Abstract
Purpose:
The use of tyrosine kinase inhibitors for the treatment for soft tissue sarcomas is increasing given promising signals of activity in a variety of tumor types. The recently completed study in non-rhabdomyosarcoma soft tissue sarcomas, ARST1321, demonstrated that the addition of pazopanib to neoadjuvant ifosfamide, doxorubicin, and radiation improved the pathological near complete response rate compared with chemoradiotherapy alone. Pharmacokinetic (PK) evaluation of doxorubicin with pazopanib has not been previously reported. As an exploratory aim, doxorubicin PK data was collected during the dose-finding phase of the study in patients receiving chemotherapy and pazopanib to assess the effect of pazopanib on doxorubicin PK parameters.
Methods:
Blood samples were collected during cycle 2 (week 4) of chemotherapy at the following time points from doxorubicin administration: predose, 5 minutes, 30 minutes, 60 minutes, 2, 4, 8, 24 ± 3, and 48 ± 3 hours after dosing. The population pharmacokinetic and individual post-hoc estimates of doxorubicin and doxorubicinol were determined by nonlinear mixed-effects modeling.
Results:
There were 52 doxorubicin and doxorubicinol samples from 7 individuals in this study (median age: 17 years; range 14–23). The doxorubicin clearance was 26.9 (16.1,36.4, 33.9) L/hr/m2 (post-hoc median and range), and 25.8 (23.3%) L/hr/m2 (population estimate and IIV (CV%)). The doxorubicinol apparent clearance was 67.5 (18.2,1701) L/hr/m2 (post-hoc median and range), and 58.7 (63.7%) L/hr/m2 (population estimate and IIV (CV%)).
Conclusion:
The PK data of 7 patients treated on ARST1321 is consistent with previously reported population and post-hoc doxorubicin clearance and doxorubicinol apparent clearance estimates, showing that the addition of pazopanib does not significantly alter doxorubicin pharmacokinetics. These data support the safety of administration of pazopanib with doxorubicin-containing chemotherapy.
Keywords: Doxorubicin, Pazopanib, Pharmacokinetics, Non-rhabdomyosarcoma Soft Tissue Sarcoma
Introduction
Non-rhabdomyosarcoma soft tissue sarcomas (NRSTS) are a heterogenous group of tumors that comprise over 35 different histologies and represent 5% of pediatric malignancies in the United States[1]. A recent study in children and adolescents showed that surgery and radiation are able to cure the majority of patients with low risk disease; however, approximately 40% of patients present with intermediate or high-risk disease which have a lower event-free survival despite the use of multimodal therapy with an ifosfamide-doxorubicin based regimen, surgery, and radiation [1, 2].
The use of tyrosine kinase inhibitors (TKI), such as pazopanib and regorafenib, have shown promising results in recent clinical trials in adult and pediatric patients with NRSTS[1, 3–6]. Pazopanib is a multi-TKI that selectively inhibits vascular endothelial growth factor receptor (VEGFR) −1, −2,−3, c-kit, and platelet derived growth factor receptor[4, 7, 8]. Based on evidence of single agent activity in clinical trials and pre-clinical data suggesting possible synergy with chemotherapy[9–11], NRG Oncology and the Children’s Oncology Group (COG) jointly conducted a randomized phase II study, ARST1321 (NCT02180867), using chemoradiotherapy with or without pazopanib[12] in pediatric and adult patients with intermediate and high grade NRSTS. The combination was overall well tolerated, with the most common serious adverse event being febrile neutropenia. At an interim analysis, the trial met its primary endpoint finding the addition of pazopanib to chemoradiotherapy resulted in an increased percentage of patients with a ≥ 90% pathologic response compared to chemoradiotherapy alone (58% vs 22%) and the trial was closed to accrual[12].
Pazopanib and other TKIs, such as sunitinib and nilotinib, modulate and inhibit the cellular ATP binding cassette (ABC) membrane transporters, potentially increasing the systemic exposure of co-administered agents such as doxorubicin[8, 13–17]. Further, pazopanib and doxorubicin both inhibit the liver uptake transporter organic anion-transporting polypeptide 1B1 (OATP1B1)[7, 18–20], raising the concern for potential drug interactions. The pharmacokinetics (PK) of doxorubicin combined with pazopanib have not been previously reported. To assess the effect of pazopanib on doxorubicin PK parameters, as an exploratory aim on ARST1321, doxorubicin PK data was collected during the dose-finding phase of the study in 7 patients receiving chemotherapy and pazopanib. Here we report the results of that analysis.
Methods
Population and Study Design
Patients were treated on the pediatric/adult phase II/III study ARST1321, which was co-led by NRG Oncology and COG. The study design has been previously reported[12]. Patients were treated with ifosfamide (intravenous over 2–4 hours, 2.5 g/m2/dose x 3 days) and doxorubicin (intravenous over 1–15 minutes, 37.5 mg/m2/dose x 2 days) at 3 week intervals with continuous pazopanib (pediatric patients (< 18 years): 350mg/m2; adult patients (≥ 18 years): 600mg daily). Participation in the PK portion of the study was optional and patient/parental consent and patient assent, as appropriate, was required.
Doxorubicin/Doxorubicinol pharmacokinetic studies
For doxorubicin and doxorubicinol PK studies, blood samples were collected during cycle 2 (week 4) of chemotherapy at the following time points: predose, 5, 30, and 60 minutes, 2, 4, 8, 24 ± 3, and 48 ± 3 hours after dosing. Blood samples (3–5 mL) were drawn into tubes containing sodium citrate and immediately centrifuged for 5 minutes at 3000g to yield plasma. Concentrations of doxorubicin and doxorubicinol were measured in plasma using validated UHPLC-MS/MS[21, 22]
Pharmacokinetic data analysis
The population pharmacokinetic (PPK) and individual post-hoc estimates of doxorubicin and doxorubicinol were determined by nonlinear mixed-effects modeling with Monolix (version 5.1.1, www.monolix.org), using the stochastic approximation expectation-maximization (SAEM) approach. The PK model has been previously described[23–25]. In brief, a linear three-compartment model with first-order elimination was used to model the doxorubicin data and a one-compartment model with first-order formation from doxorubicin and first-order elimination was used to model the doxorubicinol data. The doxorubicin parameters estimated included V1, central compartment volume (L/m2); CL clearance (L/h/m2); V2, first peripheral compartment volume (L/m2); Q2 first peripheral compartment clearance (L/h/m2); V3, second peripheral compartment volume (L/m2); Q3 second peripheral compartment clearance (L/h/m2). The doxorubicinol parameters estimated were Vmet/f, the apparent volume (L/m2); CLmet/f, the apparent clearance (L/h/m2), where f is the unidentifiable formation fraction of doxorubicinol from doxorubicin. The interindividual variability of the parameters was assumed to be log-normally distributed. A proportional residual error model was used with assumed normal distribution of the residuals. The individual post-hoc parameters are also estimated. Specifically, these are the Empirical Bayesian Estimates---the mode of the conditional parameter distribution.
Results
There were 52 doxorubicin and doxorubicinol samples from 7 individuals in this study (median age: 17 years; range 14–23; body surface area range 1.44 – 2.11). Patient demographics are reported in Supplemental Table 1. The individual concentration vs. time plots are summarized in Figure 1. Overall, the model described the doxorubicin and doxorubicinol concentrations well with no bias observed in the estimated concentration as shown in the goodness-of-fit plots Supplemental Figures 1–2. The PPK and individual post-hoc estimates are summarized in Table 1.
Figure 1:
Individual concentration vs time plots. Doxorubicin (data: black circles; model estimated fit black curve); Doxorubicinol (data: green squares; model estimated fit green curve)
Table 1:
Doxorubicin and Doxorubicinol Population Pharmacokinetics. RSE: Relative Standard Error and Bayesian Post-Hoc Pharmacokinetic Estimates (Empirical Bayesian Estimates---conditional mode)
| Parameter | Population Estimate (RSE %) | IIV (CV%) | Bayesian Post-Hoc Estimate Median | Bayesian Post-Hoc Estimate Range |
|---|---|---|---|---|
| CL (L/h/m2) | 25.8 (10.9%) | 23.3% | 26.9 | 16.1, 36.4 |
| V1 (L/m2) | 5.12 (40.2%) | 86.5% | 6.7 | 0.8, 8.7 |
| Q2 (L/h/m2) | 22.3 (17.6%) | 11.2% | 21.9 | 20.8, 24.6 |
| V2 (L/m2) | 439 (3.7%) | --- | --- | --- |
| Q3 (L/h/m2) | 9.1 (0.23%) | --- | --- | --- |
| V3 (L/m2) | 19.0 (1.0%) | --- | --- | --- |
| CLmet/f (L/h/m2) | 58.7 (24.6%) | 63.7% | 67.5 | 18.2, 170 |
| Vmet/f (L/m2) | 1010 (28.4%) | 73.3% | 983 | 386, 3870 |
Note the inter-individual variability (IIV) for V2, Q3, and V3 were not estimated.
The clearance of doxorubicin and apparent clearance of doxorubicinol given in combination with pazopanib in our current study (age range: 14–23 years old) was similar to historical controls where pazopanib was not used. Specifically, doxorubicin clearance in our current study (Table 1) was 26.9 (16.1, 36.4) L/hr/m2 (post-hoc median and range) and has been previously reported to be 25.6 (15.7, 33.9) L/hr/m2 (post-hoc median and range) for individuals from 3.3 to 21.5 years old [23], 29.6 (31%) L/hr/m2 (population estimate and IIV (CV%)) for individuals from 3 to 81 years old [24], and 24.1 (30.7%) L/hr/m2 (population estimate and IIV (CV%)) for individuals from 0.2 to 17.7 years old [25]. Additionally, doxorubicinol apparent clearance in our current study (Table 1) was 67.5 (18.2, 170) L/hr/m2 (post-hoc median and range) and has been previously reported to be 52.5 (20.3, 170.1) L/hr/m2 (post-hoc median and range) [23], and 42.5 (43%) L/hr/m2 (population estimate and IIV (CV%)) [25].
Discussion
The increasing use of combination therapy with chemotherapy and TKIs in cancer requires a better understanding of the interactions between these drugs. ARST1321 found that the addition of pazopanib to neoadjuvant ifosfamide, doxorubicin, and radiation improved the pathological near complete response rate compared with chemoradiotherapy alone [12], suggesting that this combination may be used more widely in the future. However, the ability of pazopanib to inhibit the ABC transporter and OATP1B1 raises concerns that this agent may alter the clearance of doxorubicin.
ABC transporters are responsible for the efflux of numerous anti-cancer agents from cells, including doxorubicin, and upregulation of these transporters can lead to resistance to these compounds[13]. Further, TKIs have been shown to interact with ABC transporters in other ways, with some leading to upregulation of transporters, posing potential mechanisms of resistance whereas others interact to enhance the efficacy of chemotherapeutic agents[17]. The interaction is largely dictated by the concentration of agent and the type of cell with which they are interacting[26]. One potential mechanism of inhibition may be through the ability of TKIs to compete with ATP binding domains, as has been shown for nilotinib overcoming doxorubicin resistance in cell lines[26].
Organic anion transporting polypeptides (OATPs) are transporters that are responsible for the uptake of drugs in the liver and are responsible for the distribution of multiple drugs into cells. The OATP with the highest expression in the liver is OATP1B1[18]. In vitro studies have shown that pazopanib has the potential to inhibit OATPB1[7]; however, it is not clear whether this degree of inhibition will play a physiologic role. Doxorubicin may inhibit the liver uptake transporter OATP1B1[7, 18], raising concerns for further interactions. However, based on our analysis, we found no evidence of a clinically relevant interaction of the combination of pazopanib and doxorubicin with OATP1B1.
There appeared to be no evident differences in the clearance of doxorubicin and doxorubicinol in our cohort of patients compared to historical controls. This, combined with the previously published data showing a favorable toxicity profile[12], is promising that these two agents can be safely used together without altering the pharmacokinetics of the compounds. Our study was limited by a small number of patients, mainly from a single ethnicity and restricted to a relatively narrow age group of older adolescents and young adults, raising concerns about the applicability to a larger population. Therefore, further prospective studies are warranted to confirm these findings in studies where this combination or similar combinations of these agents are being used.
Supplementary Material
Supplemental Table 1: Patient characteristics and demographics
Supplemental Figure 2: Doxorubicinol: (A) population predicted vs actual concentrations; (B) individual predicted vs actual concentrations; (C) population weighted residuals vs time; (D) individual weighted residuals vs time. Samples drawn from the individual conditional distributions (n=10 per individual) are plotted for the individual predicted and individual weighted residual plots.
Supplemental Figure 1: Doxorubicin: (A) population predicted vs actual concentrations; (B) individual predicted vs actual concentrations; (C) population weighted residuals vs time; (D) individual weighted residuals vs time. Samples drawn from the individual conditional distributions (n=10 per individual) are plotted for the individual predicted and individual weighted residual plots.
Acknowledgements:
Research reported in this publication was supported by the Children’s Oncology
Funding: This study was funded by National Clinical Trials Network Operations Center Grant U10CA180886, National Clinical Trials Network Statistics & Data Center Grant U10CA180899, National Cancer Institute IROC Operations Grant CA180803, St Baldrick’s Foundation, and Seattle Children’s Foundation, from Kat’s Crew Guild through the Sarcoma Research Fund.
Conflicts of interest/Competing interests: JG reports a grant from Eli Lilly and Company outside the submitted work. ARW reports travel reimbursement from SpringWorks Therapeutics and paid consulting for BioAtla outside the submitted work.
Footnotes
Availability of data and material: An individual level de-identified dataset containing the variables analyzed in the primary results paper can be expected to be available upon request. Requests for access to Children’s Oncology Group (COG) protocol research data should be sent to datarequest@childrensoncologygroup.org. Data are available to researchers whose proposed analysis is found by COG to be feasible and of scientific merit and who agree to the terms and conditions of use. In addition, release of data collected in a clinical trial conducted under a binding collaborative agreement between COG or the National Cancer Institute Cancer Therapy Evaluation Program and a pharmaceutical or biotechnology company must comply with the data sharing terms of the binding collaborative and contractual agreement and must receive the proper approvals.
Declarations
Code Availability: N/A
Ethics Approval: N/A
Consent to participate: N/A
Consent for publication: N/A
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Associated Data
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Supplementary Materials
Supplemental Table 1: Patient characteristics and demographics
Supplemental Figure 2: Doxorubicinol: (A) population predicted vs actual concentrations; (B) individual predicted vs actual concentrations; (C) population weighted residuals vs time; (D) individual weighted residuals vs time. Samples drawn from the individual conditional distributions (n=10 per individual) are plotted for the individual predicted and individual weighted residual plots.
Supplemental Figure 1: Doxorubicin: (A) population predicted vs actual concentrations; (B) individual predicted vs actual concentrations; (C) population weighted residuals vs time; (D) individual weighted residuals vs time. Samples drawn from the individual conditional distributions (n=10 per individual) are plotted for the individual predicted and individual weighted residual plots.