learning and confirming with preclinical studies: modeling

DMD #53926 1

Learning and Confirming with Preclinical Studies: Modeling and

Simulation in the Discovery of GDC-0917, an IAP Antagonist

Harvey Wong, Stephen E. Gould, Nageshwar Budha, Walter C. Darbonne, Edward E.

Kadel III, Hank La, Bruno Alicke, Jason S. Halladay, Rebecca Erickson, Chia Portera,

Anthony W. Tolcher, Jeffery. R. Infante, Michael Mamounas, John A. Flygare, Cornelis

E.C.A. Hop and Wayne J. Fairbrother.

Departments of Drug Metabolism and Pharmacokinetics (HW, HL, JSH, CECAH),

Translational Oncology (SEG, BA), Clinical Pharmacokinetics (NB), Oncology

Biomarkers (WCD, EEK), Exploratory Clinical Development (CP, MM), Safety

Assessment (RE), and Medicinal Chemistry (JAF), Early Discovery Biochemistry (WJF),

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

South Texas Accelerated Research Therapeutics, LLC., San Antonio, TX, USA (AWT)

Sarah Cannon Research Institute/Tennessee Oncology, Nashville, TN (JRI)

DMD Fast Forward. Published on September 16, 2013 as doi:10.1124/dmd.113.053926

Copyright 2013 by the American Society for Pharmacology and Experimental Therapeutics.

This article has not been copyedited and formatted. The final version may differ from this version.DMD Fast Forward. Published on September 16, 2013 as DOI: 10.1124/dmd.113.053926

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RUNNING TITLE: Preclinical Modeling and Simulation of GDC-0917.

Author to whom correspondence should be addressed:

Harvey Wong, PhD Drug Metabolism and Pharmacokinetics Genentech 1 DNA Way, MS 412a South San Francisco, CA, 94080 Phone: 650-225-5739 Fax: 650-467-3487 E-mail: [email protected]

Number of pages: 42

Number of Tables: 4

Number of Figures: 7

Number of References: 25

Number of Words in Abstract: 236

Introduction: 764

Discussion: 1338

List of abbreviations: AUC0-24: area under the concentration-time curve from zero to 24

hours; AUCinf :area under the concentration–time curve from zero to infinity; Cl: plasma

clearance; Cmax: highest observed plasma concentration; F: bioavailability; IV:

intravenous; I: cIAP level expressed as a percentage of the DMSO control; Iinitial: initial

value of I; Imax: the maximum value of I; IC50: GDC-0917 concentration where I is 50%

of Imax; kng: net growth rate constant; K: rate constant describing the anti-tumor effect of

GDC-0917; Kmax: maximum value of K; KC50: GDC-0917 concentration where K is

50% of Kmax; MRT: mean residence time; t1/2: half-life; PO: oral; tmax: time at which Cmax

occurred; Vss: volume of distribution at steady state.


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ABSTRACT

The application of modeling and simulation techniques is increasingly common in

preclinical stages of the drug development process. GDC-0917 [(S)-1-((S)-2-cyclohexyl-

2-((S)-2-(methylamino)propanamido)acetyl)-N-(2-(oxazol-2-yl)-4-phenylthiazol-5-

yl)pyrrolidine-2-carboxamide] is a potent second generation antagonist of inhibitor of

apoptosis (IAP) proteins being developed for the treatment of various cancers. GDC-

0917 has low to moderate clearance in mouse (12.0 ml/min/kg), rat (27.0 mL/min/kg) and

dog (15.3 mL/min/kg), and high clearance in monkey (67.6 mL/min/kg). Accordingly,

oral bioavailability was lowest in monkey compared to other species. Based on our

experience with a prototype molecule with similar structure, in vitro-in vivo extrapolation

was used to predict a moderate clearance (11.5 mL/min/kg) in human. Predicted human

volume of distribution was estimated using simple allometry at 6.69 L/kg. Translational

pharmacokinetic-pharmacodynamic (PK-PD) analysis using results from MDA-MB-231-

X1.1 breast cancer xenograft studies and predicted human pharmacokinetics suggests that

ED50 and ED90 targets can be achieved in humans using acceptable doses (72 mg and 660

mg, respectively), and under an acceptable timeframe. The relationship between GDC-

0917 concentrations and pharmacodynamic response (cIAP1 degradation) was

characterized using an in vitro PBMC immunoassay. Simulations of human GDC-0917

plasma concentration-time profile and cIAP1 degradation at the 5 mg starting dose in the

Phase 1 clinical trial agreed well with observations. This work shows the importance of

leveraging information from prototype molecules, and illustrates how modeling and

simulation can be used to add value to preclinical studies in the early stages of the drug

development process.


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INTRODUCTION

Inhibitor of apoptosis (IAP) proteins are involved in the regulation of programmed cell

death or apoptosis and were first identified in baculoviruses where they serve to suppress

host cell death responses during viral infection (Flygare and Fairbrother 2010,

Varfolomeev and Vucic 2011). IAP proteins suppress apoptosis, in part, by inhibiting

activated cytosolic cysteine/aspartate-specific proteases (caspases) that are critical for its

execution (Flygare and Fairbrother 2010, Varfolomeev and Vucic 2011). The

mammalian family of IAP proteins includes X-linked IAP (XIAP), cellular IAP 1 and 2

(cIAP1 and cIAP2), and melanoma IAP (ML-IAP). Of these, only XIAP directly inhibits

effector caspases-3 and-7, which execute the apoptotic death program. XIAP also

indirectly inhibits effector-caspase activity via inhibition of the initiator caspase-9. ML-

IAP and cIAPs directly antagonize second mitochondria-derived activator of

caspase/direct IAP-binding protein with low pI (Smac/DIABLO), an endogenous IAP

antagonist that acts by blocking XIAP-mediated inhibition of caspases. The cIAP

proteins have an additional role of preventing TNF-α-mediated activation of initiator

caspase-8, thereby stopping downstream activation of effector caspases-3,-7. Finally, the

cIAP proteins positively regulate canonical- and negatively regulate non-canonical-NF-

κB signaling and induction of NF-κB target genes, including TNF-α. Frequently, IAP

proteins are over expressed in cancer cells where they act as regulators of cancer cell

survival and are often indicators of poor prognosis (Vucic and Fairbrother, 2007).

Inhibition of IAP proteins has been shown to sensitize cancer cells to pro-apoptotic anti-

cancer agents. Therefore, the development of IAP antagonists as potential anti-cancer

agents has been of interest.


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Mathematical modeling and simulation, and the “learn-and-confirm paradigm”,

(Sheiner, 1997) have been applied widely in later stages of the drug development process,

largely with data from clinical trials (Chien et al., 2005). The “learn-and-confirm

paradigm” involves the use of mathematical models to integrate information and “learn”

about a drug candidate, but at the same time continuously “confirm” model assumptions

as new information is generated during the course of the drug development process. The

net result of successive iterations of “learning” and “confirming” is an overall better

understanding of a drug candidate’s pharmacological behavior. The application of

modeling and simulation techniques has been rapidly increasing in the preclinical drug

discovery setting as a means to reduce drug candidate attrition due to a lack of clinical

efficacy. In particular translational pharmacokinetic-pharmacodynamic (PK-PD) analysis

has been used to help interpret and translate the results of preclinical studies (Salphati et

al., 2010, Liu et al., 2011, Wong et al., 2012b, Wong et al., 2012c, Yamazaki et al.,

2012). PK-PD modeling involves the use of differential equations to quantitatively

describe drug behavior in the biological system of interest. This type of analysis is

particularly helpful in understanding concentration effect relationships where there are

apparent disconnects in drug concentration and effect related to temporal delays in drug

action. In addition, mechanism-based PK-PD models allow for known species

differences in factors such as pharmacokinetics or in vivo drug potency to be

quantitatively assessed as well as enabling a more robust characterization of

concentration-effect relationships from frequently variable in vivo preclinical efficacy

data. Finally, establishment of PK-PD models enables better integration of available data

and prospective simulations.


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We have previously reported on the biological characteristics, including the

preclinical and clinical pharmacokinetics, of GDC-0152 a potent pan-selective antagonist

of IAP proteins (Figure 1; Flygare et al., 2012). GDC-0917 [(S)-1-((S)-2-cyclohexyl-2-

((S)-2-(methylamino)propanamido)acetyl)-N-(2-(oxazol-2-yl)-4-phenylthiazol-5-

yl)pyrrolidine-2-carboxamide] is a potent second generation pan-selective IAP antagonist

with binding affinities to the BIR3 domains of X-IAP and cIAP1/2, and the BIR domain

of ML-IAP in the low nanomolar range (Ki < 60 nM) using a fluorescence polarization-

based competition assay. The previously undisclosed structure of GDC-0917 is shown in

Figure 1. GDC-0917 caused a decrease in cell viability in MDA-MB-231 human breast

cancer cells while having no effect on normal human mammary epithelial cells. In

contrast to GDC-0152, which was administered intravenously, the intended route of

administration of GDC-0917 is oral. The objective of the current study was to

characterize the preclinical pharmacokinetic properties of GDC-0917. We show how

learnings from a prototype molecule, GDC-0152, can be applied to a second-generation

molecule, GDC-0917. In particular, we show how our prior knowledge of the disposition

characteristics of GDC-0152 was leveraged in order to predict human pharmacokinetics

for GDC-0917. Furthermore, prospective human simulations were performed in order to

determine the number of patient cohorts required to achieve predicted human efficacious

doses as a means to assess the feasibility of the human clinical trial. The current study

serves as an illustration of how mathematical modeling and simulation techniques can be

used to provide an integrated preclinical assessment of a drug candidate and aid in the

translation of preclinical data to the clinic.


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MATERIALS AND METHODS

PRECLINICAL GDC-0917 IN VIVO PHARMACOKINETIC (PK) STUDIES IN

RAT, MOUSE, DOG AND MONKEY

The preclinical in vivo studies described in this manuscript were conducted according to

protocols approved by the IACUC at Genentech (South San Francisco, CA, USA).

Rat PK study: Three male Sprague-Dawley rats (Charles River Laboratories, Inc.,

Hollister, CA, USA) were given an intravenous (IV) dose of 1 mg/kg (free-base

equivalent) of GDC-0917 trifluoacetate salt (Genentech, Inc., South San Francisco, CA,

USA) in phosphate buffered saline, via femoral vein cannulae. An additional three male

rats were given a single oral (PO) dose of 5 mg/kg (free-base equivalent) of GDC-0917

trifluoacetate salt in phosphate buffered saline. At the initiation of the study, the rats

weighed from 284 to 306 g. Blood samples (~0.2 mL per sample) were collected from

each animal via jugular vein cannulae at the following timepoints: predose, 0.033, 0.083,

0.25, 0.5, 1, 2, 4, 8, and 24 hours postdose. All samples were collected into tubes

containing potassium ethylenediaminetetraacetic acid (EDTA) as an anticoagulant.

Blood samples were centrifuged within 30 minutes of collection and plasma was

harvested. Plasma samples were stored at approximately –70oC until analysis for GDC-

0917 concentrations by a liquid chromatographic-tandem mass spectrometric

(LC/MS/MS) assay method.

Mouse PK Study: Twenty-seven female SCID.bg mice (Charles River Laboratories,

Hollister, CA, USA) were given an IV dose of 1 mg/kg of GDC-0917 in 15%

hydroxypropyl-β-cyclodextrin, 20 mM succinic acid in water. An additional 27 female

SCID.bg mice were given a single PO dose of 3 mg/kg of GDC-0917 in 0.5% w/v


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methylcellulose with 0.2% v/v Tween 80. Mice weighed from 17 to 20 g at the initiation

of the study. Approximately 0.2 mL of blood was collected from each mouse by terminal

cardiac puncture. Blood samples (n = 3 mice per timepoint) were taken from

intravenously dosed mice at the following timepoints: predose; 0.033, 0.17, 0.5, 1, 3, 6,

9, and 24 hours postdose. Blood samples were collected from orally dosed mice at the

following timepoints (n = 3 mice per timepoint): predose; 0.083, 0.25, 0.5, 1, 3, 6, 9, and

24 hours postdose. All blood samples were collected into tubes containing EDTA as an

anticoagulant. Blood samples were centrifuged within 30 minutes of collection and

plasma was harvested. Plasma samples were stored at approximately –70oC until

analysis for GDC-0917 by LC/MS/MS.

Dog PK Study: Three male beagle dogs (Covance, Cumberland, VA, USA) were given a

single IV dose of 1 mg/kg of GDC-0917 in 15% hydroxypropyl-β-cyclodextrin, 20 mM

succinic acid in water. An additional three male beagle dogs (Marshall Bioresources,

Beijing, China) were administered a PO dose of 1 mg/kg of GDC-0917 in 0.5% w/v

methylcellulose with 0.2% v/v Tween 80. At the initiation of this study, dogs weighed

from 9.2 to 12.5 kg. Blood samples (~3 mL per sample) were collected from the

peripheral vein of IV and PO animals at the following timepoints: predose, 0.033, 0.083,

0.25, 0.5, 1, 3, 6, 9, and 24 hours post-dose. All blood samples were collected in tubes

containing EDTA. Blood samples were centrifuged and plasma was harvested within 1

hour of collection. Urine was collected from PO dogs into containers on wet ice during

the following intervals: predose and from 0-8 and 8-24 hours postdose. Plasma and

urine samples were stored at approximately −70°C until analysis for GDC-0917 by

LC/MS/MS.


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Monkey PK Study: Three male cynomolgus monkeys (Covance Laboratories, Inc.,

Madison, WI, USA) were given a single IV dose of 1 mg/kg GDC-0917 in 15%

hydroxypropyl-β-cyclodextrin, 20 mM succinic acid in water via a saphenous vein. An

additional three male monkeys were given a PO dose of 2 mg/kg GDC-0917 in 0.5% w/v

methylcellulose with 0.2% v/v Tween 80. At the initiation of this study, monkeys

weighed from 2.1 to 2.6 kg. Blood samples (~1 mL per sample) were collected from the

femoral vein of each animal at the following timepoints after IV dosing: predose, 0.033,

0.083, 0.25, 0.5, 1, 2, 4, 8, 12, and 24 hours postdose. Following PO dosing, blood

samples were collected at the following timepoints: predose, 0.083, 0.25, 0.5, 1, 2, 4, 8,

12, and 24 hours postdose. All blood samples were collected into tubes containing

EDTA. Blood samples were centrifuged within 1 hour of collection and plasma

harvested. Urine was collected from monkeys receiving the IV dose at predose and from

0-8 and 8-24 hours postdose in plastic containers surrounded by wet ice. Plasma and

urine samples were stored at approximately −70°C until analysis for GDC-0917 by

LC/MS/MS.

HUMAN PHARMACOKINETIC STUDY WITH GDC-0917

The human pharmacokinetic parameters were determined using data from a Phase 1 dose

escalation study using a modified continual reassessment method design in cancer

patients. Institutional Review Boards at participating institutions approved the protocol

for the Phase 1 study. Written informed consent was obtained from all trial participants.

Briefly, doses ranging from 5 to 600 mg of GDC-0917 were administered orally as a

single dose to cancer patients. Following a 3 day washout period, GDC-0917 was


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administered daily for 14 days. Plasma samples were collected at predetermined time

points after the first dose (0, 0.5, 1, 2, 3, 6, 9, 24, 48, and 72 hours) and the last dose (0,

0.5, 1, 2, 3, 6, and 24 hours) for characterization of single-dose and steady state

pharmacokinetics. Plasma samples were stored at approximately −70oC until analysis for

GDC-0917 concentrations by LC/MS/MS. Arithmetic mean concentration-time data are

presented for three Caucasian female patients following a single oral 5 mg dose of GDC-

0917. The three patients ranged from 36 to 56 years of age and their weight ranged from

66 to 99 kg.

Blood was collected for pharmacodynamics marker (cIAP1) measurement at

predose and 6 hours postdose following a single oral dose of GDC-0917. cIAP1 protein

concentrations in patient samples were determined using a cIAP1 immunoassay in

peripheral blood mononuclear cells as described below. cIAP1 levels in patient samples

taken at 6 hour postdose are expressed as a percentage of predose cIAP1 levels.

PHARMACOKINETIC ANALYSIS

All pharmacokinetic parameters were calculated by non-compartmental methods as

described in Gibaldi and Perrier (1982) using WinNonlin® version 3.2

(Pharsight Corporation; Mountain View, CA, USA). Parameters are presented as a mean

± standard deviation.

PREDICTION OF VOLUME OF DISTRIBUTION BY ALLOMETRIC SCALING

Simple allometric scaling was used to predict human volume of distribution. In vivo

volume of distribution at steady-state (Vss) estimates from preclinical pharmacokinetic


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studies were fit to the following log transformed form of the allometric equation

(Boxenbaum, 1984) by linear regression:

log(Y ) = log(a)+ b log(W )

where Y is the volume of distribution, a is the coefficient of the allometric equation, W is

the body weight, and b is the allometric exponent. Predicted human volume of

distribution was extrapolated using the fitted line and an assumed body weight of 70 kg.

PREDICTION OF HEPATIC CLEARANCE USING METABOLIC STABILITY

STUDIES IN HEPATOCYTES

The metabolic stability of GDC-0917 (1 µM) was determined in pooled (n = 5-10)

human, rat, and dog cryopreserved primary hepatocytes (CellzDirect Invitrogen

Corporation, Durham, NC, USA). Incubations (final incubation volume 250 µL) were

performed at 37oC under an atmosphere of 95/5 CO2/O2 using 0.5 million cells/mL in

hepatocyte incubation media (In Vitro Technologies, Baltimore, MD, USA). 50 µL

aliquots (cells and media) were taken at 0, 1, 2 and 3 hour following the start of the

incubation and quenched using 100 µL ice cold acetonitrile. The resulting samples were

centrifuged at 3000 g for 10 min. Supernatant (90 µL) was diluted in water (180 µL)

and % remaining of GDC-0917 was assessed by LC/MS/MS. All incubations were

performed in triplicate. Intrinsic clearance was estimated from the % GDC-0917

remaining versus time plot using standard methods described by Obach et al (1997).

Equation 1


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Intrinsic clearance was scaled to hepatic clearance using the “well-stirred” model by

standard methods (Khojasteh et al., 2011).

PREDICTION OF HALF-LIFE

Human half-life (t1/2) was predicted using the following equation:

693.02/1 ×=CL

Vt ss

where CL is the predicted human hepatic clearance based on in vitro-in vivo extrapolation

of hepatocyte metabolic stability studies with GDC-0917 and Vss is the predicted human

volume of distribution using allometry (see above).

ANTI-TUMOR EFFICACY STUDIES IN MDA-MB-231-X1.1 XENOGRAFT

MICE

Briefly, 10 million MDA-MB-231-X1.1 breast adenocarcinoma cells resuspended in

Hank’s Balanced Salt Solution and Matrigel (1:1, v/v) were implanted subcutaneously

into the upper right flank of female SCID.bg mice (Charles River Laboratories, Hollister,

CA, USA). MDA-MB-231-X1.1 cells are MDA-MB-231 cells (originally from ATCC,

Manassas, VA, USA) that were selected for improved in vivo growth rates. When tumor

volumes reached approximately 100-300 mm3, mice were assigned to treatment groups to

get a similar mean tumor size for each treatment group. Treatment groups (n = 5 per

group) were administered once daily oral doses of vehicle (15%

hydroxypropyl-β-cyclodextrin, 20 mM succinic acid in water), 0.08, 0.17, 0.34, 0.68,

Equation 2


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1.36, 2.72, 5.43, 10.87 or 16.30 mg/kg of GDC-0917 for 21 days. Tumor volumes were

measured in two dimensions (length and width) using Ultra Cal IV calipers (Model 54 10

111, Fred V. Fowler Company, Inc., Newton, MA). The following formula was used

with Excel v11.2 (Microsoft Corporation, Redmond, WA) to calculate tumor volume

(TV): TV (mm3) = (length × width2) × 0.5. Tumor sizes and body weights were recorded

twice weekly, and the mice were regularly observed over the course of the study. Mice

were euthanized if their tumor volume exceeded 2000 mm3 or if their body weight

dropped by more than 20% of the starting weight.

A satellite group of female SCID.bg mice were given single oral doses of 0.5 to

20 mg/kg of GDC-0917 in 15% hydroxypropyl-β-cyclodextrin, 20 mM succinic acid in

water to provide an assessment of the oral pharmacokinetics of GDC-0917 to support the

xenograft efficacy study. Blood was collected at 0.08, 0.25, 0.5, 1, 3, 6, 9, and 24 hours

postdose and plasma was harvested within 1 hour of blood collection. Plasma samples

were frozen at approximately −80oC until analysis for GDC-0917 concentrations using

LC/MS/MS.

PHARMACOKINETIC-PHARMACODYNAMIC (PK-PD) MODELING

PK-PD modeling was performed using SAAM II (Saam Institute, University of

Washington, Seattle, WA).

Supporting Mouse Pharmacokinetic Studies for PK-PD Modeling:

Plasma concentration-time data from female SCID.bg mice given 0.5 to 20 mg/kg

GDC-0917 were simultaneously fitted to a one-compartment model with oral absorption.


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The pharmacokinetics of GDC-0917 appeared linear over the dose range tested. Mean

estimates of the absorption rate constant (ka), the elimination rate constant (ke), and the

apparent volume of distribution (V/F) from all dose groups were used to simulate

GDC-0917 concentrations in the modeling of the MDA-MB-231-X1.1 xenograft efficacy

studies.

MDA-MB-231.X1.1 Breast Cancer Xenograft Efficacy Studies:

MDA-MB-231.X1.1 xenograft efficacy studies were fitted to an indirect response model

(Mager et al., 2003) as described by the following differential equation.

d(TV )

dt= kng(TV ) − K(TV ) (Equation 3)

where K = Kmax ×C

KC50 + C

TV (mm3) is defined as the tumor volume, t (hr) is time, kng (hr-1) is the net growth rate

constant, K (hr-1) is the rate constant describing the anti-tumor effect of GDC-0917, Kmax

(hr-1) is the maximum value of K, C (µM) is the concentration of GDC-0917, and KC50

(µM) is the GDC-0917 concentration where K is 50% of Kmax. Concentrations of

GDC-0917 in mice were simulated based upon the pharmacokinetic parameters obtained

from the supporting mouse pharmacokinetics studies described above. Mean tumor

volumes for each dose group were used for fitting. Pharmacodynamic parameters are

presented as the estimate followed by the coefficient of variation (CV) in parentheses.


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cIAP IMMUNOASSAY IN PERIPHERAL BLOOD MONONUCLEAR CELLS

(PBMCs)

The purpose of this study was to establish a cIAP1 immunoassay in peripheral blood

mononuclear cells (PBMCs) for use in the clinical trial. Whole blood was collected in

eight 8 mL cell preparation tubes (CPT; Becton Dickinson, Franklin Lakes, NJ, USA)

containing sodium heparin from each donor. Blood was pooled and transferred to 14 mL

tubes and incubated in the dark at room temperature for ~ 16 hours in the presence of

vehicle (DMSO (dimethylsulfoxide)) or 0.0001 to 10 µM of GDC-0917. Following

incubation, each blood sample was transferred back into a CPT tube and PBMCs were

harvested from blood after centrifugation as per manufacturer’s instructions and washed

in cold phosphate buffered saline. Next, resulting PBMC pellets were lysed in 100 µL 1×

Cell Extraction Buffer (Invitrogen, Grand Island, NY, USA) containing protease

inhibitors (Roche, Basel, Switzerland). Lysates were transferred to 1.5 mL microfuge

tubes and centrifuged at 13,000 × g for ten minutes at 4°C. Total cellular protein

concentration for each PBMC lysate was determined using a micro-bicinchoninic acid

assay (Pierce Biotechnology, Rockford, lL, USA). cIAP1 concentration was determined

using an internally-developed qualified cIAP1 immunoassay described as follows.

The cIAP1 assay involved incubation of the PBMC sample for ~ 16 hours with a

biotinylated goat anti-human cIAP1 polyclonal antibody (R&D Systems, Minneapolis,

MN, USA), and a ruthenium-labeled rat anti-human cIAP1 monoclonal antibody (Enzo

Life Sciences, Farmingdale, NY, USA) in assay diluent. Strepavidin-coated plates were

blocked for non-specific binding using 3 % bovine serum albumin in wash buffer (0.05%

polysorbate-20 in phosphate buffered saline) for ~16 hours at 4°C. The blocked plate was


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washed three times with 200 µL assay wash buffer. The sample/antibody mixture was

incubated with the blocked immunoassay plate for 90 minutes at room temperature. The

assay plate was again washed three times in assay wash buffer prior to addition of a

chemiluminescent substrate reactive with the ruthenium-labeled secondary anti-cIAP1

antibody. cIAP1 concentration in test samples was determined from a four-parameter

regression analysis of a standard curve based on chemiluminescence signal in the

immunoassay. cIAP1 levels in samples are expressed in this manuscript as a percentage

of the DMSO control.

The relationship between GDC-0917 concentrations and cIAP1 decrease was

characterized by estimation of pharmacodynamic parameters obtained by fitting an

inhibitory Imax model to the data using GraphPad Prism V4.02 (GraphPad Software Inc.,

San Diego, CA) based on the following equation.

I = Iinitial − Imax ×C

IC50 +C

⎛

⎝⎜

⎞

⎠⎟ Equation 4

where I (%) is the cIAP1 level expressed as a percentage of the DMSO control, Iinitial (%)

is the initial value of I, Imax (%) is the maximum value of I, IC50 (µM) is the concentration

of GDC-0917 where I is 50% of Imax, and C (µM) is the GDC-0917 concentration.

HUMAN SIMULATIONS

Simulations of human pharmacokinetics were performed using a one-compartment model

with oral absorption. Parameters used for human simulations were derived from

predicted human hepatic CL from hepatocyte metabolic stability studies, and predicted


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human volume of distribution from simple allometry. The absorption rate constant

(1.72 hr-1) used for human simulations was obtained from modeling the dog oral PK

concentration-time data using a one compartment model with oral absorption.

Bioavailability was assumed to be 1-E where E is the hepatic extraction ratio determined

with hepatocyte data using standard methods (Khojasteh et al., 2011).

The predicted human doses required for 50% (ED50) and 90% (ED90) inhibition of

tumor growth were determined using Equation 3 and pharmacodynamic parameters

derived from MDA-MB-231-X1.1 xenograft efficacy studies. Concentrations of GDC-

0917 driving the response for Equation 3 were simulated using a one compartment model

with oral absorption and the predicted human pharmacokinetic parameters described in

the previous paragraph. Simulations were performed to the last day of dosing in the

xenograft study (Day 21) which was the timepoint at which inhibition of tumor growth

was assessed. We chose not to correct for potential differences in plasma protein binding

between the mouse and human in these simulations as definitive plasma protein binding

studies were not yet available when this analysis was performed. In addition, preliminary

plasma protein binding data suggested that unbound fraction could be approximately

three-fold higher in humans (15% unbound) compared to mouse (5% unbound).

Therefore, not correcting for potential species differences in plasma protein binding

would be considered the most conservative estimate of ED50 and ED90 doses in human.

Additional simulations were performed at the starting dose of 5 mg prior to the

start of the Phase 1 clinical trial. These simulations were performed using Equation 4 and

the associated pharmacodynamics parameters estimated from the in vitro cIAP

experiment with GDC-0917 in PBMCs. GDC-0917 concentrations driving the response


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in Equation 4 for this simulation were generated using a one compartment model with

oral absorption and the predicted human PK parameters for GDC-0917. The purpose of

these simulations was to predict both the anticipated GDC-0917 concentration-time

profile, and the degree of pharmacodynamic marker (cIAP1) modulation that would be

observed in the first cohort of patients. Finally, an assessment of the number of cohorts

required to achieve human ED50 and ED90 doses was performed in order to provide an

early assessment of clinical trial feasibility and duration.


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RESULTS

Pharmacokinetics of GDC-0917 in rat, mouse, dog and monkey.

Pharmacokinetics studies were conducted in rat, mouse, dog, and cynomolgus monkey in

order to characterize GDC-0917 pharmacokinetics in preclinical species. Figure 2 shows

semilog plots of GDC-0917 plasma concentration versus time for mouse, rat, dog and

monkey following intravenous and oral administration. Derived pharmacokinetic

parameters for each species are presented in Table 1. Since GDC-0917 blood-plasma

ratio was approximately 1 (0.9-1.0) in all species tested, in vivo plasma clearance was

considered equivalent to hepatic clearance. GDC-0917 has low plasma clearance (~13%

of hepatic blood flow; Davis and Morris, 1993) in mice, at approximately 12 mL/min/kg.

It has moderate plasma clearance (~50% of hepatic blood flow) in rats and dogs, at 27

and 15.3 mL/min/kg, respectively. In monkeys, GDC-0917 has high plasma clearance of

67.6 mL/min/kg (~ 160% of hepatic blood flow). Terminal half-life (t1/2) ranged from

0.825 hour in monkeys to 6.12 hours in dogs. The volume of distribution at steady state

(Vss) was low to moderate in all species evaluated. With the exception of monkeys where

oral bioavailability was 16.8%, oral bioavailability was high, ranging from 77.0% in rats

to 97.0% in dogs. Renal clearance of GDC-0917 was assessed in dog and monkey and

found to be negligible, accounting for <1% and approximately 2% of the plasma

clearance, respectively.

Predicted Human Clearance, Volume of Distribution, and Half-life of GDC-0917


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Hepatocyte metabolic stability studies were conducted in human, rat and dog hepatocytes

in order to predict hepatic clearance in human, and to verify that an in vitro to in vivo

correlation exists in rat and dog. Table 2 presents predicted hepatic clearance based on

hepatocyte studies for rat, dog and human along with the observed in vivo plasma

clearance in rat and dog. Predicted hepatic clearance for rat and dog were within 2-fold

of the observed in vivo plasma clearance for both species. Human hepatic clearance was

predicted to be moderate at 11.5 mL/min/kg and this value was used for subsequent

human predictions and simulations.

Simple allometric scaling was performed to provide a predicted human volume of

distribution of GDC-0917 (Figure 3). Estimates of volume of distribution from mouse,

rat, dog and monkey (Table 1) were used to predict a human volume of distribution of

6.69 L/kg (Figure 3). The predicted t1/2 in human based upon the predicted volume of

distribution and clearance was 6.70 hours.

MDA-MB-231-X1.1 Breast Cancer Xenograft Efficacy Studies

Anti-tumor efficacy studies were performed in mice xenografted with MDA-MB-231-

X1.1 breast cancer in order to both demonstrate and characterize the anti-tumor activity

of GDC-0917. Figure 4A presents a tumor volume versus time profile for a range of

daily oral doses of GDC-0917 (0.08 to 16.3 mg/kg) given for 21 consecutive days.

GDC-0917 anti-tumor activity was dose dependent with slight tumor regression observed

at doses > 5.43 mg/kg. GDC-0917 was well tolerated with all dose groups experiencing

less than an 11% decrease in mean body weight.


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Xenograft PK-PD Modeling and Simulation

Supporting mouse pharmacokinetic studies were conducted to provide PK information

for PK-PD modeling of xenograft efficacy studies. Estimates of GDC-0917

pharmacokinetic parameters in mouse following oral administration of 0.5, 3, 6, and

20 mg/kg in 15% hydroxypropyl-β-cyclodextrin, 20 mM succinic acid in water are as

follows: ka = 17.6 hr-1, ke = 0.19 hr-1, and V/F = 1.74 L/kg. These PK parameters were

used to simulate GDC-0917 concentrations when fitting tumor volume data from the

xenograft efficacy study since it was not possible to collect blood from xenografted mice

that were on study.

Figure 4B compares observed and predicted tumor volumes following

simultaneous fitting of all MDA-MB-231-X1.1 tumor data to an indirect response model

(Equation 3). In general, the indirect response model was able to adequately characterize

the PK-PD relationship between GDC-0917 plasma concentrations and tumor volume

growth inhibition. Estimated in vivo GDC-0917 pharmacodynamic parameters

characterizing MDA-MB-231-X1.1 tumor growth and inhibition are presented in Table 3.

Human Simulations Determining ED50 and ED90 Doses and Clinical Trial Feasibility

Human simulations were performed in order to predict GDC-0917 doses needed to

achieve the ED50 and ED90 endpoints based on MDA-MB-231-X1.1 xenograft efficacy

studies. These simulations were performed using predicted human pharmacokinetics

described above and the pharmacodynamics parameters presented in Table 3. In

addition, the minimum number of cohorts required to reach the predicted ED50 and ED90

was assessed assuming a starting dose of 5 mg (derived from preclinical safety studies).


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Simulation results and predicted human ED50 and ED90 doses are shown in Table 3 and

Figure 5. ED50 and ED90 doses in human were predicted to be 72 mg and 660 mg,

respectively. The predicted human concentration-time profile of the ED50 and ED90

doses and their associated AUC0-24 are presented in Figure 5B and Table 3, respectively.

Based on a tradition phase 1 oncology clinical trial design (Green, et al., 2003), and

assuming no occurrence of dose limiting toxicities, a doubling of the GDC-0917 dose

with each cohort, and linear pharmacokinetics we anticipate to reach ED50 (72 mg) and

ED90 (660 mg) doses by cohorts 5 (80 mg) and cohort 8 (640 mg) (Figure 5C). Overall

these prospective simulations suggest that ED50 and ED90 are achievable in patients with

acceptable doses and within an acceptable number of cohorts.

cIAP1 Immunoassay in PBMCs

A cIAP1 immunoassay in PBMCs was established and tested in vitro prior to the

initiation of human clinical trials. Figure 6A presents percent decreases of cIAP1 relative

to a DMSO control caused by GDC-0917 added to whole blood at concentrations ranging

from 0.0001 to 10 µM. GDC-0917 induced reduction of cIAP1 levels in PBMCs in a

concentration dependent manner showing greater than 80% inhibition at concentrations

greater than 0.1 µM (56.5 ng/mL) (Figure 6A). Following fitting of the cIAP1 and GDC-

0917 concentration data to an inhibitory Imax model (Equation 4), pharmacodynamic

parameters were estimated and are presented in Figure 6B. Since the cIAP1

immunoassay involves the addition of GDC-0917 to whole blood prior to isolation of

PBMCs, these PD parameters reflect PD response to total blood concentrations rather

than unbound.


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Human Simulation of 5 mg Phase I Starting Dose

Simulations were performed prior to the start of the Phase 1 clinical trial in order to

predict both the anticipated GDC-0917 plasma concentration-time profile and the degree

of cIAP1 modulation following the administration of a 5 mg starting dose to patients.

These simulations used the predicted human PK and the PD parameters from the cIAP1

immunoassay in PBMCs (see Figure 6B). As shown in Figure 7A and Table 4, our

prediction of human pharmacokinetics at the 5 mg dose of GDC-0917 agreed well with

previously unpublished observations from the first cohort. Figure 7B presents a

simulated cIAP1 response over a 24 hour period following administration of the 5 mg

starting dose. Predicted cIAP1 levels at 6 hours postdose was approximately 32% of

predose level (Figure 7B). A mean cIAP1 decrease determined from two patient samples

collected at 6 hours postdose was comparable to the prediction, being 39% of predose

levels (Figure 7B).


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DISCUSSION

The preclinical pharmacokinetics and drug metabolism properties of a drug candidate

often serve as an early assessment of its potential to succeed as a drug. However, the

amount of information available in the early stages of the drug development process is

often limited as preclinical studies are, out of necessity, performed in a staged format.

Furthermore, the ability to translate preclinical findings to human continues to remain a

challenge. GDC-0917 is a second-generation IAP antagonist that offers the advantage of

oral dosing over our prototype molecule, GDC-0152, which was administered

intravenously, once every two weeks. Also, GDC-0917 preclinical toxicology studies

enabled continuous daily dosing allowing for a more flexible choice of dosing regimen in

the clinic. Previously, we described the application of a toxicokinetic-toxicodynamic

(TK-TD) analysis of GDC-0152 rat and dog toxicology studies (Wong et al., 2012a). We

learned from this analysis that dogs were much more sensitive to IAP antagonists than

rats, and confirmed a lower sensitivity in humans using clinical data. The described

analysis de-risked preclinical toxicity concerns around IAP proteins as drug targets

thereby enabling the advancement of a second-generation IAP antagonist. The current

work describes the integration of preclinical modeling and simulation and the “learning-

and-confirm” paradigm in the drug discovery process. We describe how we leveraged

our understanding of GDC-0152 to predict human pharmacokinetics for GDC-0917. In

addition, we show how preclinical modeling and simulation was used in the selection of

GDC-0917 as a clinical candidate.

Based on previous work with GDC-0152 (Flygare et al., 2012; Yue et al., 2013),

we anticipated that the structurally similar GDC-0917 would be eliminated mostly by


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hepatic metabolism and that renal elimination of the compound would be limited. The

low estimated renal clearance of GDC-0917 in dog and monkey (Table 1) is consistent

with these expectations. GDC-0152 human clearance was predicted using both simple

allometry and in vitro-in vivo extrapolation (Flygare et al., 2012). Both methods gave

similar estimates of human clearance (9.6 mL/min/kg [allometry] and 10 mL/min/kg [in

vitro-in vivo extrapolation]) and were successful in predicting observed clearance in

patients (9 mL/min/kg). GDC-0152 volume of distribution was predicted using simple

allometry (1.2 L/kg) and again the prediction matched well with clinical observations (0.6

L/kg) being within an acceptable 2-fold of the observed value (Flygare et al., 2012).

Allometric methods for prediction of human clearance assume a relationship

between clearance and body-weight. For compounds where clearance is due to

elimination dictated by physiological processes such as blood flow or filtration,

allometric methods work reasonably well (Lin, 1998). Therefore, for high clearance

compounds where clearance is blood flow-dependent, and compounds that are cleared

primarily via the urinary route, allometry is anticipated to perform reasonably well.

However, the rationale of using allometric scaling for human PK prediction has been

controversial (Bonate and Howard, 2000) and a known shortcoming of using in vivo

allometric methods is when there are known species differences in metabolism. As such,

we consciously decided to use in vitro-in vivo extrapolation of metabolic stability data

from human hepatocytes to predict human clearance for GDC-0917, providing that a

reasonable in vitro to in vivo correlation could be established in preclinical species.

GDC-0917 showed good preclinical pharmacokinetic properties having low to

moderate plasma clearance in all species with the exception of the monkey which


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exhibited high clearance. The two primary preclinical species used to screen

pharmacokinetics during compound optimization were the rat and dog. Therefore, early

on, we performed metabolic stability studies in hepatocytes from these two preclinical

species and human. The in vitro hepatocyte data were used to estimate hepatic clearance

in rat, dog, and human. A good in vitro to in vivo correlation was observed in rat and dog

since the predicted hepatic clearance and the observed in vivo clearance were within two

fold of each other (Table 2). With a good in vitro to in vivo correlation established using

rat and dog, and no evidence of renal elimination being a major route of GDC-0917

elimination (Table 1), human hepatic clearance was predicted to be moderate (11.5

mL/min/kg) using in vitro-in vivo extrapolation of human hepatocyte data. Similar to

GDC-0152, simple allometry was used to predict human volume of distribution (i.e.

6.69 L/kg) (Figure 3). This prediction included the volume of distribution from all

preclinical species, including the monkey, which was gathered at a later point in the

compound evaluation process. Based upon the predicted human clearance and volume of

distribution, GDC-0917 half-life in humans was estimated to be 6.70 hours.

Pharmacokinetic-pharmacodynamic modeling offers a means to improve the

translation of preclinical studies to humans (Wong et al., 2012b, Wong et al., 2012c,

Yamazaki et al., 2012). Tumor xenografted mice serve as a “workhorse” efficacy model

to assess anti-tumor effects of novel agents, and therefore are widely used in oncology

drug discovery (Johnson et al., 2001, Kelland, 2004, Peterson and Houghton, 2004,

Wong et al., 2012b). Our previous investigation showed that a correlation exists between

anti-tumor activity in xenografted/allografted mice and clinical response providing that

two conditions were fulfilled. These conditions were (1) that species differences in


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mouse and human pharmacokinetics had to be normalized, and (2) that the specific

xenograft model used in the assessment had to be relevant to human disease being treated

(Wong et al., 2012b). At the time of our work with GDC-0917, we arbitrarily set ED50

and ED90 as two efficacy endpoints from which we would evaluate the compound in a

breast cancer model. Fitting of the indirect response model (Equation 3) to tumor volume

data from MDA-MB-231-X1.1 xenografted mice allowed us to define the relationship

between GDC-0917 plasma concentrations and anti-tumor effect. This relationship is

characterized by the pharmacodynamic parameters, Kmax (the maximum value of rate

constant describing the anti-tumor effect of GDC-0917) and KC50 (GDC-0917

concentration where K is 50% of Kmax). Based on comparable estimates of Kmax to the net

growth rate constant (kng), the maximum anti-tumor effect of GDC-0917 in MDA-MB-

231-X1.1 xenografted mice is near tumor stasis. This is evident upon examination of the

tumor volume versus time plot shown in Figure 4A. In particular, at the high dose levels,

increases in dose result in very little additional activity beyond tumor stasis.

Pharmacodynamic parameters estimated from xenografted mice were fixed and

simulations were performed to identify ED50 and ED90 doses in human using predicted

human pharmacokinetics (Figure 5, Table 3). The predicted ED50 (72 mg) and ED90

(660 mg) doses for humans were within an acceptable dose load for an oncology

indication. Assuming a starting dose of 5 mg in patients, ED50 and ED90 could be

reached within 5 to 8 cohorts, respectively, (Figure 5C) suggesting that these endpoints

could be achievable within an acceptable timeframe of approximately 1 to 2 years based

on normal enrollment rates (~ 3 months per patient cohort).


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The final activity following the “learning” phase of our preclinical modeling and

simulation exercise was to “confirm” our predictions upon receiving patient data. Based

on the predicted GDC-0917 pharmacokinetics and pharmacodynamics at the 5 mg

starting dose, we anticipated observing some degree of pharmacodynamic biomarker

(cIAP1) modulation in the first patient cohort. As shown in Figure 7, our simulations

were consistent with previously unpublished clinical observations of pharmacokinetics

(Figure 7A) and pharmacodynamics (Figure 7B) in cancer patients given 5 mg of GDC-

0917. These simulations served to increase confidence in our prospective simulations,

and illustrate the value of confirming predictions upon receipt of human data.

Overall, the preclinical pharmacokinetics of GDC-0917 is favorable. The

compound exhibits low to moderate in vivo clearance in all species, other than the

monkey, and has good oral bioavailability. Based on our translational PK-PD analysis,

ED50 and ED90 are achievable in humans at acceptable doses and within a reasonable

timeframe. Our experience with GDC-0917 shows the importance of leveraging learning

from prototype molecules when advancing second-generation molecules. Finally, our

work with GDC-0917 illustrates how implementation of modeling and simulation

techniques, and the “learn and confirm” paradigm, into the early phases of the drug

development process can add value by integrating existing preclinical data and aid in its

translation to the clinic.


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ACKNOWLEDGEMENTS

The authors thank members of the Drug Metabolism and Pharmacokinetics department

and the In Vivo Studies group for their technical assistance. The authors also thank the

patients involved in the clinical studies.


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AUTHORSHIP CONTRIBUTIONS

Participated in research design: Wong, Gould, Budha, Halladay, Alicke, Erickson,

Flygare, Portera, Tolcher, Infante, Mamounas, Flygare, Fairbrother.

Conducted experiments: Wong, La, Darbornne, Kadel, Alicke, Erickson, Tolcher.

Contributed new reagents or analytical tools: Darbonne, Flygare.

Performed data analysis: Wong, Gould, Budha, Darbonne, Kadel, La, Halladay, Alicke,

Tolcher, Infante, Flygare.

Wrote or contributed to the writing of the manuscript: Wong, Darbonne, Alicke, Budha,

Portera, Tolcher, Infante, Hop, Fairbrother.


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Yu R, Zobel K, and Fairbrother WJ. (2012) The Discovery of a Potent Small-Molecule

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methanesulfonyl-piperazin-1-ylmethyl)-4-morpholin-4-yl-thieno[3,2-d]pyrimidine

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Varfolomeev, E., and Vucic, D. (2011). Inhibitor of apoptosis proteins: fascinating

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LS, Hop CECA, and Gould SE. (2012b) Anti-tumor activity of targeted and cytotoxic

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J. (2012c) Bridging the Gap between Preclinical and Clinical Studies Using PK-PD


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Modeling: An Analysis of GDC-0973, a MEK Inhibitor. Clin Cancer Res, 18: 3090-

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Yamazaki S, Vicini P, Shen Z, Zou HY, Lee J, Li Q, Christensen JG, Smith BJ and

Shetty B. (2012) Pharmacokinetic/pharmacodynamic modeling of crizotinib for

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Wong H and Khojasteh SC. (2013) Evaluation of Metabolism and Disposition of GDC-

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FOOTNOTES

Send reprint requests to: Dr. Harvey Wong, Genentech, Inc., MS# 412a, 1 DNA Way,

South San Francisco, CA 94080. E-mail: [email protected].


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FIGURE LEGENDS

Figure 1 – Structure of GDC-0152 and GDC-0917

Figure 2 – Plasma Concentration-Time Profile of GDC-0917 in (A) Mouse, (B) Rat, (C)

Dog and (D) Monkey.

Figure 3 – Predicted Human Volume of Distribution Using Allometry

Figure 4 – (A) Anti-tumor Effect of GDC-0917 on MDA-MB-231-X1.1 Xenografts.

The indicated doses have units of mg/kg. (B) Observed versus Predicted

Tumor Volumes Following Fitting of Tumor Volume Data to a PK-PD

model. The solid line is the line of unity and the dashed line is the regression

line. The slope of the regression line and correlation coefficient ( r ) are

presented in the plot.

Figure 5 – (A) Predicted Human ED50 (72 mg) and ED90 (660 mg) Using MDA-MB-

231.X1-1 Xenografts. (B) Predicted Human Pharmacokinetics at 72 and

660 mg. (C) Simulated Human Phase I Clinical Trial.

Figure 6 – (A) Plot Showing cIAP1 Response to a Range of GDC-0917 Concentrations

(0.0001 to 10 µM) in PBMCs. (B) Fit of Inhibitory Imax model (Dashed Line)


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to cIAP1 and GDC-0917 Concentrations. Pharmacodynamic Parameter

Estimates are Presented on the Plot.

Figure 7 – (A) Observed and Predicted GDC-0917 Concentration-Time Profile at a Dose

of 5 mg in Human Patients. (B) Observed and Predicted cIAP1 Modulation in

Patients Given a 5 mg Starting Dose. Observed mean cIAP decrease from

patients at 6 hours postdose was 39% of predose levels. The simulated cIAP

decrease at 6 hours postdose was 32% of predose levels.


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Table 1 Pharmacokinetics (Mean ± SD) of GDC-0917 in the Mouse, Rat, Dog, and Monkey

Parameters Mouse Rat Dog Monkey

IV administration

No. of animals 27

(3/timepoint) 3 3 3

Sex Female Male Male Male

Dose (mg/kg) 1 1 1 1

CL (mL/min/kg) 12.0 27.0 ± 1.91 15.3 ± 0.643 67.6 ± 6.17

t1/2 (hr) 1.47 0.900 ± 0.0862 6.12 ± 4.22 0.825 ± 0.0634

MRT (hr) 1.76 1.12 ± 0.0945 4.44 ± 2.38 1.05 ± 0.112

Vss (L/kg) 1.27 1.81 ± 0.0529 4.09 ± 2.27 4.25 ± 0.352

Oral administration

No. of animals 27 (3/timepoint)

3 3 3

Sex Female Male Male Male

Dose (mg/kg) 3 5a 1 2

Cmax (ng/mL) 884 545 ± 261 254 ± 129 33.6 ± 10.3

tmax (hr) 0.500 2.00 ± 1.73 1.00 ± 0.00 1.33 ± 0.577

AUCinf (ng • hr/mL) 3870 2390 ± 976 1060 ± 606 83.2 ± 12.2

F (%) 93.5 77.0 ± 31.4 97.0 ± 55.6 16.8 ± 2.45

Renal clearance (mL/min/kg)

NA NA 0.0897 ± 0.0980b 1.44 ± 1.72c

AUCinf = area under the concentration–time curve from zero to infinity; CL = plasma clearance; Cmax = highest observed plasma concentration; F = bioavailability; IV = intravenous; MRT = mean residence time; NA = not available; t1/2 = half-life; tmax = time at which Cmax occurred; Vss = volume of distribution at steady state. a free-base equivalent (trifluoroacetate salt of GDC-0917 was administered) b Renal clearance was assessed for PO group only. c Renal clearance was assessed for IV group only.


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Table 2. In Vitro Predicted Hepatic Clearance and Observed In Vivo Clearance of GDC-0917.

Species

Predicted CLhepatic (Hepatocytes) (mL/min/kg)

Observed CL (mL/min/kg)

Rat

18.3

27.0

Dog 11.9

15.3

Human

11.5 _


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Table 3. Pharmacodynamic Parameter Estimates and Predicted Human Doses Based on MDA-MB-231-X1 Breast Cancer Xenograft Efficacy Studies with GDC-0917.

Parameter

Parameter Estimatea

Kmax (hr-1)

3.28 × 10-3 (6.24)

KC50 (µM) 0.114 (20.3)

kng (hr-1)

3.15 × 10-3 (1.84)

Predicted Human Doses and Exposures

Predicted Human ED50 (mg)

72 Predicted ED50 steady-state AUC0-24 (µM×hr) 1.36

Predicted ED50 steady-state AUC0-24 (ng/mL×hr) 7.70 × 102

Predicted Human ED90 (mg) 660 Predicted ED90 steady-state AUC0-24 (µM×hr) 12.5

Predicted ED90 steady-state AUC0-24 (ng/mL×hr) 7.06 × 103

a Fitted parameter presented as estimate followed by the coefficient of variation in parentheses.


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Table 4. Pharmacokinetics of GDC-0917 in Cancer Patients Given a 5 mg Starting Dose

Parameters

Cmax (ng/mL)

tmax (hr)a

AUCinf (ng/mL×hr)

t1/2 (hr)

Observed

4.57 ± 1.67

2 – 6

37.1 ± 25.4

3.98 ± 1.56

Predicted 4.37 2.4 46.4 6.70

a observed tmax is presented as a range


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learning and confirming with preclinical studies: modeling

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