30.09.2016  Original Research Article  Ausgabe 5/2017 Open Access
Population Pharmacokinetics of Necitumumab in Cancer Patients
 Zeitschrift:
 Clinical Pharmacokinetics > Ausgabe 5/2017
Key Points
Necitumumab pharmacokinetics is characterized by targetmediated drug disposition, with a volume of distribution approximate to blood volume and a halflife of 2 weeks.

No age, sex, race, or concomitant medication factors were found influential, while weight explained a limited percentage of variability in distribution and elimination.

Simulations from the final model indicated that a only minor reduction in patient drug exposure variability would be achieved by weight or body surface areabased dosing.

1 Background
Necitumumab (also named IMC11F8) is a DNAderived, secondgeneration, recombinant human immunoglobulin G1 (IgG1), antiepidermal growth factor receptor (EGFR) monoclonal antibody (mAb) that specifically binds to the immobilized EGFR with high affinity to inhibit epidermal growth factor (EGF)induced EGFR phosphorylation [
1]. This antibody potently antagonizes the binding of the EGF ligand to EGFR, inhibits EGFstimulated activation of both EGFR and the downstream p44/p42 MAP kinases, and neutralizes EGFinduced DiFi cell proliferation. Further to inhibiting ligandinduced EGFR activation, necitumumab was capable of inducing an antibodydependent, cellmediated cytotoxic response against DiFi cells by human peripheral blood mononuclear cells. Necitumumab inhibited the interaction of EGF and EGFR with a concentration that inhibits binding by 50 % of approximately 0.9 nM (0.13 mg/L). The nonclinical pharmacology package includes in vitro studies to characterize the binding and functional inhibition of the human EGFR by necitumumab. The in vivo antitumor activity of necitumumab was evaluated in colon, nonsmallcell lung, pancreatic, and squamous cervical cancer xenograft tumor models in athymic mice. Necitumumab demonstrated significant antitumor activity as a monotherapy in colon, nonsmallcell lung, pancreatic, and squamous cervical cancer models. Antitumor activity of necitumumab in preclinical in vivo experiments was observed down to doses of 4–6 mg/kg, twice per week, associated with trough plasma concentrations of approximately 40 mg/L. Overall, the preclinical pharmacodynamic data demonstrated that necitumumab has the potential to impact human cancer progression.
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Pharmacokinetic (PK) results of noncompartmental analysis to date suggest that, as with other recombinant human (IgG1) mAbs targeting cell surface antigens [
2], necitumumab exhibited nonlinear PK characteristics [
3]. Apparent nonlinear PK profiles were observed between 100 and 1000 mg. Clearance (CL) after multiple doses was lower than that after a single dose. Following 7 weekly doses, the geometric mean accumulation ratio based on area under the curve ranged from 1.6 to 3.2. The maximum tolerated dose was determined to be 800 mg [
3]. This work aims at presenting the population PK of necitumumab in cancer patients, and describing any influential patient factors determining drug exposure.
2 Methods
Population PK analyses of the data from these five studies were performed using NONMEM (Version 7.3, ICON Plc, Ireland). Serum concentrations below the quantification limit were treated as missing values and were not included in the population pharmacokinetics. In general, missing values of independent variables (demographic and laboratory) were imputed by the last observation carried forward method. Outlier testing was performed for necitumumab concentrations, according to the US Food and Drug Administration Guidance for Industry on Population Pharmacokinetics (1999) as well as internal standard operating procedures. Outlier testing for rich sample patient profiles were based on the ±3 standard deviations criteria, while sparse sample profiles are deemed at the discretion of the analyst, where only apparently unreasonable samples are omitted, such as high concentrations after several below the quantification limit values with no dose recorded in between, quantifiable pretreatment samples, and concentration measurements without corresponding dose information.
A series of one, two, and threecompartment models were evaluated to identify the model that best described the necitumumab concentration–time data. Additional model structures were evaluated as previous studies suggested that necitumumab exhibited an exposuredependent disposition. An approximation of a targetmediated drug disposition model [
4–
6] and a Michaelis–Menten CL model were tested. Estimates of the PK parameters and error terms were obtained by fitting the concentration–time data by means of the nonlinear mixedeffects modeling program NONMEM (Version 7.3). The firstorder conditional with interaction estimation method was used for all analyses (NONMEM User’s Guide, Part VII). The interpatient variability was assumed to be lognormally distributed. Covariance between parameters was assessed using an omega block. Different residual error models—additional, proportional, and combined additive and proportional—were also evaluated.
Selection of the most appropriate PK base model was based upon agreement between predicted and observed serum concentrations, lack of pattern (that is, randomness) in the weighted residuals vs. the predicted values, nonpositive average changes in the interpatient variability, and significant decreases in the Minimum value of the objective function (MOF).
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Parameter estimate precision was evaluated through bootstrapping on the final base model to confirm that all PK parameters were well estimated. A visual predictive check (VPC) was also performed on the base model to investigate the agreement between the observed and predicted concentrations.
Once a structural and statistical model was established, the effect of patient factors was assessed for their clinical relevance on the disposition of necitumumab. Covariate testing was performed without eta correlation. A listing of patient factors and the specific PK parameters on which they were tested are specified in Table
1.
Table 1
Patient factors assessed in the population pharmacokinetic analysis
Covariate

Type

Parameters tested


Age

Continuous

CL,
V
_{1},
V
_{2}

Body size (weight, LBM, BSA)

Continuous

CL,
Q,
V
_{1},
V
_{2}

Sex

Categorical

CL,
V
_{1},
V
_{2}

Race

Categorical

CL,
V
_{1},
V
_{2}

Ethnicity

Categorical

CL,
V
_{1},
V
_{2}

Calculated creatinine CL
^{a}

Continuous

CL

Liver function tests (AST, ALT, bilirubin)

Continuous

CL,
V
_{1},
V
_{2}

Disease stage (ECOG)

Categorical

CL,
V
_{1},
V
_{2}

Concomitant gemcitabine and cisplatin

Categorical

CL,
V
_{1},
V
_{2}

Sample storage age >24 months

Categorical

Residual error

Influence of patient bodyweight on CL and volume parameters was tested and incorporated as a power function (Eq.
2) with an inclusion criteria of
p < 0.001. Simple allometric scaling was also tested, that is, a fixed power coefficient of 0.75 for CL parameters and 1 for volume of distribution parameters [
7,
8]. Inclusion of fixed power coefficients does not infer any loss in degrees of freedom; therefore, the ΔMOF significance criteria are set aside and any drop of MOF corresponds to model improvement. Lean body mass and body surface area (BSA) were also investigated.
Next, stepwise covariate modeling (SCM) was implemented using PerlSpeaks NONMEM (PsN) [
9,
10] for remaining demographic, liver function, disease state, and concomitant medication covariates. The criterion for forward inclusion was a
p value no greater than 0.01 (Δ6.635 MOF for inclusion of one parameter) with a backward deletion threshold of 0.001 (Δ10.828 MOF) for exclusion of one parameter).
Continuous covariates were tested using linear, power, and exponential models as shown in Eqs.
1–
3. Categorical covariates were tested using a categorical model, as shown in Eq.
4.
$${\text{Linear model}}\;P = \varTheta \theta_{ 1} \cdot ( 1+ \theta_{ 2} \cdot ({\text{COV  MED}}))$$
(1)
$${\text{Power model}}\;P = \theta_{ 1} \cdot \left( {{\text{COV}}/{\text{MED}}} \right)\theta_{ 2}$$
(2)
$${\text{Exponential model}}\;P = \theta_{ 1} \cdot {\text{EXP}}(\theta_{ 2} \cdot ({\text{COV  MED}}))$$
(3)
$${\text{Categorical model}}\;P = \theta_{ 1} \cdot ( 1+ \, \theta_{ 2} \cdot {\text{IND}})$$
(4)
Here,
P is the individual’s estimate of the parameter (for example, CL,
V),
θ
_{1} represents the typical value of the parameter,
θ
_{2} represents the effect of the covariate, COV is the value of the covariate, and MED is the population median of the covariate. IND is an indicator variable with a value of either 0 or 1 assigned for values of a dichotomous categorical covariate (for example, female or male) and 1 for various values of a categorical covariate ranging from 1 to
n, where
n is the number of categories (for example,
n races).
The final backward model from SCM was further reduced in a stepwise manner. If removal of a covariate did not result in a significant (
p < 0.001) MOF drop and reduce IIV by 5 %, the covariate was removed.
Because sample age stability validation at the time of the analysis only encompassed 24 months, with 14 % of samples being between 24 and 36 month of storage age, samples were flagged as above or below 24 months of age. The effect of assay age on each of the residual error terms was tested as a dichotomous categorical covariate on the residual error parameters using significance criteria above (
p < 0.001), and finally covariance was again assessed between CL and volume parameters using an omega block.
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Final model development was based on statistical significance criterias, such as a ≥10.828 point drop in MOF (
p < 0.001) and a decrease in the intersubject variability in the relevant parameters of >5 %, as well as clinical relevance criteria, such as a covariate parameter effect being a >15 % difference for a dichotomous covariate or a >15 % covariate effect at the highest or lowest observed value for a continuous variable. In addition, goodness of fit was evaluated with residual plots and VPCs. Prediction correction of VPCs was applied where appropriate [
11], to allow a comparison of model performance across dose concentrations and varying schedules. Bootstrap analysis with 1000 samples was performed to assess the precision of the final parameter estimates of base and final models, and to calculate the 95 % confidence interval for all parameters.
To evaluate the impact of the selected flat dose paradigm, simulations from the final model were performed, evaluating the distribution of predicted
C
_{ss,ave} with the 800mg dose compared with alternate weight or BSAbased dosing paradigms, assuming the same weight distribution in the phase III study SQUIRE, i.e., a median (range) of 70 (35–127) kg. The weight and BSAbased dose concentrations were selected to produce a similar median
C
_{ss,ave} as the flat dose, and this measure was chosen for comparison as it reflects the overall drug exposure. Therefore, based on each patient’s individual PK parameters (474 patients in total) from the final model,
C
_{ss,ave} values were predicted based on a flat dose, or a 11mg/kg dose or 450 mg/m
^{2} and compared with the same patient receiving 800 mg.
2.1 Dosing
Necitumumab has been administered to humans using several different dosing regimens. For the present analysis, data were pooled from studies where necitumumab was administered at two dose concentrations (600 and 800 mg) and with different dose intervals (weekly, biweekly, or days 1 and 8 of a 3week cycle), given as infusions over approximately 1 h. Necitumumab was administered both as a single agent and in combination with several chemotherapeutic drugs (cisplatin, pemetrexed, and gemcitabine) and to patients with several tumor types [
3,
12–
14].
2.2 Study Population
The population PK evaluation included 4920 necitumumab concentrations from 807 patients across five studies, with data available from 15 to 407 patients (Figs.
1,
2). Three phase I/II studies included rich PK sampling in patients with advanced solid cancers, while two phase III studies included sparse sampling (trough only) in squamous and nonsquamous nonsmallcell lung cancer patients [
3,
12–
14]. Continuous and categorical characteristics for these patients are summarized in Tables
2 and
3. One hundred and eightyfour samples, drawn from a total of 123 patients, were nonquantifiable owing to concentrations below the quantitation limit of assay. No outlier patients were identified and omitted from the analysis dataset according to the standard operating procedure, however, eight necitumumab concentrations from one patient were excluded from the analysis, as they were inconsistent with the PK properties of the drug in that concentrations reported predose were higher than those reported at 1h postend of infusion or high concentrations were reported following a concentration below the quantitation limit with no additional dosing information recorded.
Table 2
Summary of continuous patient characteristics at the time of the first dose for patients in the population pharmacokinetic analysis
Age (years)

Weight (kg)

LBM (kg)

BSA (m
^{2})

CGCL
^{a} (mL/min)

ALT (U/L)

AST (U/L)

Bilirubin (µmol/L)



All five studies JFCA/B/C/I/J


N

807

807

796

796

799

804

801

802

N
_{miss}

0

0

11

11

8

3

6

5

Minimum

19

35.0

28.2

1.23

36.0

1.40

4.90

0.300

Median

62

71.0

50.8

1.82

90.1

19.0

20.0

7.00

Maximum

84

181

90.4

2.81

751

387

216

30.8

Geometric mean

60.4

70.8

50.4

1.81

90.3

20.3

21.0

6.92

Geometric CV %

16

22

16

12

37

73

54

60

Table 3
Summary of categorical patient characteristics at the time of the first dose for patients in the population pharmacokinetic analysis
N (%)

N (%)



Sex

Concomitant cisplatin


Female

200 (25)

Without cisplatin

90 (11)

Male

607 (75)

With cisplatin

717 (89)

Race

Concomitant gemcitabine


Other

43 (5)

With gemcitabine

337 (42)

Caucasian

688 (85)

Without gemcitabine

470 (58)

Black/African American

17 (2)

ECOG


Asian

55 (7)

0

263 (33)

American Indian/Alaskan native

2 (0)

1

484 (60)

2

45 (6)


Multiple

2 (0)

Missing

15 (2)

Ethnicity

Regimen


Hispanic or Latino

86 (11)

Weekly

40 (5)

Not Hispanic or Latino

720 (89)

Every 2 weeks

6 (1)

Missing

1 (0)

Days 1 and 8 every 3 weeks

761 (94)

×
×
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3 Results
The necitumumab concentration–time data were best described by a twocompartment structural model parameterized in terms of central volume of distribution (
V
_{1}), peripheral volume of distribution (
V
_{2}), and intercompartmental CL (Q). Drug CL was best described by an approximation of the targetmediated drug disposition model [
4–
6], using [
1] a saturable component parameterized according to Michaelis–Menten (
K
_{m} and
V
_{max}) and [
2] firstorder CL describing nontargetmediated degradation, both acting in the central compartment. Exponential interpatient variability terms were included for total drug CL (CL
_{tot}),
V
_{1}, and
V
_{2}. Residual variability was accounted for by an additive/proportional error structure. The population PK parameters CL,
V
_{1},
V
_{2}, and Q were well estimated, with relative standard error (RSE) at or below 6.1 %, while the Michaelis–Menten parameters
K
_{m} and
V
_{max} had higher uncertainty (Table
4).
Table 4
Pharmacokinetic parameters with bootstrap CIs for the population base model and final model for necitumumab
Base model

Final model



Parameter description

Population estimate (95 % CI)

Interpatient variability (95 % CI)

Population estimate (95 % CI)

Interpatient variability (95 % CI)

CL
^{a} (CL
_{tot})

32.4 (29.6–35.7)

28.8 (25.5–31.9)


CL (L/h)

0.0113 (0.0103–0.0123)

0.0114 (0.0106–0.0124)


K
_{m} (mg/L)

9.96 (5.57–18.2)

7.97 (3.97–15.4)


V
_{max} (mg/h)

0.626 (0.467–0.819)

0.565 (0.406–0.731)


Central volume of distribution,
V
_{1} (L)
^{b}

3.60 (3.40–3.91)

27.8 (23.3–36.4)

3.41 (3.23–3.66)

21.1 (17.4–27.1)

Intercompartmental CL,
Q (L/h)

0.0198 (0.0164–0.0237)

0.0183 (0.0155–0.0215)


Peripheral volume of distribution,
V
_{2} (L)
^{b}

3.31 (3.01–3.58)

60.1 (46.0–72.8)

3.29 (3.04–3.56)

55.4 (41.4–68.6)

WeightCL
^{c} and
Q
^{d}

0.768 (0.640–0.912)


Weight
V
_{1}
^{e} and
V
_{2}
^{f}

0.498 (0.317–0.647)


Interpatient variability correlation Coefficient (CL
_{tot} and
V
_{1})

0.715 (0.563–0.861)

0.609 (0.437–0.793)


Residual error


Additive (µg/mL)

10.3 (8.27–15.9)

10.8 (8.42–16.1)


Proportional

23.8 (21.6–25.3)

23.7 (21.6–25.4)

Once the base structural model was established, potentially significant covariates were evaluated as described. Estimation of power coefficients for bodyweight resulted in a better fit to data than fixed power allometry, and was retained through the covariate search. The resulting covariate model after forward inclusion and backward deletion from the SCM, using total patient bodyweight or lean body mass performed very similarly (ΔMOF = 2.54); therefore, total bodyweight was selected as being a more practical measure for clinical purposes.
After application of prespecified inclusion/exclusion criteria, only patient bodyweight remained as a significant covariate for CL and volume parameters. Parameters were estimated to be less than proportionally dependent on weight; for CL parameters, the power coefficient was estimated at 0.768 and for volume parameters it was 0.498. The population PK parameters CL,
K
_{m},
V
_{max},
V
_{1},
V
_{2}, and
Q were well estimated, as were the covariate bodyweight effects. Final parameter estimates are shown in Table
4.
The estimated interpatient variability for CL
_{tot},
V
_{1}, and
V
_{2} was significantly decreased compared with the base model. The interaction between CL
_{tot} and
V
_{1} was well characterized. CL
_{tot} is the sum of linear and nonlinear clearances [CL
_{tot} = CL +
V
_{max}/(
C +
K
_{m})]. Simulations were used to calculate the population estimates of
V
_{ss} (6.97 L) and CL
_{tot} parameters (0.0141 L/h) at steady state following the phase III study dosing with 800 mg on days 1 and 8 of a 21day cycle [CL
_{tot,ss} = CL +
V
_{max}/(
C
_{ss,ave} +
K
_{m}),
V
_{ss} =
V
_{1} +
V
_{2}].
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Goodness of fit for the final population model is represented graphically (Fig.
3) by the agreement between predicted values (Population prediction (PRED) and Individual Prediction (IPRED)) and observed concentrations (Dependent value (DV)), as well as the randomness of the individual and conditional weighted residuals. In general, there was a random distribution of residual values suggesting no consistent bias in the predicted necitumumab concentrations, as further demonstrated by the VPC (see Fig.
4).
×
×
Simulations from the final model evaluated the distribution of predicted
C
_{ss,ave}, resulting from a 800mg flat dose compared with a weight or BSAbased dosing paradigm using 11.5 mg/kg or 450 mg/m
^{2} respectively, with results shown in Fig.
5.
×
4 Discussion
It is well known that PK properties of mAbs differ significantly from those of smallmolecule drugs. Disposition of mAbs is usually influenced by targetrelated factors including target antigen expression level and turnover rate, immune reactions to the antibody, target antigen distribution, and binding affinity [
2,
15]. In solid tumors, the distribution may depend on the architecture and physiology of the tumors [
16,
17].
The PK parameters of necitumumab were well characterized by an approximation of the targetmediated drug disposition model [
4–
6]. The model contained a central and a peripheral volume of distribution, and elimination was characterized by a linear and a saturable component, assumed to reflect binding to target. The population estimates of
V
_{ss} (6.97 L) and CL
_{tot} parameters (0.0141 L/h) at steady state following dosing of 800 mg on days 1 and 8 of a 21day cycle were in range with other human IgG mAbs studied in the clinic [
2,
16,
17], and translated into a functional halflife of approximately 2 weeks, with steady state achieved after three to four cycles of treatment.
The nonlinear CL term
K
_{m} was estimated to 7.97 mg/L, which is well exceeded with continuous treatment in the studied dose range. This means that linear kinetics could be assumed under normal clinical conditions, which leads to predictable effects of potential dose adjustments.
Effects of different covariates (including sex, age, bodyweight, race, albumin, hepatic function, and renal function) were investigated for influence on the disposition of necitumumab in the population PK analysis. Patient bodyweight was the only significant covariate on the necitumumab disposition that was retained in the final model, with a less than proportional effect on both CL and distribution parameters. The influence of bodyweight on steady state was moderate (typical CL
_{tot} ranged from 77 to 131 % and typical
V
_{ss} from 84 to 120 % of the median at the 5th and 95th weight percentiles) in comparison to overall variability. Interpatient variability in the PK parameters was moderate to high (28.8 % for CL, 21.1 % for
V
_{1}, and 55.4 % for
V
_{2}) as commonly observed with mAbs [
18]. Interoccasion variability was not assessed owing to computational issues, as expected given studies with rich sampling only contained information from a singledose administration, while longterm studies encompassed only trough sampling. The lack of correlation between liver or renal status and CL
_{tot} can be expected given the known mechanisms involved for the CL of antibodies, while a relationship to patient bodyweight could be expected [
16–
18]. Even though inclusion of patient bodyweight as a covariate fulfilled all statistical criteria, comparison of base and final model goodnessoffit plots (Fig.
3) clearly indicates the overall contribution to variability from bodyweight is limited. The simulations comparing a flat dose vs. a bodyweightadjusted dose show that weight or BSAbased dosing would not significantly decrease the overall variability in exposure, but rather increase the risk of dosing and administration errors, and why a flat dose paradigm would be preferred. During the clinical development of necitumumab, a target concentration of 40 mg/L has been used, as derived from the concentration needed to achieve tumor growth inhibition in in vivo xenograft experiments. The work presented here can be used to support the use of a flat dose in clinical development, but future work is needed to establish the exposure response of necitumumab, and whether the target concentration desired is representative of clinical anticancer activity, and whether the proposed dose intensity is sufficient to achieve efficacy.
5 Conclusions
Nonlinear mixedeffects modeling of serum concentration data across five clinical studies in various development phases indicate that necitumumab exhibits targetmediated drug disposition, as commonly observed with mAbs, and that pharmacokinetics can be expected to be linear in the studied dose ranges when administered as repeated infusions. No age, sex, race, or concomitant medication factors were found influential, while weight was a statistically significant factor for both distribution and elimination. Simulations from the final model indicate that only a limited reduction in patient drug exposure would be achieved by weightor BSAbased dosing. The necitumumab effective halflife was estimated to approximately 2 weeks, and steady state was achieved within three to four cycles of treatment. The phase III dosing schedule of 800 mg dosed on days 1 and 8 of a 21day schedule resulted in serum concentrations well exceeding the 40mg/L threshold indicated by preclinical experiments.
Acknowledgments
The authors are indebted to Michael Heathman for expert input and modeling advice.
Compliance with Ethical Standards
The studies used in this work were conducted in compliance with the Declaration of Helsinki, International Conference on Harmonization Guidelines for Good Clinical Practice, and applicable local regulations. The protocol was approved by the ethics committees of all participating centers and all patients provided written informed consent before study entry.
Funding
The authors are employed by Eli Lilly & Co, and all work presented was funded by the company.
Conflict of interest
Amanda Long, Emmanuel Chigutsa and Johan Wallin are all employees of Eli Lilly & Co, and hold stock options or shares in the company.
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