Research Article

A Translational Quantitative Systems Pharmacology Model for CD3 BispecificMolecules: Application to Quantify T Cell-Mediated Tumor Cell Killingby P-Cadherin LP DART®

Alison Betts,1,2,10 Nahor Haddish-Berhane,3 Dhaval K. Shah,4 Piet H. van der Graaf,2 Frank Barletta,5

Lindsay King,6 Tracey Clark,7 Cris Kamperschroer,8 Adam Root,1 Andrea Hooper,5 and Xiaoying Chen9

Received 21 December 2018; accepted 8 April 2019; published online 22 May 2019

Abstract. CD3 bispecific antibody constructs recruit cytolytic T cells to kill tumor cells,offering a potent approach to treat cancer. T cell activation is driven by the formation of atrimolecular complex (trimer) between drugs, T cells, and tumor cells, mimicking an immunesynapse. A translational quantitative systems pharmacology (QSP) model is proposed for CD3bispecific molecules capable of predicting trimer concentration and linking it to tumor cell killing.The model was used to quantify the pharmacokinetic (PK)/pharmacodynamic (PD) relationshipof a CD3 bispecific targeting P-cadherin (PF-06671008). It describes the disposition of PF-06671008 in the central compartment and tumor in mouse xenograft models, including binding totarget and Tcells in the tumor to form the trimer. Themodel incorporates Tcell distribution to thetumor, proliferation, and contraction. PK/PD parameters were estimated for PF-06671008 and atumor stasis concentration (TSC) was calculated as an estimate of minimum efficacious trimerconcentration. TSC values ranged from 0.0092 to 0.064 pM across mouse tumor models. Themodel was translated to the clinic and used to predict the disposition of PF-06671008 in patients,including the impact of binding to soluble P-cadherin. The predicted terminal half-life of PF-06671008 in the clinicwas approximately 1 day, and P-cadherin expression and number of Tcells inthe tumor were shown to be sensitive parameters impacting clinical efficacy. A translational QSPmodel is presented for CD3 bispecific molecules, which integrates in silico, in vitro and in vivo datain a mechanistic framework, to quantify and predict efficacy across species.

KEY WORDS: CD3 bispecific; translational modeling; quantitative systems pharmacology; PK/PD;immunotherapy.

INTRODUCTION

Immunotherapy, which recruits a patient’s own immunesystem to kill cancer cells, has begun to revolutionize cancertreatment (1). Within the class of immune-oncology therapies

are the bispecific immune cell re-targeting molecules (2).These are typically recombinant bispecific antibodies, orantibody fragments, with one binding domain targeting aspecific tumor antigen of choice and the other domaintargeting CD3 on T cells. Because CD3 serves as the signaling

The original version of this article was revised: Typesetting error occurredand Figure 1a and Figure 1b were altered during the uploading process.

DART is a registered trademark of Macrogenics Inc.

Electronic supplementary material The online version of this article(https://doi.org/10.1208/s12248-019-0332-z) contains supplementarymaterial, which is available to authorized users.1 Department of Biomedicine Design, Pfizer Inc., 610 Main Street,Cambridge, Massachusetts 02139, USA.

2Division of Systems Biomedicine and Pharmacology, Leiden AcademicCentre for Drug Research, 2300 RA, Leiden, The Netherlands.

3 Janssen Research & Development, LLC, Spring House, Pennsylva-nia 19477, USA.

4Department of Pharmaceutical Sciences, 455 Kapoor Hall, Univer-sity at Buffalo, The State University of New York, Buffalo, NewYork 14214-8033, USA.

5Oncology Research Unit, Pfizer Inc., 401 N Middletown Rd., PearlRiver, New York 10965, USA.

6Department of Biomedicine Design, Pfizer Inc., 1 Burtt Road,Andover, Massachusetts, USA.

7 Established Med Business, Pfizer Inc., Eastern Point Rd, Groton,Connecticut 06340, USA.

8Department of Immunotoxicology, Pfizer Inc., 558 Eastern PointRoad, Groton, Connecticut 06340, USA.

9Department of Clinical Pharmacology, Pfizer Inc., 10555 ScienceCenter Dr., San Diego, California 92121, USA.

10 To whom correspondence should be addressed. (e–mail:alison.betts@pfizer.com)

The AAPS Journal (2019) 21: 66DOI: 10.1208/s12248-019-0332-z

1550-7416/19/0400-0001/0 # 20192019 The Author(s), corrected publication 2019

component of the T cell receptor (TCR) complex, these CD3bispecific molecules enable T cells to circumvent the need forthe interaction between the TCR and antigen presented bymajor histocompatibility complex (MHC) class I molecules.This expands the repertoire of T cells able to recognize thetumor and stimulate them to act as effector cells (3). Similarto the standard immune synapse formation, once a thresholdof bispecific-mediated molecular interactions has beenreached, CD3 signals the T cell to initiate a cytotoxic responsetoward the adjacent tumor cell expressing the specific antigen.Cytotoxicity is mediated by the release of cytotoxic granulescontaining perforin and granzymes by the T cell. Perforin is apore-forming protein enabling entry of granzymes, and thegranzymes trigger a caspase cascade that leads to apoptosis.Activation of T cells leads to the transient release of cytokinesand T cell proliferation, recruitment, and infiltration into thetumor environment, which drives serial killing of tumor cells.

In 2014, blinatumomab (CD3-CD19) was the first CD3bispecific construct approved in the USA for the treatment ofresistant/refractory B cell acute lymphocytic leukemia (B-ALL)(4). Blinatumomab is also being investigated in phase 2 clinicaltrial in patients with resistant/refractory non-Hodgkin’s lym-phoma (NHL) (5). The first generation bispecific T cellretargetingmolecules such as blinatumomab are tandemly linkedsingle-chain Fv (scFv) known as bispecific T cell engager (BiTE)molecules (2,3). These molecules are around 50 kDa and have ashort circulating half-life (approx. 2 h) requiring constantinfusion through the use of a pump to achieve a stabletherapeutic exposure of the molecule (6). New generation CD3bispecifics with a variety of formats are being tested in clinicaltrials. These include PF-06671008 which is a P-cadherin-specificLP DART: a molecule based on the DART® platform, butcontaining a human IgG1 Fc domain to extend the half-life (7).This bispecific targets CD3 and P-cadherin expressed on solidtumors. P-cadherin is a member of a family of molecules thatmediate calcium-dependent cell-cell adhesion and has beenreported to correlate with increased tumor cell motility andinvasiveness when over-expressed (8–10). Upregulation of P-cadherin has been reported in breast, gastric, endometrial,colorectal, and pancreatic carcinomas and correlates with poorsurvival of breast cancer patients (11–14). In contrast, P-cadherinhas low expression in normal tissues, making it an attractivetarget for immunotherapy (12). In preclinical studies, in vitro andin vivo data indicate that PF-06671008 is a highly potentmoleculeeliciting P-cadherin expression-dependent cytotoxic T cell activ-ity across a range of tumor indications (15). In addition, PF-06671008 is stable and has desirable biophysical and PKproperties with a half-life of 3.7–6 days in mouse (7,15). PF-06671008 is currently being investigated in phase 1 clinical trialsin patients with advanced solid tumors with the potential to haveP-cadherin expression (https://clinicaltrials.gov/ct2/show/NCT02659631).

In order to characterize the in vivo efficacy of PF-06671008 in tumor-bearing mice, a quantitative systemspharmacology (QSP) model was established. This modelintegrates the PK of PF-06671008, its binding to shed P-cadherin and circulating T cells in the systemic circulation, itsbiodisposition in the tumor and the formation of atrimolecular complex (trimer) with T cells, and P-cadherinexpressing tumor cells in the tumor microenvironment(TME). The model incorporates T cell kinetics in the tumor

including T cell proliferation and contraction. The concentra-tion of the trimer in the tumor is used to drive efficacy inmouse using an optimized transduction model of tumor cellgrowth and killing. In this manuscript, we discuss the use ofthe model to characterize the underlying pharmacology inmouse, and translation of the preclinical efficacy data to theclinic by incorporation of predicted human PK and diseaseparameters. The quantitative translational framework forCD3 bispecific molecules presented here can aid in drugdesign, candidate selection, and clinical dosing regimenprojection.

MATERIALS AND METHODS

In Vivo Studies. All procedures in animals were ap-proved by the Pfizer Institutional Animal Care and UseCommittees and studies were performed according toestablished guidelines.

PF-06671008 Mouse PK Study. PF-06671008 was admin-istered as a single intravenous (IV) dose of 0.05 or 0.5 mg/kgto HCT-116 tumor-bearing female NOD-scid IL-2rgnull

(NSG) mice, (n = 3/time point/dose) with or without humanperipheral blood mononuclear cell (PBMC) engraftment.Mice were injected with 5 × 106 HCT-116 cells in Matrigelsubcutaneously in the dorsal left flank. When the tumors hadgrown to approximately 0.5 g in size (after 14 days), the micewere administered PF-06671008. Serum and tumor sampleswere collected at predetermined time points from 5 min to240 h post-dose.

ELISA Assay to Quantify PF-06671008. PF-06671008concentrations in mouse serum and tumor homogenate weredetermined using an enzyme-linked immunosorbant assay(96-well format) with colorimetric detection. Briefly, thecapture protein was a polyclonal goat antibody recognizingthe CD3 scFv domain and the detection antibody was a goatanti-human IgG-Biotin (Qualex), followed by HRP-streptavidin conjugate (Jackson ImmunoResearch, WestGrove, PA). Optical density was measured on a spectropho-tometer (molecular devices). The lower limit of quantitation(LLOQ) of the assay was 12.5 ng/mL for serum samples and1.5 ng/mL for tumor samples. The minimal required dilutionwas 1:25 for serum and 1:6 for tumor.

Flow Cytometric Tumor-Infiltrating LymphocyteAnalysis. HCT-116 tumor-bearing mice (n = 3) engrafted withhuman PBMC and administered a single IV dose of 0.01,0.05, or 0.5 mg/kg PF-06671008 were euthanized pre-dose and24, 72, and 144 h following dosing to assess tumor-infiltratinghuman CD3+ lymphocytes. Tumor samples were collectedinto gentleMACS C tubes containing human tumor celldissociation buffer (Miltenyi Biotech) and processed tosingle-cell suspensions using the manufacturer’s suggestedprotocol for soft human tumors using the gentleMACS tissuedissociator (Miltenyi Biotech). After subsequent washingsteps and live cell counting (using a hemocytometer andtrypan blue exclusion), 1 × 106 live cells from each samplewere collected and stained with CD3 FITC (BD Pharmingen)for 30 min on ice. Samples were analyzed using LSRII with

66 Page 2 of 16 The AAPS Journal (2019) 21: 66

FACS Diva software (BD Pharmingen). Absolute numbers ofCD3+ T cells per gram of tumor were then calculated usingthe number of CD3+ events and sample tumor weight.

PF-06671008 Mouse Xenograft Studies. Mouse xeno-graft studies were completed in human T cell engrafted(HCT-116) or adoptive transfer (HCT-116 or SUM-149)established tumor models. In the human T cell engraftedmodel, tumor cells (5 × 106 HCT-116) were implantedsubcutaneously (SC) into the right flank of 6–8-week-oldfemale NSG mice as a 0.2 mL bolus mixed with 4 mg/mLCultrex basement membrane extract (Trevigen) in PBS.Seven days prior to randomization, mice were inoculatedwith 5 × 106 or 2.5 × 106 freshly isolated human PBMC as anintraperitoneal injection of 0.2 mL cell suspension in PBS. Inaddition to vehicle (PBS), dose levels of 0.01, 0.05, 0.1, 0.15,and 0.5 mg/kg PF-06671008 were administered for HCT-116studies (n = 10/dose). The doses were administered IV as aq7d × 2 regimen.

For the T cell adoptive transfer established tumor model,8- to 10-week-old NSG mice were inoculated with either 5 ×106 HCT-116 cells in the flank or 5 × 106 SUM-149 cells in themammary fat pad in a total injection volume of 0.2 mL, 7 daysprior to randomization. HCT-116 cells were suspended inPBS, while SUM-149 cells were suspended in growth mediaand mixed 1:1 with Matrigel basement membrane matrix (BDBiosciences, San Jose, CA). T cells, which had been isolatedfrom PBMCs, were activated and expanded using DynabeadsHuman T-Expander CD3/CD28 magnetic beads (Life Tech-nologies) for 6–9 days, depending on the study, wereharvested, and resuspended in PBS at 1 × 107 cells/mL forin vivo inoculation. An initial dose of PF-06671008 or vehiclewas administered to mice on day 0 and on the following day,mice were inoculated with 0.5, 1, 2, 2.5, or 5 × 106 T cells/miceIV. In addition to vehicle, dose levels of 0.05, 0.15, and0.5 mg/kg PF-06671008 were administered for HCT116xenograft studies and 0.05, 0.15, and 0.5 mg/kg PF-06671008for SUM149 xenograft studies (n = 10/dose). The doses wereadministered IV as a q7d × 3 or q7d × 5 regimen.

Tumor volume was measured using a digital Verniercaliper (Mitutoyo America, Aurora, IL), and volumes werecalculated by the use of the modified ellipsoid formula ½(width2 × length). Tumor measurements were collected twiceweekly, with continuous health monitoring, until the animalshad to be euthanized due to tumor burden or health concernsout to a maximum of 16 days (HCT-116 in the human T cellengrafted model), 42 days (SUM-149 adoptive transfer), or65 days (HCT-116 adoptive transfer). For a full description ofthe mouse xenograft studies, please refer to reference (15).

PF-06671008 Cynomolgus Monkey PK Study. The PK ofPF-06671008 in cynomolgus monkey was evaluated followingIV bolus and SC administration at weekly escalating doses for1 month. The IV doses administered were 1.1/3.3, 3.3/10, or10/20 μg/kg/week, and the SC dose administered was 10/30 μg/kg/week. This study has been described previously (16).

Measurement of Soluble P-Cadherin. Baseline sPcadlevels were measured in cynomolgus monkey, healthy volun-teer, and cancer patient serum samples (Bioreclamation).

sPcad levels were also measured in cynomolgus monkey aftertreatment with PF-06671008 (in-house samples). A qualifiedMeso Scale Discovery (MSD) human P-cadherin kit was usedto measure soluble P-cadherin levels, as described previously(16).

Modeling of Mouse Tumor Growth Inhibition Data. AQSP model was constructed to describe the disposition of PF-06671008 and T cells in the central compartment and tumor ofthe xenograft mouse models (Fig. 1a). The model accountsfor the binding of PF-06671008 to tumor cells and T cells inthe extracellular space of the TME to form trimers. Thetrimers are assumed to drive tumor cell killing. Description ofall the symbols and parameters used in the mouse equationsare shown in Tables I and II.

1. Modeling of PF-06671008 and T cells in central/peripheralcompartments and distribution to the tumor: Followingsystemic administration to mouse, PF-06671008 is assumedto be able to distribute to a peripheral compartment,distribute to the tumor, bind to circulating T cells, or becleared from the central compartment. In the mousemodel, PF-06671008 does not bind to sPcad.

& Mouse PK: The mouse serum concentration profiles inhuman PBMC-engrafted mice, following IV administrationof PF-06671008 at 0.05 and 0.5 mg/kg, were described usinga two-compartment model with linear elimination from thecentral compartment (Eqs. 1 and 2). C1, C2, and C3 are theconcentrations of the drug, PF-06671008, in plasma, periph-eral compartment, and tumor, respectively. kel is theelimination rate of PF-06671008 from the central compart-ment. k12 and k21 are the inter-compartmental rateconstants describing the distribution of PF-06671008 be-tween the central compartment and the peripheral com-partment. These values were fixed in the subsequent TGImodeling. Distribution of free PF-06671008 to the extracel-lular environment of the tumor was characterized usingtumor disposition equations (Eqs. 3 and 4) that have beendescribed previously (19,25,26). Briefly, P is the rate ofpermeability and D is the diffusion of the drug, across andaround the tumor blood vessels. Rcap is the radius of thetumor blood capillary, Rkrogh is the average distancebetween 2 capillaries, Rtumor is the radius of the tumor,and ε is the tumor void volume for the drug.

& Binding to T cells: Binding of PF-06671008 to circulat-ing T cells was determined from CD3 binding (konCD3 andkoffCD3), the number of CD3 receptors per T cell (CD3) andnumber of T cells in the central compartment, or plasma(Tcellsp). These values were used to calculate total CD3 inthe central compartment (TotCD3p) (Eq. 5). Binding toCD3 (and P-cadherin) was determined using surfaceplasmon resonance (SPR) assays run on a Biacore instru-ment as described previously (7). The number of CD3receptors per T cell was taken from literature data (17,18).The number of T cells administered per mouse was used toinform the initial number of T cells in the centralcompartment. See Eq. 6 for binding of PF-06671008 toCD3 in the central compartment. DCD3p and DCD3t arethe concentration of drug-CD3 dimers in plasma and tumor,respectively.

Page 3 of 16 66The AAPS Journal (2019) 21: 66

& T cell trafficking: Following administration of T cells tothe mouse, T cells were assumed to be able to distribute tothe tumor (Eqs. 7 and 8), bind to PF-06671008, or becleared from the central compartment. k12T and k21T are therate constants describing the distribution of T cells betweenthe central compartment and the tumor. kelT is theelimination rate of the T cells from the central compart-ment. These parameters were determined from modeling ofin-house PBMC data in tumor-bearing mice (not shownhere). A lag time of 5 days was introduced to accommodatethe disposition and start of the proliferation of T cells at thetumor site. This was informed from in-house immunohisto-chemistry data and was equivalent to the time of T cellobservation in the tumor. Tcellstm are T cells which havemigrated from the central compartment to TME during the5 days.

2. Modeling of T cell proliferation and trimer formation inthe TME

T cell kinetics in the tumor: CD3+ cells/mg tumor weremeasured in HCT-116 tumor-bearing mice, engrafted withhuman PBMCs, following administration of PF-06671008 at0.01, 0.05, or 0.5 mg/kg (described above). This data was used todetermine the proliferation rate of T cells in the tumor. Therelationship between CD3+ cells/mg tumor with time at eachdose level was described using an exponential function. Theslope of each line represents the rate of proliferation of CD3+cells and was plotted versus PF-06671008 dose. An empiricalmodel was then used to describe the CD3+ proliferation rates(Prate) as a function of dose (Eq. 9). Please see SupplementaryMaterial for additional information and plots. T cells migratinginto the TME during the 5-day lag time (Tcellstm) undergoproliferation for 7 days (Eq. 10). Following proliferation, T cellsundergo contraction which was characterized using mono-exponential decline (k_exhaust) (Eq. 11). The time (7 days) andthe rate of decline (0.0412 1/h) were estimated from literaturedata (20). It was assumed that T cell proliferation was onlytaking place in the tumor environment and that proliferationand contraction rates were the same in the human T cellengraftment and adoptive transfer mouse tumor models.

& Trimer formation: In theTME, PF-06671008 can bind to P-cadherin on tumor cells or to CD3 on Tcells to form dimers, orboth tumor cells and T cells to form the active trimers. Thebinding constants between drug and P-cadherin are konPcad andkoffPcad and the binding constants between drug and CD3 arekonCD3 and koffCD3. In addition to binding affinity values, trimerformation was a function of P-cadherin receptors per tumor cell(mPcad), number of tumor cells (Tumorcellst), CD3 receptorsper T cell (CD3), and number of T cells in the TME (Tcellst).These values were used to calculate total Pcad (TotPcadt) andtotal CD3 (TotCD3t) in the TME (Eqs. 12 and 13). P-cadherinreceptor expression in HCT-116 and SUM-149 tumor cell lineswas determined by phycoerythrin (PE) labeling of anti-P-cadherin mAb and flow cytometry to determine the number ofPE-labeled antibodies bound per cell. This study has beendescribed previously (15). Internalization rate of drug bound toP-cadherin (kint) in the tumor was determined from the mousePK study, completed in the presence of PBMCs. The number oftumor cells was determined from xenograft data. The numberof CD3 receptors/T cell was taken from published data (17,18).See Eqs. 14–16 for binding of PF-06671008 to P-cadherin andCD3 to form dimers and trimers.

3. Tumor growth inhibition: The mouse xenograft PK/PDrelationship was established by relating mouse PF-06671008 trimer concentration in the TME to measuredxenograft tumor volume data using an optimized celldistribution transduction model (27). The presented modelis a modified version of the model by Simeoni et al. (22).Briefly, the unperturbed tumor growth was fitted first usingindividual animal growth data from the vehicle controlgroup, using a logistic model describing linear (kg) andexponential (kg0) growth. The measured initial tumorvolume in each animal was used to inform the initialconditions (M1). M1–M4 are the tumor volume in thegrowth compartment and three transduction compart-ments, respectively. w is the total tumor volume (mm3).The inter-individual variability of the growth parameters

++

koffPcad

+

mPcad

CD3

Trimer

T-cells

TumorCell

TumorCells

Tumor

+

kg,

kmax 1/t 1/t 1/t

Effect

kel +

CD3

Periph

eral

k int

Prate

kexhaust

kg0

k21T

k12T

kelT

konPcad

koffCD3

konCD3 koffCD3konCD3

koffPcad

konPcad

k12k21

Cen

tral

sPcad+ksyn

kdegkdegcx

CD3 bispecific Concentration

Tum

or C

ell K

illin

g (%

)

Emax

Efficacy Window

Optimal [CD3 bispecific] for Trimer

Low [CD3 bispecific]: Few Trimers

Excess [CD3 bispecific]: No Trimers, only Dimers

a

b

Fig. 1. a Translational quantitative systems pharmacology model forCD3 bispecific molecules. Parameter descriptions and values aresummarized in Tables I to III. The figure represents both mouse andhuman models, with the following exceptions: binding to sPcad wasonly included in the human model and T cell proliferation/ exhaustionin the tumor were only included in the mouse model. b Schematic ofthe bell-shaped concentration relationship which can be observed forCD3 bispecific molecules. Formation of trimers between drugs, Tcells, and tumor cells, is required for efficacy. The QSP model predictstrimer concentration and links it to tumor cell killing

66 Page 4 of 16 The AAPS Journal (2019) 21: 66

and the maximum tumor volume (Mmax) obtained from theunperturbed growth model were then fixed in the simulta-neous estimation of growth and drug effect parametersfrom the complete tumor volume data set. Tumor cellkilling was driven by the concentration of the trimolecularcomplex (Trimer). τ is the transduction time, kmax is themaximum kill rate, kc50 is the concentration of the trimerin the tumor at half the maximal kill rate, and ψ is theconstant for switching from exponential to linear growthpatterns. Equations 17–22 describe the tumor growthinhibition modeling.

& Determination of tumor static concentration (TSC):TSC is the concentration of trimers at which tumor growthand death rate are equal, and is defined as the minimalefficacious concentration (Ceff). This PK/PD-derived pa-rameter combines growth information and drug effect,providing insight into the efficacy of PF-06671008 in mousexenograft models. TSC was used as a translational factor forextrapolation of xenograft data to the clinic. See Eq. 23 forTSC calculation.

An 80% confidence interval on TSC was calculated usingparametric bootstrap by resampling from the estimatedparameters using a log-normal distribution.

dC1dt

¼ −kel � C1−k12� C1þ k21� C2� V2V1

−konCD3

� C1� TotCD3p−DCD3p� �þ kof f CD3

�DCD3p−Tumor Disposition� TVV1

; C1 t ¼ 0ð Þ

¼ Dose in nmols ð1Þ

dC2dt

¼ k12� C1� V1V2

−k21� C2; C2 t ¼ 0ð Þ ¼ 0 ð2Þ

Tumor Disposition ¼ 2� P� Rcap

R2krogh

þ 6�DR2

tumor

!� C1−

C3ε

� �ð3Þ

dC3dt

¼ Tumor disposition−konPcad � C3

� TotPcadt−DPcadt−Trimerð Þε

� �þ kof f Pcad

�DPcadt−konCD3 � C3

� TotCD3t−DCD3t−Trimerð Þε

� �þ kof f CD3

�DCD3t; C3 t ¼ 0ð Þ¼ 0 ð4Þ

TotCD3p ¼ Tcellsp � CD3

6:023� 1023

� �� 1� 109� � ð5Þ

dDCD3pdt

¼ konCD3 � C1� TotCD3p−DCD3p� �

−kof f CD3 �DCD3p;DCD3 t ¼ 0ð Þ ¼ 0

ð6Þ

dTcellspdt

¼ −kelT � Tcellsp−k12T � Tcellsp þ k21T

�Tcellstm � TVV1

;Tcellsp t ¼ 0ð Þ ¼ Tcellp0

ð7Þ

dTcellstmdt

¼ k12T � Tcellsp � V1TV

−k21T � Tcellstm; Tcellstm t ¼ 0ð Þ ¼ 0

ð8Þ

Table I. Model Variables and Terms Used in Equations

Variable Definition Unit

TotPcadt Total Pcad in the tumor nMTotCD3p Total CD3 in the central compartment nMTotCD3t Total CD3 in the tumor nMTcellsp T cells in the central compartment Cells/LTcellstm T cells migrated from plasma to tumor, during 5-day lag time Cells/LTcellst T cells in the tumor Cells/LDose Dose of PF-06671008 nmolsC1 Concentration of PF-06671008 in central compartment nMC2 Concentration of PF-06671008 in the peripheral compartment nMC3 Concentration of PF-06671008 in the tumor nMDCD3p Dimer of PF-06671008-CD3 in the central compartment nMDCD3t Dimer of PF-06671008-CD3 in the tumor nMDPcadp Dimer of PF-06671008-Pcad in the central compartment nMDPcadt Dimer of PF-06671008-Pcad in the tumor nMTrimer Trimer of PF-06771008-CD3-Pcad in the tumor nMw Total tumor volume mm3

M1, M2, M3, M4 Tumor volume in growth and three transduction compartments nM

Page 5 of 16 66The AAPS Journal (2019) 21: 66

TableII.Mou

seMod

elParam

eters

Param

eter

Definition

Unit

Value

(CV%)

Source

Binding

k onCD3,k o

ffCD3,

Kd_C

D3

Binding

ofPF-06671

008to

CD3

1/nM

/h,1

/hnM

1.72,1

9.66

11.4

(7)

k onPcad,k

offPcad,

Kd_P

cad

Binding

ofPF-06671

008to

P-cad

herin

1.57,0

.74

0.47

Cen

tral/periphe

ral

compa

rtmen

tV1

Volum

eof

distribu

tion

inthe

centralcompa

rtmen

tmL/kg

49.6

(9)

Estim

ated

from

mou

sePK

data.

kel=

CL/V

1k1

2=CLd/V

1k2

1=Cl d/V

2

V2

Volum

eof

distribu

tion

inthe

periph

eral

compa

rtmen

tmL/kg

60.7

(16)

CL

Clearan

cemL/h/kg

0.45

(12)

CLd

Inter-compa

rtmen

talclearance

mL/h/kg

4.95

(28)

OmegaCL

Interindividu

alva

riab

ility

inclearance

–0.064(41)

aAdd

itiveerror

–0.067(32)

bPropo

rtiona

lerror

–0.207(15)

Tcellsp0

Num

berof

Tcells

administered/mou

seCells/L

0,2.5e8,

5e8,

1e9,

1.25e9

,2.5e9

See“M

etho

ds”

section

CD3

CD3ex

pression

onTcells

Recep

tors/cell

100,00

0(17,18)

Tum

ordispositionof

PF-066

71008/Tcells

PPermea

bilityof

drug

into

tumor

μm/day

334

(19)

DDiffusivityof

drug

into

tumor

cm2 /da

y0.022

εVoidfraction

intumor

fordrug

–0.24

Rcap

Cap

illaryradius

μm8

Rkrog

hAve

rage

distan

cebe

twee

n2

capilla

ries

μm75

Tlag

Lag

timeforTcell

disposition/on

setof

Tcell

proliferationin

thetumor

Day

5Se

tem

pirically,

usingin-hou

seda

tak e

lT,k

12T,k

21T

Tcellredistribu

tion

from

the

centralcompa

rtmen

tto

the

tumor

1/da

y2.51,0

.002

,0.0005

Tum

orcompa

rtmen

tPrate

Tcellproliferationrate

1/h

Fun

ctionof

dose.

Eq.

(9)

See“M

etho

ds”

section

k _exhau

stc

Slop

eof

Tcellde

cline

1/h

0.0412

Interpolated

from

(20)

Tum

orcellst

Num

berof

tumor

cells

Cells/g

oftumor

1e8

(21)

mPcad

P-cad

herinex

pression

ontumor

cells

Recep

tors/cell

28,706

(HCT-116)

17,500

(SUM-149)

(15)

Rtumor

Tum

orradius

cmCalculatedfrom

wMea

sured

k int

P-cad

herininternalizationrate

Day

−1

0.1728

(−)

Estim

ated

from

mou

setumor

PK

data.R

epresents

96hha

lf-lifeof

internalization.

Tum

orgrow

thinhibition

Mou

setumor

mod

els

HCT116a

HCT116b

SUM14

9b

k g0

Exp

onen

tial

tumor

grow

thrate

1/da

y0.30

(−)

0.19

(3)

0.12

(3)

Estim

ated

inmou

semod

elsfrom

unpe

rturbe

dtumor

grow

thda

ta

k gLinea

rtumor

grow

thrate

mm

3 /da

y10

5(4)

123(2)

74.3

(5)

Mmax

Maxim

umtumor

volume

mm

33.8×10

3(−)

6.0×10

3(−)

5.8×10

3(−)

ψ–

20(−)

Fixed

basedon

(22)

66 Page 6 of 16 The AAPS Journal (2019) 21: 66

Prate ¼ 0:0144þ dose

þ 1:5e−5� �

� dose; ð9Þ

Tcellt ¼ Tcelltm � ePrate�t for t≤7 days; after 5 day lag time ð10Þ

Tcellt ¼ Tcelltm � ePrate�7� �� e−0:0412� t−7ð Þ for t

> 7 days; after 5 days lag time ð11Þ

TotPcadt ¼ Tumorcellst �mPcad

6:023� 1023

� �� 1� 109� �

; ð12Þ

TotCD3t ¼ Tcellst � CD3

6:023� 1023

� �� 1� 109� �

; ð13Þ

dDCD3tdt

¼ konCD3 � C3� TotCD3t−DCD3t−Trimerð Þε

� �−kof fCD3

�DCD3t−konPcad �DCD3t � TotPcadt−DPcadt−Trimerð Þε

� �

þkof f Pcad � Trimer; DCD3t t ¼ 0ð Þ ¼ 0

ð14Þ

dDPcadtdt

¼ konPcad � C3� TotPcadt−DPcadt−Trimerð Þε

� �−kof f Pcad

�DPcadt−konCD3 �DPcadt � TotCD3t−DCD3t−Trimerð Þε

� �

þkof f CD3� Trimer−kint �DPcadt; DPcadt t ¼ 0ð Þ ¼ 0

ð15Þ

dTrimerdt

¼ konCD3 �DPcadt � TotCD3t−DCD3t−Trimerð Þε

� �−kof f CD3

�Trimer þ konPcad �DCD3t � TotPcadt−DPcadt−Trimerð Þε

� �

−koff Pcad � Trimer; Trimer t ¼ 0ð Þ ¼ 0

ð16Þ

kkill ¼ kmax � Trimerkc50 þ Trimer

ð17Þ

dM1

dt¼

kg0 � 1−w

Mmax

� ��M1

1þ kg0kg

� w� �ψ� �1=ψ

−kkill �M1; M1 t ¼ 0ð Þ ¼ TV ð18Þ

dM2

dt¼ kkill �M1−

M2

τ;M2 t ¼ 0ð Þ ¼ 0 ð19Þ

dM3

dt¼ M2−M3

τ;M3 t ¼ 0ð Þ ¼ 0 ð20Þ

dM4

dt¼ M3−M4

τ;M4 t ¼ 0ð Þ ¼ 0 ð21Þ

w ¼ M1 þM2 þM3 þM4 ð22Þ

Switch

betw

eenex

pone

ntial

andlin

eargrow

thph

ases

k max

Max

imum

killing

rate

1/da

y0.74

(7)

1.32

(7)

2.71

(14)

Estim

ated

inmou

semod

els

kC50

Con

centration

atha

lfmaxim

umkillrate

nM1.0×10

−4(6)

6.9×10

−5(7)

2.0×10

−4(15)

τTransdu

ctiontimebe

twee

ntumor

compa

rtmen

tsDay

4.78

(10)

3.99

(1)

2.25

(3)

Omegak g

0Inter-individu

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riab

ility

inex

pone

ntialgrow

thrate

0.46

(14)

0.34

(11)

0.12

(25)

Omegak g

Inter-individu

alva

riab

ility

inlin

eargrow

thrate

0.35

(13)

0.16

(13)

0.16

(28)

aAdd

itiveerror

5(−)

60(−)

60(−)

bPropo

rtiona

lerror

0.26

(3)

0.06

(6)

0.01(50)

TSC

(pM)

(80%

confi

dence

interval)

Tum

orstatic

concen

tration

ofthetrim

er0.064(0.044,0

.096)

0.011(0.009

6,0.01

3)0.0092

(0.007

1,0.012)

aTcellen

graftedtumor

mod

elbTcellad

optive

tran

sfer

tumor

mod

elcOnset

ofex

haustion

ofTcells

setto

7da

ysafterdispositionin

thetumor

MW

tof

PF-066

71008=10

5kD

a,MWtof

sPcad=85

kDa

Page 7 of 16 66The AAPS Journal (2019) 21: 66

TSC ¼ kg0 � kC50kmax−kg0

; ð23Þ

Modeling. All modeling was performed using Monolixsoftware v4.3.3 (Paris, France). The quality of the modelfitting was assessed using the following:

1. Diagnostic plots: (a) plots of observations versuspopulation/individual predictions and comparisonwith line of unity, (b) plots of weighted residualsversus time/concentration and check for systematicdeviation from zero, (c) visual predictive checks ofobservations and predictions for all individuals at eachdose level to check for goodness of fit.

2. Diagnostic criteria: (a) reasonable precision of theparameter estimates (RSE/CV%), (b) lack of correla-tion between model predicted parameters (< 0.95), (c)lack of shrinkage (η−) as a check for model over-parameterization (< 40%), (d) reduction in objectivefunction values and/or Aikake and Schwarz criterionfor model comparison.

Translation of the Model to Human

Prediction of Human PK. Human PK parameters werepredicted from cynomolgus monkey PK parameters using atwo-compartmental PK model which incorporates binding tosPcad (Table III and Fig. 1a). PK parameters were scaledfrom monkey to a human using allometric exponents of 0.9for clearance, 1 for the volume of distribution, and − 0.25 forabsorption rate. These exponents were selected as they havebeen previously identified as optimal for monoclonal anti-bodies (28). The degradation rate of sPcad (kdeg) was scaledfrom monkey to human using an exponent of − 0.25. Thedegradation rate of the PF-06671008-sPcad complex (kdegcx)was assumed to be the same as PF-06671008 elimination rate.

Prediction of Clinical PK/PD. The QSP model used todescribe the PK/PD relationship in the mouse was translated to ahuman using the physiological parameters and assumptionsdescribed in Table III. An important difference from the mousemodel is that PF-06671008 binds to circulating target (sPcad) toformdrug-P-cadherin (DPcadp) dimers in the central compartmentin the human model. The additional model equations are shown inEqs. 24–26. In addition, T cell proliferation/contraction kineticswere not included in the human model. Instead, a “steady-state”number of T cells in the tumor are assumed (Tcellst). All modelsimulations were completed using Berkeley-Madonna v8.3.18.

dC1dt

¼ −kel � C1−k12� C1þ k21� C2� V2V1

−konCD3

� C1� TotCD3p−DCD3p� �þ kof f CD3

�DCD3p−konPcad � C1� sPcadþ kof f Pcad

�DPcadp−Tumor Disposition� TVV1

;C1 t ¼ 0ð Þ

¼ Dose in nmols ð24Þ

dsPcaddt

¼ ksyn−kdeg� sPcad−konPcad � C1� sPcad

þkof f Pcad �DPcadp; sPcad t ¼ 0ð Þ ¼ sPcad in nM

ð25Þ

dDPcadpdt

¼ konPcad � C1� sPcad−kof f Pcad �DPcadp� �

−kdegcx

�DPcadp; DPcadp t ¼ 0ð Þ ¼ 0

ð26Þ

Sensitivity Analyses. Local sensitivity analyses were per-formed to assess the sensitivity of the QSP model to P-cadherin receptor expression on tumor cells, and to tumor Tcell (effector) to tumor cell ratio (E:T), as these arepotentially variable parameters in patients. P-cadherin recep-tor numbers of 1000, 3000, 10,000, and 28,706 were used forsimulations with the human model. These values representedthe range of P-cadherin expression measured across humantumor cell lines (15). The nominal value of E:T used in themodel was 1:150, which is thought to be representative of asolid tumor (21,24). In the sensitivity analysis, E:T ratios oftenfold lower (1:15) and tenfold higher (1:1500) than thenominal value were investigated in the human model. Forquantitative comparison, sensitivity was represented as pre-dicted tumor trimer concentration at each expression level, orE:T ratio, following an IV dose of 0.1 μg/kg PF-06671008 QWto cancer patients.

RESULTS

Serum and Tumor PK of PF-06671008 in Mouse. PKprofiles of PF-06671008 in PBMC engrafted and non-PBMCengrafted HCT-116 tumor-bearing mice following single-doseIVadministration at 0.05 and 0.5mg/kg are shown in Fig. 2a. Thearea under the curve (AUC) of PF-06671008 in serum was doseproportional between 0.05 and 0.5 mg/kg and similar betweenPBMC engrafted and non-PBMC-engrafted mice (Supplemen-tal Fig. 1). In contrast, the tumor AUC from the study withPBMC engraftment was more than fivefold higher than thestudy without PBMCs (Fig. 2b). This was attributed to areduction in the internalization of PF-06671008 bound to P-cadherin on tumor cells in the presence of PBMCs.

The serum PK in the PBMC-engrafted mice was used forPK modeling. The estimated serum PK parameter estimatesfor PF-06671008 are shown in Table II, and the goodness of fitplots are shown in Supplemental Fig. 1. The tumor internal-ization rate in the presence of PBMCs was used in the TGIPK/PD modeling (Table II).

Tumor T Cell Kinetics. HCT-116 tumor-bearing miceengrafted with PBMCs and administered PF-06671008showed dose-dependent increases of tumor-infiltrating/prolif-erating CD3+ lymphocytes (TILs) over time (Fig. 3). Therelationship with time was transformed to calculate aproliferation rate of CD3+ cells as a function of the dose

66 Page 8 of 16 The AAPS Journal (2019) 21: 66

which was used to describe tumor T cell kinetics in the QSPmodel (Supplementary Fig. 2).

PK/PD Relationship of PF-06671008 in Mouse XenograftModels. The QSP model (Fig. 1a) was used to fit the tumorgrowth inhibition data obtained from the HCT-116 and SUM-149 mouse xenograft studies. The tumor trimer concentrationwas used as a driver of tumor cell killing. Estimated modelparameters with percent coefficient of variation (CV) andcalculated tumor trimer TSCs with 80% confidence intervalsare shown in Table II. Parameters were estimated with goodprecision as assessed by the percentage CV for all cell lines.The goodness of fit and model performance were assessedusing the goodness of fit plots (population prediction,individual prediction, and visual predictive check) that areshown in Supplemental Fig. 3 for HCT-116 in the T cellengrafted model and for HCT-116 and SUM-149 in T celladoptive transfer experimental model. Overall, the medianresponse and variability of all cell lines were described wellby the mechanistic model. The calculated population medianTSCs were 0.064, 0.011, and 0.0092 pM for HCT-116 in T cellengrafted model and for HCT-116 and SUM-149 in T celladoptive transfer experimental models, respectively. The Cefffor tumor stasis is defined as the geometric mean of the TSCsin three mouse xenograft models and was calculated to be0.028 pM trimer concentration in the tumor.

Serum P-Cadherin Concentrations Across Species. Theconcentrations of sPcad in serum samples from cynomolgusmonkey, healthy humans, and cancer patients are shown inTable IV. There was no difference in sPcad levels in theserum of healthy human volunteers and cancer patients.Higher variability was observed in the lung and colorectalcancer samples compared to samples from breast cancerpatients or healthy humans. Levels of sPcad in cynomolgusmonkeys were similar to those in human.

Clinical PK Predictions for PF-06671008. The predictedhuman PK parameters for PF-06671008 are shown inTable III. The predicted human CL and Vss were 4.6 mL/h/kg and 251 mL/kg, respectively, and the terminal half-life waspredicted to be approximately 1 day.

Clinical PK/PD Predictions for PF-06671008 and Sensi-tivity to P-Cadherin Expression on Tumor Cells and T CellNumber. To translate the QSP model from mouse to human,the predicted human PK was incorporated along withassumptions and parameters describing the human physiol-ogy (Table III). The model simulated serum PK and tumortrimer concentrations following IV infusion of PF-06671008 at 0.01, 0.1, and 1 μg/kg QW to cancer patientsare shown in Fig. 4 a and b, respectively. Expression levels ofP-cadherin on tumor cells are expected to vary acrosspatients. To investigate the potential impact on tumor trimerconcentrations, a sensitivity analysis was performed varyingP-cadherin receptor numbers from 1000 to 28,706 (HCT-116). Predicted tumor trimer concentration increases withincreasing receptor expression (Fig. 5a) suggesting that P-cadherin expression is a sensitive parameter. Tumor immunestatus is also likely to vary across patients. The nominal E:T

ratio in the model is assumed to be low (1:150) in a solidtumor (21,24). To investigate the potential impact of tumor Tcell number on tumor trimer concentrations, a sensitivityanalysis was performed varying E:T ratio from 1:15 to 1:1500and assuming a constant number of tumor cells. Predictedtumor trimer concentration correlates with E:T ratio(Fig. 5b), suggesting T cells in the tumor is a sensitiveparameter.

DISCUSSION

Complex Exposure-Response Relationships for CD3Bispecific Molecules

Bispecific antibodies are emerging as a leading class ofbiotherapeutic drugs in oncology, with the potential toenhance efficacy, increase tumor targeting and reduce sys-temic toxicity compared to their monospecific counterparts.These formats can vary in their molecular weight, PK, andability to support immune effector functions. Perhaps moresignificantly, they can also vary in geometry a number ofantigen binding sites, and the intrinsic affinity of individualarms (29). As a result of this complexity, dose-responserelationships for bispecific antibodies can be non-intuitive anddifficult to rationalize.

An additional complexity emerges for the CD3 bispecific Tcell retargetingmodality, where efficacy is driven by the formationof a trimer between the drug, Tcell, and tumor cell. A bell-shapedconcentration-response relationship can be observed (Fig. 1b),which is a well-described phenomenon for ternary complexes(30–33). When concentrations of antibodies are low, conditionsfavor the formation of trimers, with an optimal antibodyconcentration needed for trimer formation. However, as concen-trations increase further, antibodies will be in excess and theequilibrium will shift to the formation of dimers betweenantibodies and T cell, or antibodies and tumor cell. This resultsin a decrease of response as dimers cannot trigger cytotoxicity.Since trimer concentration is a function of drug Kd values, tumorantigen expression, CD3 expression, and E:T ratio, a single drugconcentration could potentially result in different trimer concen-trations. Therefore, interpretation of response by drug exposurealone can be misleading. For the CD3 bispecific moleculediscussed in this manuscript (PF-06671008), a bell-shaped dose-response relationship was not observed in mouse xenograftstudies. This is probably because there was high P-cadherinexpression on the tumor cell lines studied and good infiltration ofT cells. In addition, PF-06671008 is a potent drug with low-Kdvalues for P-cadherin and CD3. As a result sufficient, trimerconcentrations were achieved at each dose for efficacy. The bell-shaped relationship has been confirmed for other CD3 bispecificmolecules in-house, where target expression is lower and/oraffinity is weaker. It has also been observed in the literature frommodeling of in vitro and in vivo experimental data (34,35).

Translational QSP Model for CD3 Bispecific Molecules

QSP models, which map out the causal path between drugadministration and effect in a mechanistic framework, can be a

Page 9 of 16 66The AAPS Journal (2019) 21: 66

useful tool to deconvolute complex mechanisms (36). Someexamples of the use of mechanistic PK/PD models to quantifyand understand the system dynamics of CD3 bispecificmolecules are emerging in the literature. For example, Jianget al. (34) proposed a cell-killing model based on target cell-biologic-effector cell complex formation and used it to describeand predict in vitro cytotoxicity data for multiple T cellredirecting bispecific antibodies under different experimental

conditions. Campagne et al. (37) developed a PK/PD model fora bispecific CD123/CD3 DART molecule in non-humanprimates. The model describes DART molecule binding toperipheral CD3 expressing cells and CD123+ cells, T celltrafficking, activation, and expansion, and resulting peripheraldepletion of CD123 cells.

In this manuscript, a translational QSP model isproposed for CD3 bispecific T cell retargeting molecules,

Table III. Predicted Human Parameters Used in Simulations

Parameter Definition Unit Value (CV%) Source

Binding konCD3,koffCD3,Kd_CD3

Binding of PF-06671008to CD3

1/nM/h, 1/hnM

1.72, 19.66, 11.4 (7)

konPcad,koffPcad,Kd_Pcad

Binding of PF-06671008to P-cadherin

1.57, 0.74, 0.47

Central/peripheralcompartment

V1 Volume of distributionin central compartment

mL/kg 40.2 Allometrically scaledfrom cynomolgusmonkey PKanalysis (16)

kel = CL/V1k12 =CLd/V1k21 =Cld/V2

V2 Volume of distributionin peripheralcompartment

mL/kg 211

CL Clearance mL/h/kg 4.61CLD Inter-compartmental

clearancemL/h/kg 25.2

sPcad sPcadherin concentrationin central compartment

nM 1.1 (0.4–4.1) Measured in-house(= 92.7 ng/mL)

Median value ofhealthy subjectsand patient data

kdeg sPcad degradation rate 1/h 0.15 Allometrically scaledfrom cynomolgusmonkey PK analysis(0.31 1/h in cyno) (16)

kdegcx sPcad-PF-06671008complex degradationrate

1/h 0.115 Assumed to equalPF-06671008elimination rate(CL/V1)

Tcellsp T cell concentration incentral compartment

Cells/μL 5000 (23)

CD3 CD3 expression onT cells

Receptors/cell 100,000 (17,18)

Tumor dispositionof PF-06671008/T cells

P Permeability of druginto tumor

μm/d 334 (19)

D Diffusivity of drug intotumor

cm2/d 0.022

ε Void fraction in tumorfor drug

– 0.24

Rcap Capillary radius μm 8Rkrogh Average distance between

2 capillariesμm 75

Tcellsta Number of T cells in

tumorCells/g of tumor 6.49e5 (24)

Tumorcellst Number of Tumor cells Cells/g of tumor 1e8 (21)mPcad P-cadherin expression

on tumor cellsReceptors/cell 28,706 (15)

Rtumor Tumor radius cm 1 Assumedkint Internalization rate

with PBMCsDay−1 0.1728 (−) Estimated from mouse

tumor PK data.Represents 96 hhalf-life ofinternalization.

aAssume no proliferation in tumor; ksyn = kdeg*sPcad. MWt of PF-06671008 = 105 kDa, MWt of sPcad = 85 kDa

66 Page 10 of 16 The AAPS Journal (2019) 21: 66

capable of predicting trimer formation and linking it totumor cell killing in in vivo efficacy models. In addition, themechanistic nature of the model enables the integration ofpatient data/parameters and subsequent clinical predictions.The model consists of 3 parts describing the central, tumor,and effect compartments (Fig. 1a). The first part includes thebispecific antibody PK, binding to circulating T cells, andbinding to the soluble target (when applicable) in the centralcompartment. The second part describes the distribution ofthe antibodies to the tumor compartment using mechanistictumor penetration equations, and parameters calculatedbased on the drug’s molecular weight and tumor size(19,26,38). If the model is being used for a liquid tumor,these drug exchange tumor penetration parameters cansimply be removed, as liquid tumors are assumed to provideless of a diffusion barrier than solid tumors, and equilibriumcan be assumed between drug concentration in the centralcompartment and tumor interstitium. In the tumor compart-ment, the model incorporates binding of the drug to CD3 onT cells and the specific antigen on tumor cells to forminactive dimers and ultimately the active trimers. In themouse model, a simple description of T cell expansion andcontraction is included, constructed using mouse TIL dataand published information. For translation of this model to

human, data on T cell kinetics was not available and insteada baseline concentration of T cells was assumed with noproliferation.

In the third part of the model, the trimer concentration isused as the basis for quantifying tumor volume reduction using atumor growth inhibitionmodel. Themodel used is a transductionmodel describing tumor cell growth and tumor cell killing (as afunction of the tumor trimer concentration). The model param-eters from eachmouse study can be used to calculate a secondaryparameter called the TSC. This is the concentration of trimerwhere the tumor is neither growing nor regressing and can beconsidered as the minimum concentration of tumor trimerrequired for efficacy. The TSC is a useful parameter which canbe used as a pharmacodynamic index to rank compounds, or tounderstand the difference in compound potency across mousexenograft models, or as the denominator in therapeutic indexcalculations.

Application of the QSP Model to Quantify PK/PD Relation-ship for PF-06671008 in Mouse Xenograft Models

The model was used to quantify the preclinical PK/PDrelationship of a CD3 bispecific molecule targeting P-cadherin (PF-06671008). To implement the model, the first

a

b

Fig. 2. a Serum and b tumor PK profiles of PF-06671008 in PBMCengrafted and non-PBMC engrafted HCT-116 tumor-bearing micefollowing single-dose intravenous administration at 0.05 and 0.5 mg/kg

Page 11 of 16 66The AAPS Journal (2019) 21: 66

step was to collect drug and system parameters describingthe mechanism of action in the mouse. To calculate trimerconcentration in the tumor, receptor expression of P-cadherin was determined for the HCT-116 and SUM-149human tumor cell lines used in the mouse xenograftexperiments. P-cadherin receptor expression in both celllines (28,706 for HCT-116 and 17,500 for SUM-149) waslower than the expression of CD3 on T cells (100,000(17,18)). This is typical for CD3 bispecific molecules as anexpression of most tumor targets is less than 100,000 and as aresult, tumor antigen receptor expression can be limiting anda key driver of efficacy. This was exemplified for acarcinoembryonic antigen T cell bispecific (CEA-TCB) forthe treatment of solid tumors. CEA-TCB activity was foundto be strongly correlated with CEA expression, with a higherpotency observed in highly CEA-expressing tumor cells,with a threshold of 10,000 CEA-binding sites/cell (39).Target affinity data was also required to calculate trimerconcentration. PF-06671008 binds to P-cadherin with a Kd of0.47 nM and CD3 with a Kd of 11.4 nM (7). Binding to thetumor target antigen is often more potent than binding toCD3 on T cells in order to target the CD3 bispecific towardthe tumor and away from peripheral tissues (35). Inaddition, strong binding to CD3 has been shown to drivemore rapid clearance of an anti-CD3/anti-CLL1 bispecific inpreclinical in vivo models (40).

The QSP model was used to integrate the mouse PKfor PF-06671008 with the TGI data and to calculate TSCsin T cell engrafted (HCT-116) and T cell adoptive transfer(HCT-116 and SUM-149) established mouse tumor models.TSC values were very similar in the adoptive transfer

model for both the SUM-149 and HCT-116 tumor cell lines(0.0092 and 0.011 pM respectively, with overlapping 80%confidence intervals). In contrast, a sixfold higher TSCvalue was obtained in the T cell engrafted versus T celladoptive transfer model with the same cell line (HCT116,0.064pM), and the respective 80% confidence intervals donot overlap. This is probably due to differences in T cellengraftment between the two mouse tumor models. In theT cell engrafted model, the T cells are administered asfreshly isolated human PBMCs, 7 days prior to drugadministration. In contrast, in the adoptive transfer model,activated T cells are given 1 day post-drug treatment.There are also other factors which can result in differentTSCs including initial tumor size and differences in tumorgrowth rates.

Translation of the Model to the Clinic

The first step in translation to human was the predictionof the clinical PK parameters. For PF-06671008, circulatingsoluble target can act as a sink for the drug and reduce freedrug exposure by forming complexes with PF-06671008. Thereduction of free sPcad concentrations in cynomolgusmonkey following dosing of PF-06671008 has been reportedpreviously (16). The human PK of PF-06671008 was pre-dicted from cynomolgus monkey PK using a two-compartmental PK model which incorporates binding tosPcad. Levels of sPcad were measured in healthy volunteersand in breast, colon, and lung cancer patients and the medianconcentration in cancer patients was used in the humanmodel.

Fig. 3. PF-06671008-induced tumor T cell proliferation in mice bearing HCT-116 tumors with humanPBMC engraftment. Number of CD3+ cells/mg of the tumor (with standard deviations) is plotted againsttime following IV administration of control and PF-06671008 at 10 μg/kg, 50 μg/kg, and 500 μg/kg

66 Page 12 of 16 The AAPS Journal (2019) 21: 66

The next step in the clinical translation process wasthe incorporation of human systems parameters into theQSP model. These parameters are summarized in Table IIIand include T cell concentration in the circulation andtumors, tumor cell concentration, and typical tumor vol-umes in cancer patients. Values for all of these parameterswere obtained from the literature. CD3 receptor expres-sion was kept the same as the mouse model (which usedhuman T cells or PBMCs). P-cadherin expression of 28,706was used in the clinical simulations. This was the value

from the HCT-116 cell line and represents a medium-highlevel expression of P-cadherin measured across humantumor cell lines used in in vitro cytotoxicity experiments(874–37,582 (15)).

The model simulated serum PK and tumor trimerconcentrations following IV infusion of PF-06671008 at 0.01,0.1, and 1 μg/kg QW to cancer patients are shown in Fig. 4 aand b, respectively. In human, the terminal half-life of PF-06671008 was predicted to be approximately 1 day. Theconcentration of trimer in the tumor, which is the morerelevant concentration for efficacy, accumulates slowly (Cmaxapprox. 2 days post first dose) and persists for longer(Fig. 4b). This is due to slow diffusion of the drug into thetumor and formation of a more stable trimer which isretained within the TME. Since receptor expression of tumortarget was known to be a key parameter, a sensitivity analysiswas completed using the human model with P-cadherinexpression varying from 1000 to 28,706 receptors/cell. Thisanalysis confirmed that P-cadherin receptor expression was asensitive parameter and that concentration of trimer formedin the tumor correlates with expression level (Fig. 5a). Thishas an impact on predicted clinical efficacy with a higher doserequired for efficacy in patients with lower P-cadherinexpression. In addition, the T cell number in the tumor wasfound to be a sensitive parameter (Fig. 5b), with a higherpredicted concentration of trimer in the tumor with increas-ing E:T ratio. High doses of PF-06671008 were also simulated,to check to see where the bell-shaped relationship might beobserved. At doses of > 1.8 mg/kg, a reduction in tumortrimer concentration is predicted with increasing dose levels(Supplementary Fig. 4). However, at these doses, thepredicted trimer concentrations in the tumor are high enoughthat good responses would be expected (assuming the doseswould be tolerated). A translational flow diagram describing thesteps taken to translate CD3 bispecific drugs from preclinicalTGI data in the mouse to human is shown in Fig. 6.

The translational QSP model described for CD3bispecific compounds can be used to drive decision-making at different stages of the drug discovery anddevelopment continuum. At early stages, the model canbe used to provide guidance on the compound selection, bypredicting optimal Kd values for CD3 and the tumorantigen. This can be achieved by modeling of in vitro data,using a reduced version of the model without the PK(central and peripheral) compartments. For example, themodel was previously used to describe the in vitro

Table IV. Concentration of Soluble P-Cadherin in Cynomolgus Monkey and Human Serum

Species Disease state n Soluble P-cadherin concentration

Median (ng/mL) Range (ng/mL)

Cynomolgus monkeya Healthy 32 47 29–273Cynomolgus monkeyb Healthy 4 68 57–74Humana Healthy 40 90 45–150Humana Breast cancer patients 23 78 32–190Humana Colon cancer patients 31 102 36–328Humana Lung cancer patients 25 102 65–320

a Samples from Bioreclamation (Westbury, NY)b Samples from in-house studies

b

a

Fig. 4. Model simulated a serum PK and b tumor trimer concentra-tions following IV infusion of PF-06671008 at 0.01, 0.1, and 1 μg/kgQW to cancer patients

Page 13 of 16 66The AAPS Journal (2019) 21: 66

exposure-response of PF-06671008 in cytotoxicity assaysand was able to simultaneously describe the kinetics oftumor and T cells at various E:T ratios (16). Once a leadcompound has been selected, the model can be used topredict clinical doses and regimens and to optimizeefficient clinical study design (41). A precision medicineapproach could be adopted, whereby parameters in themodel such as immune cell numbers, or tumor targetexpression levels, are tailored to individual characteristicsof patients. This could result in a recommendation ofdifferent doses for different patients. The model has alsobeen used to predict clinical starting dose for PF-06671008using a minimal biological effect level approach (MABEL),which is recommended for CD3 bispecific constructs due totheir immune agonistic activity following target engage-ment (16,42). A recent analysis by the FDA concluded that

receptor occupancy-based methods were not advised forCD3 bispecifics. The QSP modeling approach is moresuitable to determine MABEL as efficacy is driven bydrug bound to both T cells and tumor cell, rather receptoroccupancy of either target singly. It is also independent ofE:T ratio or other experimental specificities.

The model in its current state is very useful for a range oftasks from optimization of drug design to clinical dosepredictions. However, opportunities exist to improve themodel. For example, the current model includes an empiricaldescription of T cell activation/proliferation in mouse,constructed based on TIL analysis across dose and time. Amore mechanistic model could be developed by collectionand characterization of more tumor lymphocyte kinetic dataacross species. In addition, the model is based upon a “well-mixed” hypothesis in which tumor target and T cells are

a

b

Fig. 5. Model simulated tumor trimer concentrations at a different P-cadherinreceptor expression values (1000–28,706 receptors/cell) and b different E:Tratios (1:1500–1:15) following IV infusion of PF-06671008 at 0.1 μg/kg QW tocancer patients

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assumed to be homogeneously distributed throughout thetumor environment with equal opportunity for trimer forma-tion. However, tumors are known to be a complex environ-ment with a heterogeneous distribution of T cells and tumorcells expressing target. Future versions of the model will takethis into account.

CONCLUSION

The mechanistic PK/PD model and translationalframework described for CD3 bispecific molecules providea holistic solution for quantitative decision-makingthroughout the drug discovery and development process.In this manuscript, the use of the model to characterizethe in vivo PK/PD relationship of a P-cadherin/CD3bispecific construct (PF-06671008) across mouse efficacymodels is described. The model can also be translated tothe clinic for human PK/PD predictions and sensitivityanalysis to determine important parameters driving effi-cacy. The model can be applied at early stages to aid inthe design of CD3 bispecific constructs and to selectmolecules with optimal properties.

ACKNOWLEDGMENTS

The author would like to thank Brian Peano, MikeCinque, Ed Rosfjord, Tim Fisher, Konstantinos Tsaparikos,and Hui Wang for generation of the mouse xenograft datafor PF-06671008. In addition, many thanks to HannahJones, Wenlian Qiao, and Jatin Narula for useful discus-sions and their critical review of this manuscript.

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