Found in Translation: Maximizing the Clinical Relevance of ... · Experimental Design and Results: In this work, we propose a pharmacokinetic-based strategy to select nonclinical

Cancer Therapy: Preclinical

Found in Translation: Maximizing the ClinicalRelevance of Nonclinical Oncology StudiesMary E. Spilker1, Xiaoying Chen1, Ravi Visswanathan1, Chandra Vage2, Shinji Yamazaki1,Gang Li3, Judy Lucas4, Erica L. Bradshaw-Pierce5, and Paolo Vicini1

Abstract

Purpose: The translation of nonclinical oncology studies is asubject of continuous debate. We propose that translationaloncology studies need to optimize both pharmacokinetic (drugexposure) and pharmacodynamic (xenograft model) aspects.While improvements in pharmacodynamic translatability can beobtained by choosing cell lines or patient-derived xenograftmodels closer to the clinical indication, significant ambiguityand variability exists when optimizing the pharmacokinetic trans-lation of small molecule and biotherapeutic agents.

Experimental Design and Results: In this work, we propose apharmacokinetic-based strategy to select nonclinical doses forapproved drug molecules. We define a clinically relevant dose(CRD) as the dosing regimen in mice that most closely approx-

imates the relevant pharmacokinetic metric in humans. Suchmetrics include area under the time–concentration curve andmaximal or minimal concentrations within the dosing interval.Themethodology is applied to six drugs, including targeted agentsand chemotherapeutics, small and large molecules (erlotinib,dasatinib, vismodegib, trastuzumab, irinotecan, and capecita-bine). The resulting efficacy response at the CRD is comparedwith clinical responses.

Conclusion: We conclude that nonclinical studies designedwith the appropriate CRDs of approved drug molecules willmaximize the translatability of efficacy results, which is criticalwhen testing approved and investigational agents in combina-tion. Clin Cancer Res; 1–11. �2016 AACR.

IntroductionAs oncology research advances, the field is increasingly aware of

the biological and methodological complexities intrinsic to can-cer treatment and oncology drug development. It is no longersufficient to focus on a single aspect of amolecular pathway whileignoring redundancies and alternative pathways. This realizationnaturally shifts the focus of researchers to combination therapies,which are becoming more commonplace in treatment regimens(1). However, combination approaches amplify the already chal-lenging paradigm of drug discovery and development in oncol-ogy. Furthermore, it is not feasible to test multiple drug permuta-tions in humans to select the best combination, as this approachquickly leads to a combinatorial problem (2). Thus, while con-siderable attention is being devoted to optimizing dosing regi-

mens in the clinic (3), increased emphasis is also being placed onnonclinical studies and their ability to assess synergistic combina-tions and evaluate early drug discovery questions related toscheduling and dosing regimens. This necessitates that nonclin-ical systems become as translatable as possible and closer to thehuman paradigm.

In this regard, progress is being made in the biological trans-latability of nonclinical tumor models to human cancers. Forexample, genetically engineered mouse models (GEMM) andpatient-derived xenograft (PDX) models are utilized more fre-quently in nonclinical studies and are thought to better reflect thegenetic and biologic characteristics of human cancers than tradi-tional xenograft models derived from standard cell lines (4).While it is certainly important for the biological aspect (pharma-codynamics) of the model system to be as close as possible to thetarget indication, an equally important aspect of nonclinicalstudies that does not receive much attention is the translatabilityof the drug concentrations (pharmacokinetics) used to probe theunderlying biology. We posit that this is a critical element in thedesign of translatable nonclinical studies.

It can be challenging to identify a dose in mice (the mostcommon nonclinical species for oncology studies) that appro-priately reflects clinical drug concentrations. This may be onereason why doses used in published nonclinical studies can varywidely and often reflect the MTD in nonclinical species, ratherthan a clinically relevant dose (CRD). A literature survey of 5different drugs showed significant variability in the selected doseanddosing protocol from study to study (Fig. 1).While it has beencommon practice to evaluate drug response at the mouse MTD,this canhave limited value froma translational perspective as drugconcentrations can vary significantly between an MTD dose inmice and concentrations achieved in humans, especially forcytotoxic compounds. If the drug concentrations at the mouse

1Department of Pharmacokinetics, Dynamics and Metabolism – New BiologicalEntities, Pfizer Worldwide Research and Development, San Diego, California.2Department of Pharmacokinetics, Dynamics and Metabolism – New BiologicalEntities, Pfizer Worldwide Research and Development, Groton, Connecticut.3Ignyta, Inc., San Diego, California. 4Oncology Research Unit, Pfizer WorldwideResearch and Development, Pearl River, New York. 5Global DMPK, TakedaCalifornia, San Diego, California.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Current address for C. Vage: DSAR US, Sanofi Genzyme, Waltham, MA; andcurrent address for P. Vicini, Clinical Pharmacology and DMPK, MedImmune,Cambridge, United Kingdom.

Corresponding Author: Mary E. Spilker, Pfizer Inc., 10646 Science Center Drive,San Diego CA 92121. Phone: 858-526-4016; Fax: 484-323-8332; E-mail:[email protected]

doi: 10.1158/1078-0432.CCR-16-1164

�2016 American Association for Cancer Research.

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MTD are significantly higher than those attainable in humans, theclinical relevance of the study results is questionable. Recent workhas also suggested that nonclinical tumor growth inhibition (TGI)results determined at concentrations that are relevant to humanexposures better correlate to clinical outcomes than do responsesdetermined atmouseMTDconcentrations (5). Furthermore, oncean oncology drug has progressed to the clinic and its recom-mended phase II dose (RP2D), or its approved dose has beendefined, alongwith plasma concentration data, future nonclinicalstudies can leverage this information todesigndosingprotocols intumor-bearing mice within drug concentration ranges that arerelevant to patients.

For many drugs, access to the appropriate clinical dosingprotocol and summary pharmacokinetic data is not the majorhurdle when determining a CRD, as this information can mostlybe found within the public domain (Table 1). The more chal-lenging aspect is the method by which clinical information isback-extrapolated to nonclinical species, which has been tackledwith a great degree of variability. Some studies have scaled theclinical dose between species using an approach that normalizesthe dose by body surface area. This scaling approach was initiallydeveloped in an attempt to scale toxic doses between species (6).While it is helpful in the forward scaling from nonclinical speciesto humans for first-in-human studies (7), it is not optimal in theback-extrapolation to nonclinical studies, where efficacy read-outs are the primary focus. The method also ignores inter-species

differences in plasma protein binding (PPB) and each molecule'sADME (absorption, distribution, metabolism, and excretion)characteristics.

The work presented here highlights an alternative approach toestimate CRDs for nonclinical studies that leverages human andmouse pharmacokinetic information. Simply put, the proposedmethod attempts to match unbound drug plasma concentrationsobserved in humans by adjusting the mouse dose and dosingschedule to arrive at similar unbound plasma concentrations inmice. For most drugs, it is unrealistic to directly match the fullpharmacokinetic profile in humans and mice, therefore, a sum-mary pharmacokinetic metric (Cmax, AUC, etc.) that is mostclosely related to the efficacy is used to arrive at a dose or rangeof doses that best represents the same metric in humans. Anoverview of the method is shown in Fig. 2 and described in detailin the Materials and Methods section. In principle, this concept isstraightforward, yet in reality the details of how to perform theback-extrapolation are not trivial and require investigation andconfirmation that the appropriate pharmacokinetic metric is

Table 1. Online sources used in this work to retrieve summary information about specific drugs

Drug Subject Link

Erlotinib EMA Scientific Discussion http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Scientific_Discussion/human/000618/WC500033991.pdf

Erlotinib Label http://www.accessdata.fda.gov/drugsatfda_docs/label/2010/021743s14s16lbl.pdfIrinotecan Label http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/020571s042lbl.pdfDasatinib Label http://www.accessdata.fda.gov/drugsatfda_docs/label/2010/021986s7s8lbl.pdfVismodegib Label http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/203388lbl.pdfVismodegib Clinical pharmacology and

biopharmaceutics reviewhttp://www.accessdata.fda.gov/drugsatfda_docs/nda/2012/203388Orig1s000ClinPharmR.pdf

Vismodegib FDA briefing package http://www.fda.gov/downloads/AdvisoryCommittees/CommitteesMeetingMaterials/Drugs/OncologicDrugsAdvisoryCommittee/UCM277585.pdf

Trastuzumab Label http://www.accessdata.fda.gov/drugsatfda_docs/label/2010/103792s5250lbl.pdfDruginformation

drugs@FDA http://www.accessdata.fda.gov/scripts/cder/drugsatfda/

Translational Relevance

Translatable nonclinical studies are a vital component ofcancer research. The complexity of the disease requires con-trolled studies that can explore wide-ranging hypotheses andtreatment scenarios prior to testing in humans. To increaseconfidence in the translation of results from nonclinical stud-ies, the entire nonclinical experiment should be designed andevaluated from the perspective of the corresponding humanscenario. This requires assessing not only the genetic andbiological correspondence of the tumor model (the pharma-codynamics), but also the drug concentrations used to perturbthe underlying biology (the pharmacokinetics). This workpresents an approach to ensure that nonclinical studies arecarried outwithin clinically relevant drug concentration rangesand illustrates its application through a series of case studies.

Figure 1.

Doses ranges observed in literature survey of in vivo combination studies. Giventhe variety of dosing protocols utilized, a weekly dose was calculated forcomparability. Search terms: drug name þ "combination" þ "xenograft".Acceptance criterion: the in vivo drug response (e.g., tumor growth inhibition)was reported.

Spilker et al.

Clin Cancer Res; 2017 Clinical Cancer ResearchOF2




matched between humans and mice. For example, we hypoth-esize that the efficacy, as defined by the nonclinical TGIresponse, of most molecularly targeted therapies is related tothe average drug exposure within the dosing interval. Therefore,for this class of oncology drugs, the back-extrapolation will bedesigned to match unbound average drug concentrations atsteady state within the dosing interval (uCav,ss). This strategywill be applied to four molecularly targeted drugs and a test ofour hypothesis against the results (5) presented by Wong andcolleagues will be provided. Two additional case studies will bepresented to illustrate the unique challenges that arise whenworking with cytotoxic prodrugs.

Materials and MethodsOutlined here are themost critical steps to calculate theCRD for

nonclinical studies (Fig. 2). The steps are a mixture of literaturesearches and data processing that can be applied to any com-pound for which a RP2D or an approved dose has been defined.

Step 1. Define human pharmacokineticsFor drugs that have reached their RP2D or have been approved,

details about the drug's exposure can be utilized to execute futurenonclinical studies at achievable clinical concentrations. Theclinical information required for the calculation is the plasmaprotein binding (PPB) or fraction unbound (Fu) in plasma andpharmacokinetic parameters such as the average (Cav,ss), maximal

(Cmax,ss), and minimal (Cmin,ss) drug concentration and the areaunder the concentration–time curve (AUCss) within the dosinginterval, at steady state.

Step 2. Select scaling methodSelection of the best metric for back-extrapolation of the drug

of interest should be based on biological considerations such asthe mechanisms of anticancer action. For example, molecularlytargeted therapies calculate the CRD by matching the humanand mouse average unbound plasma exposure at steady state(uCav,ss). While this metric has also been utilized for cytotoxicagents, it is unclear whether this is the best method or whether itmay be better to match Cmax or another metric. Dose fraction-ation studies are a useful approach to determine the pharma-cokinetic metric most closely related to the pharmacology. Theseare pharmacology (TGI) studies that change the dose and dosingschedule between treatment groups so that AUC remain con-stant between groups, but Cmax and Cmin values differ. If con-sistent TGI responses are achieved in groups with matchingAUCs, then AUC is considered the primary driver of the phar-macology. If the groups with higher Cmax values consistentlyshow better TGI responses, then it is likely that Cmax is playing arole in the pharmacology and may be the most appropriatemetric to match in the CRD calculation. Pump studies whichprovide a constant level of exposure may also be useful whenevaluating whether a threshold (Cmin) concentration is the mostmeaningful back-extrapolation metric.

Clinical response100

50

00 4 8 12 16 20 24

Time (months)

Time (days)

Sur

viva

l pro

babi

lity

(%)

Tum

or v

olum

e (m

m3 )

1. Human pharmacokinetics (AUC, Cmax, Cmin...)

2. Matching strategy Select Human PK Metric (e.g. AUC) to match.

3. Mouse pharmacokinetics(AUC, Cmax, Cmin...)

4. Select mouse dose tomatch human PK metric

Met

ric

Mouse dose Clinicaldose

Clinically relevant dose (CRD)

Translatable nonclinicalstudies

Goal:More translatablebiological response.

Clinicallyrelevant

dose

2,000VehicleDose 1

Dose 2CRD

Dose 4

1,500

1,000

500

00 3 6 9 12 15 18 21

Figure 2.

A graphical summary of the required steps for calculation of the CRD for anticancer compounds with the goal of improving translation to the clinic.

Maximizing Clinical Relevance of Nonclinical Studies

www.aacrjournals.org Clin Cancer Res; 2017 OF3




Step 3. Define nonclinical pharmacokineticsPlasma drug concentrations from the appropriate animal

model should be acquired. When possible, it is preferable thattumor-bearing animals be used to minimize drug distributiondifferences between tumor-bearing and na€�ve animals. Action-able knowledge about mouse pharmacokinetics can beachieved by collecting drug concentrations over time and fittinga mathematical model to the data to calculate pharmacokineticmetrics for a variety of dosing regimens. Alternatively, if theplasma concentration is intensely sampled, the pharmacoki-netic metrics can be calculated directly from the data usingnoncompartmental approaches (8, 9). The values should becalculated or simulated at steady state, to match the steady-statevalues obtained in humans. It is suggested that at least two doselevels with sufficient exposure separation be explored to assessany nonlinearities in the pharmacokinetics. The PPB or Fu inthe mouse strain of interest should also be obtained to converttotal drug concentrations to unbound drug concentrations forthe cross-species comparison.

Step 4. Adjust nonclinical dose to match human metricThemouse dose can be adjusted through simulations or simple

scaling methods (if the pharmacokinetics is linear) to match thehuman metric agreed upon in Step 2. After selecting the CRD, aprospective experiment should be carried out tomeasure the drugconcentrations at that dose to verify the calculation. This isparticularly important if the CRD calculations are based solelyon literature information.

Underlying assumptions (Table 2)The approach outlined here is consistent with the free drug

hypothesis (10). Briefly, the concept puts forth that only theunbound drug concentrations can interact with the target to elicita pharmacologic effect. Thus, to execute a study atmatching in vivounbound drug concentrations, the PPB in humans and nonclin-ical species must be taken into account.

The CRD method further assumes that the tumor evaluated inmice has similar properties to human tumors in terms of itsarchitecture (e.g., vasculature, cellular density), transporters, andother aspects that impact the tissue pharmacokinetics of the drug.This assumption allows for the use of plasmapharmacokinetics asa surrogate for tumor pharmacokinetics, further assuming thatwhen steady-state drug levels are achieved, tumor concentrationswill be in equilibriumwith the plasma concentrations. The use oftumor concentrations in translational modeling has been advo-cated when there is a clear plasma–tumor disconnect (11). How-ever, it is usually difficult to estimate unbound drug in tumortissue and this data may not be available in humans. The suit-ability of these assumptions should be assessed independently foreach case.

Data extraction and modelingTo illustrate that the CRD calculation is viable for both aca-

demic and industrial scientists, the majority of data reported herewas collected from openly available sources, including journalarticles, drug package inserts and information available on theDrugs@FDA website (Table 1). Data was extracted from pub-lished plots using the program DigitizeIt v2.0.3 (http://www.digitizeit.de). Mathematical modeling and simulations, whereapplicable, were performed using the SAAM II v2.0 software(ref. 12; The Epsilon Group).

The PK-TGI models previously implemented by Wong andcolleagues (5) were used to assess the translatability of studiesexecuted at the CRD for molecularly targeted drugs. For thisanalysis, the original model parameters describing the humanpharmacokinetics were modified to reflect mouse pharmacoki-netics at the CRD. The mathematical models were then executedand the resulting TGIwas assessed at 21 days following the start ofdosing, using the same methodology as in the original work byWong and colleagues (5).

ResultsThree case studies illustrating the CRD methodology are pre-

sented for a molecularly targeted agent (erlotinib) and twochemotherapeutic compounds (irinotecan and capecitabine). Forsimplicity, mean values of the clinical pharmacokinetic metricsare reported throughout the text, while their associated variability(SDs and coefficients of variation) are provided in Table 3. Whilethe general strategy is consistent between compounds, each casestudy presents a slightly different challenge in the back-extrapo-lation of concentrations from humans to mice and how they canbe addressed in practice. Additional molecularly targeted thera-pies (dasatinib, vismodegib, and trastuzumab) are included in theSupplementary Material and contribute to the assessment oftranslational relevance.

Case 1: erlotinibHuman pharmacokinetics. Erlotinib is a small-molecule EGFRinhibitor approved for the treatment of patients with non–smallcell lung cancer (NSCLC), where the recommended dose is 150mg taken orally, once a day (erlotinib label; Table 1). Informationabout erlotinib's human exposure at this dose is available fromthe EMA Scientific Discussion (Table 1) and suggest that inhumans, the uCav,ss is 142.8 ng/mL. Erlotinib accumulates inhumans, so additional relevant estimates of steady-state exposureare uCmax,ss (165.6 ng/mL) and uCmin,ss (102.8 ng/mL). Thesevalues were calculated from the reported total drug metricsassuming a Fu of 0.08 in humans [EMA Scientific Discussion(Table 1)].

Scaling method. It is assumed that matching erlotinib's uCav,ss

within the dosing interval would be an appropriate metric fortranslation.

Mouse pharmacokinetics at the mouse dose adjusted to matchhuman metric. A mathematical model for mouse exposure oferlotinib was derived from internal data and was used to sim-ulatemultiple dosing schemes. Amouse Fuof 0.035was used forthe calculations.

To find the CRD, different doses and dosing schedules weresimulated and the average unbound drug concentrations within

Table 2. Primary assumptions of the CRD methodology

Free (unbound) drug interacts with target to have a pharmacologic effect.* The method uses mouse and human PPB to correct for different levels of

protein binding in humans and mice.Plasma concentrations are a surrogate for the site of action (tumor)concentrations.

* This requires that mechanisms governing drug uptake, distribution, andinteraction with the target are similar in human and mouse tumors.

A pharmacokinetic metric (e.g., AUC, Cmax, etc.) is correlated with thepharmacologic response in humans and mice.

Spilker et al.





Table

3.Sum

marypha

rmacokine

ticmetrics

from

clinically

approve

ddosesan

dco

rrespond

ingmouseCRD

Erlotinib

Dasatinib

aVismodeg

iba

Trastuzu

mab

aIrinotecan

(SN38

)Cap

ecitab

ine

(5-FU)

Clinical

inform

ation

Clinical

dose

andregim

en150mgQDp.o.

100mgQDp.o.

150mgQDp.o.

4mg/kgi.v.w

eek

1an

d2mg/kgi.v.

QW

afterwee

k1

125mg/m

2QWx3

350mg/m

2Q3W

850

–1,2

50mg/m

2

BID

p.o.o

nday

s1–14

ofa3-wee

kcycle

Hum

anFu

0.083

0.06

<0.01

1.00

0.04–0.06

0.9

Cmax,ss(ng/m

L)165.6(75.2)

7.7[57%

]14,300(5,10

0)

123,000

26.3

(11.9

)56

.0(28.2)

n.r.

Cmin,ss(ng/m

L)102.8(68.6)

n.r.

n.r.

79,000

n.r.

n.r.

n.r.

AUCtau,ss(ng� hr/mL)

3,400(1,800)

28.7

[43%

]n.r.

14,900mg

/h� m

L22

9(108)

474

(245)

n.r.

Tau

(h)

2424

n.r.

168

2424

n.r.

Cav,ss(ng/m

L)142.8(76.1)

1.2[43%

]11,800(4800)

89,000

n.r.

n.r.

400ng

/g

Metricmatch

edUnb

oun

dplasm

aCav,ss

Unb

oun

dplasm

aCav,ss

Totalplasm

aCav,ss

Totalplasm

aCav,ss

Totalplasm

aSN

38AUC

5-FUtumorco

ncen

trations

Non

clinical

inform

ation

MouseCRDan

dregim

en25

mg/kg

BID

p.o.

50mg/kg

QDp.o.

4.5

mg/kg

QDp.o.

75mg/kg

QDp.o.

13mg/kgi.v.

W1&4.5

mg/kgi.v.Q

W2–5.5mg/kgi.v.,

QDx3

4–12mg/kgi.v.,

Q4D

120–25

0mg/kg

QDp.o.(dep

enden

tup

onxeno

graftmodel)

MouseFu

0.035

0.08

0.0134(0.0017)

1.00

0.012–0

.034

0.89

Cmax,ss(ng/m

L)27

8484

3.57

20,700

133,000

80–220

160–4

80

n.c.

Cmin,ss(ng/m

L)44.7

11.4

0.06

4,850

72,700

<1�

10�3

<1�

10�3

n.c.

AUCtau,ss(ng� h/m

L)3,20

03,20

028

.16.1�

106

15,300mg

/h� m

L117–

321

233–70

0n.c.

Tau

(h)

2424

2450

4168

2424

n.c.

Cav,ss(ng/m

L)133

133

1.17

12,100

95,000

4.9–13.4

9.7–2

9.2

�400ng

/g

NOTE:V

alue

sreported

asmea

n(SD)ormea

n[CV%].Matchingmetrics

inhu

man

san

dmicearebolded

toea

seco

mparisons.

aDasatinib,v

ismodeg

ib,a

ndtrastuzumab

analyses

areinclud

edin

Sup

plemen

tary

materials.

Abbreviations:B

ID,twicedaily;n.r.,no

treported

;n.c.,no

tcalculated

;p.o.,orally;Q

D,onceaday

;QW,o

nceawee

k;QWx3

,oncewee

klyfor3wee

ks;W

1,wee

k1;QDx3

,oncedailyfor3co

nsecutiveday

s;Q4D,o

nceev

ery4days.






the dosing interval were calculated. Doses of 25 mg/kg twice dailyand 50 mg/kg once a day result in average drug concentrations of130.5 and132.6ng/mL, respectively,which are reasonable approx-imations of the clinical uCav,ss. This is especially true when con-sidering the variable pharmacokinetics observed clinically (Table3). Because of erlotinib's short half-life in mice (4.3 hours in miceversus 36.2 hours in humans), the uCmax,ss (278.0 and 483.9 ng/mL) and uCmin,ss (44.7 and 11.4 ng/mL) values at the two non-clinical doses differ from the human values. While the 25 mg/kgtwice daily and 50 mg/kg once a day doses provide equivalentaverage concentrations, the 25mg/kg twice daily dose provides theclosest match to the full human exposure profile (Fig. 4A).

Case 2: irinotecanThe back-extrapolation of exposures from humans to mice is

particularly challenging for cytotoxic compounds such as iri-notecan. As with most oncology drugs, cytotoxics are typicallydosed in humans near their human MTDs; however, this classof drugs is often accompanied by dosing holidays that allow thepatient time to recover from the drug's toxic effects before thenext dose is administered. This creates a challenge when defin-ing the correct dosing schedule in animals. The other challeng-ing aspect specific to irinotecan is that it is a prodrug for SN38,making SN38 the most meaningful drug concentration thatshould be matched between humans and mice. To furthercomplicate this assessment, it is currently unclear whether Cmax

or AUC is the best metric to match when performing back-extrapolation for cytotoxic molecules. We will assume that theappropriate metric to match between humans and mice is theSN38 plasma AUC.

Human pharmacokinetics. A range of irinotecan doses and doseschedules can be used in the clinic, depending on the conditionbeing treated. Two example dosing regimens are 125mg/m2 givenas a 90-minute i.v. infusion onceweekly for 3weeks followed by a

2-week dosing holiday, and 350 mg/m2 given as a 90-minute i.v.infusion once every 3 weeks (irinotecan label, Table 1). These twodosing regimens require different nonclinical CRDs and dosingschedules.

SN380s clinical AUC0-24 was reported to be 229 ng�h/mL at a125mg/m2 dose and 474 ng�h/mL at a 340mg/m2 dose. At thesedoses, the terminal half-life of SN38 has been reported to bebetween 10 and 20 hours in humans (irinotecan label, Table 1).As SN38 concentrations would be negligible at the end of thedosing interval, no accumulation of the compound in plasma isexpected with each additional dose. Therefore, pharmacokineticcomparisons following the first dose are sufficient for the backextrapolation, as accumulating steady-state plasma concentra-tions will not be achieved with a dosing regimen of once weeklyor longer.

Scaling method. In the back-extrapolation, SN38 exposure withinthe dosing interval will be matched. In addition, as SN38 has asimilarly high PPB in humans (94%–96%; ref. 13) and mice(96.6%–98.8%; ref. 14), total SN38 concentrations were used inthis assessment.

Mouse pharmacokinetics at the mouse dose adjusted to matchhuman metric. The mouse pharmacokinetics was simulatedusing the mathematical model developed by Zamboni andcolleagues(15). After simulating a range of doses, it was foundthat for the lower human dose of 125mg/m2 given once weekly,the matching dose in mice ranges from 2 to 5.5 mg/kg [AUC0–24

range: 117–321 (nonclinical); 121–337 (clinical) ng�h/mL],with the best match of the mean value occurring at 4 mg/kg(233 ng�h/mL).

The model predicts that irinotecan doses of 4–12 mg/kg willcover the range of clinical exposures that can be observed at the340 mg/m2 dose [AUC0–24 range: 233–700 (nonclinical); 229–719 (clinical) ng�h/mL]. The best approximation to the clinicallyobserved mean value is 8 mg/kg (Fig. 4E), which results in amodel-derived AUC of 466 ng�h/mL. Given that the originalclinical dose of interest is 350 mg/m2, not 340 mg/m2, a slightlyhigher dose may be warranted. Systemic SN38 concentrationshave been reviewed in a recent publication which arrives at asimilar conclusion, suggesting that a 10 mg/kg dose in mice willencompass the range of reported SN38 clinical exposures (16).

A particularly challenging aspect for irinotecan CRD is selectingan optimal dosing interval. SN38 is reported to have a terminalhalf-life of 10 to 20 hours in humans, whichmeans that nearly allof the drug will be cleared in 2–5 days, yet the dosing interval is 1–3 weeks. In mice, the terminal half-life is reported to be approx-imately 2.5 hours (range: 1–6hours), so themajority of the drug iscleared within a day. Given the long interval during which nosystemic exposure to SN38 occurs in either humans ormice, thereis uncertainty about the appropriate dosing schedule to use innonclinical studies.

One method to adjust dosing schedules is to match thenumber of half-lives within the dosing interval. Assuming anaverage human half-life of 15 hours, there will be 34 half-liveswithin a 3-week dosing interval and 11 half-lives within a 1-week dosing interval. For the mouse dosing scheme to span thesame number of half-lives, the dosing intervals should be 3.5and 1.2 days, respectively. Using this approach, a recommen-dation to match the SN38 exposure in humans subsequent to a350 mg/m2 once every 3 weeks clinical dose of irinotecan is to

Figure 3.

Comparison of 21-day TGI evaluated with human pharmacokinetics (PK) ormouse pharmacokinetics at the CRD. The plots show that the TGI responsesdriven by human PK (from Wong and colleagues; ref. 5) or mousepharmacokinetics at the CRD (this report) are consistent with one another,suggesting that matching unbound AUC levels between either humansor mice is appropriate for the targeted therapies reviewed here. Solid line,y ¼ 0.906x þ 6.55; R2 ¼ 0.94; dotted line, unity line.

Spilker et al.





dose irinotecan in mice at 4–12 mg/kg given once every 4 days.For the lower, more frequent monotherapy dose of 125 mg/m2

once weekly for 3 weeks, the recommended dose would be 2–5.5 mg/kg given once daily for 3 days, followed by a 2-dayholiday in each cycle.

Case 3: capecitabineHuman pharmacokinetics. Capecitabine is the oral prodrug for 5-fluorouracil (5-FU). In humans, the monotherapy regimen forcapecitabine is 850 mg/m2 to 1,250 mg/m2 twice daily (12 hoursapart) for 1–14 days in a 3-week cycle (17). The clinical data used

Figure 4.

Steady-state human and mouse plasma concentrations. Clinical concentrations are represented by black symbols; mouse concentrations are represented by lines.See Supplementary Materials for a full listing of referenced data and CRD calculations for dasatinib, vismodegib, and trastuzumab.






to assess humanexposurewas extracted from the studybySchullerand colleagues (18), in which 11 colorectal patients due forresection of the primary tumor were given capecitabine at1,255 mg/m2 twice daily for 5–7 days and tumor, plasma, andnormal tissue were collected at 2–12 hours after the last dose. Thetumor 5-FU concentration values ranged from 45.8–1,868 ng/g,highlighting the large degree of variability present in the clinicaldata. Combining these measurements into a single time course(Fig. 4F), the average tumor 5-FU concentrationwithin the dosinginterval is approximately 400 ng/g.

Scaling method.Given the potential differences in conversion ofcapecitabine to 5-FU in humans and mice, 5-FU levels are thebest comparator. In addition, capecitabine is designed to gen-erate greater 5-FU concentrations in tumor tissue comparedwith systemic concentrations, due to higher tumor levels of thespecific enzymes involved in its conversion to 5-FU (19).Therefore, 5-FU concentrations in human tumors (18) andmouse xenograft tumors (19) were chosen as the metric tomatch in the back-extrapolation of capecitabine for nonclinicalstudies.

Mouse pharmacokinetics at themouse dose adjusted tomatch humanmetric. In a previous study, mouse xenograft 5-FU tumor con-centrations were obtained at multiple time points following asingle oral dose of 539 mg/kg capecitabine (19). Four differentcolon tumor models were evaluated in this study (HCT116,CXF280, Colo205, and WiDr). The data provided in Fig. 2 of theoriginal article were digitized and an estimate of the averagetumor concentrations over 24 hours ranged from 861 to 1,826ng/g. As summarized in Fig. 4F, these concentrations encompassthe higher end of the clinical tumor exposures measured bySchuller and colleagues (18). On the basis of this data, a 500mg/kg daily oral dose of capecitabine appears to provide tumorexposures that would encompass the higher end of clinicallyobservable concentrations. Lower doses could be explored if amore conservative approach was desired, for example assumingthe 5-FU concentrations extrapolate linearly from the 539 mg/kgdose to lower doses, then, depending on the tumor xenograft, adose of 120–250 mg/kg would provide average tumor 5-FUconcentrations near 400 ng/g. In this example, literature data areused to estimate a potential dose for future studies. It is recom-mended that tumor concentrations be measured during futurestudies to verify that appropriate concentrations are achieved inthe tumor of interest.

Comparison of nonclinical and clinical efficacy for targetedtherapies

To test the hypothesis that matching average steady-statedrug concentrations is an appropriate back-extrapolation meth-od for molecularly targeted therapies, an extension of theanalysis by Wong and colleagues was performed (5). Wongand colleagues' study showed that a better correlation betweenthe nonclinical TGI and the overall clinical response could beobtained when the mouse efficacy (TGI) model was driven bythe clinical pharmacokinetics, rather than the mouse pharma-cokinetic profiles at mouse MTDs of erlotinib, dasatinib, vis-modegib, and trastuzumab. The goal of the current work was toreplicate this finding using mouse pharmacokinetic profiles atthe CRDs, rather than using the human pharmacokinetics asoriginally assessed (5). Dasatinib, vismodegib, and trastuzu-

mab CRD calculations closely follow that provided by theerlotinib case study and are included in the SupplementalMaterial. Fig. 3 illustrates that there is good agreement betweenthe original approach with clinical pharmacokinetics and themouse CRD approach when comparing the simulated 21-dayTGI responses. While the sample size is low, this trend supportsthat the average drug concentration at steady state is an appro-priate metric for the back-translation of molecularly targeteddrug exposures from humans to mice. These results leverage theprevious finding that translatable nonclinical results can beobtained when assessed at relevant drug concentrations andincreases confidence that mouse pharmacokinetics can be tai-lored to provide the relevant drug concentrations.

In addition to the previous assessment that supports the useof Cav,ss as an appropriate back-extrapolation metric, it is alsoimportant to examine the biological responses in mice andhumans at the matching doses. Erlotinib's response in differenttumor models highlights the importance of evaluating efficacyat relevant concentrations to achieve translatable responses.Nonclinically, erlotinib was studied using the wild-type non–small cell lung cancer (NSCLC) H460a and A549 cell lines (20)to investigate its ability to induce TGI. At the mouse MTD of100 mg/kg once a day orally, erlotinib induced an encouraging71% TGI in H460a and 93% TGI in A549. However, at a dose of25 mg/kg once a day orally, which is closer to the estimatedCRD, only 46% (H460a) and 48% (A549) TGI was observed.The lower TGI results at the 25 mg/kg dose appear to beconsistent with the clinical results from the monotherapy armof the TRIBUTE trial (21), where NSCLC patients were dosed at150 mg once a day and showed no difference with placebo inoverall survival, time to progression or objective response,except for self-reporting nonsmokers. On the other hand, inEGFR-mutant cell lines, nonclinical evidence showed that EGFRinhibitors such as erlotinib are more effective at the CRD (50mg/kg), resulting in tumor stasis at the end of treatment in theNR6-EGFR Del(746-752) tumors and slight tumor growth inthe NR6-EGFR L858R tumors (22). This encouraging biologicalresponse has also been shown in the clinic, where the EURTACtrial for NSCLC with EGFR mutations (exon 19 deletion orL858R mutation in exon 21) showed a HR of 0.37 for thesepatients compared with standard chemotherapy (23).

Dasatinib's translation provides a unique perspective on therequired level and duration of target (phospho-BCR-ABL)modulation. Nonclinical investigations of dasatinib (24) inthe K562 CML xenograft model suggest that phospho-BCR-ABLcoverage at an EC90 was needed for over 3 hours in mousemodels and more than 6–8 hours clinically, which occur atunbound plasma concentration of 0.87 ng/mL. Clinically, at anoral dose of 70 mg daily, a phase II trial of dasatinib showed90% complete hematologic response (25) with correspondingplasma concentrations greater than 0.87 ng/mL for severalhours. In a subsequent phase III study, 92% complete hema-tologic response was achieved at a dasatinib dose of 100 mgdaily (26). From pharmacokinetic data obtained in healthyvolunteers (27) at 100 mg, the average unbound concentrationwould be 0.78 ng/mL, again supporting target coverage abovean EC90 for a duration of time. Thus, it can be appreciated fromthe plot of human and mouse pharmacokinetic profiles (Fig.4B) that the mouse CRD will result in a similar length of timeover the EC90 (dotted line) as obtained clinically, which resultsin a clinically relevant biomarker response.

Spilker et al.





DiscussionTranslatable nonclinical studies are a vital component of cancer

research (28). The complexity of the disease requires controlledstudies that can explore wide-ranging hypotheses and treatmentscenarios prior to testing in humans. To increase confidence in thetranslation of results from nonclinical studies, the entire nonclin-ical experiment should be designed and evaluated from theperspective of the corresponding human scenario. This requiresassessing not only the genetic and biological correspondence ofthe tumor model (the pharmacodynamics), but also the drugconcentrations used to perturb the underlying biology (the phar-macokinetics). The work presented here has focused onapproaches to ensure that nonclinical studies are carried outwithin clinically relevant drug concentration ranges.

The proposed method uses plasma drug levels in the extrap-olation from humans to mice. Ideally, the drug concentrationsand/or the biomarker response at the site of action, namely thetumor, should bematched. Exposure at the site of action is one ofthe "three pillars" of drug discovery and development (29), withthe other two being target modulation and subsequent efficacy.However, this information has not routinely been collected inoncology clinical trials, and is often confounded by the hetero-geneity of the tumor (30) and the uncertainty in assessing the freedrug concentration in tumor tissue. While we are hopeful that inthe future it may be possible to match biomarker (target) mod-ulation in mice and humans, which would be the biologicallymost appropriate way to design equivalent regimens, for themajority of drugs this is an unrealistic expectation at this time.Therefore, the best option at themoment is tomatch plasma drugconcentrations.

The method presented here makes the assumption that theunbound plasma concentrations reflect unbound tumor concen-trations, and further assumes that the general delivery and trans-porter profiles of human tumors are reasonably approximated inthe xenograft, PDX, or GEMM models used in the nonclinicalsetting (Table 2). While tumors are heterogeneous, on a systemicscale, we believe the initial assumption is reasonable for small-molecule drugs that reach and maintain steady-state concentra-tions in plasma. In this case, when steady state is reached, the freedrug in the tissue is in equilibrium with plasma. Given thisassumption, using unbound steady-state plasma concentrationsas the matching entity is sufficient. The assumption needs to befurther investigated for monoclonal antibodies and small-mole-cule drugs that may not reach steady-state levels within therequired dosing intervals.

The CRD method has primarily has been applied to exampleswhere a single active component is responsible for the pharma-cologic response. However, situations may arise where both theparentmolecule and itsmetabolite(s) are active.Givendifferencesin metabolic pathways between species, the kinetics of the parentandmetabolite can differ, necessitating that the active parent andactive metabolite concentrations be measured in both humansand mice prior to making the CRD calculation. While the secondcase study (irinotecan) illustrates how to handle the prodrugscenario, situations involving both an active parent and activemetabolite are more complex. One approach to managing thissituation is to create a composite time–concentration profile ofthe active components, where each contributing profile is weight-ed by its corresponding potency value against the target. Thiscomposite profile can thenbeused in theCRDcalculationonce an

appropriate back-extrapolationmetric is defined. Thus, one couldenvision that if the parent and metabolite are equipotent, theunbound concentrations will equally contribute to a compositeconcentration profile, while if the parent is twice as potent as theactive metabolite, then the parent's unbound concentrations willweigh 2-fold more than the metabolite's concentrations in thecalculation of the CRD. While this is fairly easy to implement,provided the needed data are in place, some additional effort willbe required to define the most appropriate back-extrapolationmetric for the composite exposure. In this regard, the methodol-ogy can, at least in principle, account for the differing contribu-tions of drugs with active parent and metabolite molecules.

For molecularly targeted therapeutics, the proposed methodassumes that AUC (or Cav,ss) is the appropriate metric to match.The initial assessment (Fig. 3) suggests that this assumption isreasonable. To mitigate the risk that this assumption is incorrectand that either Cmax or Cmin values may be a more appropriatemetric to match, alternative dosing schedules such as twice dailydosing can be used. Depending on the drug's pharmacokinetics,these schedulesmay better align all threemetrics (Cmax,Cmin, andCav) to human concentration values than does a once daily dosingprotocol. Erlotinib is one example where twice daily dosingmitigates extreme concentration swings while maintaining thesame average drug concentration as once a day dosing (Fig. 4A).

It is commonly thought that drug concentrations need to bemaintained above a minimal threshold (e.g., a potency level) toachieve efficacy. Matching a minimal concentration betweenhumans and mice is difficult to implement, in part becausenonclinical species typically have a faster drug clearance. Tomatchminimal drug concentrations would require increased dosingfrequencies or increased doses, which may result in high Cmax

values and off-target treatment effects that could confound theinterpretation of the study. An additional knowledge gap is thetime interval over which concentrations need to be maintainedabove the threshold value. Finally, for the majority of drugsexplored nonclinically, TGI appears to be an AUC or Cav-drivenresponse. While this may be due to limitations in the methodsused to assess the concentration–response relationship, it couldalso point toward the need for more biomarker information.Therefore, as mentioned previously, a more appealing approachwould be to use time-dependent or maximum biomarker mod-ulation as the matching metric, rather than a minimal thresholdconcentration.

While this work provides an approach to estimate the CRD, it isimportant to acknowledge that there are experimental studies thatshould be designed with doses that potentially go beyond theCRD. For example, exploratory studies investigating a new tumormodel should be designed to fully understand the exposure–response profile across a range of doses/concentrations. In addi-tion, pharmacokinetic–pharmacodynamic studies used to devel-op robust concentration–response relationship for a given drugrequires minimal and maximal responses. Depending on thesensitivity of the tumor model to the drug, doses beyond theCRD may be required to maximally inhibit the target, whichprovides important information for the concentration–responserelationship. However, knowing where the CRD falls in compar-ison to the doses tested provides an important data point for theinterpretation of the in vivo study from a translational perspective.On the other hand, studies that are primarily designed to assesscombination efficacyof an investigational agentwith an approveddrug should select the approved agent's doses to be at or near the






CRD and explore the full efficacy profile of the novel investiga-tional agent by including it at multiple dose levels.

It is strongly recommended that drug concentrations, at min-imum, be sparsely sampled in efficacy studies to strengthen theconclusions by ensuring the appropriate drug concentrationswereachieved in the study. Strain differences in mice have beenobserved for various drugs, both in terms of the fraction unboundand pharmacokinetic profiles, suggesting that a dose in onemouse strain may not result in equivalent drug concentrationsin a different mouse strain.

The method presented here is an initial approach to matchclinical andnonclinical concentrations.While it is a fairly straight-forward approach to the problem, it has various implementationchallenges and limitations (Table 4). Some additional areas offuture research include expanding single dose CRD estimates intoranges that capture the inter-individual variability present in theclinical data and the uncertainty in conversion factors such as PPB.Themethodmay also benefit from the expanded use of modelingand simulation techniques, such as physiologically based phar-macokinetic (PBPK) models, to evaluate plasma and tissuedrug concentrations. Furthermore, the challenges associated withthe back-translation of nonuniformor prolonged dosing intervals

(e.g., drugs given once every 3 weeks) need to be addressed formany cytotoxic drugs.

In conclusion, when translational in vivo studies are executed, itis critical to carefully consider the drug concentrations achieved inthe studies andhow they relate to thedrug concentration toleratedin humans. Most importantly, the majority of informationrequired to better approximate clinically relevant drug concentra-tions for translatable nonclinical studies exists in the publicdomain. Therefore, nonclinical researchers should utilize allavailable pharmacokinetic and pharmacodynamic informationwhen designing translational studies.

Disclosure of Potential Conflicts of InterestP. Vicini holds ownership interest (including patents) in University of

Washington Center for Commercialization. No potential conflicts of interestwere disclosed by the other authors.

Authors' ContributionsConception and design: M.E. Spilker, S. Yamazaki, G. Li, J. Lucas,E.L. Bradshaw-Pierce, P. ViciniDevelopment of methodology: M.E. Spilker, R. Visswanathan, S. Yamazaki,E.L. Bradshaw-Pierce, P. ViciniAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): R. Visswanathan, S. Yamazaki, J. LucasAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): M.E. Spilker, X. Chen, R. Visswanathan, C. Vage,S. Yamazaki, G. Li, P. ViciniWriting, review, and/or revision of the manuscript: M.E. Spilker, R. Visswa-nathan, C. Vage, S. Yamazaki, G. Li, J. Lucas, E.L. Bradshaw-Pierce, P. ViciniAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): S. YamazakiStudy supervision: S. Yamazaki, G. Li

AcknowledgmentsThe authors would like to acknowledge Shubha Bagrodia, Alison Betts,

Deepak Dalvie, Ashwin Gollerkeri, Justine Lam, Mauricio Leal, Hui Wang,Catherine Yeh, Michael Zager, Cathy Zhang, and Matthew Zierhut for theirdiscussions of this topic and comments on the manuscript.

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received May 7, 2016; revised August 1, 2016; accepted August 11, 2016;published OnlineFirst August 22, 2016.

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Table 4. Limitations and challenges in the application of the CRDmethodology

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Published OnlineFirst August 22, 2016.Clin Cancer Res Mary E. Spilker, Xiaoying Chen, Ravi Visswanathan, et al. Nonclinical Oncology StudiesFound in Translation: Maximizing the Clinical Relevance of

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Found in Translation: Maximizing the Clinical Relevance of ... · Experimental Design and Results: In this work, we propose a pharmacokinetic-based strategy to select nonclinical