Introduction
Glasdegib (PF-04449913) is a potent, selective, oral inhibitor of the Hedgehog signaling pathway. Based on the primary analysis of the BRIGHT AML 1003 trial, which demonstrated superior overall survival (OS) with glasdegib + low-dose cytarabine (LDAC) versus LDAC alone, glasdegib + LDAC was approved in the United States for the treatment of patients with newly diagnosed acute myeloid leukemia (AML) who are unable to receive intensive chemotherapy (ICT) as a result of comorbidities or older (≥ 75 years) age [
1,
2]. Long-term (> 40 months) follow-up of BRIGHT AML 1003 showed a sustained, statistically significant improvement in OS among patients with AML receiving glasdegib + LDAC versus LDAC alone (hazard ratio [HR] 0.495, 95% confidence interval [CI] 0.325–0.752;
P = 0.0004; median OS 8.3 vs. 4.3 months); the respective 2-year survival probability was 19.0 versus 2.8% [
3]. The rate of complete remission was higher with glasdegib + LDAC versus LDAC alone (19.2 vs 2.6%) [
4]. Glasdegib + LDAC treatment was associated with a reduced risk of cytopenias; with cycle 1 recovery of absolute neutrophil count (≥ 1000/µL, 45.1%), platelets (≥ 10 g/dL, 43.1%), and hemoglobin (≥ 100,000/µL, 33.3%) seen in a meaningful proportion of patients [
5]. Additionally, more patients receiving glasdegib + LDAC (29.3%) were transfusion-independent vs. LDAC alone (5.6%) [
6]. Long-term follow-up confirmed that the treatment combination was associated with an acceptable safety profile, with little additional toxicity (primarily related to nausea, and the inhibition of the Hedgehog signaling pathway [e.g., dysgeusia, muscle spasms]) [
3] seen with glasdegib + LDAC versus LDAC alone. Currently, glasdegib, at a dose of 100 mg once daily (QD), is under further clinical evaluation in combination with a hypomethylating agent or ICT in patients with AML and myelodysplastic syndromes (MDS) [
7,
8].
This population pharmacokinetic (PK)/pharmacodynamic (PD) analysis evaluated the time course of survival in patients with AML who were ineligible for ICT, comparing glasdegib + LDAC treatment relative to LDAC alone treatment (treatment–response), and explored the relationship between glasdegib exposure and OS (exposure–response). The effect of other covariates, including patient demographics, disease characteristics, and baseline laboratory values influencing OS probability were also investigated.
Materials and methods
Clinical studies
BRIGHT AML 1003 (ClinicalTrials.gov identifier: NCT01546038) was an open-label, randomized, multicenter, phase 1b/2 trial for which the methods have previously been published [
1,
9]. Briefly, BRIGHT AML 1003 enrolled adult patients aged ≥ 55 years with newly diagnosed, previously untreated AML or high-risk MDS (World Health Organization 2008 classification), who were ineligible for ICT. The phase 1b portion evaluated glasdegib (100 or 200 mg QD) in combination with LDAC (Arm A) or decitabine (Arm B) [
9]. In the phase 2 portion of the study, patients were randomized 2:1 to receive glasdegib 100 mg QD + LDAC or LDAC alone with OS as a primary efficacy endpoint [
1]. Patients were followed for up to 4 years from the first dose. OS was defined as the date of randomization to the time of death for any reason. If death was not documented, censoring occurred at the date on which the subject was last known to be alive. Response to treatment was assessed based on the International Working Group response criteria guidelines for MDS and AML [
10,
11]. The study was conducted in accordance with the Declaration of Helsinki. All patients provided written informed consent before study procedures began, and the protocol was approved by institutional review boards at each study site.
The population PK/PD analysis followed a prespecified analysis plan for data handling, model selection and evaluation, and testing of covariate effects.
Study data
Using data from the phase 2 portion of the trial, the study population for the treatment–response analysis included all patients with AML who were enrolled in the glasdegib + LDAC or LDAC alone arm. The exposure–response analysis included a subset of patients with AML from the phase 2 glasdegib + LDAC arm who received at least one dose of glasdegib and had glasdegib PK information available.
An exploratory treatment–response analysis evaluating glasdegib in combination with a hypomethylating agent was also conducted, adding data from the phase 1b portion of the study in patients with AML (n = 5) and MDS (n = 2) who received glasdegib 100 or 200 mg QD with decitabine to the treatment–response analysis population (glasdegib + LDAC and LDAC alone in AML). The study data cut-off for all analyses was based on the primary completion date of 3 January 2017.
Parametric time-to-event model for OS
All OS response endpoints were captured as events and non-events and, therefore, the models were developed using time-to-event (TTE) analyses. Parametric survival models were used to assess the relationship of OS with study treatment (treatment–response analysis) and with glasdegib exposure (exposure–response analysis), and to explore covariates. The TTE models for OS were developed from survival data using a cumulative hazard distribution function [
12]. Constant or time-varying cumulative hazard distribution functions, including exponential, Weibull, and log-logistic distributions, were evaluated using the available data. The distribution that best fits the data was selected as the base model. All TTE analyses were performed using nonlinear mixed-effects modeling (NONMEM) software (version 7.3.0, ICON Development Solutions, Ellicott City, MD, USA).
Covariate analysis
Based on clinical relevance, mechanistic plausibility, and visual inspection of the Kaplan–Meier Mean Covariate (KMMC) plots, potential covariates were selected and tested for significance. This was implemented using the stepwise covariate model (SCM) building procedure in Perl-speaks-NONMEM version 4.2.0 [
13,
14]. In the KMMC methodology, the mean of each covariate was plotted for all individuals remaining in the study at every inflection point of the Kaplan–Meier OS curve. A strong relationship observed between a covariate and parameters of the TTE model suggested that the covariate influenced the OS curve. The mean value of a covariate that influenced OS would be expected to increase or decrease over time, whereas the mean value of a covariate that did not affect OS would be expected to remain constant over time [
15].
Intrinsic and extrinsic variables (e.g., study treatment, demographic characteristics, disease characteristics, and baseline safety laboratory values) were evaluated, using the SCM approach, for inclusion in the base models of the treatment–response and exposure–response analyses. The SCM approach involved both forward addition and backward elimination with a significance level of α < 0.05 and α < 0.001, respectively.
Demographic covariates including baseline body weight, baseline age, sex, and race were tested. Disease characteristics tested included de novo or secondary disease, cytogenetic risk, prior treatment with hypomethylating agents, and baseline Eastern Cooperative Oncology Group performance status (ECOG PS). The following baseline laboratory tests and other factors were also evaluated: creatinine clearance, aspartate transaminase, white blood cells, percentage of bone marrow blasts, and percentage of peripheral blasts.
Categorical covariates were included in the base model using a linear model. Continuous covariates were evaluated using a linear, exponential, power, or maximal-effect model. The covariates were screened for pairwise correlation and the more clinically relevant covariate was selected to be included in the model. If a baseline covariate value was found to be missing and the covariate was measured at post-baseline visits, that value was then imputed using the value at the first available, or earliest, post-baseline visit. If a covariate value was entirely missing for the patient, the baseline value was imputed as the population median baseline value.
Derivation of PK exposure metrics
To derive summary measures of glasdegib exposure, individual empirical Bayes estimates of PK parameters were generated from a previously described population PK model [
16]. Because duration of treatment may significantly impact efficacy, glasdegib exposure metrics that were not time dependent or that were earlier in the treatment course were selected in the exposure–response analysis. The selected exposure metrics included: first dose maximum concentration (
Cmax), end of cycle 1
Cmax, end of cycle 1 minimum concentration, cycle 1 cumulative area under the concentration–time curve (AUC), cycle 1 average concentration (
Cavg), average AUC over the dosing interval, and overall
Cavg. A cycle was defined as 28 days.
Cavg was calculated by dividing AUC by time. Both the raw scale and natural log-transformed exposure metrics were tested through the SCM approach on the base model.
Model evaluation
Model adequacy and predictive performance was evaluated based on change in objective function value, precision of parameter estimates, and graphical presentation of model-predicted Kaplan–Meier curves overlaid with observed Kaplan–Meier curves. The performance of the final model was evaluated by simulating survival data (
n = 500) using parameter estimates from the final model and conducting a visual predictive check (VPC) [
17,
18]. All post-processing graphical and statistical analyses were completed with R version 3.2.2 (R Foundation for Statistical Computing, Vienna, Austria).
Discussion
This study characterized the time course of survival with glasdegib + LDAC relative to LDAC alone (treatment–response) and explored whether the differences in glasdegib exposure at the dose of 100 mg QD significantly affected OS (exposure–response) based on data from the BRIGHT AML 1003 trial in patients with newly diagnosed AML who were ineligible for ICT. For both the treatment–response and exposure–response study populations, the survival function was best characterized by an exponential TTE distribution model.
The treatment–response analysis indicated that treatment arm (glasdegib + LDAC or LDAC alone) had a statistically significant impact on OS. The addition of glasdegib to LDAC resulted in a 58% reduction in the risk of death, translating to a median OS prolonged by approximately 5 months (HR 0.42, 95% CI 0.28–0.66). These results are similar to those reported from the primary analysis (the same data cut-off used in this analysis) of BRIGHT AML 1003 using a Cox proportional hazards model in patients with AML (HR 0.46, 95% CI 0.30–0.71; median OS 8.3 vs. 4.3 months) [
2]. Together these results support the survival benefit of glasdegib + LDAC (vs. LDAC alone) in the treatment of patients with newly diagnosed AML ineligible for ICT.
In the exploratory treatment–response analysis in patients with AML and MDS, glasdegib + decitabine treatment had an estimated 61.8% base hazard reduction from the standard-of-care therapy, LDAC alone. Although the sample size in the exploratory analysis with glasdegib + decitabine was small, the estimated OS of 11.1 months compares favorably to the observed clinical data (median OS, 11.5 months) and the historically reported median OS of 7.7 months for decitabine monotherapy [
9,
20]. A randomized, double-blind, multicenter, placebo-controlled phase 3 trial (BRIGHT AML 1019; ClinicalTrials.gov identifier: NCT03416179) of glasdegib in combination with azacitidine in patients with newly diagnosed AML is ongoing. The choice of azacitidine as the combination agent was based on pre-clinical evidence of synergistic effect between a Smoothened inhibitor and azacitidine, and experience from another phase 1b clinical trial involving dosing of glasdegib plus azacitidine in patients with AML and MDS (BRIGHT 1012; NCT02367456) [
21,
22].This trial also includes a second randomized, double-blind, placebo-controlled cohort investigating glasdegib in combination with ICT in patients with newly diagnosed AML [
8].
In the phase 2 portion of BRIGHT AML 1003, all patients in the glasdegib + LDAC arm were randomized to receive glasdegib 100 mg QD and were permitted to reduce the glasdegib dose for the management of adverse events (AEs). The exposure–response analysis demonstrated that variability in glasdegib exposures at the 100 mg QD dose did not impact the risk of death or the OS curves in patients with AML. Therefore, the survival benefit of glasdegib + LDAC was determined not to be glasdegib exposure–dependent; however, these results are limited by the availability of only one glasdegib dose level in BRIGHT AML 1003 (100 mg QD). Long-term follow-up of BRIGHT AML 1003 in patients with AML confirmed that glasdegib + LDAC was well tolerated [
6]. However, some patients may require dose modifications to manage the occurrence of AEs; the most common AEs associated with glasdegib + LDAC treatment in the first 90 days and after 90 days were anemia and diarrhea, respectively. In the glasdegib + LDAC arm, 14/75 (18.7%) patients had glasdegib dose reduced at any time on study (data unpublished). Of these, 13/75 (17.3%) patients had glasdegib dose reductions due to treatment-related AEs (data unpublished). The proportion of patients needing dose reduction at the 100 mg QD dose is considered low. The exposure–response analysis suggests that the management of AEs with dose reduction of glasdegib may allow patients with AML to remain on treatment without impacting the survival benefit of glasdegib + LDAC.
In both the treatment–response and exposure–response analyses, demographic characteristics (e.g., age, sex, baseline weight, race) and baseline safety laboratory values were evaluated as potential sources of variability affecting OS, but none were significant covariates on the base hazard. Additionally, baseline disease characteristics (ECOG PS, de novo or secondary disease, cytogenetic risk, and prior use of hypomethylating agents) were also explored as potential covariates, but none of these characteristics impacted the probability of an event that would modify the OS curves. These results demonstrate that the survival benefit associated with glasdegib treatment is independent of patient demographics, baseline safety laboratory values, and baseline disease characteristics, and support the broad use of glasdegib 100 mg QD in combination with chemotherapy in patients who are ineligible to receive ICT.
In conclusion, the addition of glasdegib to LDAC chemotherapy resulted in significant OS benefit in patients with AML who were ineligible to receive ICT. The survival function was best characterized by an exponential TTE distribution model. The addition of glasdegib to LDAC chemotherapy resulted in a 58% reduction in the risk of death. Variability in glasdegib exposures, demographics, baseline safety laboratory values, and disease characteristics did not impact the probability of an event modifying the OS curves. Together these results support the broad use of glasdegib 100 mg QD with chemotherapy in the treatment of this AML subpopulation.
Compliance with ethical standards
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.