Operating Characteristics of Tumor Kinetic Response

Assessments in Early Phase Oncology Trials

Dean Bottino1, Arijit Chakravarty2, Eric Westin3 Millennium Pharmaceuticals, Inc., a wholly owned

subsidiary of Takeda Pharmaceuticals Company Limited (1) Clinical Pharmacology, (2) Drug

Metabolism & Pharmacokinetics, (3) Oncology Clinical Research

Summary: While the RECIST criteria have been a

valuable tool in standardizing anticancer treatment

response assessment, they do not take into account the

tumor growth rate prior to treatment intervention, which

can be highly heterogeneous, particularly in phase 1 all-

comers trials. We describe methodology whereby

additional pre-study scans can be used to estimate each

patient's pre-treatment growth rate and therefore

treatment benefit, defined to be the observed deflection

from that initial rate. We show that this methodology

outperforms RECIST percent change from baseline in

terms of accuracy of estimation of antitumor effect, even

when RECIST percent change from baseline is enhanced

to account for ‘placebo’ tumor growth rates. We therefore

anticipate that this kinetic based measure of antitumor

effect will enable more precise dose, exposure, and

biomarker vs. response relationships, leading to more

informed decisions in early oncology development.

128 64 32 16 8

53.3

73.6

106

163

291

1

1

1

2

2

2

median doubling time (weeks) =ln(2)/g

100*s

qrt

(e

g2

-1)=

%C

V o

f gro

wth

rate

g (

k)

p(|2-| 0.1)/p(|

1-| 0.1)

BC

LC

1

2

3

4

5

6

7

89

10

11

12

min

max

0.25

0.354

0.5

0.707

1

1.41

2

2.83

4

Median doubling time(weeks)

%CV o

f gro

wth

rate

g

PTK >

CFB m

eth

od

PTK <

CFB m

eth

od

-8 -1 8 160

50

100

150

200

250

weeks of treatment

tum

or

burd

en (

mm

)

Spaghetti plot: gmedian

=8 wks, CV(g) = 291%

0,

0,)(

)(

0

0

tey

teyty

tkg

gt

obs

freq

g

Median doubling time = 8 weeks = log(2)/gmed

%CV of growth rate g = 291 %

~%CV

gmed

PD

SD PR

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

normalized kill rate

est -

using baseline only: p(|kest

-|<tol*)=0.1461

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

normalized kill rate

est -

using pre-study + baseline: p(|kest

-|<tol*)=0.5784

Log(

Y)

-8 -1 0 8 16w

g gnet

kest = g – gnet

Log(

Y)

-8 -1 0 8 16w

gnet

g

kest = g – gnet

6. Conclusion: Pretreatment Tumor Kinetic (PTK) method, which uses a pre-study scan, is more accurate than Change From Baseline (CFB) method in this polygon spanned by published tumor kinetic parameter values in Breast Cancer1, Lung Cancer2, and 12 all-comers trials.3 This accuracy advantage is maintained over a range of pre-study scan times (4-16 weeks before treatment, results for 8w shown). 1. Heuser et al, Cancer (1979) 43:1888-1894. 2. Usuda et al, Cancer (1994) 74:8. 3. Ferte et al, Clin Cancer Res (2014) 20:46.

PTK method: 57.8%

of GRI estimates GRIPTK are within tolerance of

true growth rate inhibition GRITRUE

CFB method: 14.6%

of GRI estimates GRICFB are within tolerance of

true growth rate inhibition GRITRUE

Therefore PTK method is

times more accurate than CFB method for this choice of

growth rate median & %CV

Pretreatment Tumor Kinetic (PTK) method

Change From Baseline (CFB) method

3.96

1. Each point on the axes below represents a different patient population described by median and spread (%CV) of untreated tumor growth rates.

Tum

or

burd

en (

mm

)

Tumor burden (mm)

2. Next, we draw 10000 patients from each population distribution and simulate their tumor burden time courses, including ~8.5% random measurement error and assuming gtreated/g uniformly distributed between -1 and 1 to generate RECIST response rates typical of targeted ph1 monotherapy trials.

2b. For reference purposes, we report the RECIST response rates from the simulated tumor burden trajectories as a pie chart overlaid on the axes on the right.

3. For each patient, we estimate the study drug’s antitumor Growth Rate Inhibition in two different ways: • The Pretreatment Tumor Kinetic method (PTK, left) uses the patient’s historical pre-study scan to estimate the ‘placebo’ growth rate g. This is the ‘beyond RECIST’ estimate. • The Change From Baseline method (CFB, right) instead uses the median on-treatment growth rate of patients with progressive disease to estimate the ‘placebo’ growth rate g. This was intended to represent the best possible estimate obtainable without a pre-study scan. In both methods, we estimate the net growth rate on treatment gnet and report the ‘Growth Rate Inhibition’ as GRI = 1 - gnet/g.

4. We then compare the PTK and CFB methods to address the key question: what is the incremental benefit of obtaining a pre-study scan in addition to the baseline and on-treatment assessments we typically collect? We do this by summarizing each method’s accuracy as the percent of growth rate inhibition (GRI) estimates falling within a given tolerance of the true GRI originally used to simulate the tumor trajectories. For the particular case shown (8w doubling time, 291% CV), the PTK method, which uses the pre-study scan, is 57.8% accurate while the CFB method, which does not, is only 14.6% accurate.

5. Finally, we report the PTK/CFB ‘accuracy ratio’ for this choice of population parameters as a heat-mapped pixel in the axes above, where green represents population parameters for which the pre-study scan provides additional accuracy and red where it does not.

1-GRITRUE 1-GRITRUE

GRI C

FB-G

RI t

rue

GRI P

TK-G

RI t

rue

Objectives: To investigate

and compare the operating

characteristics of kinetic-

based methods for

quantifying antitumor

effects of investigational

anticancer agents, in

particular the Change From

Baseline (CFB) method and

the Pre-treatment Tumor

Kinetics (PTK) method

which requires an

additional pre-baseline

tumor burden assessment

[1-2].

Methods: Simulated data was generated from a (N=10^5) virtual

patient population having log-normally distributed (exponential) tumor

growth rates (TGRs), with median (mTGR) and %CV (cvTGR) of

TGR tested over ranges encompassing clinically observed values

[1,3,4]. Normalized growth rate inhibition GRI= (growth-kill)/growth

were uniformly distributed from -1 to 1, resulting in simulated

RECIST response frequencies similar to those observed in early phase

oncology trials. Exponential tumor burden measurement error (8.5%)

as fitted from a recent scan-to-scan variability study [5] was also

simulated. For each virtual patient, the CFB estimate of GRICFB was

calculated via log linear regression to assessments at -1 (baseline), 8

and 16 weeks after start of treatment. The PTK estimate GRIPTK for

each patient was calculated via piecewise log linear regression to

assessments at -8 (pre-study), -1 (baseline), 8 and 16 weeks after start

of treatment. Accuracy of each method for a given (mTGR, cvTGR)

parameter set was defined as the fraction of GRI estimates falling

within an arbitrary tolerance (+/- 0.1) of the true GRI values.

Results: While the PTK method was not universally more

accurate than the CFB method over all (mTGR, cvTGR)

parameter values tested, it was consistently more accurate

than CFB over the clinically observed ranges. Specifically,

PTK advantage over CFB was most pronounced in

populations with fast growing tumors and highly

heterogeneous TGRs, while CFB was actually more

accurate than PTK in populations with very slow growing

tumors and relatively homogenous TGRs. Increasing the

time span between the pre-study assessment and the start

of treatment from 4 weeks to 16 weeks further increased

the advantage of PTK over CFB.

Conclusions: While the PTK

method outperforms the CFB

method in all clinically

feasible scenarios tested thus

far, the absolute accuracy

advantage of PTK over CFB

varies from negligible to

significant with increasing

mTGR, cvTGR, and time

between pre-study baseline

scans. This anticipated

accuracy advantage should be

weighed against the minimal

additional cost of reading the

pre-study scan required for

the PTK method.

References

[1] Charles Ferte et al, “Tumor Growth Rate Is an Early Indicator of

Antitumor Drug Activity in Phase I Clinical Trials,” Clin Cancer Res

January 1, 2014 20; 246.

[2] Sylvie Retout et al, “A model-based approach to optimize

detection of treatment effects in early oncology trials,” J Clin Oncol

31, 2013 (suppl; abstr e13508).

[3] Louis Heuser et al, “Growth Rates of Primary Breast Cancers,”

Cancer 43:1888- 1894, 1979.

[4] Katsuo Usuda et al, “Tumor Doubling Time and Prognostic

Assessment of Patients with Primary Lung Cancer,” CANCER

October 15,2994, Volume 74, No. 8.

[5] Geoffrey Oxnard et al, “Variability of Lung Tumor

Measurements on Repeat Computed Tomography Scans Taken

Within 15 Minutes,” J. Clinical Oncology, Volume 29, No. 23,

August 10 2011.