%.

Table 2: Stepwise and statistical values used for discrimination

Model No.	Model Description	OFV (-2 log-likelihood)	ΔOFV	p
Forward-inclusion
1	Base model	2569
2	Age (months) on Volume of distribution	2535	-34	<0.05
3	Age (months) on Clearance	2557	-12	<0.05
4	BSA on Clearance	2558	-11	<0.05
5	Age (months) and BSA on Clearance	2556	-13	<0.05
6	Age (months) on Volume of distribution and Clearance	2528	-41	<0.05
Backward-elimination
7	Removed BSA from model 5	2557	+1	>0.05
8	Removed Age (Months) from model 6	2536	+8	<0.05

ΔOFV: change in Objective Function Value

Applying age as a covariate on Clearance (Age on CL) also significantly decreased OFV (ΔOFV = -12). A similar reduction in OFV was also observed with BSA as a covariate on Clearance (BSA on CL). However, the redundancy of both covariates became apparent when both were included simultaneously, as OFV did not decrease further. As the current cohort of patients was already dosed according to the existing practice of BSA-based dosing, we decided to keep age as the sole covariate for V and CL in the final model. Other tested covariates – baseline lab values (S. bilirubin, S. creatinine, S. albumin, ALP, AST, ALT) and baseline anti-asparaginase antibody status did not significantly decrease in OFV. Equations for the final covariate model are shown in Eqs. 1 and 2:

CL = 0.0117972* Age (months)^ 1.59 ------------ (1)

V = 0.00329742 * Age (months)^ 1.28 ------------ (2)

The parameter estimates from the final model and the associated 95% confidence intervals are provided in Table 3.

Table 3: The parameter estimates from the final model

Parameter	Estimate	SE	CV%	2.5% CI	97.5% CI
tvKa (1/hr)	0.027	0.003	11.494	0.021	0.033
tvV (L)	0.003	0.001	36.817	0.001	0.006
tvCl (L/hr)	0.012	0.001	8.211	0.010	0.014
dVd AGE	1.281	0.075	5.837	1.134	1.428
dCL dAGE	1.598	0.323	20.212	0.962	2.235
SD	48.15	3.70	7.69	40.85	55.44

CI: Confidence interval; CV: coefficient of variability; dCl dAGE = fixed parameter coefficient of age on Clearance; dVd AGE = fixed parameter coefficient of age on Volume of distribution; SD= standard deviation; SE: standard error; tvCL = typical value of Clearance; tvka = typical value of absorption rate constant; tvV = typical value of Volume of distribution

3.4. Model validation:

The model's performance was validated using 1000 replicates generated from the original dataset to evaluate the stability of the final model. The mean values of pharmacokinetic parameters were within the 95% CI of the bootstrap values, indicating that all pharmacokinetic parameters can be estimated with acceptable precision (Table 4).

Table 4: Estimates of population pharmacokinetic parameters obtained by fitting the final model to bootstrap samples

Parameter	Mean	SE	CV%	Median	2.50% CI	97.50% CI
tvKa (1/hr)	0.034	0.004	12.767	0.036	0.025	0.042
tvV (L)	0.525	0.797	151.830	0.003	0.001	1.741
tvCl (L/hr)	0.012	0.002	15.905	0.011	0.009	0.014
dCl dAGE	0.988	0.793	80.176	1.171	0.000	2.203
dV dAGE	0.971	0.652	67.106	1.298	0.000	1.701
SD	48.50	4.04	8.32	47.71	38.64	56.54

CI: Confidence interval; CV: coefficient of variability; dCl dAGE = fixed parameter coefficient of age on Clearance; dV dAGE = fixed parameter coefficient of age on Volume of distribution; SD = standard deviation; SE: standard error; tvCL = typical value of Clearance; tvka = typical value of absorption rate constant; tvV = typical value of Volume of distribution

The visual predictive checks show that the observed concentrations correspond with the 90% prediction intervals of the 5th, 50th, and 95th percentiles, calculated from 1000 simulated datasets, as shown in Figure 2.

3.5 Effect of age on Pharmacokinetic parameters:

The impact of a patient's age on pegasparaginase pharmacokinetic parameters was assessed. The volume of distribution increased as the patients' age increased. However, the patient's age had no significant influence on drug clearance. C_max and AUC were compared in different age groups (Table 5). C_max in patients over 120 months was 28 % lower than in patients under 120 months (p<0.05). A similar reduction was observed in AUC_last (22%) between two age groups, but it was not statistically significant. By stratifying patients into four age groups (75-100 months, 101-125 months, 126-150 months, and 151 – 200 months), we found that C_max and AUC decrease with age (Figure 3).

Table 5: Observed and Predicted PK parameters

	Observed^$		Predicted NMD*			Predicted MD^#
	C_max (IU/L)	AUC_last (IU.hr)/L	C_max (IU/L)	AUC_last (IU.hr)/L	AUC_inf (IU.hr)/L	C_max (IU/L)	AUC_inf (IU.hr)/L
Patient age ≤10 yrs (n=15)	503.0 ± 23.3	83762 ± 4088.6	438 ± 19.5	77587 ± 6268.9	92932 ± 10480.6	475.8 ± 20.7	100859.9 ± 11170.9
Patient age >10 yrs (n=14)	358.9 ± 41.8	64620 ± 8500.7	362 ± 25.8	64193 ± 6729.5	78469 ± 10880.1	475.4 ± 33.5	103280.3 ± 14625.0
All (n=29)	433.4 ± 26.8	74521 ± 4877.3	401 ± 17.3	71121 ± 4681.2	85950 ± 7537	475.6 ± 19.1	102028.4 ± 8959.1

All continuous data is shown as Mean ± SE;

$: obtained by Noncompartmental analysis (NCA)

*NMD: Non-Modified Dose- 1000 IU/ m² BSA

#MD: Modified Dose – according to formula (4*age in months+715)/m² BSA

3.6. Simulation:

The final PopPK model was used to predict the concentration-time profiles of pegasparaginase in the same set of patients in whom the study was conducted (Table 5 and Figure 3). Concentrations predicted from non-modified doses (NMD) were comparable with those observed in the study, indicating that the developed model could accurately predict the levels. Furthermore, predicted C_max and AUC_infofthe modified dose (MD) were uniform across the different age groups.

3.7. Effect of pegasparaginase on lab parameters:

Changes in the safety lab parameters (like blood glucose, antithrombin III, and S. fibrinogen) post-treatment with pegasparaginase showed no differential effects when patients were stratified by age.

After the induction phase, the patient's Minimal Residual Disease (MRD) status was assessed; five out of 29 patients remained MRD positive, with more patients in the age group of more than 120 months (p-ns). The average Cmax in MRD negative and positive patients was 437.25 IU/L and 373.73 IU/L, respectively (p-ns). The average Cmax and AUC_last in the patients aged ≤ 120 months and who were MRD negative was 470.9 ± 27.6 IU/L and 102871.3 ± 7681.9 IU.hr/L, respectively.

L-asparaginase has been a component of ALL treatment regimens for over 40 years. Despite high inter-individual variability in the drug levels, the recommended dosing regimen of asparaginase or pegasparaginase is based on the Patient's Body Surface Area (BSA). Hence, population pharmacokinetic (PopPK) analysis was undertaken to identify any covariates that could address the variability of the pegasparaginase pharmacokinetics.

Currently, the US-FDA recommended dose of pegasparaginase is 2500 IU/m². Based on individual tolerability, the dosing practices of pegasparaginase are varied in different parts of the world. The safe and effective dose of pegasparaginase in the Indian ALL-patients is 1000 IU/m² body surface area.

The PopPK model was developed from the pharmacokinetic data of a bioequivalent study of pegasparaginase. A one-compartment model with first-order absorption and first-order elimination fitted the data. There was high variability in the population-predicted data of the base model. Age, as a covariate on the Volume of distribution, could explain the variability to a large extent, especially the variability in C_max. The covariate, patient's age, also affected the drug clearance, but to a lesser extent than its effect on the drug's Volume of distribution.

To assess the impact of age on the pharmacokinetics of pegasparaginase, C_max and AUC of different age groups were compared. It was observed that there was a significant difference in C_max achieved in different age groups. Moreover, C_max and AUC decreased as the age of the child increased. When stratified by age groups, the observed variability could be due to the variability in the hydrolytic enzymes present in the reticuloendothelial system, the site of metabolism/elimination of asparaginase [10],^,[11].

We assessed whether the variable concentrations achieved in the patients of different age groups had influenced pegasparaginase safety and efficacy. As it is known to be a hepatic and pancreatic toxic drug, its effects were analyzed when stratified by age; patients of all age groups well-tolerated Pegasparaginase. Furthermore, we observed no trend in the age-stratified liver or pancreatic adverse effects.

Asparaginase tends to lower blood glucose levels. Tanaka and colleagues reported repeated episodes of hypoglycemia in a patient on treatment with combination chemotherapy containing asparaginase [12]. In our study, too, we observed the fall in blood glucose levels was more at the t_max of pegasparaginase. The fall in blood glucose is of significant concern, especially in young children in whom hypoglycemic episodes may go unnoticed. Furthermore, our study found that young children tend to achieve a higher asparaginase concentration than older children. However, our study did not observe clinically significant hypoglycemic episodes even in young children, indicating that the concentrations achieved by the current dosing regimen are safe.

Asparaginase can induce bleeding and thrombotic events by causing an imbalance in the coagulation factors and the proteins regulating it by its lytic action. We regularly monitored the patients' coagulation profile (PT, aPTT, INR) in addition to serum antithrombin III (AT) and serum fibrinogen levels while they were on treatment with pegasparaginase [13]. We observed a relative fall in the fibrinogen and AT by day seven and day 14 of pegasparaginase administration from their baseline levels (data not shown). However, the fall in these levels was not significant enough to cause hemorrhagic or thrombotic events. We did not observe any correlation between the patient's age and change in AT or fibrinogen levels.

Asparaginase is a critical drug in combination with chemotherapy protocols for ALL patient management. The efficacy of ALL management is assessed by the Patient's Minimal Residual Disease (MRD) status. Age is an important prognostic factor in pediatrics ALL [14]. Significant variability is reported in pediatric ALL survival by age at diagnosis, wherein survival peaked among children diagnosed at 1–4 years and steadily declined among those diagnosed at older ages [15]. We observed that the average C_max concentrations were lower in the patients who remained MRD positive than those who turned MRD negative. However, it should be interpreted with the background knowledge that various other factors are critical in ALL patients' responses, like concomitant combination chemotherapeutic drugs, steroids' use, anti-asparaginase antibodies, etc. Moreover, the study's small sample size limits drawing a firm conclusion.

We observed that older children achieved lower drug levels than younger children, which follows our finding of the PopPK model that age is a significant covariate. Pegasparaginase drug levels achieved in the younger children were well tolerated and had no critical adverse drug reactions compared to older children. Hence, increasing the drug levels in older children to match those in younger children can lead to an increased duration of asparagine depletion and subsequently increase efficacy. With the results achieved from our study, we attempted to derive an age-based dosing formula to understand dosing requirements to achieve uniform pharmacokinetics across all age groups.

A trough concentration above or equal to 100 IU/L is necessary to achieve therapeutic benefit in ALL management [16]. The number of patients with trough concentrations less than 100 IU/L was similar in both age groups. There was no significant difference in observed mean trough concentrations in the two age groups (113.3 IU/L vs. 116.7 IU/L in the ≤ 120 months vs. > 120 months, respectively). With a small sample size, it is difficult to comment on the effect of age on trough concentration.

Using the C_max and AUC of pegasparaginase observed in the older and younger children, we simulated different dosing regimens for the pegasparaginase. The main objective of this simulation was to achieve, among other age groups, a target C_max and AUC_inf, which was observed in children aged less than 120 months and who became MRD negative [i.e., of approximately 470 IU/L and 100000 (IU.hr)/L respectively]. Starting with the linear regression obtained from the Age vs. C_max plot (Fig. 3), after several trials of different equations, we arrived at an equation "(4*age in months + 715) IU/m² BSA,". Dosing pegasparaginase as per the equation would provide comparable C_max and AUC in children under 15 (figure – 3). Using this equation, we simulated the C_max and AUC in different age groups, which predicted that C_max and AUC were comparable in all the age groups of patients (Table 5). However, these observations must be validated in a more extensive cohort study.

Age of the Patient emerged as a significant covariate contributing to the variability observed in the pegasparaginase pharmacokinetics. Dosing patients based on age and their Body Surface Area could help achieve uniform drug exposure across the different age groups of pediatric patients.

Author Contribution

AP, MN: Concept, Manuscript writingAP: Population Pharmacokinetic analysisVG, GN, SB: Manuscript editing

ACKNOWLEDGEMENTS:

We acknowledge Gennova Biopharmaceuticals Limited for funding the Pegasparaginase bioequivalence study.

DATA AVAILABILITY STATEMENT:

Most of the data of the manuscript is available in the manuscript. If the reviewers or editors require additional data, the corresponding author is willing to submit the same.

Fu CH, Sakamoto KM (2007) PEG-asparaginase. Expert Opin Pharmacother 8:1977–1984. https://doi.org/10.1517/14656566.8.12.1977
Stock W, Douer D, DeAngelo DJ, et al (2011) Prevention and management of asparaginase/pegasparaginase-associated toxicities in adults and older adolescents: recommendations of an expert panel. Leukemia & Lymphoma 52:2237–2253. https://doi.org/10.3109/10428194.2011.596963
Sassen SDT, Mathôt RAA, Pieters R, et al (2017) Population pharmacokinetics of intravenous Erwinia asparaginase in pediatric acute lymphoblastic leukemia patients. Haematologica 102:552–561. https://doi.org/10.3324/haematol.2016.149195
Pinheiro JPV, Boos J (2004) The best way to use asparaginase in childhood acute lymphatic leukaemia–still to be defined? Br J Haematol 125:117–127. https://doi.org/10.1111/j.1365-2141.2004.04863.x
Asselin B, Rizzari C (2015) Asparaginase pharmacokinetics and implications of therapeutic drug monitoring. Leuk Lymphoma 56:2273–2280. https://doi.org/10.3109/10428194.2014.1003056
Vrooman LM, Kirov II, Dreyer ZE, et al (2013) Preliminary Results Of a Pharmacokinetic Study Of Intravenous Asparaginase Erwinia Chrysanthemi Following Allergy To E Coli-Derived Asparaginase In Children, Adolescents, and Young Adults With Acute Lymphoblastic Leukemia Or Lymphoblastic Lymphoma. Blood 122:3904. https://doi.org/10.1182/blood.V122.21.3904.3904
Schrey D, Borghorst S, Lanvers-Kaminsky C, et al (2010) Therapeutic drug monitoring of asparaginase in the ALL-BFM 2000 protocol between 2000 and 2007. Pediatr Blood Cancer 54:952–958. https://doi.org/10.1002/pbc.22417
Nookala Krishnamurthy M, Narula G, Gandhi K, et al (2020) Randomized, Parallel Group, Open-Label Bioequivalence Trial of Intramuscular Pegaspargase in Patients With Relapsed Acute Lymphoblastic Leukemia. JCO Glob Oncol 6:1009–1016. https://doi.org/10.1200/GO.20.00113
Sankaran H, Sengupta S, Purohit V, et al (2020) A comparison of asparaginase activity in generic formulations of E.coli derived L- asparaginase: In-vitro study and retrospective analysis of asparaginase monitoring in pediatric patients with leukemia. Br J Clin Pharmacol 86:1081–1088. https://doi.org/10.1111/bcp.14216
Brueck M, Koerholz D, Nuernberger W, et al (1989) Elimination of l-asparaginase in children treated for acute lymphoblastic leukemia. Dev Pharmacol Ther 12:200–204
Keating MJ, Holmes R, Lerner S, Ho DH (1993) L-asparaginase and PEG asparaginase–past, present, and future. Leuk Lymphoma 10 Suppl:153–157. https://doi.org/10.3109/10428199309149129
Graham M (2003) Pegaspargase: a review of clinical studies. Advanced Drug Delivery Reviews 55:1293–1302. https://doi.org/10.1016/S0169-409X(03)00110-8
Nesbit ME, Ertel I, Hammond GD (1981) L-Asparaginase as a single agent in acute lymphocytic leukemia: survey of studies form Childrens Cancer Study Group. Cancer Treat Rep 65 Suppl 4:101–107
Terwilliger T, Abdul-Hay M (2017) Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J 7:e577. https://doi.org/10.1038/bcj.2017.53
Hossain MJ, Xie L, McCahan SM (2014) Characterization of pediatric acute lymphoblastic leukemia survival patterns by age at diagnosis. J Cancer Epidemiol 2014:865979. https://doi.org/10.1155/2014/865979
van der Sluis IM, Vrooman LM, Pieters R, et al (2016) Consensus expert recommendations for identification and management of asparaginase hypersensitivity and silent inactivation. Haematologica 101:279–285. https://doi.org/10.3324/haematol.2015.137380

No competing interests reported.

Population Pharmacokinetics of Pegasparaginase in pediatric ALL patients: Age as a significant covariate

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusions

Figures

1. INTRODUCTION

2. PATIENTS AND METHODS

2.1. Pharmacokinetic data analysis:

2.2. Covariate analysis:

2.3. Model validation:

2.4. Simulation and dosing regimen optimization

2.5. Pegasparaginase toxicity and efficacy evaluation

3. RESULTS

4. DISCUSSION

5. CONCLUSION

Declarations

Author Contribution

ACKNOWLEDGEMENTS:

DATA AVAILABILITY STATEMENT:

References

Additional Declarations

Status:

Version 1