ORIGINAL ARTICLE

A population pharmacokinetic/pharmacodynamic modelof thrombocytopenia characterizing the effect of trastuzumabemtansine (T-DM1) on platelet counts in patientswith HER2-positive metastatic breast cancer

Brendan C. Bender • Franziska Schaedeli-Stark • Reinhold Koch •

Amita Joshi • Yu-Waye Chu • Hope Rugo • Ian E. Krop •

Sandhya Girish • Lena E. Friberg • Manish Gupta

Received: 7 February 2012 / Accepted: 20 July 2012 / Published online: 12 August 2012

� Springer-Verlag 2012

Abstract

Purpose Trastuzumab emtansine (T-DM1) is an antibody-

drug conjugate in the development for the treatment of

human epidermal growth factor receptor 2-positive cancers.

Thrombocytopenia (TCP) is the dose-limiting toxicity of

T-DM1. A semimechanistic population pharmacokinetic/

pharmacodynamic (PK/PD) model was developed to char-

acterize the effect of T-DM1 on patient platelet counts.

Methods A PK/PD model with transit compartments that

mimic platelet development and circulation was fit to

concentration-platelet–time course data from two T-DM1

single-agent studies (TDM3569g; N = 52 and TDM4258g;

N = 112). NONMEM� 7 software was used for model

development. Data from a separate phase II study

(TDM4374g; N = 110) were used for model evaluation.

Patient baseline characteristics were evaluated as covari-

ates of model PD parameters.

Results The model described the platelet data well and

predicted the incidence of grade C3 TCP. The model

predicted that with T-DM1 3.6 mg/kg given every 3 weeks

(q3w), the lowest platelet nadir would occur after the first

dose. Also predicted was a patient subgroup (46 %) having

variable degrees of downward drifting platelet–time pro-

files, which were predicted to stabilize by the eighth

treatment cycle to platelet counts above grade 3 TCP.

Baseline characteristics were not significant covariates of

PD parameters in the model.

Conclusions This semimechanistic PK/PD model accu-

rately captures the cycle 1 platelet nadir, the downward

drift noted in some patient platelet–time profiles, and the

*8 % incidence of grade C3 TCP with T-DM1 3.6 mg/kg

q3w. This model supports T-DM1 3.6 mg/kg q3w as a

well-tolerated dose with minimal dose delays or reductions

for TCP.

Keywords Trastuzumab emtansine � T-DM1 �Thrombocytopenia � Population pharmacokinetic/

pharmacodynamic model � Semimechanistic �Cumulative TCP

Introduction

Trastuzumab emtansine (T-DM1) is an antibody-drug

conjugate (ADC) in development for the treatment of

human epidermal growth factor receptor 2 (HER2)-positive

cancers [1–4]. It is composed of the potent antimicrotubule

maytansinoid derivative DM1 conjugated to the HER2-

targeted monoclonal antibody trastuzumab via a stable

B. C. Bender � A. Joshi � Y.-W. Chu � S. Girish (&) � M. Gupta

Genentech, Inc., South San Francisco, CA, USA

e-mail: girish.sandhya@gene.com

B. C. Bender

e-mail: brendan.bender@farmbio.uu.se

B. C. Bender � L. E. Friberg

Department of Pharmaceutical Biosciences,

Uppsala University, Uppsala, Sweden

F. Schaedeli-Stark � R. Koch

F. Hoffman-La Roche Ltd., Basel, Switzerland

H. Rugo

UCSF Helen Diller Family Comprehensive Cancer Center,

San Francisco, CA, USA

I. E. Krop

Dana–Farber Cancer Institute, Boston, MA, USA

Present Address:M. Gupta

Bristol–Myers Squibb, Lawrenceville, NJ, USA

123

Cancer Chemother Pharmacol (2012) 70:591–601

DOI 10.1007/s00280-012-1934-7

thioether linker, MCC ([N-maleimidomethyl] cyclohexane-

1-carboxylate). T-DM1 binds to HER2-overexpressing

cells and undergoes receptor-mediated internalization,

resulting in the intracellular release of Lys–MCC–DM1

and subsequent tumor cell death [1]. In two phase II studies

[3, 4], T-DM1 3.6 mg/kg administered intravenously every

3 weeks (q3w) demonstrated activity against HER2-positive

metastatic breast cancer (MBC) that was previously treated

with HER2-directed therapy, with objective response rates of

25.9 and 34.5 % by independent radiologic review.

Thrombocytopenia (TCP) was among the more fre-

quently observed toxicities in clinical studies of T-DM1

[2–4]. In the phase I dose-escalation study (TDM3569g)

[2], the maximum tolerated T-DM1 dose was 3.6 mg/kg

q3w, with grade 4 TCP as the dose-limiting toxicity (DLT)

at 4.8 mg/kg q3w. Platelet counts of most patients treated

at the maximum tolerated dose followed a characteristic

pattern: after the administration of T-DM1, platelet counts

decreased to a nadir by day 8, with recovery to baseline

levels by day 1 of the next cycle. A slow downward drift in

the platelet–time profile was noted in some patients over

multiple cycles. In three early clinical studies of single-

agent T-DM1, platelet declines were mostly grade 1 or 2

and no clinically significant bleeding events were reported

[2–4]. The mechanisms of T-DM1-induced TCP are not

known. In contrast to platelets, other hematologic lineages

were relatively spared from T-DM1; leukopenia, neutro-

penia, and anemia were all observed at much lower inci-

dences compared with TCP.

This report describes the development of a semimech-

anistic population pharmacokinetic/pharmacodynamic

(PK/PD) model, which was designed to (1) describe the

time course of platelet response to T-DM1, (2) test model

structures to support hypotheses regarding the mecha-

nism(s) of the effect of T-DM1 on platelet counts, (3)

evaluate patient baseline characteristics as covariates of

model PD parameters, and (4) predict platelet response and

incidence rates of grade C3 TCP in future clinical studies

of T-DM1.

Materials and methods

Patient population

The PK/PD model was based on 4,340 assessments of

platelet concentration from 164 patients enrolled in two

studies: the phase I study TDM3569g (N = 52) and the

phase II study TDM4258g (N = 112). Patients in the phase

I study received T-DM1 intravenously qw (weekly) or

q3w. Doses in the q3w regimen were 0.3 mg/kg (n = 3),

0.6 mg/kg (n = 1), 1.2 mg/kg (n = 1), 2.4 mg/kg (n = 1),

3.6 mg/kg (n = 15), and 4.8 mg/kg (n = 3). Doses in the

qw regimen were 1.2 mg/kg (n = 3), 1.6 mg/kg (n = 3),

2.0 mg/kg (n = 3), 2.4 mg/kg (n = 16), and 2.9 mg/kg

(n = 3). Patients in the phase II study received T-DM1

3.6 mg/kg q3w. Data from 110 patients enrolled in the

phase II study TDM4374g, evaluating T-DM1 3.6 mg/kg

q3w (1,841 assessments of platelet concentration), were

used for external evaluation of the model.

Baseline demographic and disease characteristics from

studies TDM3569g, TDM4258g, and TDM4374g have

been reported elsewhere and are similar across studies [2–

5]. Demographic values (mean ± SD) are summarized

briefly here: age (54 ± 10 years), weight (72.1 ± 17.7 kg),

observed baseline platelet count (267 ± 105 9 1,000/lL),

creatinine clearance (96 ± 34 mL/min), serum creatinine

(75 ± 56 lmol/L), albumin (39 ± 5 g/L), total protein

(70.4 ± 7.3 g/L), alanine transaminase (ALT) (31 ±

21 IU/L), aspartate transaminase (AST) (39 ± 28 IU/L),

total bilirubin (8.1 ± 5.3 lmol/L), tumor burden (based on

the sum of the longest diameters of target lesions reported

at baseline) (9.3 ± 7.2 cm), and HER2 expression

(84.4 ± 126 ng/mL). Patient races were of the following

percentages: White, 79 %; Black, 8 %; Hispanic or Latino,

7 %; American Indian or Alaska Native, 4 %; Asian, 2 %.

Patients received a median duration of seven cycles of

T-DM1 (range 1–34 cycles). In all three studies, T-DM1

treatment continued until progressive disease or unaccept-

able T-DM1 toxicity. Dose delays or dose reductions to

3.0 mg/kg due to TCP or other reasons occurred in 24

patients (*10 %) in the 3.6 mg/kg q3w group. TCP of

grades 1–4 was reported in 31 % (73/237) of patients

treated at 3.6 mg/kg q3w, with approximately 8 % expe-

riencing grade C3 TCP.

Platelet measurements

Hematologic sampling was conducted at multiple time

points throughout the studies. Patients with baseline

platelet counts \100 9 1,000/lL were excluded from the

studies. For patients receiving q3w regimens in study

TDM3569g, platelet counts were assessed on days 1, 2, 4,

7–8, 11, and 18 in cycle 1. Typical collection times in

subsequent cycles were days 1, 4, and 7–8. For patients

receiving qw regimens, platelet counts were assessed on

days 1, 2, 4, 7–8, 11, 14–15, and 18 in cycle 1, with pre-

dose weekly sampling beginning on day 21. For

TDM4258g, platelet counts were assessed every 7 days in

all cycles. For TDM4374g, platelet counts were assessed

weekly during cycle 1 and on days 1 and 8 of all sub-

sequent cycles. When hematologic toxicity occurred, more

frequent sampling was conducted in accordance with the

clinical protocol.

592 Cancer Chemother Pharmacol (2012) 70:591–601

123

T-DM1 population PK modeling

A T-DM1 population PK model was previously developed

from the phase I and II studies [5]. Briefly, plasma T-DM1

concentrations were measured using an enzyme-linked

immunosorbent assay (ELISA), which captured any trast-

uzumab molecule conjugated to DM1 (1–8 DM1 molecules

per antibody). In cycles 1 and 4, frequent PK sampling

post-T-DM1 infusion was conducted, while PK sampling

was limited to pre- and post-T-DM1 infusion in other

cycles. A linear two-compartment PK model with first-

order elimination from the central compartment best

described the T-DM1 plasma concentration–time data. PK

parameter estimates and interindividual variability (IIV)

were: clearance (CL) = 0.7 L/day (21), central compartment

volume (V1) = 3.33 L (13 %), peripheral compartment vol-

ume (V2) = 0.89 L (50 %), and inter-compartmental clear-

ance (CLd) = 0.78 L/day (IIV not estimated). Shrinkage

values for CL, V1, and V2 were 8, 18, and 35 %, respectively.

For PK/PD modeling, the post hoc Bayesian estimates of

individual PK parameters were used as input to model the

respective patient platelet response.

T-DM1 population PK/PD modeling of platelet

response

A schematic of the semimechanistic population PK/PD

model that was developed to describe the T-DM1-driven

platelet–time course is shown in Fig. 1. This model

structure was based on the PK/PD model of myelosup-

pression proposed by Friberg et al. [6, 7], which has been

widely used to characterize the time course of leukocyte,

neutrophil, and platelet counts during cytotoxic drug

treatment [8–12]. The final model consisted of a T-DM1

drug effect inhibiting the proliferation rate of a prolifera-

tive platelet pool (PP) compartment, three transit com-

partments (T1, T2, and T3), and a circulating platelet (PLT)

compartment. As shown in Fig. 1, individual patient PK

parameters from the population PK model provided

T-DM1 central compartment concentrations (C), which

acted as linear inhibitory drug effects (C 9 Slope) on the

proliferation rate (KPROL) of the PP compartment. Other

system-related parameters that were estimated included

the mean transit time (MTT; h), equal to the number of

intercompartmental transits divided by the transit rate

constant (4/Ktr; h-1); BASE (91,000/lL), the total baseline

platelet count at time = 0; GAM, a dimensionless feed-

back term that increases the proliferation rate when platelet

counts (PLT) drop below BASE.

Modifications to the myelosuppression model [6] (i.e.,

the addition of structural components and parameters) were

made taking into account notable observations from the

clinical studies. First, in most patients, platelet reduction to

nadir and return to baseline appeared stable throughout the

course of treatment; however, in some patients, platelet–

time profiles drifted slowly down over multiple cycles of

T-DM1. The rate and extent of this decline was modeled by

the introduction of an additional slow T-DM1-related drug

KPROL = Ktr

Cavg • Kdeplete

C • Slope

Ktr = 4 / MTT

Kel = Ktr

Ktr Ktr

Ktr

T1 T2 T3

PlateletProliferationPool (PP)

Transit Compartments

CirculatingPlatelets

(PLT)

GAM

Feedback = BASEPLT

V2

T-DM1Dose

CLd

CL

V1

BASE2

BASE1

Fig. 1 Schematic of the semiphysiologic PK/PD model describing

the time course of platelet response after T-DM1 administration

(modified from Friberg et al. [6]). PP the proliferative platelet pool

compartment; T1, T2, and T3 transit compartments; PLT circulating

platelet compartment; BASE baseline platelet count at time = 0,

modeled as BASE1 ? BASE2 in the proliferating platelet compart-

ment; BASE/PLT baseline platelet count/platelet count at time (t);BASE1 amount of baseline proliferating PP that is nondepletable;

BASE2 amount of baseline proliferating PP that is depletable by the

Kdeplete 9 Cavg rate; Cavg average T-DM1 concentration over dosing

intervals; CL clearance; CLd intercompartmental clearance;

C 9 Slope T-DM1 drug effect; GAM feedback parameter; Kdeplete

rate of depletion of BASE2 PP; Kel rate of physiologic elimination of

circulating platelets; KPROL rate of PP proliferation; Ktr transit rate

constant between transit compartments; MTT mean transit time

through transit compartment chain; Slope drug potency parameter; V1

T-DM1 central volume of distribution; V2 T-DM1 peripheral volume

of distribution

Cancer Chemother Pharmacol (2012) 70:591–601 593

123

effect on a depletable fraction of the proliferating PP.

Specifically, the PP compartment was modeled as a nonde-

pletable (BASE1) proliferating pool and as a depletable

(BASE2) proliferating pool of platelets. The drug effect

(Cavg 9 Kdeplete) was incorporated to slowly deplete the

BASE2 pool over time, where Kdeplete (L/mg 9 week-1) is

the depletion rate constant and Cavg (mg/L) is the average

T-DM1 concentration over dosing intervals. BASE2 is a

function of time (t), where BASE2(t) = BASE2 9 EXP(-

Kdeplete 9 Cavg 9 t). Given that BASE = BASE1 ?

BASE2, the total ‘‘baseline’’ platelet count at time (t) is cal-

culated as BASE(t) = BASE1 ? (BASE - BASE1) 9

EXP(-Kdeplete 9 Cavg 9 t). Thus, BASE1 and Kdeplete were

additional system- and drug-related parameters to be esti-

mated, respectively. During model building, Kdeplete param-

eter estimates resulted in a skewed, apparently bimodal

distribution with a tenfold difference between the mode

values. A mixture model implementation in NONMEM� 7

software (ICON, Dublin, Ireland) was used to estimate the

probability of a lower (POP1) or higher (POP2) value of

Kdeplete, resulting in two patient subgroups with an apparent

stable platelet–time profile (POP1) or with an apparent

decline (POP2).

The second modification was applied in consideration of

the clinical observation that platelet count nadirs were

generally lowest after the first T-DM1 dose, seen primarily

with the q3w regimen. This phenomenon was modeled

using two separate Slope parameters (i.e., Slope1 for the

first dose only and Slope2 for all subsequent doses) for both

q1w and q3w data.

The final PK/PD model differential equations describing

the platelet–time course following T-DM1 dosing are

shown below. A(1) and A(2) represent T-DM1 mass in the

PK central and peripheral compartments, respectively. The

T-DM1 concentration–time course is described as A(1)/V1.

Platelet count (PLT) was the dependent variable (DV) in

the model.

dA 1ð Þ=dt ¼ CLd=V2 � A 2ð Þ � CLd=V1 � A 1ð Þ� CL=V1 � A 1ð Þ

dA 2ð Þ=dt ¼ CLd=V1 � A 1ð Þ � CLd=V2 � A 2ð ÞConcentration tð Þ ¼ A 1ð Þ=V1

Drug Effect1 ¼ Slope � Concentration tð ÞDrug Effect2 ¼ Kdeplete � Cavg

Ktr ¼ 4=MTT

BASE tð Þ ¼ ðBASE� BASE1Þ � EXPð�Drug Effect2

� tÞ þ BASE1

dPP=dt ¼ �Ktr � PP þ Ktr � PP� ð1� Drug Effect1Þ� BASE tð Þ=PLTð ÞGAM

dT1=dt ¼ �Ktr � T1 þ Ktr � PP

dT2=dt ¼ �Ktr � T2 þ Ktr � T1

dT3=dt ¼ �Ktr � T3 þ Ktr � T2

dPLT=dt ¼ �Ktr � PLT þ Ktr � T3

Data analysis

PK/PD model building was performed using the first-order

conditional estimation (FOCE) method with INTERAC-

TION using NONMEM 7 [13]. Platelet count data were log-

transformed. Log-normal parameter distributions were used

for IIV, where the parameter for an ith patient was represented

by Parameteri = Typical Value 9 exp(gi), where gi repre-

sents the IIV. The residual error was modeled as a propor-

tional error, which is additive in the log domain. The

objective function value (OFV) was used for the comparison

of hierarchical models, using the log-likelihood ratio test. A

difference in OFV of[3.84, corresponding to a significance

level of P \ 0.05, was used for discrimination between two

nested models that differed in one parameter.

Model building

During the model-building process, a linear (Slope 9 C)

versus a nonlinear ([Emax 9 C]/[EC50 ? C]) drug effect, a

drug effect on PP, T1, T2, T3, or PLT compartments, as well

as variable numbers of transit compartments (n = 2–5),

were considered. With regard to the downward drift in

platelet profiles observed in some patients, numerous

approaches were taken: (1) modeling was done with and

without mixture model implementation, (2) the platelet

proliferation pool was not divided and thus only a single

BASE parameter was estimated, (3) Kdeplete,POP1 was fixed

at 0, (4) the downward drift was modeled as driven by

cumulative T-DM1 exposure, (5) the downward drift was

modeled as driven by a time effect on the feedback (GAM)

parameter, and (6) with regard to the low cycle 1 platelet

count nadirs, a single Slope parameter was tried, as well as

using an intraoccasion variability (IOV) on Slope; a similar

IOV approach was performed with the GAM parameter.

All models described above were compared with respect to

their OFV, diagnostic plots, successful parameter estima-

tion, and mechanistic plausibility.

Covariate analysis

A covariate analysis strategy was developed a priori to

identify patient baseline characteristics that might explain

sources of IIV on the model drug-related parameters of

Kdeplete and Slope, as well as system-related parameters of

BASE, BASE1, and MTT. Tested baseline covariates

included patient age, weight, race, observed baseline

594 Cancer Chemother Pharmacol (2012) 70:591–601

123

platelet count, creatinine clearance, serum creatinine,

albumin, total protein, ALT, AST, total bilirubin, tumor

burden (based on the sum of the longest diameters of target

lesions reported at baseline), HER2 expression, and prior

myelosuppressive chemotherapies (i.e., paclitaxel, doce-

taxel, or carboplatin).

The covariate analysis was performed using the linear-

ized FOCE method with stepwise covariate model building

[14] to screen all available covariates for significance on

any parameter. If significant covariate parameter correla-

tions were found, they were assessed by stepwise addition,

followed by backwards elimination using the nonlinear

FOCE method.

Model evaluation

Model evaluation was performed using standard diagnostic

plots, visual predictive checks (VPCs), external evaluation,

and predictability of TCP. For VPCs, prediction intervals with

90 % confidence intervals were obtained by simulating 100

data sets from the model using the original data set. Simula-

tions were constrained to 210 days and for the two largest-dose

groups in the model-building dataset, 3.6 mg/kg q3w

(n = 127) and 2.4 mg/kg qw (n = 16). The predicted versus

observed platelet response were examined to determine whe-

ther observed 5th, 50th, and 95th percentiles of data fell within

the confidence intervals around these percentiles (Fig. 2).

To assess model predictability of patient platelet

response in subsequent studies, platelet observations from

the phase II study TDM4374g (n = 110; 3.6 mg/kg q3w)

were used as an external evaluation dataset. For simula-

tions, results from the population PK analysis [5] were

incorporated with results from this PK/PD analysis. Spe-

cifically, the model-simulated T-DM1 concentrations were

based on PK parameter estimates, PK variability, the effect

of body weight on CL and V1, and the correlation between

CL and V1 as reported [5]. Platelet response simulations

were, in turn, driven by these T-DM1 concentrations using

the final PD parameters and IIVs shown in Table 1. Patient

body weights were also simulated by the model using a

median weight of 69.6 kg and an IIV of 25 %, as calcu-

lated from the model-building dataset, which contained 164

patients. This approach was taken in order to predict

patient platelet response a priori and assumes a similar

patient weight distribution in future studies. This was

confirmed for patients in the evaluation dataset,

TDM4374g, where patients had a median weight of

69.7 kg and an IIV of 25 %.

One hundred simulation replicates were generated at a

dose of 3.6 mg/kg q3w, with 110 simulated patients per

replicate to match the patient number from TDM4374g.

Nominal time points and dosing were used without patient

dropout or dose reduction. Observed platelet count data

from TDM4374g were then overlaid with model simula-

tions to assess whether observed 5th, 50th, and 95th per-

centiles of data fell within the confidence intervals around

these percentiles. The median line through the observed

data was plotted and compared with the 50th percentile

window. The median line from the model-building dataset

observations was also plotted for comparison.

For a secondary evaluation, the PK/PD model was

applied to this dataset with MAXEVAL = 0 in order to

assign patients from TDM4374g to either the Kdeplete,POP1

or Kdeplete,POP2 subgroups; the MAXEVAL = 0 approach

was used as a rapid method to stratify patients only for

visualization purposes, and parameter values for the eval-

uation dataset were not re-estimated. Simulations of

platelet response were also stratified by the Kdeplete,POP1 and

Kdeplete,POP2 subgroups, and the 90 % prediction interval

and 50th percentile were plotted with respective 95 %

confidence intervals. The observed platelet count data from

the Kdeplete,POP1 or Kdeplete,POP2 subgroups were then over-

laid with model simulations to assess whether observed

5th, 50th, and 95th percentiles of data fell within the

confidence intervals around these percentiles. The median

line through the observed data was plotted and compared

with the 50th percentile window. The median line from the

model-building dataset observations was also plotted for

comparison.

Finally, the ability of the model to predict the incidence

of grade C3 TCP by day 63 was evaluated. Day 63 was

chosen as the end point since most patients were still on

Median of observed data 5th and 95th percentiles of observed data

95th-percentile confidence interval

50th-percentile confidence interval

5th-percentile confidence interval

100

50

500

200

Pla

tele

t Cou

nt (

• 10

00/µ

L)

50

Time (days)

200100 150

Fig. 2 Visual predictive check of the final model simulations at a

T-DM1 dose of 3.6 mg/kg q3w with model dataset observations. The

solid red line represents the median of the observed data (opencircles). The red shaded region represents the 95 % confidence

interval of the model simulated 50th percentile. The outer blueshaded regions represent the 95 % confidence interval around the

model simulated 5th and 95th percentiles. The stippled red linesrepresent the 5th and 95th percentiles of the observed platelet

observations

Cancer Chemother Pharmacol (2012) 70:591–601 595

123

study, and grade C3 TCP (when observed) was usually

reached within three treatment cycles. The observed inci-

dence of grade C3 TCP from the model dataset and eval-

uation dataset was overlaid with the simulated predictions.

These probabilities were further separated by quartiles of

observed baseline platelet count, T-DM1 Cmax (calculated

by Dose/V1), and T-DM1 area under the curve (AUC)

(calculated by Dose/CL). Simulations included patient

dosing history from the model dataset.

Results

Final population PK/PD model

The final population PK/PD model parameter estimates based

on the model-building dataset (TDM3569g and TDM4258g)

are shown in Table 1. There were no apparent trends in PK/

PD model parameter estimates across doses (0.3–4.8 mg/kg)

or between schedules (qw and q3w). The relative standard

errors (RSE) were below 25 % for all parameters, indicating

that parameters could be estimated with good precision.

Values for system-related parameters GAM, MTT, and

BASE were 0.135, 37.4 h, and 255 9 1,000/lL, respec-

tively. BASE was composed of a 46 % BASE1 nondepletable

proliferating PP (118 9 1,000/lL) and a 54 % BASE2

depletable proliferating PP (137 9 1,000/lL).

The implementation of two separate Slope parameters (for

the initial dose and for subsequent doses) caused a drop of

OFV by 325 points compared with a model with a single Slope

parameter. The T-DM1 drug effect was greater after the initial

T-DM1 dose (Slope1 = 0.00297 L/mg) versus subsequent

doses (Slope2? = 0.00182 L/mg), corresponding to a typical

platelet count nadir of 115 9 1,000/lL in the first cycle and

then a higher platelet nadir of 145 9 1,000/lL in the second

cycle. Eighty-five percent of patients in the model-building

dataset demonstrated this pattern of lowest nadir in the first

cycle, with variable degrees of increased nadir counts in the

second and subsequent cycles. This observation is illustrated

in Fig. 3b for patient ID = 143 and in Fig. 4b.

The rates of the downward drift in platelet–time profiles

were quantified by the Kdeplete parameter multiplied by the

Cavg, and patients were assigned either to POP1 with a low

Kdeplete or to POP2 with a higher Kdeplete by the mixture model.

The probability of POP1 was estimated at 55 %, and the

typical values for Kdeplete were 0.000625 L/mg 9 week-1 and

0.00842 L/mg 9 week-1 for the two subgroups. The actual

percentages of patients in the model-building dataset assigned

to POP1 and POP2 were 61 % and 39 %, respectively. For the

typical patient in POP2 treated with 3.6 mg/kg T-DM1 q3w

(Cavg = 17.1 mg/L), the rate for BASE2 depletion (Kdeplete 9

Cavg) is 0.144 week-1, corresponding to a time to reach steady

state of 24 weeks. Therefore, the platelet–time profile is

expected to stabilize after 24 weeks (eight treatment cycles).

For the typical patient in POP1 treated with 3.6 mg/kg q3w,

the rate for BASE2 depletion is 0.0107 week-1, predicting

extremely slow declines of platelet–time profiles that stabilize

after 324 weeks. This is longer than current T-DM1 treatment

periods, and these platelet–time profiles are considered stable.

Approximately 22 % of all patients had Kdeplete 9 Cavg values

[0.144 week-1 (i.e., above the typical value for POP2); their

platelet–time profiles drifted down faster and stabilized earlier

than eight cycles. Representative patients, ID = 143 (POP1)

and ID = 223 (POP2), from each subgroup are shown in

Table 1 Population parameter estimates for the final model

Parameter Parameter descriptions Unit Estimate RSE

(%)

IIV

(%)

IIV

RSE (%)

Slope1 T-DM1 drug effect for first dose L/mg 0.00297 4.01 36.1 8.04

Slope2 T-DM1 drug effect for subsequent doses L/mg 0.00182 5.19 56.3 9.94

MTT Mean transit time h 37.4 0.0204 24.5 4.18

GAM Feedback term – 0.135 0.0439 – –

BASE Baseline platelet count at time = 0 91,000/lL 255 2.64 32.3 6.88

BASE1 Baseline platelet count not depleted 91,000/lL 118 7.59 37.4 23.4

BASE2 Baseline platelet count depleted by (Cavg 9 Kdeplete) rate 91,000/lL 137 – – –

Kdeplete,POP1 Depletion rate of BASE2 platelet pool for population 1 patients L/mg 9 week-1 0.000625 24.8 88.1a 13.7

Kdeplete,POP2 Depletion rate of BASE2 platelet pool for population 2 patients L/mg 9 week-1 0.00842 19.4 88.1a 13.7

P(1) Probability of patient in POP1 – 0.554 – – –

P(2) Probability of patient in POP2 – 0.446 17.7 – –

Res err Residual error – 18.4 % 3.10 – –

Cavg average T-DM1 concentration (mg/L) over dosing intervals, IIV interindividual variability, RSE relative standard error, T-DM1 trastuzumab

emtansinea The OMEGA SAME option was used for IIV on Kdeplete

596 Cancer Chemother Pharmacol (2012) 70:591–601

123

Fig. 3b. After stabilizing, the model predicts that patients

have a nondepletable PP (BASE1) and that platelet–time

profiles will have less amplitude of platelet drop and return to

baseline. This is also illustrated in Fig. 3b (patient ID = 223).

Covariate analysis

None of the available patient baseline covariates were

found to significantly correlate with any PD parameter.

Notably, because of eligibility criteria, patients in study

TDM4258g had received numerous prior treatments,

including known myelosuppressive agents. However,

patients with prior paclitaxel treatment (55 %), docetaxel

treatment (54 %), or carboplatin treatment (44 %) did not

have lower predose platelet counts (; BASE), higher drug

sensitivity (: Slope), or greater platelet–time profile

declines (: Kdeplete) compared with those who had not

received paclitaxel, docetaxel, or carboplatin.

500

300

0

100

0 100 200 300 400 500 600 700

DVIPREPRED

Time (days)

ID 120 - 1.2 mg/kg - qw

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tele

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nt (

• 10

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L)

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300

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100

0 20 40 60 80 100 120 140

DVIPREPRED

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ID 121 - 1.6 mg/kg - qw

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ID 147 - 2 mg/kg - qwP

late

let C

ount

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1000

/µL)

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300

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100

0 50 100 150

DVIPREPRED

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ID 152 - 2.4 mg/kg - qw

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300

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ID 114 - 1.2 mg/kg - q3w

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ID 131 - 4.8 mg/kg - q3w

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0 100 200 300 400

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ID 143 - 3.6 mg/kg - q3w

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tele

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tele

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nt (

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let C

ount

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/µL)

500

300

0

100

0 50 100 150 200

DVIPREPRED

Time (days)

ID 223 - 3.6 mg/kg - q3w

a

b

Fig. 3 Representative model

fits for data from patients treated

with T-DM1 either weekly

(a) or once every 3 weeks (b).

Vertical hashes indicate dosing

times. DV observed platelet

count, IPRE model-predicted

individual platelet count, PREDmodel-predicted population

platelet count, qw once weekly,

q3w once every 3 weeks

Cancer Chemother Pharmacol (2012) 70:591–601 597

123

Model diagnostics

The PK/PD model described all platelet profiles, which

included multiple dose levels and regimens, and no

indications of model misspecifications were evident from

standard model diagnostic plots (data not shown). The VPC

on the largest cohort of the modeling dataset (3.6 mg/kg

q3w) indicated that the model described the data well

(Fig. 2). A VPC was attempted for the 2.4 mg/kg qw

(n = 15), but the low number of patients caused inflation

of the variability and made the VPC noninformative.

However, the median line through the observations was

within the 50th percentile interval.

Representative model fits for individual patients are

shown for qw regimens (Fig. 3a) and q3w regimens

(Fig. 3b). Results of model simulations with the external

evaluation dataset are shown in Fig. 4. The simulations,

which used nominal dosing and time points, described the

platelet response for all patients and patient subpopulations

well. Figure 4a shows model simulations and patient data

for all patients. Figure 4b shows model simulations and

patient data of the Kdeplete,POP1 subgroup, that is, those with

stable platelet–time profiles. Figure 4c shows model sim-

ulations and patient data of the Kdeplete,POP2 subgroup, that

is, those with downward drifting platelet–time profiles.

Patient platelet responses were similar between the model-

building dataset (TDM3569g and TDM4258g) and the

evaluation dataset (TDM4374g). Given this similarity,

there is no need to update the model-building dataset with

TDM4374g data.

Predicted incidence of grade C3 TCP

Figure 5a shows box plots of the model-predicted proba-

bility of grade C3 TCP overlaid with observed probabilities

from the model-building and evaluation datasets. Box plots

are shown for data from all patients (All), and by quartiles

of patient baseline platelet count (lower 25, 25–75 %, and

upper 75 %). The model predicted the *8 % incidence

rate of grade C3 TCP for this patient population, as well as

the incidence rates, by quartiles, of baseline platelet count.

A higher incidence of grade C3 TCP occurs when baseline

platelet counts are B200 9 1,000/lL (i.e., in the lowest

quartile of baseline platelet counts, compared with the

entire patient dataset). This is illustrated by the correlation

between observed baseline platelet counts and observed

nadirs shown in Fig. 5b. The upper quartile of T-DM1

exposure metrics (i.e., Cmax and AUC) did not show such a

profoundly increased incidence of grade C3 TCP compared

with the entire patient dataset (data not shown).

Discussion

In most patients, T-DM1 q3w administration has been

associated with a predictable cyclic pattern of platelet

decline to nadir and return to baseline between doses. Platelet

500

200

100

50

20

0 50

95th-percentile confidence interval

Time (days)100 200

50th-percentile confidence interval

5th-percentile confidence interval

95th-percentile confidence interval

50th-percentile confidence interval

5th-percentile confidence interval

150

Pla

tele

t Cou

nt (

• 10

00/µ

L)

b

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200

100

50

20

0 50

Time (days)100 200150

Pla

tele

t Cou

nt (

• 10

00/µ

L)a

95th-percentile confidence interval

50th-percentile confidence interval

5th-percentile confidence interval

Evaluation dataset observationsMedian evaluation datasetMedian model-building dataset

500

200

100

50

20

Pla

tele

t Cou

nt (

• 10

00/µ

L)

c

0 50

Time (days)

100 200150

Fig. 4 Model-predicted 90 % window and observed platelet counts

versus time for the overall patient platelet response and stratified by

two patterns of platelet response; nominal time points and dosing

were used for the simulations: a overall platelet response, b platelet

count nadir followed by a return to baseline between treatment cycles,

c downward drift in nadir and postnadir counts over time. Gray lineindicates cutoff for grade 3 thrombocytopenia (50 9 1,000/lL)

598 Cancer Chemother Pharmacol (2012) 70:591–601

123

counts reach grade C3 TCP levels in only a minority of

patients (7–8 %) [3, 4, 15] and have not been associated with

serious hemorrhage. This cyclic pattern is similar to that of

some conventional chemotherapeutic agents, which are

often associated with myelosuppression [6]. In addition to

this acute drop in platelet count to nadir by day 8, which

appeared lowest during cycle 1, a slow downward drift in the

platelet–time profile was noted in some patients receiving

repeated doses of T-DM1. The mechanisms of patient

platelet response to T-DM1 are currently unknown.

The semimechanistic PK/PD model proposed by Friberg

et al. [6, 7] has been used extensively in drug development

to quantify and describe drug-induced myelosuppression

[8–12]. The term ‘‘semimechanistic’’ derives from com-

partments representative of bone marrow, blood cell mat-

uration, and circulating blood cells; typically, the model

incorporates a drug effect on the bone marrow progenitor

compartment, mimicking chemotherapeutic mechanisms of

toxicity. Using this model as a template, structural modi-

fications were incorporated to test hypotheses of the

potential mechanisms of T-DM1-mediated effects on

platelets and to test patient baseline characteristics as

covariates of PK/PD model parameters.

As an ADC, T-DM1 can be viewed as being composed

of multiple components with regard to PK drivers of tox-

icity or efficacy: (1) the ADC, T-DM1; (2) the parent

antibody, trastuzumab; and (3) the cytotoxic agent, DM1.

For PK/PD modeling, T-DM1 was used as the PK driver of

platelet response for several reasons. First, trastuzumab, as

a single agent, has not been associated with myelosup-

pressive effects [16]. Second, although there is little clin-

ical experience with DM1 administered as a free drug,

maytansine, the parent drug of DM1 and of similar struc-

ture, showed no substantial myelosuppressive effects in

phase I [17] and phase II [18] studies. Third, TCP has not

been reported as a significant toxicity with other DM1-

containing ADCs currently in clinical development [19–

21]. Preclinical investigations are ongoing to characterize

the effect of T-DM1 treatment on platelets and to confirm

the T-DM1 moiety responsible.

The data on platelet concentrations, obtained from

multiple dose levels and schedules of T-DM1, provided

variable T-DM1 concentration–time and platelet profiles.

These data, together with the proposed model, support a

linear effect of T-DM1 on the (nonobserved) platelet pro-

liferating pool in line with the observed delayed nadir on

day 8 relative to drug administration. Other hypotheses

of the relationship between T-DM1 concentrations and

platelet response, including a direct drug effect on the

(observed) PLT compartment, were also tested but were

not well supported by the modeling results. Incorporating

two separate Slope parameters best captured the observa-

tion that cycle 1 platelet nadirs are lower than in sub-

sequent cycles. This is an empirical modification of the

model that was justified by a highly significant improve-

ment in the model fit, which was confirmed with the

evaluation data set; the underlying mechanism is currently

unknown.

The downward drift in platelet–time profiles over time,

readily observable in some patients, was modeled using an

additional T-DM1-related effect. Although the true mech-

anism or moiety involved is unclear, the incorporation of a

T-DM1-related effect on the platelet baseline (BASE2) is

supported by the data. This phenomenon is not without

precedent, since patterns of the decline of platelet and

neutrophil counts over time have previously been described

as cumulative myelosuppression for cytotoxic drugs. In

patients with advanced breast cancer who received FLAC

(5-fluorouracil, leucovorin, doxorubicin, and cyclophos-

phamide) [22], cumulative TCP was the DLT. In work by

Maze et al. [23], the cumulative TCP with nitrosoureas

noted clinically was reproduced in a murine model and a

reduced number of primitive hematopoietic proliferation

cells was reported.

To model this downward drift in platelet–time profiles,

an additional mechanistic assumption was hypothesized

0.50

0.40

0.30

0.20

0.10

0.00

All <201

Simulation median ± SDModel datasetEvaluation dataset

Baseline Platelet Count (• 1000/µL)201–338 >338

Pro

babi

lity

of G

rade

≥3 T

CP

500

400

250200

140

100

70

50

35

2520

100 200

Cycle 1 nadirCycle 2,3 nadirs

Baseline Platelet Count (• 1000/µL)

300 400 500 600 700

Pla

tele

t Nad

ir C

ount

(•

1000

/µL)

a

b

Fig. 5 a Box plots are stratified by ‘‘All’’ baseline platelet counts and

by quartiles of observed baseline platelet counts (lower 25 %,

25–75 %, and upper 75 %). b Correlation is shown between the

observed baseline platelet count and platelet nadir at 3.6 mg/kg q3w.

SD standard deviation, TCP thrombocytopenia

Cancer Chemother Pharmacol (2012) 70:591–601 599

123

and incorporated into the structure of the model proposed

by Friberg et al. [6, 7]. The PP compartment was modeled

as two fractions, hypothesized as sensitive and nonsensitive

fractions of the platelet proliferation pool. In this model,

the sensitive lineage is affected by drug exposure and is

depleted with a time-dependent rate of decline. This long-

term drug effect on the baseline platelet count is modeled

on top of the acute drug effect that is hypothesized to cause

the oscillation in platelet count. Due to the slower nature of

the platelet profile decline, the average T-DM1 concen-

tration over dose intervals was used as a measure of drug

exposure rather than the T-DM1 concentration–time pro-

file, to drive this effect. However, the true PK driver is

unknown.

The structural model modifications captured not only the

rate of platelet profile decline and the new apparent steady-

state platelet baseline but also the reduction in the ampli-

tude of platelet decline and rebound, further supporting this

model structure. The mixture model implementation for the

parameter Kdeplete captured the apparent bimodal distribu-

tion of the observed platelet–time profile decline, with an

extremely slow decline in about 55 % of patients (POP1)

and a notable, highly variable decline in about 45 % of

patients (POP2), which typically stabilizes by week 24

(eight treatment cycle). Overall, 22 % of patients are pre-

dicted to have a more rapid platelet profile decline, which

stabilizes sooner than eight treatment cycles and which is

readily observed without modeling so long as there is

adequate platelet sampling. Although it was not possible to

correlate the two patient subgroups with any of the inves-

tigated covariates, the mixture model implementation

allowed us to adequately quantify the rate and extent of

decline as well as the proportion of patients likely to

experience such a decline.

Given these modeling results, and evidence in the lit-

erature for this phenomenon [22, 23], it is plausible that

T-DM1 affects a platelet proliferation lineage or cofactor;

when this lineage is depleted, the new platelet baseline is

derived from a lineage that is less sensitive to T-DM1. The

model structure predicts that patients’ PPs are not entirely

depleted, and, as shown in Fig. 4c, the nondepletable PP is

enough to maintain platelet count nadirs [80 9 1,000/lL,

above grade 3 TCP (50 9 1,000/lL) [24]. It should be

noted that this model is currently based on data from

patients receiving continuous T-DM1 treatment. It is

unknown how discontinuation of T-DM1 administration

affects platelet response(s).

To assess whether a patient’s platelet response to

T-DM1 (i.e., magnitude of platelet count drop to nadir or

downward drift in platelet–time profile) could be identified

a priori, a covariate analysis was performed using the final

model. Ultimately, no baseline demographic or patho-

physiologic characteristic was identified as a covariate

relating to platelet response, though some hypotheses were

ruled out. These included (1) baseline hepatic transaminase

(ALT/AST) levels on Kdeplete, with which patients with

compromised liver function may have had reduced

thrombopoietin or other cofactor(s) that would have

affected platelet regulation; (2) BASE on Slope, in which a

larger pool of baseline platelets may have correlated with

decreased drug effect; and (3) prior chemotherapies on

Slope or Kdeplete, in which myelosuppressive agents (e.g.,

paclitaxel, docetaxel, and carboplatin) taken before T-DM1

treatment may have exacerbated the platelet response to

T-DM1.

Although assessed covariates did not contribute to the

understanding of the mechanisms underlying platelet

responses, model predictions did provide useful informa-

tion regarding patient propensity for grade C3 TCP. Prior

to this modeling analysis, there was concern that patients

with greater T-DM1 exposure at studied doses might have

an increased likelihood of developing grade C3 TCP.

However, platelet observations and model simulations at

3.6 mg/kg q3w showed that patients with the greatest

(upper quartile) T-DM1 exposure (T-DM1 Cmax or T-DM1

AUC) did not have a markedly increased incidence of

grade C3 TCP compared with the entire population. Based

on the current dataset, only baseline platelet counts

B200 9 1,000/lL were associated with an increased risk

of grade C3 TCP, suggesting that these patients should be

carefully monitored.

The model also suggests that more frequent T-DM1

dosing can modulate toxicity. As shown in Fig. 3b

(ID = 143) and Fig. 3a (ID = 152), the population pre-

dicted platelet counts (PRED) returned to baseline prior to

the next T-DM1 dose with the 3.6 mg/kg q3w regimen,

while the dose frequency of the 2.4 mg/kg qw regimen

allowed only a partial return to baseline, though within the

normal range. For each of these cohorts, platelet nadirs

were approximately 150 9 1,000/lL, yet the 2.4 mg/kg qw

regimen resulted in roughly twice as much overall T-DM1

exposure. Given the potential for exposure-driven T-DM1

antitumor activity, a weekly T-DM1 treatment regimen

may be similarly beneficial to patients, without any clini-

cally significant exacerbations in TCP.

The population PK/PD model reported here describes

the clinical platelet response to T-DM1 well and can

potentially be used to predict the incidence of TCP, thereby

optimizing the safety of T-DM1. Utilizing structural model

modifications to the standard PK/PD myelosuppression

model [6, 7], our model suggests that (1) TCP is less

pronounced after the first T-DM1 dose, (2) platelet–time

profiles that drift slowly downward over time will even-

tually stabilize above grade 3 TCP, and (3) an individual

patient’s platelet response to T-DM1 cannot be predicted

a priori from any baseline characteristics tested. We

600 Cancer Chemother Pharmacol (2012) 70:591–601

123

conclude that T-DM1 3.6 mg/kg q3w is safe in patients with

HER2-positive breast cancer and necessitates minimal dose

delays and reductions due to clinically significant TCP.

Planned and ongoing clinical studies are evaluating T-DM1

in combination with various chemotherapeutic agents, some

of which demonstrate thrombocytopenic effects; the appli-

cation of this T-DM1 PK/PD model in the combination

therapy setting is currently under investigation.

Acknowledgments The study was funded by Genentech, Inc.

Support for third-party writing assistance was provided by Genentech,

Inc.

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