population pharmacokinetics of rilotumumab, a fully human monoclonal antibody against hepatocyte...

RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism

Population Pharmacokinetics of Rilotumumab, a Fully HumanMonoclonal Antibody Against Hepatocyte Growth Factor, inCancer 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

1Amgen Inc., Thousand Oaks, California2SGS Exprimo NV, Mechelen, Belgium3Amgen Inc., Barcelona, Spain4Amgen Inc., South San Francisco, California

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 ingastric 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 rilotumumabexhibited linear and time-invariant kinetics over a dose range of 0.5–20 mg/kg. Typical systemic clearance and central volume of distributionwere 0.184 L/day and 3.56 L, respectively. Body weight is the most significant covariate, and sex, cancer type, coadministration ofchemotherapeutics, baseline plasma HGF and tumor MET levels, and renal and hepatic functions did not have an effect on rilotumumabpharmacokinetics. The concentration–time profiles for the rilotumumab clinical regimens were projected well with the model. C© 2013Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:328–336, 2014Keywords: rilotumumab; population pharmacokinetics; cancer; monoclonal antibody; hepatocyte growth factor; MET; clinical pharma-cokinetics; pharmacokinetic/pharmacodynamic models; population pharmacokinetics/pharmacodynamics

INTRODUCTION

A key consideration in developing anticancer drugs is their se-lectivity against cancer cells while sparing normal cells. MET, areceptor tyrosine kinase, is dysregulated in many cancers andis activated by the binding of its only known ligand hepatocytegrowth factor (HGF), also known as scatter factor, which inturn activates downstream signaling for tumor cell prolifera-tion, migration, and survival.1,2 MET and HGF are frequentlyoverexpressed in various human cancers, including breast, gas-tric, colorectal, head and neck, nonsmall cell lung, renal, andliver cancers.2 Increased levels of MET and HGF have been as-sociated with advanced disease and poor prognosis.1–4 As theHGF/MET pathway plays critical roles in various malignan-cies, it has been recognized as a potential target for developingcancer therapeutics.

Rilotumumab, previously known as AMG 102, is an investi-gational, fully human, monoclonal antibody [immunoglobulin G(IgG)2] against HGF that prevents the activation of its receptorMET.5 The pharmacokinetics of rilotumumab as a monother-apy or combination therapy was evaluated in several phase1 and 2 clinical trials. In these studies, rilotumumab exhib-ited linear pharmacokinetic behaviors up to 20 mg/kg adminis-tered intravenously every 2 (Q2W) or 3 weeks (Q3W).6–11 Thepharmacokinetics of rilotumumab appeared to be similar in

Correspondence to: Min Zhu (Telephone: +805-447-2236; Fax: +805-376-1871; E-mail: [email protected])

This article contains supplementary material available from the authors uponrequest 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

patients with different tumor types6 and was not affected bythe coadministration of chemotherapy (e.g., carboplatin, cis-platin, 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 pharmacokinet-ics of other coadministered drugs (e.g., carboplatin, cisplatin,etoposide, mitoxantrone, bevacizumab, and motesanib).8,9,12

Targeting the HGF/MET pathway with rilotumumab hasbeen investigated in solid tumors in the preclinical and clin-ical setting for nearly a decade.5–21 In a phase 2 clinical trialin gastric and esophagogastric junction cancer, rilotumumab incombination with epirubicin, cisplatin, and capecitabine signif-icantly improved progression-free survival and overall survivalin patients who had high-tumor MET expression.13,20 Further-more, the effect of rilotumumab on survival in patients withgastric cancer appeared to be associated with rilotumumabconcentrations.11,13,14

A clearer understanding of the rilotumumab disposition inpatients with cancer and the factors that may affect its expo-sure are important for the development of rilotumumab as ananticancer therapy. This is the first manuscript to characterizethe population pharmacokinetics of rilotumumab with pooleddata from multiple phase 1 and 2 studies. The primary objec-tives of this analysis were to (1) characterize the time-course ofrilotumumab serum concentrations following intravenous ad-ministration in patients with cancer; (2) quantify the degreeof interpatient variability of rilotumumab pharmacokinetic pa-rameters; and (3) assess intrinsic and extrinsic factors as po-tential sources of variability in rilotumumab exposure (e.g., de-mographics, baseline plasma HGF and tumor MET levels, andconcomitant medications).

328 Zhu et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:328–336, 2014

RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism 329

MATERIALS AND METHODS

Data Source

The population pharmacokinetic analysis of rilotumumab in-cluded 2479 serum concentrations (586 from intensive sam-pling schedules and 1893 from sparse sampling schedules) col-lected from 393 patients with cancer from seven clinical trials.Study descriptions are provided in Table 1. Additional detailsof these clinical trials are reported elsewhere.6–8,10,12,16,21 Allpatients gave written informed consent before screening afterbeing advised of the potential risks and benefits, as well as theinvestigational nature of the study. The studies were approvedby the investigational review boards at each site and compliedwith the principles of good clinical practice as defined by theInternational Conference on Harmonization and the principlesof the Declaration of Helsinki.

Bioanalytical Assays

Rilotumumab serum concentrations were determined by arilotumumab-specific enzyme-linked immunosorbent assay us-ing recombinant human HGF (capture reagent; Amgen Inc.,Thousand Oaks, California) for capturing a biotinylated poly-clonal rabbit anti-rilotumumab antibody (Amgen Inc.) for de-tection, as previously described.6,18 The lower limits of quantifi-cation were 31 (studies one to four) and 90 ng/mL (studies fiveto seven). The interassay coefficients of variation ranged from7% to 10%, and the average assay accuracy ranged from 3% to7%.

Plasma HGF concentrations were determined by a hu-man HGF/scatter factor immunoassay kit (R&D Systems,Minneapolis, Minnesota) that detects pro-HGF, rilotumumab-bound HGF, and free HGF, as previously described.9

Tumor MET expression in archival patient tumor sampleswere determined by immunohistochemistry.

Software

Revisions and model-specific datasets were made with SAS soft-ware version 9.2 (SAS Institute Inc., Cary, North Carolina).The population pharmacokinetic analysis was conducted bynonlinear mixed effects modeling (NONMEM) using the first-order conditional estimation method with interaction, as im-plemented in the NONMEM version 7.1.2 software package(ICON Development Solutions, Ellicott City, Maryland). Com-pilations were achieved using the Intel Fortran 11.1 compiler(Intel Corporation, Santa Clara, California). Graphical datavisualization, evaluation of NONMEM outputs, constructionof goodness-of-fit plots, and graphical model comparisons wereconducted using S-PLUS software version 8.1 (TIBCO SoftwareInc., Palo Alto, California).

Population Pharmacokinetic Analysis

The analysis included four steps: (1) construction of a basemodel; (2) covariate analysis to obtain a final model; (3) modelevaluation; and (4) model-based simulations.

Step 1: A linear two-compartment structural model withfirst-order elimination from the central compartment was se-lected as a base model to characterize systemic clearance (CL),intercompartmental clearance (Q), volume of distribution ofthe central compartment (Vc), and volume of distribution ofthe peripheral compartment (Vp). The interindividual variabil-ity was assessed for CL, Q, Vc, and Vp with an exponential T

able

1.S

um

mar

yof

Stu

dies

Stu

dyN

um

ber

(Cli

nic

alT

rial

s.go

vId

enti

fier

)

Nu

mbe

rof

Pat

ien

tsw

ith

PK

Dat

aR

ilot

um

um

abD

ose

Can

cer

Typ

eS

tudy

Dru

gsP

KS

ampl

ing

Stu

dy1

(NA

a)1

400.

5,1,

3,5,

10,o

r20

mg/

kgi.v

.Q2W

Adv

ance

dso

lid

tum

ors

Ril

otu

mu

mab

mon

oth

erap

yIn

ten

sive

and

spar

seS

tudy

2(N

Ab)2

143,

10,o

r20

mg/

kgi.v

.Q2W

Adv

ance

dso

lid

tum

ors

Ril

otu

mu

mab

+bev

aciz

um

abor

mot

esan

ibIn

ten

sive

and

spar

se

Stu

dy3

(NC

T00

4274

40)3

6010

or20

mg/

kgi.v

.Q2W

Mal

ign

ant

glio

ma

Ril

otu

mu

mab

mon

oth

erap

yS

pars

eS

tudy

4(N

CT

0042

2019

)457

10or

20m

g/kg

i.v.Q

2WA

dvan

ced

ren

alce

llca

rcin

oma

Ril

otu

mu

mab

mon

oth

erap

yS

pars

eS

tudy

5(N

CT

0071

9550

)588

5,7.

5,or

15m

g/kg

i.v.Q

3WL

ocal

lyad

van

ced/

met

asta

tic

gast

ric/

EG

Jju

nct

ion

aden

ocar

cin

oma

Ril

otu

mu

mab

+epi

rubi

cin

,cis

plat

in,a

nd

cape

cita

bin

eS

pars

e

Stu

dy6

(NC

T00

7889

57)6

595

or10

mg/

kgi.v

.Q2W

Wil

d-ty

peK

RA

Sm

etas

tati

cco

lore

ctal

can

cer

Ril

otu

mu

mab

+pan

itu

mu

mab

Inte

nsi

vean

dsp

arse

Stu

dy7

(NC

T00

7911

54)7

7515

mg/

kgi.v

.Q3W

Sm

allc

elll

un

gca

nce

rR

ilot

um

um

ab+e

topo

side

+cis

plat

inor

carb

opla

tin

Spa

rse

aA

mge

nst

udy

nu

mbe

r20

0401

67.

bA

mge

nst

udy

nu

mbe

r20

0501

77.

EG

J,es

oph

agog

astr

icju

nct

ion

;i.v

.,in

trav

enou

s;N

A,n

otav

aila

ble;

PK

,ph

arm

acok

inet

ic;Q

2W,e

very

two

wee

ks;Q

3W,e

very

3w

eeks

.

DOI 10.1002/jps.23763 Zhu et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:328–336, 2014

330 RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism

random-effect model. The covariances between individual esti-mates of CL and Vc and between Vc and Vp were estimated.

Residual variability was described by a proportional errormodel, with separate proportional residual variability for in-tensive and sparse samples.

One- and three-compartment models were also tested andnested models were compared with minimum value of objec-tive functions (MVOF).22 Goodness-of-fit plots were used formodel diagnostics. Shrinkage toward the population mean wascalculated as previously described.23

Step 2: Effect of baseline factors and patient demograph-ics 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 biliru-bin, aspartate aminotransferase, alanine aminotransferase, al-kaline phosphatase, Eastern Cooperative Oncology Group per-formance status (0 vs. 1 and 2), blood urea nitrogen, baselinelevels 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) followedby backward elimination (MVOF > 7.879, df = 1, p < 0.005)approach was utilized.24 Continuous covariates were evaluatedusing power equations after centering on the median, whereascategorical variables were incorporated into the model as indexvariables. If the magnitude of the change in a parameter be-cause of the influence of a covariate was less than 20% over therange of values that were evaluated, the covariate factor wasnot considered to be clinically relevant and, consequently, wasexcluded from the model.

Step 3: The final pharmacokinetic model was evaluated byperforming a visual predictive check on rilotumumab serumconcentrations to assess the ability of the model to produce sim-ilar results as those derived from the original dataset. A totalof 300 dataset replicates were simulated with the final model.Percentiles (5th, 50th, and 95th) and their 95% prediction in-tervals (PIs) of the simulated rilotumumab concentration datawere plotted and overlaid with the observed median and 90%PIs of rilotumumab concentration–time profiles.

A nonparametric bootstrap was used as an internal evalua-tion method to qualify the estimates of the model parameters.25

The mean and 95% PI of the parameter estimates from the boot-strap replicates were compared with the estimated parametersfrom the original dataset.

Step 4: Steady-state profiles of rilotumumab with the Q2Wand Q3W regimens at clinically tested doses (7.5, 10, 15, and20 mg/kg) were simulated using the final pharmacokineticmodel. Significant covariates that were included in the finalmodel were sampled (with replacement) for 3000 patients fromall patients in the seven studies. The proportional residual vari-ability for sparse sampling was used in the simulation.

RESULTS

Rilotumumab Dataset for the Pharmacokinetic Model

Seventy-five of the 2479 evaluable measurements (3%) were ex-cluded because of concentrations below the limit of quantifica-tion and sampling errors. Generally, baseline values, such as de-mographics, were similar among the studies included in theseanalyses (Table 2). Baseline laboratory measures related tobaseline disease characteristics, liver function, and renal func-tion were also comparable among the studies. In this dataset, T

able

2.B

asel

ine

Ch

arac

teri

stic

sof

Pat

ien

tsIn

clu

ded

inth

eP

har

mac

okin

etic

Mod

elin

gD

atas

et

Ch

arac

teri

stic

saS

tudy

1(n

=40)

Stu

dy2

(n=1

4)S

tudy

3(n

=60)

Stu

dy4

(n=5

7)S

tudy

5(n

=88)

Stu

dy6

(n=5

9)S

tudy

7(n

=75)

All

(n=3

93)

Wei

ght

(kg)

75.0

(46.

4–13

0.5)

73.8

(46.

8–97

.3)

83.2

(47.

5–14

4.0)

80.1

(43.

8–16

8.2)

67.5

(35.

0–10

3.8)

77.0

(44.

8–11

8.0)

70.0

(44.

0–10

9.9)

73.3

(35.

0–16

8.2)

Age

(yea

rs)

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)

60.0

(42.

0–74

.0)

60(1

9–84

)M

en,(

n,%

)17

(42.

5)7

(50.

0)38

(63.

3)41

(71.

9)61

(69.

3)34

(57.

6)55

(73.

3)25

3(6

4.4)

EC

OG

PS

0,(n

,%)

NA

14(1

00)

NA

28(4

9.1)

37(4

2.1)

30(5

0.8)

23(3

0.7)

118

(40.

3)C

auca

sian

,(n

,%)

35(8

7.5)

11(7

8.6)

54(9

0.0)

55(9

6.5)

68(7

7.3)

58(9

8.3)

60(8

0.0)

341

(86.

8)A

lbu

min

(g/L

)37

.5(2

5.0–

46.0

)37

.0(2

5.0–

44.0

)42

.0(3

6.0–

49.0

)40

.0(2

9.0–

47.0

)37

.8(1

6.1–

48.3

)40

.0(2

0.3–

55.0

)N

A40

.0(1

6.1–

55.0

)T

otal

bili

rubi

n(:

mol

/L)

8.6

(3.4

–23.

9)9.

4(3

.4–2

2.2)

5.1

(1.7

–17.

1)5.

1(1

.7–1

3.7)

8.0

(3.0

–31.

0)9.

1(3

.4–3

1.0)

8.0

(3.4

–23.

0)7.

2(1

.7–3

1.0)

ALT

(U/L

)20

.5(1

1.0–

79.0

)29

.5(1

0.0–

102.

0)28

.0(1

1.0–

110.

0)21

.0(9

.0–6

93.0

)18

.0(7

.0–1

31.0

)24

.0(7

.0–8

4.0)

23.0

(8.4

–172

.0)

23.0

(7.0

–419

.0)

AS

T(U

/L)

24.0

(14.

0–70

.0)

29.5

(14.

0–16

8.0)

21.0

(10.

0–43

.0)

23.0

(10.

0–41

9.0)

21.0

(7.0

–255

.0)

31.0

(14.

0–14

8.0)

22.0

(10.

0–12

0.0)

22.0

(7.0

–693

.0)

Alk

alin

eph

osph

atas

e(U

/L)

92.5

(66.

0–97

0.0)

122.

5(5

7.0–

484.

0)75

.5(3

5.0–

210.

0)79

.0(3

5.0–

925.

0)10

2.0

(31.

0–29

68.0

)12

4.0

(58.

0–84

8.0)

96.0

(36.

0–67

3.0)

92.0

(31.

0–29

68.0

)C

reat

inin

ecl

eara

nce

b(m

L/m

in)

82.3

(41.

8–17

3.2)

63.2

(32.

8–18

2.1)

119.

4(4

3.7–

263.

5)69

.0(2

8.7–

185.

9)86

.9(4

2.0–

188.

8)87

.9(4

7.4–

156.

6)89

.8(6

0.1–

151.

5)88

.3(2

8.7–

263.

5)H

GF

(ng/

mL

)10

70.0

(95.

0–15

,515

.0)

1255

.0(9

5.0–

3280

.0)

648.

4(9

5.0–

4814

.5)

1048

.0(4

56.9

–18,

053.

8)12

35.9

(95.

0–32

,536

.7)

986.

5(9

5.0–

9289

.5)

NA

982.

5(9

5.0–

32,5

36.7

)

aR

esu

lts

ofco

nti

nu

ous

vari

able

sw

ere

repo

rted

asm

edia

n(r

ange

).bIn

the

popu

lati

onph

arm

acok

inet

ican

alys

is,c

reat

inin

ecl

eara

nce

was

set

to15

0mL

/min

wh

enth

eca

lcu

late

dva

lue

was

abov

e15

0mL

/min

.A

LT,a

lan

ine

amin

otra

nsf

eras

e;A

ST,

aspa

rtat

eam

inot

ran

sfer

ase;

EC

OG

PS,

Eas

tern

Coo

pera

tive

On

colo

gyG

rou

ppe

rfor

man

cest

atu

s;H

GF,

hep

atoc

yte

grow

thfa

ctor

;an

dN

A,n

otav

aila

ble.

Zhu et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:328–336, 2014 DOI 10.1002/jps.23763


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, and236 patients (60%) received rilotumumab in combination withother medications (Table 1).

Base Development Model

A two-compartment model was found to better describe the rilo-tumumab concentration data than a one-compartment model(�MVOF = −790), and the three-compartment model did notshow any significant improvement over the two-compartmentmodel (�MVOF = −3.58). Therefore, an open two-compartmentlinear model was selected as a base model for further develop-ment. The interindividual variability was 32.9% and 25.1% forthe model parameters CL and Vc, respectively. The proportionalresidual variability was 20.5% and 27.6% for intensive and non-intensive pharmacokinetics, respectively. Simplification of theresidual error to one integrated proportional residual variabil-ity for both intensive and nonintensive pharmacokinetics re-sulted in a significant increase in MVOF (�MVOF = 49.3).Therefore, the two separate residual variabilities for intensiveand 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 wereselected to further test their effect on CL and Vc. HGF andtumor MET levels were also tested for effect on CL.

In the final model, body weight and age had a significant ef-fect on both CL and Vc (Fig. 1). The inclusion of the body weighteffect on CL and Vc improved the goodness-of-fit, relative to thebase model (�MVOF = −214) and reduced interindividual vari-ability by 3% and 5% on the model parameters for CL and Vc,respectively. The power coefficients associated with the bodyweight effect were 0.625 and 0.611 on CL and Vc, respectively.It can be calculated that a 10% increase in body weight was as-sociated with a 6% increase in CL and Vc with limited effect onthe elimination rate constant. Furthermore, implementation ofthe age effect on CL and Vc was associated with a significantreduction in objective function value (�MVOF = −24.8), butthere was no further reduction on interpatient variability onthose parameters. The power coefficients associated with theage effect were 0.268 and 0.229 on CL and Vc, respectively. Itcan be calculated that a 10% increase from the median age (60years old) was associated with an additional 2%–3% increase inCL and Vc on top of the weight effect, with limited effect on theelimination rate constant. All other covariates did not show asignificant change on MVOF on either CL or Vc. The populationpharmacokinetic parameters of the final model are provided inTable 3.

None of the pharmacokinetic parameter estimates were dosedependent in the model-based assessment, indicating that rilo-tumumab exhibited linear pharmacokinetic behaviors over thetested dose range from 0.5 to 20 mg/kg and over the treatment



Table 3. 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) 0.184 (2.5) 0.184 (0.175–0.194)Weight on CL (power) 0.625 (17.9) 0.623 (0.402–0.849)Age on CL (power) 0.268 (56.0) 0.259 (-0.034–0.550)Typical Vc (L/70 kg per 60 years) 3.56 (1.5) 3.56 (3.45–3.67)Weight on Vc (power) 0.611 (8.4) 0.607 (0.504–0.715)Age on Vc (power) 0.229 (42.3) 0.218 (0.032–0.400)Q (L/day) 0.833 (12.3) 0.818 (0.612–1.11)VP (L) 2.50 (6.8) 2.47 (2.18–2.84)

Interindividual variability (% CV)TCL 29.8 (10.7) 29.8 (26.3–33.2)TVc 19.8 (15.1) 19.8 (16.7–22.6)TQ 71.0 (42.7) 67.5 (2.2–100)TVp 37.3 (41.4) 37.6 (20.8–51.9)D CL-Vc 0.0286 (21.2) 0.0289 (0.0175–0.0409)DVc-Vp 0.0436 (30.0) 0.0428 (0.0153–0.0678)

Proportional residual (% CV)Fintensive PK 20.1 (8.6) 19.9 (18.1–23.1)Fsparse PK 26.9 (4.8) 26.8 (24.2–29.1)

% 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, residualerror.

period in the patient populations. The finding is consistent withearlier evaluations with a noncompartmental model.6–10,12,13

The goodness-of-fit plots for the final pharmacokinetic modeldemonstrated that the model adequately fitted the rilotu-mumab concentrations. The observed concentrations versuspopulation- and individual-predicted concentrations were welldistributed around the line of identity. Values for conditionalweighted residuals were homogeneously distributed aroundzero, suggesting no apparent bias in the predictions of highand low concentrations over time (Supplementary Fig. S1) andconfirming that rilotumumab exhibited time-invariant phar-macokinetics over the tested dose range and treatment periodin the patient populations included in this analysis.

Our assessment for the final model showed that the distri-butions of post-hoc ETAs were close to the normal distributionwith a mean of zero and respective variance of the population.The calculated shrinkages to the mean of individual random ef-fects of CL and Vc were acceptable (0.23 and 0.24, respectively),and the shrinkage to the mean of individual random effects ofVp and Q were high (0.43 and 0.68, respectively).

Model Evaluation

The distribution of observed and simulated concentrationranges was demonstrated via a visual predictive check. For aqualified model, approximately 90% of observed concentrationsshould fall in the 90% prediction intervals of the simulated rilo-tumumab time profile. The analysis revealed that 93.4% (95%PI, 92.2–94.6) of the observed dose-normalized rilotumumabconcentrations were within the 90% prediction intervals of thesimulated dose-normalized concentration–time profiles (Fig. 2),indicating that the model described the observed data reason-ably well with a slight overprediction of variability.

The model parameter estimates from the final model weresimilar to the median of the nonparametric bootstrap replicates(Table 3), and all were contained within the 95% PI obtainedfrom the bootstrap analysis.

Model-Based Simulations

The simulated pharmacokinetic profiles with the Q2W andQ3W regimens at clinically tested doses (7.5, 10, 15, and20 mg/kg) are presented in Figure 3. It was calculated thatsteady state was achieved after at least five doses of rilotu-mumab, and accumulation ratios for peak and trough concen-trations were 2.1 and 3.0 for the Q2W regimen, respectively, and1.7 and 2.3 for the Q3W regimen, respectively. The steady-statemean peak and trough concentrations under a 20-mg/kg Q2Wregimen were predicted to be 836 and 426 :g/mL, respectively,and those under a 15-mg/kg Q3W regimen were predicted to be497 and 193 :g/mL, respectively. These values are much higherthan the rilotumumab-binding affinity to human HGF (target,Kd = 6 ng/mL); therefore, no target-mediated rilotumumab dis-position was expected at the clinical dose range, which is con-sistent with the fact that there was no detectable effect of HGFon the pharmacokinetic parameters.

DISCUSSION

In the present analysis, the population pharmacokinetics ofrilotumumab has been characterized using an open linear two-compartment pharmacokinetic model. After intravenous infu-sion of rilotumumab in patients with cancer, the estimatedrilotumumab volume of distribution (Vc, 3.6 L; Vp, 2.5 L) wassimilar to that of most monoclonal antibodies, of which the typ-ical median (range) Vc of most IgG1 or IgG2 therapeutic mono-clonal 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 Theestimated typical rilotumumab CL in patients with cancer (0.2L/day) was comparable to the typical CL estimates of most ther-apeutic monoclonal antibodies with linear CL characteristics(0.2–0.5 L/day) and comparable to the estimated CL of endoge-



Figure 2. Visual predictive check plot for all patients following the first treatment. The blue circles show the observed data normalized bydose, 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 solidand dashed black lines show the median and 5th and 95th percentiles of the simulated rilotumumab concentrations. The orange-shaded areasrepresent the 95% confidence intervals of the respective lines. The simulated values were computed from 300 trials simulated using the covariatevalues of the analysis dataset.

Figure 3. Simulated plasma concentration–time profile at steady state following rilotumumab administration at (a) 10 mg/kg Q2W, (b) 20mg/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 thesimulated rilotumumab concentrations (n=3000 per dose group). Q2W, every two weeks; Q3W, every three weeks.



Table 4. Summary of the Final Model-Predicted AUCtau Values atSteady State Following 15 mg/kg Q3W Rilotumumab Administration

Body Weight Range AUCtau (mg/mLh)a

Overall 141 (68.1–299)Lower quartile (≤63 kg) 127 (68.1–264)Upper quartile (≥88 kg) 165 (85.1–285)

aData are expressed as median (minimum–maximum).

nous IgG (0.1–0.2 L/day).26 Similar to other monoclonal anti-bodies, rilotumumab is expected to be cleared primarily by non-specific fluid-phase endocytosis and proteolysis.27 In this pro-cess, rilotumumab is broken down to component amino acids,and the degradation products are unlikely to be toxic. As thecatabolism mainly occurs in the lysosome, the involvement ofthe liver or impact of liver impairment on degradation of rilo-tumumab through intracellular catabolism is expected to benegligible. Consistent results were found in our analysis; an as-sociation between measures of liver function and rilotumumabCL was not detectable.

The pharmacokinetic parameters of rilotumumab are mainlyinfluenced by the patient’s weight. Body weight is the mostinfluential covariate contributing to interindividual pharma-cokinetic variability (Fig. 1). However, it should be noted thatthe relationship between steady-state AUCtau and weight stillshowed a slightly positive trend (Table 4) with the weight-based dosing. For the patients at the lower quartile of bodyweight (≤63 kg), the medium-predicted exposures were about10% lower than those in the overall population, whereas forthe patients at the higher quartile of body weight (≥88 kg), themedian-predicted exposures were about 15% higher than thosein the overall population, indicating that exposures in patientswith extreme lower or higher weight are expected to be slightlylower and higher, respectively, than those in other patients.

Although the age effect on pharmacokinetic parameters wasdetected in this dataset, the magnitude is modest and inclusionof age effect in the model did not reduce interpatient variabil-ity on CL and Vc. Furthermore, the bootstrap analysis showedthat the 95% CI of the parameter estimate on the age effect onclearance included zero, indicating the uncertainty in the esti-mation of the age effect on pharmacokinetics. It can be seen inFigure 1 that data from patients younger than 40 years old (6%of the total number of patients) appear to mainly contributeto the CL–age and Vc–age relationships. Further calculationsuggested that the CL was within 0.85- and 1.1-fold of the me-dian, and the Vc was within 0.9- and 1.1-fold of the median inpatients at least 40 years old. Therefore, the clinical relevanceof this age effect on rilotumumab pharmacokinetics appears tobe negligible.

The renal filtration of protein drugs is a size-specific mecha-nism of elimination. Renal filtration and subsequent proteoly-sis are often a primary mechanism of elimination for a proteinwith a molecular weight less than 70 kDa. As rilotumumabhas a molecular weight of 150 kDa, involvement of renal excre-tion of rilotumumab is unlikely. Consistently, within the rangeof covariates evaluated, there was no association between themeasures of renal function (e.g., creatinine clearance) and therilotumumab CL detected in the population pharmacokineticanalysis. Furthermore, rilotumumab exposure is not expectedto be affected by renal impairment (i.e., creatinine clearance<90 mL/min).

On the basis of the estimated typical pharmacokinetic pa-rameters, the calculated average elimination half-life of rilo-tumumab was 23 days, which is consistent with the half-lifereported for IgGs and was independent of body weight andage. On the basis of the current knowledge,27 the neonatal Fcreceptor (FcRN) may play an important role in rilotumumab’slong half-life. Rilotumumab homeostasis may be maintained byintracellular catabolism and FcRN-mediated recycling. Morethan 90% of steady state therefore can be attained in approx-imately 3 months following multiple dose regimens. At steadystate, rilotumumab exposure levels are expected to accumulateby three- or two-fold under the Q2W or Q3W regimens, respec-tively.

Neutralizing anti-rilotumumab antibodies have not been de-tected in any cancer patient studied for this fully human mon-oclonal antibody, and no data suggested that binding anti-rilotumumab antibodies affect the drug concentration levels.

Rilotumumab has shown high-binding affinity specifically tothe human HGF ligand, with an in vitro KD value of 41 pM(∼6 ng/mL).5 The effect of the baseline HGF plasma level onrilotumumab pharmacokinetics was examined in our analysis.As shown in Table 2, the interpatient variability in baselineHGF was large. There was no significant association detectedbetween the baseline HGF plasma level and rilotumumab CL,indicating that rilotumumab exposure is not affected by thebaseline HGF plasma level at the clinical doses. Thus, linearpharmacokinetics was observed.

The effect of cancer type, coadministered therapeutic agents[e.g., monoclonal antibodies (panitumumab and bevacizumab),small molecule chemotherapy agents (etoposide, cisplatin, andcarboplatin), and an experimental-targeted small moleculeagent (motesanib)], and study setting were examined indepen-dently for effect on the pharmacokinetic parameters of rilotu-mumab. As none of these factors showed an effect on the rilotu-mumab pharmacokinetics, rilotumumab exposure is expectedto be comparable regardless of cancer type and comedications.Further analyses on the pharmacokinetics of coadministeredtherapeutic agents suggested that rilotumumab did not affectthe pharmacokinetics of these cotherapy agents.8,12

In summary, this work improved our understanding of rilo-tumumab pharmacokinetics and assessed the potential effectof intrinsic and extrinsic factors on rilotumumab exposure inpatients with cancer. Its pharmacokinetic properties were pre-dictable and similar to most therapeutic monoclonal antibodies.Rilotumumab clearance was not affected by baseline HGF andMET levels in tumors, hepatic and renal functions, and con-comitantly administered medications. Clinical doses of 15–20mg/kg given Q2W or Q3W appeared to provide a concentrationrange required for clinical efficacy.16 Rilotumumab is currentlybeing investigated in a randomized phase 3 study in gastricand gastroesophageal junction cancer, in which a rilotumumabdose of 15 mg/kg Q3W is being tested in combination with ECX(RILOMET-1, ClinicalTrials.gov identifier: NCT01697072).

CONCLUSION

The pharmacokinetics of rilotumumab in patients with vari-ous types of solid tumors was characterized using a popula-tion pharmacokinetic approach. The results showed that rilo-tumumab exhibited linear and time-invariant kinetics over adose range of 0.5–20 mg/kg, and its clearance and volume of



distribution parameters were comparable with endogenousIgG. Body weight is the main covariate on the pharmacokineticparameters, and sex, cancer type, coadministration of cother-apy agents, baseline HGF and MET levels, and organ functionsdid not have an effect on rilotumumab pharmacokinetics.

ACKNOWLEDGMENTS

This study was funded by Amgen Inc. Per O. Gisleskog, PhD,received research funding from Amgen Inc. for this study. Wewould like to thank the patients, investigators, and the medi-cal, nursing, and laboratory staff who participated in the rilo-tumumab clinical trials. We would also like to thank Yun Lan,PhD, of Amgen Inc. for the HGF assay data, Mark Ma, PhD,and Teresa Wong of Amgen Inc. for support with the analyticalassay development sample analysis, and Jenilyn Virrey, PhD,of Amgen Inc. for writing assistance with this manuscript. Wewould also like to thank the Journal of Pharmaceutical Sci-ences reviewers for their helpful comments on the manuscript.The following authors are employees of and own stock in Am-gen Inc.: Min Zhu, PhD; Sameer Doshi, MS; Kelly S. Oliner,PhD; Juan Jose Perez Ruixo, PhD; Elwyn Loh, MD; and YilongZhang, PhD.

REFERENCES

1. Jung KH, Park BH, Hong SS. 2012. Progress in cancer therapy tar-geting c-Met signaling pathway. Arch Pharm Res 35:595–604.2. Gherardi E, Birchmeier W, Birchmeier C, Vande Woude G. 2012.Targeting MET in cancer: Rationale and progress. Nat Rev Cancer12:89–103.3. Nakajima M, Sawada H, Yamada Y, Watanabe A, Tatsumi M, Ya-mashita J, Matsuda M, Sakaguchi T, Hirao T, Nakano H. 1999. Theprognostic significance of amplification and overexpression of c-met andc-erb B-2 in human gastric carcinomas. Cancer 85:1894–1902.4. Taniguchi K, Yonemura Y, Nojima N, Hirono Y, Fushida S, FujimuraT, Miwa K, Endo Y, Yamamoto H, Watanabe H. 1998. The relationbetween the growth patterns of gastric carcinoma and the expressionof hepatocyte growth factor receptor (c-met), autocrine motility factorreceptor, and urokinase-type plasminogen activator receptor. Cancer82:2112–2122.5. Burgess T, Sun J, Meyer S, Tsuruda TS, Sun J, Elliott G, ChenQ, Haniu M, Barron WF, Juan T, Zhang K, Coxon A, Kendall RL.2010. Biochemical characterization of AMG 102: A neutralizing, fullyhuman monoclonal antibody to human and nonhuman primate hepa-tocyte growth factor. Mol Cancer Ther 9:400–409.6. Gordon MS, Sweeney CS, Mendelson DS, Eckhardt SG, Anderson A,Beaupre DM, Branstetter D, Burgess TL, Coxon A, Deng H, Kaplan-Lefko P, Leitch IM, Oliner KS, Yan L, Zhu M, Gore L. 2010. Safety,pharmacokinetics, and pharmacodynamics of AMG 102, a fully humanhepatocyte growth factor-neutralizing monoclonal antibody, in a first-in-human study of patients with advanced solid tumors. Clin CancerRes 16:699–710.7. Wen PY, Schiff D, Cloughesy TF, Raizer JJ, Laterra J, Smitt M, WolfM, Oliner KS, Anderson A, Zhu M, Loh E, Reardon DA. 2011. A phaseII study evaluating the efficacy and safety of AMG 102 (rilotumumab)in patients with recurrent glioblastoma. Neuro Oncol 13:437–446.8. Rosen PJ, Sweeney CJ, Park DJ, Beaupre DM, Deng H, Leitch IM,Shubhakar P, Zhu M, Oliner KS, Anderson A, Yee LK. 2010. A phaseIb study of AMG 102 in combination with bevacizumab or motesanib inpatients with advanced solid tumors. Clin Cancer Res 16:2677–2687.9. Ryan CJ, Rosenthal M, Ng S, Alumkal J, Picus J, Gravis G, FizaziK, Forget F, Machiels JP, Srinivas S, Zhu M, Tang R, Oliner KS, JiangY, Loh E, Dubey S, Gerritsen WR. 2013. Targeted MET inhibition incastration-resistant prostate cancer: a randomized phase II study and

biomarker analysis with rilotumumab plus mitoxantrone and pred-nisone. Clin Cancer Res 19:215–224.10. Schoffski P, Garcia JA, Stadler WM, Gil T, Jonasch E, Tagawa ST,Smitt M, Yang X, Oliner KS, Anderson A, Zhu M, Kabbinavar F. 2011.A phase II study of the efficacy and safety of AMG 102 in patients withmetastatic renal cell carcinoma. BJU Int 108:679–686.11. Zhu M, Tang R, Doshi S, Oliner KS, Kathman S, Gisleskog PO,Dubey S, Jiang Y, Donehower RC, Iveson T, Loh E. 2012. Exposure–response (E-R) analysis of rilotumumab (R, AMG 102) plus epiru-bicin/cisplatin/capecitabine (ECX) in patients (pts) with locally ad-vanced or metastatic gastric or esophagogastric junction (G/EGJ) can-cer (abstract). J Clin Oncol 30(suppl):2535.12. Lorigan P, Soria J-C, Stephenson J, Maru A, Gervais R, Zhu M,McCaffery I, Jiang Y, McGreivy J, Glisson B. 2010. Safety and phar-macokinetics of first-line AMG 479 (MAb to IGF1R) or AMG 102(MAb to HGF/SF) with platinum-based chemotherapy in extensive-stage small cell lung cancer (SCLC) (abstract). Ann Oncol 21(suppl8):444P (viii149).13. Davidenko I, Iveson T, Donehower RC, Tjulandin S, Deptala A,Jiang Y, Zhu M, Oliner KS, Dubey S, Loh E. 2012. Updated efficacy,biomarker, and exposure-response data from a phase 2 study of rilotu-mumab (R) plus epirubicin, cisplatin, and capecitabine (ECX) in gas-tric (G) or esophagogastric junction (EGJ) cancer. Ann Oncol 23(suppl9):ix230, 687P.14. Doshi S, Kathman S Jr, Tang R, Gisleskog PO, Loh E, Dubey S,Jiang Y, Oliner KS, Donehower RC, Iveson T, Zhu M. 2012. Model-ing the combined efficacy of rilotumumab (R; AMG 102) plus epiru-bicin/cisplatin/capecitabine (ECX) for the treatment of locally advancedor metastatic gastric or esophagogastric junction (G/EGJ) cancer (ab-stract). J Clin Oncol 30(suppl):2594.15. Gisleskog PO, Lu JF, Perez-Ruixo JJ, Magnusson MO, Zhu M. 2010.A mechanistic model to characterize binding dynamics of AMG 102 toHGF ligands in cancer patients. Clin Pharm Ther 87(suppl 1):S83.16. Iveson T, Donehower RC, Davidenko I, Tjulandin S, Deptala A,Harrison M, Loh E, Jiang J, Oliner KS, Dubey S. 2011. Safety and effi-cacy of epirubicin, cisplatin, and capecitabine (ECX) plus rilotumumab(AMG 102) as first-line treatment for unresectable, locally advanced ormetastatic, gastric or esophagogastric junction (EGJ) adenocarcinoma(abstract #446.504). Eur J Cancer 47(suppl 1):S443.17. Jun HT, Sun J, Rex K, Radinsky R, Kendall R, Coxon A, Burgess TL.2007. AMG 102, a fully human anti-hepatocyte growth factor/scatterfactor neutralizing antibody, enhances the efficacy of temozolomide ordocetaxel in U-87 MG cells and xenografts. Clin Cancer Res 13:6735–6742.18. Kakkar T, Ma M, Zhuang Y, Patton A, Hu Z, Mounho B. 2007.Pharmacokinetics and safety of a fully human hepatocyte growth factorantibody, AMG 102, in cynomolgus monkeys. Pharm Res 24:1910–1918.19. Kathman S, Tang R, Doshi S, Zhu M, Hale M. 2012. Exposure–discrete time survival analysis to assess the effect of a drug com-bination for gastric cancer: A case study of rilotumumab and epiru-bicin/cisplatin/capecitabine. Poster presented at the American Collegeof Clinical Pharmacy Annual Meeting, Hollywood, Florida.20. Oliner KS, Tang R, Anderson A, Lan Y, Iveson T, Donehower RC,Jiang Y, Dubey S, Loh E. 2012. Evaluation of MET pathway biomarkersin a phase II study of rilotumumab (R, AMG 102) or placebo (P) in com-bination with epirubicin, cisplatin, and capecitabine (ECX) in patients(pts) with locally advanced or metastatic gastric (G) or esophagogastricjunction (EGJ) cancer (abstract #4005). J Clin Oncol 30(suppl 15).21. Eng C, Van Cutsem E, Nowara E, Swieboda-Sadlej A, Tebbutt NC,Mitchell EP, Davidenko I, Oliner K, Chen L, Huang J, McCaffery I,Loh E, Smethurst D, Tabernero J. 2011. A randomized, phase Ib/IItrial of rilotumumab (AMG 102; ril) or ganitumab (AMG 479; gan) withpanitumumab (pmab) versus pmab alone in patients (pts) with wild-type (WT) KRAS metastatic colorectal cancer (mCRC): Primary andbiomarker analyses (abstract #3500). J Clin Oncol 29(suppl).22. Hooker AC, Staatz CE, Karlsson MO. 2007. Conditional weightedresiduals (CWRES): A model diagnostic for the FOCE method. PharmRes 24:2187–2197.



23. Karlsson MO, Savic RM. 2007. Diagnosing model diagnostics. ClinPharmacol Ther 82:17–20.24. Jonsson EN, Karlsson MO. 1998. Automated covariate model build-ing with NONMEM. Pharm Res 15:1463–1468.25. Ette EI. 1997. Stability and performance of a population pharma-cokinetic model. J Clin Pharmacol 37:486–495.

26. Dirks NL, Meibohm B. 2010. Population pharmacokinetics oftherapeutic monoclonal antibodies. Clin Pharmacokinet 49:633–659.27. Wang W, Wang EQ, Balthasar JP. 2008. Monoclonal antibody phar-macokinetics and pharmacodynamics. Clin Pharmacol Ther 84:548–558.


population pharmacokinetics of rilotumumab, a fully human monoclonal antibody against hepatocyte...

Documents