RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
Population Pharmacokinetics of Rilotumumab, a Fully Human Monoclonal Antibody Against Hepatocyte Growth Factor, in Cancer Patients MIN ZHU,1 SAMEER DOSHI,1 PER O. GISLESKOG,2 KELLY S. OLINER,1 JUAN JOSE PEREZ RUIXO,3 ELWYN LOH,4 YILONG ZHANG1 1
Amgen Inc., Thousand Oaks, California SGS Exprimo NV, Mechelen, Belgium 3 Amgen Inc., Barcelona, Spain 4 Amgen Inc., South San Francisco, California 2
Received 28 June 2013; revised 20 September 2013; accepted 7 October 2013 Published online 1 November 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.23763 ABSTRACT: Rilotumumab is an investigational, fully human, monoclonal antibody immunoglobulin G2 against hepatocyte growth factor (HGF) that blocks the binding of HGF to its receptor MET and has shown trends toward improved survival in a phase 2 clinical trial in gastric cancer. To characterize rilotumumab pharmacokinetics in patients with cancer and to identify factors affecting the pharmacokinetics, rilotumumab concentration data from seven clinical trials were analyzed with a nonlinear mixed-effect model. We found that rilotumumab exhibited linear and time-invariant kinetics over a dose range of 0.5–20 mg/kg. Typical systemic clearance and central volume of distribution were 0.184 L/day and 3.56 L, respectively. Body weight is the most significant covariate, and sex, cancer type, coadministration of chemotherapeutics, baseline plasma HGF and tumor MET levels, and renal and hepatic functions did not have an effect on rilotumumab C 2013 pharmacokinetics. The concentration–time profiles for the rilotumumab clinical regimens were projected well with the model. Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:328–336, 2014 Keywords: rilotumumab; population pharmacokinetics; cancer; monoclonal antibody; hepatocyte growth factor; MET; clinical pharmacokinetics; pharmacokinetic/pharmacodynamic models; population pharmacokinetics/pharmacodynamics
INTRODUCTION A key consideration in developing anticancer drugs is their selectivity against cancer cells while sparing normal cells. MET, a receptor tyrosine kinase, is dysregulated in many cancers and is activated by the binding of its only known ligand hepatocyte growth factor (HGF), also known as scatter factor, which in turn activates downstream signaling for tumor cell proliferation, migration, and survival.1,2 MET and HGF are frequently overexpressed in various human cancers, including breast, gastric, colorectal, head and neck, nonsmall cell lung, renal, and liver cancers.2 Increased levels of MET and HGF have been associated with advanced disease and poor prognosis.1–4 As the HGF/MET pathway plays critical roles in various malignancies, it has been recognized as a potential target for developing cancer therapeutics. Rilotumumab, previously known as AMG 102, is an investigational, fully human, monoclonal antibody [immunoglobulin G (IgG)2] against HGF that prevents the activation of its receptor MET.5 The pharmacokinetics of rilotumumab as a monotherapy or combination therapy was evaluated in several phase 1 and 2 clinical trials. In these studies, rilotumumab exhibited linear pharmacokinetic behaviors up to 20 mg/kg administered intravenously every 2 (Q2W) or 3 weeks (Q3W).6–11 The pharmacokinetics of rilotumumab appeared to be similar in Correspondence to: Min Zhu (Telephone: +805-447-2236; Fax: +805-3761871; E-mail: [email protected]) This article contains supplementary material available from the authors upon request or via the Internet at http://onlinelibrary.wiley.com/. Journal of Pharmaceutical Sciences, Vol. 103, 328–336 (2014) C 2013 Wiley Periodicals, Inc. and the American Pharmacists Association
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patients with different tumor types6 and was not affected by the coadministration of chemotherapy (e.g., carboplatin, cisplatin, etoposide, epirubicin, capecitabine, and mitoxantrone) or targeted agents (e.g., bevacizumab and motesanib).8,9,11,12 Rilotumumab also did not appear to affect the pharmacokinetics of other coadministered drugs (e.g., carboplatin, cisplatin, etoposide, mitoxantrone, bevacizumab, and motesanib).8,9,12 Targeting the HGF/MET pathway with rilotumumab has been investigated in solid tumors in the preclinical and clinical setting for nearly a decade.5–21 In a phase 2 clinical trial in gastric and esophagogastric junction cancer, rilotumumab in combination with epirubicin, cisplatin, and capecitabine significantly improved progression-free survival and overall survival in patients who had high-tumor MET expression.13,20 Furthermore, the effect of rilotumumab on survival in patients with gastric cancer appeared to be associated with rilotumumab concentrations.11,13,14 A clearer understanding of the rilotumumab disposition in patients with cancer and the factors that may affect its exposure are important for the development of rilotumumab as an anticancer therapy. This is the first manuscript to characterize the population pharmacokinetics of rilotumumab with pooled data from multiple phase 1 and 2 studies. The primary objectives of this analysis were to (1) characterize the time-course of rilotumumab serum concentrations following intravenous administration in patients with cancer; (2) quantify the degree of interpatient variability of rilotumumab pharmacokinetic parameters; and (3) assess intrinsic and extrinsic factors as potential sources of variability in rilotumumab exposure (e.g., demographics, baseline plasma HGF and tumor MET levels, and concomitant medications).
DOI 10.1002/jps.23763
Amgen study number 20040167. Amgen study number 20050177. EGJ, esophagogastric junction; i.v., intravenous; NA, not available; PK, pharmacokinetic; Q2W, every two weeks; Q3W, every 3 weeks. b
a
75 Study 7 (NCT00791154)7
15 mg/kg i.v. Q3W
59 Study 6 (NCT00788957)6
5 or 10 mg/kg i.v. Q2W
329
Sparse
Intensive and sparse
Sparse Sparse Sparse 10 or 20 mg/kg i.v. Q2W 10 or 20 mg/kg i.v. Q2W 5, 7.5, or 15 mg/kg i.v. Q3W 60 57 88
Number of Patients with PK Data Study Number (ClinicalTrials.gov Identifier)
Summary of Studies
The analysis included four steps: (1) construction of a base model; (2) covariate analysis to obtain a final model; (3) model evaluation; and (4) model-based simulations. Step 1: A linear two-compartment structural model with first-order elimination from the central compartment was selected as a base model to characterize systemic clearance (CL), intercompartmental clearance (Q), volume of distribution of the central compartment (Vc ), and volume of distribution of the peripheral compartment (Vp ). The interindividual variability was assessed for CL, Q, Vc , and Vp with an exponential
Table 1.
Population Pharmacokinetic Analysis
Rilotumumab Dose
Revisions and model-specific datasets were made with SAS software version 9.2 (SAS Institute Inc., Cary, North Carolina). The population pharmacokinetic analysis was conducted by nonlinear mixed effects modeling (NONMEM) using the firstorder conditional estimation method with interaction, as implemented in the NONMEM version 7.1.2 software package (ICON Development Solutions, Ellicott City, Maryland). Compilations were achieved using the Intel Fortran 11.1 compiler (Intel Corporation, Santa Clara, California). Graphical data visualization, evaluation of NONMEM outputs, construction of goodness-of-fit plots, and graphical model comparisons were conducted using S-PLUS software version 8.1 (TIBCO Software Inc., Palo Alto, California).
Study 3 (NCT00427440)3 Study 4 (NCT00422019)4 Study 5 (NCT00719550)5
Software
Rilotumumab monotherapy Rilotumumab+bevacizumab or motesanib Malignant glioma Rilotumumab monotherapy Advanced renal cell carcinoma Rilotumumab monotherapy Locally advanced/ metastatic Rilotumumab+epirubicin, cisplatin, and gastric/EGJ junction adenocarcinoma capecitabine Wild-type KRAS metastatic Rilotumumab+panitumumab colorectal cancer Small cell lung cancer Rilotumumab+etoposide+cisplatin or carboplatin
Cancer Type
Rilotumumab serum concentrations were determined by a rilotumumab-specific enzyme-linked immunosorbent assay using recombinant human HGF (capture reagent; Amgen Inc., Thousand Oaks, California) for capturing a biotinylated polyclonal rabbit anti-rilotumumab antibody (Amgen Inc.) for detection, as previously described.6,18 The lower limits of quantification were 31 (studies one to four) and 90 ng/mL (studies five to seven). The interassay coefficients of variation ranged from 7% to 10%, and the average assay accuracy ranged from 3% to 7%. Plasma HGF concentrations were determined by a human HGF/scatter factor immunoassay kit (R&D Systems, Minneapolis, Minnesota) that detects pro-HGF, rilotumumabbound HGF, and free HGF, as previously described.9 Tumor MET expression in archival patient tumor samples were determined by immunohistochemistry.
Advanced solid tumors Advanced solid tumors
Bioanalytical Assays
0.5, 1, 3, 5, 10, or 20 mg/kg i.v. Q2W 3,10, or 20 mg/kg i.v. Q2W
Study Drugs
The population pharmacokinetic analysis of rilotumumab included 2479 serum concentrations (586 from intensive sampling schedules and 1893 from sparse sampling schedules) collected from 393 patients with cancer from seven clinical trials. Study descriptions are provided in Table 1. Additional details of these clinical trials are reported elsewhere.6–8,10,12,16,21 All patients gave written informed consent before screening after being advised of the potential risks and benefits, as well as the investigational nature of the study. The studies were approved by the investigational review boards at each site and complied with the principles of good clinical practice as defined by the International Conference on Harmonization and the principles of the Declaration of Helsinki.
40 14
Data Source
Study 1 (NAa )1 Study 2 (NAb )2
PK Sampling
MATERIALS AND METHODS
Intensive and sparse Intensive and sparse
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
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70.0 (44.0–109.9) 73.3 (35.0–168.2) 60.0 (42.0–74.0) 60 (19–84) 55 (73.3) 253 (64.4) 23 (30.7) 118 (40.3) 60 (80.0) 341 (86.8) NA 40.0 (16.1–55.0) 8.0 (3.4–23.0) 7.2 (1.7–31.0) 23.0 (8.4–172.0) 23.0 (7.0–419.0) 22.0 (10.0–120.0) 22.0 (7.0–693.0) 96.0 (36.0–673.0) 92.0 (31.0–2968.0) 89.8 (60.1–151.5) 88.3 (28.7–263.5) NA 982.5 (95.0–32,536.7)
Study 7 (n=75) Study 6 (n=59) Study 5 (n=88) Study 4 (n=57) Study 3 (n=60) Study 2 (n=14) Study 1 (n=40)
Results of continuous variables were reported as median (range). In the population pharmacokinetic analysis, creatinine clearance was set to 150mL/min when the calculated value was above 150mL/min. ALT, alanine aminotransferase; AST, aspartate aminotransferase; ECOG PS, Eastern Cooperative Oncology Group performance status; HGF, hepatocyte growth factor; and NA, not available. b
a
Seventy-five of the 2479 evaluable measurements (3%) were excluded because of concentrations below the limit of quantification and sampling errors. Generally, baseline values, such as demographics, were similar among the studies included in these analyses (Table 2). Baseline laboratory measures related to baseline disease characteristics, liver function, and renal function were also comparable among the studies. In this dataset,
Weight (kg) 75.0 (46.4–130.5) 73.8 (46.8–97.3) 83.2 (47.5–144.0) 80.1 (43.8–168.2) 67.5 (35.0–103.8) 77.0 (44.8–118.0) Age (years) 59.0 (24.0–78.0) 70.0 (41.0–84.0) 53.5 (19.0–71.0) 59.0 (39.0–84.0) 61.0 (27.0–77.0) 63.0 (37.0–78.0) Men, (n, %) 17 (42.5) 7 (50.0) 38 (63.3) 41 (71.9) 61 (69.3) 34 (57.6) ECOG PS 0, (n, %) NA 14 (100) NA 28 (49.1) 37 (42.1) 30 (50.8) Caucasian, (n, %) 35 (87.5) 11 (78.6) 54 (90.0) 55 (96.5) 68 (77.3) 58 (98.3) Albumin (g/L) 37.5 (25.0–46.0) 37.0 (25.0–44.0) 42.0 (36.0–49.0) 40.0 (29.0–47.0) 37.8 (16.1–48.3) 40.0 (20.3–55.0) Total bilirubin (:mol/L) 8.6 (3.4–23.9) 9.4 (3.4–22.2) 5.1 (1.7–17.1) 5.1 (1.7–13.7) 8.0 (3.0–31.0) 9.1 (3.4–31.0) ALT (U/L) 20.5 (11.0–79.0) 29.5 (10.0–102.0) 28.0 (11.0–110.0) 21.0 (9.0–693.0) 18.0 (7.0–131.0) 24.0 (7.0–84.0) AST (U/L) 24.0 (14.0–70.0) 29.5 (14.0–168.0) 21.0 (10.0–43.0) 23.0 (10.0–419.0) 21.0 (7.0–255.0) 31.0 (14.0–148.0) Alkaline phosphatase (U/L) 92.5 (66.0–970.0) 122.5 (57.0–484.0) 75.5 (35.0–210.0) 79.0 (35.0–925.0) 102.0 (31.0–2968.0) 124.0 (58.0–848.0) Creatinine clearanceb (mL/min) 82.3 (41.8–173.2) 63.2 (32.8–182.1) 119.4 (43.7–263.5) 69.0 (28.7–185.9) 86.9 (42.0–188.8) 87.9 (47.4–156.6) HGF (ng/mL) 1070.0 (95.0–15,515.0) 1255.0 (95.0–3280.0) 648.4 (95.0–4814.5) 1048.0 (456.9–18,053.8) 1235.9 (95.0–32,536.7) 986.5 (95.0–9289.5)
Rilotumumab Dataset for the Pharmacokinetic Model
Characteristicsa
RESULTS
Baseline Characteristics of Patients Included in the Pharmacokinetic Modeling Dataset
random-effect model. The covariances between individual estimates of CL and Vc and between Vc and Vp were estimated. Residual variability was described by a proportional error model, with separate proportional residual variability for intensive and sparse samples. One- and three-compartment models were also tested and nested models were compared with minimum value of objective functions (MVOF).22 Goodness-of-fit plots were used for model diagnostics. Shrinkage toward the population mean was calculated as previously described.23 Step 2: Effect of baseline factors and patient demographics on CL and Vc were screened with the covariate analysis. Table 2 summarized these covariates including body weight, age, sex, race, creatinine clearance, serum albumin, total bilirubin, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, Eastern Cooperative Oncology Group performance status (0 vs. 1 and 2), blood urea nitrogen, baseline levels of HGF and MET, and coadministration of agents (yes vs. no, specified in Table 1). Forward inclusion (MVOF < 6.635, df = 1, p < 0.01) followed by backward elimination (MVOF > 7.879, df = 1, p < 0.005) approach was utilized.24 Continuous covariates were evaluated using power equations after centering on the median, whereas categorical variables were incorporated into the model as index variables. If the magnitude of the change in a parameter because of the influence of a covariate was less than 20% over the range of values that were evaluated, the covariate factor was not considered to be clinically relevant and, consequently, was excluded from the model. Step 3: The final pharmacokinetic model was evaluated by performing a visual predictive check on rilotumumab serum concentrations to assess the ability of the model to produce similar results as those derived from the original dataset. A total of 300 dataset replicates were simulated with the final model. Percentiles (5th, 50th, and 95th) and their 95% prediction intervals (PIs) of the simulated rilotumumab concentration data were plotted and overlaid with the observed median and 90% PIs of rilotumumab concentration–time profiles. A nonparametric bootstrap was used as an internal evaluation method to qualify the estimates of the model parameters.25 The mean and 95% PI of the parameter estimates from the bootstrap replicates were compared with the estimated parameters from the original dataset. Step 4: Steady-state profiles of rilotumumab with the Q2W and Q3W regimens at clinically tested doses (7.5, 10, 15, and 20 mg/kg) were simulated using the final pharmacokinetic model. Significant covariates that were included in the final model were sampled (with replacement) for 3000 patients from all patients in the seven studies. The proportional residual variability for sparse sampling was used in the simulation.
All (n=393)
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
Table 2.
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Figure 1. Covariate plots: (a) clearance versus body weight, (b) central volume of distribution versus body weight, (c) clearance versus age, and (d) central volume of distribution versus age. Each line represents the curve fit of observed data under a power function.
157 patients (40%) received rilotumumab monotherapy, and 236 patients (60%) received rilotumumab in combination with other medications (Table 1). Base Development Model A two-compartment model was found to better describe the rilotumumab concentration data than a one-compartment model (MVOF = −790), and the three-compartment model did not show any significant improvement over the two-compartment model (MVOF = −3.58). Therefore, an open two-compartment linear model was selected as a base model for further development. The interindividual variability was 32.9% and 25.1% for the model parameters CL and Vc , respectively. The proportional residual variability was 20.5% and 27.6% for intensive and nonintensive pharmacokinetics, respectively. Simplification of the residual error to one integrated proportional residual variability for both intensive and nonintensive pharmacokinetics resulted in a significant increase in MVOF (MVOF = 49.3). Therefore, the two separate residual variabilities for intensive and nonintensive pharmacokinetics were selected. Covariate Analysis and Final Model After the initial exploratory covariate screening, body weight, sex, age, race, and baseline levels of creatinine clearance were selected to further test their effect on CL and Vc . HGF and tumor MET levels were also tested for effect on CL. DOI 10.1002/jps.23763
In the final model, body weight and age had a significant effect on both CL and Vc (Fig. 1). The inclusion of the body weight effect on CL and Vc improved the goodness-of-fit, relative to the base model (MVOF = −214) and reduced interindividual variability by 3% and 5% on the model parameters for CL and Vc , respectively. The power coefficients associated with the body weight effect were 0.625 and 0.611 on CL and Vc , respectively. It can be calculated that a 10% increase in body weight was associated with a 6% increase in CL and Vc with limited effect on the elimination rate constant. Furthermore, implementation of the age effect on CL and Vc was associated with a significant reduction in objective function value (MVOF = −24.8), but there was no further reduction on interpatient variability on those parameters. The power coefficients associated with the age effect were 0.268 and 0.229 on CL and Vc , respectively. It can be calculated that a 10% increase from the median age (60 years old) was associated with an additional 2%–3% increase in CL and Vc on top of the weight effect, with limited effect on the elimination rate constant. All other covariates did not show a significant change on MVOF on either CL or Vc . The population pharmacokinetic parameters of the final model are provided in Table 3. None of the pharmacokinetic parameter estimates were dose dependent in the model-based assessment, indicating that rilotumumab exhibited linear pharmacokinetic behaviors over the tested dose range from 0.5 to 20 mg/kg and over the treatment Zhu et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:328–336, 2014
332 Table 3.
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
Rilotumumab Population Pharmacokinetic Parameters from the Final Model
Model Parameter
Typical Value (% RSE)
Bootstrap Median (95% CI)
Typical CL (L/day per 70kg per 60 years) Weight on CL (power) Age on CL (power) Typical Vc (L/70 kg per 60 years) Weight on Vc (power) Age on Vc (power) Q (L/day) VP (L)
0.184 (2.5) 0.625 (17.9) 0.268 (56.0) 3.56 (1.5) 0.611 (8.4) 0.229 (42.3) 0.833 (12.3) 2.50 (6.8)
0.184 (0.175–0.194) 0.623 (0.402–0.849) 0.259 (-0.034–0.550) 3.56 (3.45–3.67) 0.607 (0.504–0.715) 0.218 (0.032–0.400) 0.818 (0.612–1.11) 2.47 (2.18–2.84)
Interindividual variability (% CV) TCL TVc TQ TVp D CL-Vc DVc-Vp
29.8 (10.7) 19.8 (15.1) 71.0 (42.7) 37.3 (41.4) 0.0286 (21.2) 0.0436 (30.0)
29.8 (26.3–33.2) 19.8 (16.7–22.6) 67.5 (2.2–100) 37.6 (20.8–51.9) 0.0289 (0.0175–0.0409) 0.0428 (0.0153–0.0678)
20.1 (8.6) 26.9 (4.8)
19.9 (18.1–23.1) 26.8 (24.2–29.1)
Proportional residual (% CV) Fintensive PK Fsparse PK
% RSE is expressed as one significant digit, and others are expressed as three significant digits. CI, confidence interval; CL, clearance; CV, coefficient of variation; PK, pharmacokinetics; Q, intercompartmental clearance; Vp, peripheral volume of distribution; RSE, relative standard error = (standard error/parameter estimate)×100; Vc , central volume of distribution; T, between patient variability; D, covariance; F, residual error.
period in the patient populations. The finding is consistent with earlier evaluations with a noncompartmental model.6–10,12,13 The goodness-of-fit plots for the final pharmacokinetic model demonstrated that the model adequately fitted the rilotumumab concentrations. The observed concentrations versus population- and individual-predicted concentrations were well distributed around the line of identity. Values for conditional weighted residuals were homogeneously distributed around zero, suggesting no apparent bias in the predictions of high and low concentrations over time (Supplementary Fig. S1) and confirming that rilotumumab exhibited time-invariant pharmacokinetics over the tested dose range and treatment period in the patient populations included in this analysis. Our assessment for the final model showed that the distributions of post-hoc ETAs were close to the normal distribution with a mean of zero and respective variance of the population. The calculated shrinkages to the mean of individual random effects of CL and Vc were acceptable (0.23 and 0.24, respectively), and the shrinkage to the mean of individual random effects of Vp and Q were high (0.43 and 0.68, respectively). Model Evaluation The distribution of observed and simulated concentration ranges was demonstrated via a visual predictive check. For a qualified model, approximately 90% of observed concentrations should fall in the 90% prediction intervals of the simulated rilotumumab time profile. The analysis revealed that 93.4% (95% PI, 92.2–94.6) of the observed dose-normalized rilotumumab concentrations were within the 90% prediction intervals of the simulated dose-normalized concentration–time profiles (Fig. 2), indicating that the model described the observed data reasonably well with a slight overprediction of variability. The model parameter estimates from the final model were similar to the median of the nonparametric bootstrap replicates (Table 3), and all were contained within the 95% PI obtained from the bootstrap analysis. Zhu et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:328–336, 2014
Model-Based Simulations The simulated pharmacokinetic profiles with the Q2W and Q3W regimens at clinically tested doses (7.5, 10, 15, and 20 mg/kg) are presented in Figure 3. It was calculated that steady state was achieved after at least five doses of rilotumumab, and accumulation ratios for peak and trough concentrations were 2.1 and 3.0 for the Q2W regimen, respectively, and 1.7 and 2.3 for the Q3W regimen, respectively. The steady-state mean peak and trough concentrations under a 20-mg/kg Q2W regimen were predicted to be 836 and 426 :g/mL, respectively, and those under a 15-mg/kg Q3W regimen were predicted to be 497 and 193 :g/mL, respectively. These values are much higher than the rilotumumab-binding affinity to human HGF (target, Kd = 6 ng/mL); therefore, no target-mediated rilotumumab disposition was expected at the clinical dose range, which is consistent with the fact that there was no detectable effect of HGF on the pharmacokinetic parameters.
DISCUSSION In the present analysis, the population pharmacokinetics of rilotumumab has been characterized using an open linear twocompartment pharmacokinetic model. After intravenous infusion of rilotumumab in patients with cancer, the estimated rilotumumab volume of distribution (Vc , 3.6 L; Vp , 2.5 L) was similar to that of most monoclonal antibodies, of which the typical median (range) Vc of most IgG1 or IgG2 therapeutic monoclonal antibodies is 3.6 (1.4–6.4) L, and the median (range) Vp is 2.3 (0.9–4.2) L.26 The estimated total volume of distribution (i.e., Vc + Vp ) of rilotumumab in patients with solid tumors (6.1 L) was similar to that of endogenous IgG (6.2 L).26 The estimated typical rilotumumab CL in patients with cancer (0.2 L/day) was comparable to the typical CL estimates of most therapeutic monoclonal antibodies with linear CL characteristics (0.2–0.5 L/day) and comparable to the estimated CL of endogeDOI 10.1002/jps.23763
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Figure 2. Visual predictive check plot for all patients following the first treatment. The blue circles show the observed data normalized by dose, and the solid and dashed blue lines show the median and 5th and 95th percentiles of the data normalized by dose for cycle 1. The solid and dashed black lines show the median and 5th and 95th percentiles of the simulated rilotumumab concentrations. The orange-shaded areas represent the 95% confidence intervals of the respective lines. The simulated values were computed from 300 trials simulated using the covariate values of the analysis dataset.
Figure 3. Simulated plasma concentration–time profile at steady state following rilotumumab administration at (a) 10 mg/kg Q2W, (b) 20 mg/kg Q2W, (c) 7.5 mg/kg Q3W, and (d) 15 mg/kg Q3W. The solid lines and shaded area represent the median and 95% prediction interval of the simulated rilotumumab concentrations (n=3000 per dose group). Q2W, every two weeks; Q3W, every three weeks.
DOI 10.1002/jps.23763
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Table 4. Summary of the Final Model-Predicted AUCtau Values at Steady State Following 15 mg/kg Q3W Rilotumumab Administration Body Weight Range
AUCtau (mg/mLh)a
Overall Lower quartile (≤63 kg) Upper quartile (≥88 kg) a
141 (68.1–299) 127 (68.1–264) 165 (85.1–285)
Data are expressed as median (minimum–maximum).
nous IgG (0.1–0.2 L/day).26 Similar to other monoclonal antibodies, rilotumumab is expected to be cleared primarily by nonspecific fluid-phase endocytosis and proteolysis.27 In this process, rilotumumab is broken down to component amino acids, and the degradation products are unlikely to be toxic. As the catabolism mainly occurs in the lysosome, the involvement of the liver or impact of liver impairment on degradation of rilotumumab through intracellular catabolism is expected to be negligible. Consistent results were found in our analysis; an association between measures of liver function and rilotumumab CL was not detectable. The pharmacokinetic parameters of rilotumumab are mainly influenced by the patient’s weight. Body weight is the most influential covariate contributing to interindividual pharmacokinetic variability (Fig. 1). However, it should be noted that the relationship between steady-state AUCtau and weight still showed a slightly positive trend (Table 4) with the weightbased dosing. For the patients at the lower quartile of body weight (≤63 kg), the medium-predicted exposures were about 10% lower than those in the overall population, whereas for the patients at the higher quartile of body weight (≥88 kg), the median-predicted exposures were about 15% higher than those in the overall population, indicating that exposures in patients with extreme lower or higher weight are expected to be slightly lower and higher, respectively, than those in other patients. Although the age effect on pharmacokinetic parameters was detected in this dataset, the magnitude is modest and inclusion of age effect in the model did not reduce interpatient variability on CL and Vc . Furthermore, the bootstrap analysis showed that the 95% CI of the parameter estimate on the age effect on clearance included zero, indicating the uncertainty in the estimation of the age effect on pharmacokinetics. It can be seen in Figure 1 that data from patients younger than 40 years old (6% of the total number of patients) appear to mainly contribute to the CL–age and Vc –age relationships. Further calculation suggested that the CL was within 0.85- and 1.1-fold of the median, and the Vc was within 0.9- and 1.1-fold of the median in patients at least 40 years old. Therefore, the clinical relevance of this age effect on rilotumumab pharmacokinetics appears to be negligible. The renal filtration of protein drugs is a size-specific mechanism of elimination. Renal filtration and subsequent proteolysis are often a primary mechanism of elimination for a protein with a molecular weight less than 70 kDa. As rilotumumab has a molecular weight of 150 kDa, involvement of renal excretion of rilotumumab is unlikely. Consistently, within the range of covariates evaluated, there was no association between the measures of renal function (e.g., creatinine clearance) and the rilotumumab CL detected in the population pharmacokinetic analysis. Furthermore, rilotumumab exposure is not expected to be affected by renal impairment (i.e., creatinine clearance
Population Pharmacokinetics of Rilotumumab, a Fully Human Monoclonal Antibody Against Hepatocyte Growth Factor, in Cancer Patients MIN ZHU,1 SAMEER DOSHI,1 PER O. GISLESKOG,2 KELLY S. OLINER,1 JUAN JOSE PEREZ RUIXO,3 ELWYN LOH,4 YILONG ZHANG1 1
Amgen Inc., Thousand Oaks, California SGS Exprimo NV, Mechelen, Belgium 3 Amgen Inc., Barcelona, Spain 4 Amgen Inc., South San Francisco, California 2
Received 28 June 2013; revised 20 September 2013; accepted 7 October 2013 Published online 1 November 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.23763 ABSTRACT: Rilotumumab is an investigational, fully human, monoclonal antibody immunoglobulin G2 against hepatocyte growth factor (HGF) that blocks the binding of HGF to its receptor MET and has shown trends toward improved survival in a phase 2 clinical trial in gastric cancer. To characterize rilotumumab pharmacokinetics in patients with cancer and to identify factors affecting the pharmacokinetics, rilotumumab concentration data from seven clinical trials were analyzed with a nonlinear mixed-effect model. We found that rilotumumab exhibited linear and time-invariant kinetics over a dose range of 0.5–20 mg/kg. Typical systemic clearance and central volume of distribution were 0.184 L/day and 3.56 L, respectively. Body weight is the most significant covariate, and sex, cancer type, coadministration of chemotherapeutics, baseline plasma HGF and tumor MET levels, and renal and hepatic functions did not have an effect on rilotumumab C 2013 pharmacokinetics. The concentration–time profiles for the rilotumumab clinical regimens were projected well with the model. Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:328–336, 2014 Keywords: rilotumumab; population pharmacokinetics; cancer; monoclonal antibody; hepatocyte growth factor; MET; clinical pharmacokinetics; pharmacokinetic/pharmacodynamic models; population pharmacokinetics/pharmacodynamics
INTRODUCTION A key consideration in developing anticancer drugs is their selectivity against cancer cells while sparing normal cells. MET, a receptor tyrosine kinase, is dysregulated in many cancers and is activated by the binding of its only known ligand hepatocyte growth factor (HGF), also known as scatter factor, which in turn activates downstream signaling for tumor cell proliferation, migration, and survival.1,2 MET and HGF are frequently overexpressed in various human cancers, including breast, gastric, colorectal, head and neck, nonsmall cell lung, renal, and liver cancers.2 Increased levels of MET and HGF have been associated with advanced disease and poor prognosis.1–4 As the HGF/MET pathway plays critical roles in various malignancies, it has been recognized as a potential target for developing cancer therapeutics. Rilotumumab, previously known as AMG 102, is an investigational, fully human, monoclonal antibody [immunoglobulin G (IgG)2] against HGF that prevents the activation of its receptor MET.5 The pharmacokinetics of rilotumumab as a monotherapy or combination therapy was evaluated in several phase 1 and 2 clinical trials. In these studies, rilotumumab exhibited linear pharmacokinetic behaviors up to 20 mg/kg administered intravenously every 2 (Q2W) or 3 weeks (Q3W).6–11 The pharmacokinetics of rilotumumab appeared to be similar in Correspondence to: Min Zhu (Telephone: +805-447-2236; Fax: +805-3761871; E-mail: [email protected]) This article contains supplementary material available from the authors upon request or via the Internet at http://onlinelibrary.wiley.com/. Journal of Pharmaceutical Sciences, Vol. 103, 328–336 (2014) C 2013 Wiley Periodicals, Inc. and the American Pharmacists Association
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patients with different tumor types6 and was not affected by the coadministration of chemotherapy (e.g., carboplatin, cisplatin, etoposide, epirubicin, capecitabine, and mitoxantrone) or targeted agents (e.g., bevacizumab and motesanib).8,9,11,12 Rilotumumab also did not appear to affect the pharmacokinetics of other coadministered drugs (e.g., carboplatin, cisplatin, etoposide, mitoxantrone, bevacizumab, and motesanib).8,9,12 Targeting the HGF/MET pathway with rilotumumab has been investigated in solid tumors in the preclinical and clinical setting for nearly a decade.5–21 In a phase 2 clinical trial in gastric and esophagogastric junction cancer, rilotumumab in combination with epirubicin, cisplatin, and capecitabine significantly improved progression-free survival and overall survival in patients who had high-tumor MET expression.13,20 Furthermore, the effect of rilotumumab on survival in patients with gastric cancer appeared to be associated with rilotumumab concentrations.11,13,14 A clearer understanding of the rilotumumab disposition in patients with cancer and the factors that may affect its exposure are important for the development of rilotumumab as an anticancer therapy. This is the first manuscript to characterize the population pharmacokinetics of rilotumumab with pooled data from multiple phase 1 and 2 studies. The primary objectives of this analysis were to (1) characterize the time-course of rilotumumab serum concentrations following intravenous administration in patients with cancer; (2) quantify the degree of interpatient variability of rilotumumab pharmacokinetic parameters; and (3) assess intrinsic and extrinsic factors as potential sources of variability in rilotumumab exposure (e.g., demographics, baseline plasma HGF and tumor MET levels, and concomitant medications).
DOI 10.1002/jps.23763
Amgen study number 20040167. Amgen study number 20050177. EGJ, esophagogastric junction; i.v., intravenous; NA, not available; PK, pharmacokinetic; Q2W, every two weeks; Q3W, every 3 weeks. b
a
75 Study 7 (NCT00791154)7
15 mg/kg i.v. Q3W
59 Study 6 (NCT00788957)6
5 or 10 mg/kg i.v. Q2W
329
Sparse
Intensive and sparse
Sparse Sparse Sparse 10 or 20 mg/kg i.v. Q2W 10 or 20 mg/kg i.v. Q2W 5, 7.5, or 15 mg/kg i.v. Q3W 60 57 88
Number of Patients with PK Data Study Number (ClinicalTrials.gov Identifier)
Summary of Studies
The analysis included four steps: (1) construction of a base model; (2) covariate analysis to obtain a final model; (3) model evaluation; and (4) model-based simulations. Step 1: A linear two-compartment structural model with first-order elimination from the central compartment was selected as a base model to characterize systemic clearance (CL), intercompartmental clearance (Q), volume of distribution of the central compartment (Vc ), and volume of distribution of the peripheral compartment (Vp ). The interindividual variability was assessed for CL, Q, Vc , and Vp with an exponential
Table 1.
Population Pharmacokinetic Analysis
Rilotumumab Dose
Revisions and model-specific datasets were made with SAS software version 9.2 (SAS Institute Inc., Cary, North Carolina). The population pharmacokinetic analysis was conducted by nonlinear mixed effects modeling (NONMEM) using the firstorder conditional estimation method with interaction, as implemented in the NONMEM version 7.1.2 software package (ICON Development Solutions, Ellicott City, Maryland). Compilations were achieved using the Intel Fortran 11.1 compiler (Intel Corporation, Santa Clara, California). Graphical data visualization, evaluation of NONMEM outputs, construction of goodness-of-fit plots, and graphical model comparisons were conducted using S-PLUS software version 8.1 (TIBCO Software Inc., Palo Alto, California).
Study 3 (NCT00427440)3 Study 4 (NCT00422019)4 Study 5 (NCT00719550)5
Software
Rilotumumab monotherapy Rilotumumab+bevacizumab or motesanib Malignant glioma Rilotumumab monotherapy Advanced renal cell carcinoma Rilotumumab monotherapy Locally advanced/ metastatic Rilotumumab+epirubicin, cisplatin, and gastric/EGJ junction adenocarcinoma capecitabine Wild-type KRAS metastatic Rilotumumab+panitumumab colorectal cancer Small cell lung cancer Rilotumumab+etoposide+cisplatin or carboplatin
Cancer Type
Rilotumumab serum concentrations were determined by a rilotumumab-specific enzyme-linked immunosorbent assay using recombinant human HGF (capture reagent; Amgen Inc., Thousand Oaks, California) for capturing a biotinylated polyclonal rabbit anti-rilotumumab antibody (Amgen Inc.) for detection, as previously described.6,18 The lower limits of quantification were 31 (studies one to four) and 90 ng/mL (studies five to seven). The interassay coefficients of variation ranged from 7% to 10%, and the average assay accuracy ranged from 3% to 7%. Plasma HGF concentrations were determined by a human HGF/scatter factor immunoassay kit (R&D Systems, Minneapolis, Minnesota) that detects pro-HGF, rilotumumabbound HGF, and free HGF, as previously described.9 Tumor MET expression in archival patient tumor samples were determined by immunohistochemistry.
Advanced solid tumors Advanced solid tumors
Bioanalytical Assays
0.5, 1, 3, 5, 10, or 20 mg/kg i.v. Q2W 3,10, or 20 mg/kg i.v. Q2W
Study Drugs
The population pharmacokinetic analysis of rilotumumab included 2479 serum concentrations (586 from intensive sampling schedules and 1893 from sparse sampling schedules) collected from 393 patients with cancer from seven clinical trials. Study descriptions are provided in Table 1. Additional details of these clinical trials are reported elsewhere.6–8,10,12,16,21 All patients gave written informed consent before screening after being advised of the potential risks and benefits, as well as the investigational nature of the study. The studies were approved by the investigational review boards at each site and complied with the principles of good clinical practice as defined by the International Conference on Harmonization and the principles of the Declaration of Helsinki.
40 14
Data Source
Study 1 (NAa )1 Study 2 (NAb )2
PK Sampling
MATERIALS AND METHODS
Intensive and sparse Intensive and sparse
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70.0 (44.0–109.9) 73.3 (35.0–168.2) 60.0 (42.0–74.0) 60 (19–84) 55 (73.3) 253 (64.4) 23 (30.7) 118 (40.3) 60 (80.0) 341 (86.8) NA 40.0 (16.1–55.0) 8.0 (3.4–23.0) 7.2 (1.7–31.0) 23.0 (8.4–172.0) 23.0 (7.0–419.0) 22.0 (10.0–120.0) 22.0 (7.0–693.0) 96.0 (36.0–673.0) 92.0 (31.0–2968.0) 89.8 (60.1–151.5) 88.3 (28.7–263.5) NA 982.5 (95.0–32,536.7)
Study 7 (n=75) Study 6 (n=59) Study 5 (n=88) Study 4 (n=57) Study 3 (n=60) Study 2 (n=14) Study 1 (n=40)
Results of continuous variables were reported as median (range). In the population pharmacokinetic analysis, creatinine clearance was set to 150mL/min when the calculated value was above 150mL/min. ALT, alanine aminotransferase; AST, aspartate aminotransferase; ECOG PS, Eastern Cooperative Oncology Group performance status; HGF, hepatocyte growth factor; and NA, not available. b
a
Seventy-five of the 2479 evaluable measurements (3%) were excluded because of concentrations below the limit of quantification and sampling errors. Generally, baseline values, such as demographics, were similar among the studies included in these analyses (Table 2). Baseline laboratory measures related to baseline disease characteristics, liver function, and renal function were also comparable among the studies. In this dataset,
Weight (kg) 75.0 (46.4–130.5) 73.8 (46.8–97.3) 83.2 (47.5–144.0) 80.1 (43.8–168.2) 67.5 (35.0–103.8) 77.0 (44.8–118.0) Age (years) 59.0 (24.0–78.0) 70.0 (41.0–84.0) 53.5 (19.0–71.0) 59.0 (39.0–84.0) 61.0 (27.0–77.0) 63.0 (37.0–78.0) Men, (n, %) 17 (42.5) 7 (50.0) 38 (63.3) 41 (71.9) 61 (69.3) 34 (57.6) ECOG PS 0, (n, %) NA 14 (100) NA 28 (49.1) 37 (42.1) 30 (50.8) Caucasian, (n, %) 35 (87.5) 11 (78.6) 54 (90.0) 55 (96.5) 68 (77.3) 58 (98.3) Albumin (g/L) 37.5 (25.0–46.0) 37.0 (25.0–44.0) 42.0 (36.0–49.0) 40.0 (29.0–47.0) 37.8 (16.1–48.3) 40.0 (20.3–55.0) Total bilirubin (:mol/L) 8.6 (3.4–23.9) 9.4 (3.4–22.2) 5.1 (1.7–17.1) 5.1 (1.7–13.7) 8.0 (3.0–31.0) 9.1 (3.4–31.0) ALT (U/L) 20.5 (11.0–79.0) 29.5 (10.0–102.0) 28.0 (11.0–110.0) 21.0 (9.0–693.0) 18.0 (7.0–131.0) 24.0 (7.0–84.0) AST (U/L) 24.0 (14.0–70.0) 29.5 (14.0–168.0) 21.0 (10.0–43.0) 23.0 (10.0–419.0) 21.0 (7.0–255.0) 31.0 (14.0–148.0) Alkaline phosphatase (U/L) 92.5 (66.0–970.0) 122.5 (57.0–484.0) 75.5 (35.0–210.0) 79.0 (35.0–925.0) 102.0 (31.0–2968.0) 124.0 (58.0–848.0) Creatinine clearanceb (mL/min) 82.3 (41.8–173.2) 63.2 (32.8–182.1) 119.4 (43.7–263.5) 69.0 (28.7–185.9) 86.9 (42.0–188.8) 87.9 (47.4–156.6) HGF (ng/mL) 1070.0 (95.0–15,515.0) 1255.0 (95.0–3280.0) 648.4 (95.0–4814.5) 1048.0 (456.9–18,053.8) 1235.9 (95.0–32,536.7) 986.5 (95.0–9289.5)
Rilotumumab Dataset for the Pharmacokinetic Model
Characteristicsa
RESULTS
Baseline Characteristics of Patients Included in the Pharmacokinetic Modeling Dataset
random-effect model. The covariances between individual estimates of CL and Vc and between Vc and Vp were estimated. Residual variability was described by a proportional error model, with separate proportional residual variability for intensive and sparse samples. One- and three-compartment models were also tested and nested models were compared with minimum value of objective functions (MVOF).22 Goodness-of-fit plots were used for model diagnostics. Shrinkage toward the population mean was calculated as previously described.23 Step 2: Effect of baseline factors and patient demographics on CL and Vc were screened with the covariate analysis. Table 2 summarized these covariates including body weight, age, sex, race, creatinine clearance, serum albumin, total bilirubin, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, Eastern Cooperative Oncology Group performance status (0 vs. 1 and 2), blood urea nitrogen, baseline levels of HGF and MET, and coadministration of agents (yes vs. no, specified in Table 1). Forward inclusion (MVOF < 6.635, df = 1, p < 0.01) followed by backward elimination (MVOF > 7.879, df = 1, p < 0.005) approach was utilized.24 Continuous covariates were evaluated using power equations after centering on the median, whereas categorical variables were incorporated into the model as index variables. If the magnitude of the change in a parameter because of the influence of a covariate was less than 20% over the range of values that were evaluated, the covariate factor was not considered to be clinically relevant and, consequently, was excluded from the model. Step 3: The final pharmacokinetic model was evaluated by performing a visual predictive check on rilotumumab serum concentrations to assess the ability of the model to produce similar results as those derived from the original dataset. A total of 300 dataset replicates were simulated with the final model. Percentiles (5th, 50th, and 95th) and their 95% prediction intervals (PIs) of the simulated rilotumumab concentration data were plotted and overlaid with the observed median and 90% PIs of rilotumumab concentration–time profiles. A nonparametric bootstrap was used as an internal evaluation method to qualify the estimates of the model parameters.25 The mean and 95% PI of the parameter estimates from the bootstrap replicates were compared with the estimated parameters from the original dataset. Step 4: Steady-state profiles of rilotumumab with the Q2W and Q3W regimens at clinically tested doses (7.5, 10, 15, and 20 mg/kg) were simulated using the final pharmacokinetic model. Significant covariates that were included in the final model were sampled (with replacement) for 3000 patients from all patients in the seven studies. The proportional residual variability for sparse sampling was used in the simulation.
All (n=393)
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Table 2.
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Figure 1. Covariate plots: (a) clearance versus body weight, (b) central volume of distribution versus body weight, (c) clearance versus age, and (d) central volume of distribution versus age. Each line represents the curve fit of observed data under a power function.
157 patients (40%) received rilotumumab monotherapy, and 236 patients (60%) received rilotumumab in combination with other medications (Table 1). Base Development Model A two-compartment model was found to better describe the rilotumumab concentration data than a one-compartment model (MVOF = −790), and the three-compartment model did not show any significant improvement over the two-compartment model (MVOF = −3.58). Therefore, an open two-compartment linear model was selected as a base model for further development. The interindividual variability was 32.9% and 25.1% for the model parameters CL and Vc , respectively. The proportional residual variability was 20.5% and 27.6% for intensive and nonintensive pharmacokinetics, respectively. Simplification of the residual error to one integrated proportional residual variability for both intensive and nonintensive pharmacokinetics resulted in a significant increase in MVOF (MVOF = 49.3). Therefore, the two separate residual variabilities for intensive and nonintensive pharmacokinetics were selected. Covariate Analysis and Final Model After the initial exploratory covariate screening, body weight, sex, age, race, and baseline levels of creatinine clearance were selected to further test their effect on CL and Vc . HGF and tumor MET levels were also tested for effect on CL. DOI 10.1002/jps.23763
In the final model, body weight and age had a significant effect on both CL and Vc (Fig. 1). The inclusion of the body weight effect on CL and Vc improved the goodness-of-fit, relative to the base model (MVOF = −214) and reduced interindividual variability by 3% and 5% on the model parameters for CL and Vc , respectively. The power coefficients associated with the body weight effect were 0.625 and 0.611 on CL and Vc , respectively. It can be calculated that a 10% increase in body weight was associated with a 6% increase in CL and Vc with limited effect on the elimination rate constant. Furthermore, implementation of the age effect on CL and Vc was associated with a significant reduction in objective function value (MVOF = −24.8), but there was no further reduction on interpatient variability on those parameters. The power coefficients associated with the age effect were 0.268 and 0.229 on CL and Vc , respectively. It can be calculated that a 10% increase from the median age (60 years old) was associated with an additional 2%–3% increase in CL and Vc on top of the weight effect, with limited effect on the elimination rate constant. All other covariates did not show a significant change on MVOF on either CL or Vc . The population pharmacokinetic parameters of the final model are provided in Table 3. None of the pharmacokinetic parameter estimates were dose dependent in the model-based assessment, indicating that rilotumumab exhibited linear pharmacokinetic behaviors over the tested dose range from 0.5 to 20 mg/kg and over the treatment Zhu et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:328–336, 2014
332 Table 3.
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Rilotumumab Population Pharmacokinetic Parameters from the Final Model
Model Parameter
Typical Value (% RSE)
Bootstrap Median (95% CI)
Typical CL (L/day per 70kg per 60 years) Weight on CL (power) Age on CL (power) Typical Vc (L/70 kg per 60 years) Weight on Vc (power) Age on Vc (power) Q (L/day) VP (L)
0.184 (2.5) 0.625 (17.9) 0.268 (56.0) 3.56 (1.5) 0.611 (8.4) 0.229 (42.3) 0.833 (12.3) 2.50 (6.8)
0.184 (0.175–0.194) 0.623 (0.402–0.849) 0.259 (-0.034–0.550) 3.56 (3.45–3.67) 0.607 (0.504–0.715) 0.218 (0.032–0.400) 0.818 (0.612–1.11) 2.47 (2.18–2.84)
Interindividual variability (% CV) TCL TVc TQ TVp D CL-Vc DVc-Vp
29.8 (10.7) 19.8 (15.1) 71.0 (42.7) 37.3 (41.4) 0.0286 (21.2) 0.0436 (30.0)
29.8 (26.3–33.2) 19.8 (16.7–22.6) 67.5 (2.2–100) 37.6 (20.8–51.9) 0.0289 (0.0175–0.0409) 0.0428 (0.0153–0.0678)
20.1 (8.6) 26.9 (4.8)
19.9 (18.1–23.1) 26.8 (24.2–29.1)
Proportional residual (% CV) Fintensive PK Fsparse PK
% RSE is expressed as one significant digit, and others are expressed as three significant digits. CI, confidence interval; CL, clearance; CV, coefficient of variation; PK, pharmacokinetics; Q, intercompartmental clearance; Vp, peripheral volume of distribution; RSE, relative standard error = (standard error/parameter estimate)×100; Vc , central volume of distribution; T, between patient variability; D, covariance; F, residual error.
period in the patient populations. The finding is consistent with earlier evaluations with a noncompartmental model.6–10,12,13 The goodness-of-fit plots for the final pharmacokinetic model demonstrated that the model adequately fitted the rilotumumab concentrations. The observed concentrations versus population- and individual-predicted concentrations were well distributed around the line of identity. Values for conditional weighted residuals were homogeneously distributed around zero, suggesting no apparent bias in the predictions of high and low concentrations over time (Supplementary Fig. S1) and confirming that rilotumumab exhibited time-invariant pharmacokinetics over the tested dose range and treatment period in the patient populations included in this analysis. Our assessment for the final model showed that the distributions of post-hoc ETAs were close to the normal distribution with a mean of zero and respective variance of the population. The calculated shrinkages to the mean of individual random effects of CL and Vc were acceptable (0.23 and 0.24, respectively), and the shrinkage to the mean of individual random effects of Vp and Q were high (0.43 and 0.68, respectively). Model Evaluation The distribution of observed and simulated concentration ranges was demonstrated via a visual predictive check. For a qualified model, approximately 90% of observed concentrations should fall in the 90% prediction intervals of the simulated rilotumumab time profile. The analysis revealed that 93.4% (95% PI, 92.2–94.6) of the observed dose-normalized rilotumumab concentrations were within the 90% prediction intervals of the simulated dose-normalized concentration–time profiles (Fig. 2), indicating that the model described the observed data reasonably well with a slight overprediction of variability. The model parameter estimates from the final model were similar to the median of the nonparametric bootstrap replicates (Table 3), and all were contained within the 95% PI obtained from the bootstrap analysis. Zhu et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:328–336, 2014
Model-Based Simulations The simulated pharmacokinetic profiles with the Q2W and Q3W regimens at clinically tested doses (7.5, 10, 15, and 20 mg/kg) are presented in Figure 3. It was calculated that steady state was achieved after at least five doses of rilotumumab, and accumulation ratios for peak and trough concentrations were 2.1 and 3.0 for the Q2W regimen, respectively, and 1.7 and 2.3 for the Q3W regimen, respectively. The steady-state mean peak and trough concentrations under a 20-mg/kg Q2W regimen were predicted to be 836 and 426 :g/mL, respectively, and those under a 15-mg/kg Q3W regimen were predicted to be 497 and 193 :g/mL, respectively. These values are much higher than the rilotumumab-binding affinity to human HGF (target, Kd = 6 ng/mL); therefore, no target-mediated rilotumumab disposition was expected at the clinical dose range, which is consistent with the fact that there was no detectable effect of HGF on the pharmacokinetic parameters.
DISCUSSION In the present analysis, the population pharmacokinetics of rilotumumab has been characterized using an open linear twocompartment pharmacokinetic model. After intravenous infusion of rilotumumab in patients with cancer, the estimated rilotumumab volume of distribution (Vc , 3.6 L; Vp , 2.5 L) was similar to that of most monoclonal antibodies, of which the typical median (range) Vc of most IgG1 or IgG2 therapeutic monoclonal antibodies is 3.6 (1.4–6.4) L, and the median (range) Vp is 2.3 (0.9–4.2) L.26 The estimated total volume of distribution (i.e., Vc + Vp ) of rilotumumab in patients with solid tumors (6.1 L) was similar to that of endogenous IgG (6.2 L).26 The estimated typical rilotumumab CL in patients with cancer (0.2 L/day) was comparable to the typical CL estimates of most therapeutic monoclonal antibodies with linear CL characteristics (0.2–0.5 L/day) and comparable to the estimated CL of endogeDOI 10.1002/jps.23763
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Figure 2. Visual predictive check plot for all patients following the first treatment. The blue circles show the observed data normalized by dose, and the solid and dashed blue lines show the median and 5th and 95th percentiles of the data normalized by dose for cycle 1. The solid and dashed black lines show the median and 5th and 95th percentiles of the simulated rilotumumab concentrations. The orange-shaded areas represent the 95% confidence intervals of the respective lines. The simulated values were computed from 300 trials simulated using the covariate values of the analysis dataset.
Figure 3. Simulated plasma concentration–time profile at steady state following rilotumumab administration at (a) 10 mg/kg Q2W, (b) 20 mg/kg Q2W, (c) 7.5 mg/kg Q3W, and (d) 15 mg/kg Q3W. The solid lines and shaded area represent the median and 95% prediction interval of the simulated rilotumumab concentrations (n=3000 per dose group). Q2W, every two weeks; Q3W, every three weeks.
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Table 4. Summary of the Final Model-Predicted AUCtau Values at Steady State Following 15 mg/kg Q3W Rilotumumab Administration Body Weight Range
AUCtau (mg/mLh)a
Overall Lower quartile (≤63 kg) Upper quartile (≥88 kg) a
141 (68.1–299) 127 (68.1–264) 165 (85.1–285)
Data are expressed as median (minimum–maximum).
nous IgG (0.1–0.2 L/day).26 Similar to other monoclonal antibodies, rilotumumab is expected to be cleared primarily by nonspecific fluid-phase endocytosis and proteolysis.27 In this process, rilotumumab is broken down to component amino acids, and the degradation products are unlikely to be toxic. As the catabolism mainly occurs in the lysosome, the involvement of the liver or impact of liver impairment on degradation of rilotumumab through intracellular catabolism is expected to be negligible. Consistent results were found in our analysis; an association between measures of liver function and rilotumumab CL was not detectable. The pharmacokinetic parameters of rilotumumab are mainly influenced by the patient’s weight. Body weight is the most influential covariate contributing to interindividual pharmacokinetic variability (Fig. 1). However, it should be noted that the relationship between steady-state AUCtau and weight still showed a slightly positive trend (Table 4) with the weightbased dosing. For the patients at the lower quartile of body weight (≤63 kg), the medium-predicted exposures were about 10% lower than those in the overall population, whereas for the patients at the higher quartile of body weight (≥88 kg), the median-predicted exposures were about 15% higher than those in the overall population, indicating that exposures in patients with extreme lower or higher weight are expected to be slightly lower and higher, respectively, than those in other patients. Although the age effect on pharmacokinetic parameters was detected in this dataset, the magnitude is modest and inclusion of age effect in the model did not reduce interpatient variability on CL and Vc . Furthermore, the bootstrap analysis showed that the 95% CI of the parameter estimate on the age effect on clearance included zero, indicating the uncertainty in the estimation of the age effect on pharmacokinetics. It can be seen in Figure 1 that data from patients younger than 40 years old (6% of the total number of patients) appear to mainly contribute to the CL–age and Vc –age relationships. Further calculation suggested that the CL was within 0.85- and 1.1-fold of the median, and the Vc was within 0.9- and 1.1-fold of the median in patients at least 40 years old. Therefore, the clinical relevance of this age effect on rilotumumab pharmacokinetics appears to be negligible. The renal filtration of protein drugs is a size-specific mechanism of elimination. Renal filtration and subsequent proteolysis are often a primary mechanism of elimination for a protein with a molecular weight less than 70 kDa. As rilotumumab has a molecular weight of 150 kDa, involvement of renal excretion of rilotumumab is unlikely. Consistently, within the range of covariates evaluated, there was no association between the measures of renal function (e.g., creatinine clearance) and the rilotumumab CL detected in the population pharmacokinetic analysis. Furthermore, rilotumumab exposure is not expected to be affected by renal impairment (i.e., creatinine clearance
