68Ga
Abstract #4948: PET/CT clinical protocol design for the novel, first in class
68
guanylyl cyclase C targeted peptide MLN6907 ([ Ga]MLN6907)
Jacob Y
1
Hesterman ,
1
Orcutt ,
2
Yardibi ,
2
Mettetal ,
Kelly Davis
Ozlem
Jay
Shu-Wen
2
1
2
2
Cvet , Jack Hoppin , Thea Kalebic , Daniel P. Bradley
2
Teng ,
labeled
Donna
1inviCRO,
LLC, Boston, MA
2Takeda Pharmaceutical International Corporation, Cambridge, MA
Problem Statement
Methods (ctd)
Acquisition and interpretation guidelines for clinical PET/CT imaging in oncology have
typically been designed for whole-body 18F-FDG imaging and may not be optimized for
assessment of other PET imaging tracers. Here we describe a methodology of PET/CT
study design for the novel first in class 68Ga-labeled Guanylyl cyclase C (GCC)
targeted peptide, [68Ga]MLN6907, based on a combination of in vitro, ex vivo, and in
vivo preclinical imaging studies [1] and model-based estimation of tumor parameters
from simulated clinical PET data. The goal of the study design is to optimize the
estimation accuracy for GCC antigen density.
Methods
Preclinical in vitro, ex vivo, and in vivo experiments were performed to experimentally
determine affinity, internalization, vascularity, antigen density, and blood PK and
background parameters of [68Ga]MLN6907. Radioactive binding and internalization
assays were used with GCC-293 cells (HEK-293 cells overexpressing GCC) to
measure binding affinity and internalization half-life of peptide-GCC bound complex.
Radioactive binding assays and vascular casting using Microfil were used with a
variety of primary human tumor lines to determine a physiological range of GCC
density (Bmax) and the vascularity parameter R, respectively. Rat and non-human
primate imaging and biodistribution studies were performed to determine background
and blood PK parameters.
Peptide Parameters
Parameter
Value
KD (nM)
3.5*
MW
2.1 kDa
ke
1h
t1/2,alpha
0.92 min
t1/2,beta
69.4 min
A (fraction alpha)
0.78
Summary of Results
Body and liver regions-of-interest (ROIs) were
generated from the 4D XCAT phantom [3].
Spherical lesions were simulated and
embedded within the liver ROI. Time-activity
curves were generated for the background,
liver, and lesion regions over a time span of
zero to four hours post-injection. Background
and liver TACs were derived from rat dosimetry
studies. Lesion TACs were generated using
the tumor model described above under a
variety of antigen density and vascularity
combinations. TACs were generated assuming
injected dose of either 3mCi or 5mCi of 68GaDOTA-labeled compound. Simulated TACs and
lesion-bearing phantoms were integrated into a
model of a clinical PET scanner (AnyScan
PET/CT, Mediso Kft.). List-mode data were
provided in one-second increments over a four
hour simulated acquisition.
Figure 1: a) XCAT whole-body phantom, b) Example body,
liver, and tumor region used in simulation study.
a)
b)
c)
d)
e)
We assume kon = 105 M-1s-1
Injected Dose and Subject/Tumor Parameters
A distributed tumor model [2] was used to generate lesion time-activity curves (TACs)
as input for the phantom. The model, described below, was also then used to estimate
antigen density and vascularity from the simulated PET data.
f)
g)
• Data were reconstructed at 4mm isotropic voxel size using a dedicated web-based
reconstruction engine.
• Reconstructions were generated to simulate seven realistic clinical acquisition
intervals, 0 − 30, 0 − 45, 0 − 60, 30 − 60, 30 − 90, 45 − 90, and 60 − 90 minutes.
• For each of those seven acquisition intervals, reconstructions were generated at
each of 2, 3, 5, and 10 minute windows. A cross-section of a simulated data
reconstruction at several time points is shown in Figure 2.
• The system point spread function was assumed as a 3D Gaussian and performance
was evaluated at full-width at half maximum (FWHM) values ranging from 4mm to
28mm. Example TACs with and without partial volume correction for liver and tumor
regions are shown in Figure 3.
where ∇2 denotes the Laplacian in cylindrical coordinates, [C] denotes the free compound concentration, [B] denotes the bound
compound/antigen concentration, [Ag] denotes the unbound antigen concentration, [I] denotes the concentration of internalized compound,
D denotes the compound diffusion coefficient in tissue, kon denotes the compound/antigen association rate constant,koff denotes the
compound/antigen dissociation rate constant, ε denotes the compound void fraction in the tissue, ke,B denotes the internalization rate
constant of the compound/antigen bound complex, ke,Ag denotes the internalization rate constant of the antigen, kresid denotes the rate of
release of compound or compound signal from the intracellular compartment, RS denotes the antigen synthesis rate, R denotes the Krogh
cylinder radius, Rcap denotes the capillary radius, P denotes the tumor capillary permeability, Ag0 denotes the initial antigen density, and [C]p
denotes the plasma concentration of the compound as a function of time, also known as the arterial input function.
•
Estimates for three different simulated
tumors were observed to perform equally
well when simulated with 3 mCi and 5 mCi
injected doses.
•
Parameter estimation as a function of
tumor volume performed well within a
tumor range of approximately 1-5 cm.
Below 1 cm, large variability was observed
in estimates. Above 5 cm, parameter
estimates were reproducible, but biased.
•
Parameter estimation was robust as a
function of reconstruction window (i.e., 2
min vs 3 min vs 5 min vs 10 min time
points).
•
Generally, parameter estimation was robust
across a variety of acquisition intervals
(i.e., 0-30 min vs 30-60 min). Pooling of
estimates across all six evaluated tumors
yielded a recommended focused imaging
range from 30-90 minutes post-injection.
a)
b)
Figure 3: a) Liver, measured tumor, and true
tumor time-activity curves shown with and without
partial volume correction, b) Error in estimate of
antigen density as a function of partial volume
correction full-width at half-maximum (mm).
Conclusion
a)
Example MIP views of simulated data
for b) 0-3, c) 9-12, d) 21-24, e) 51-54,
f) 69-72 minutes post-injection.
An optimal partial volume correction FWHM
of approximately 17mm (i.e., 15-19mm)
was observed based on overall
performance in terms of estimation of
antigen density and vascularity.
b)
Figure 2: a) Example MIP view of a
three-lesion phantom. Red lines
indicate axial FOV in simulated data.
Parameter Bodyweight Injected Mass Injected Activity Tumor size Ag0
Rcap R
Value
70 kg
38 µg
3 or 5 mCi
Varies
Varies
8 µm Varies
(1-10 nM)
(20-150 µm)
•
• For each combination of acquisition interval, temporal window, and partial volume
correction FWHM, estimated tumor TACs were generated by calculating the mean
signal concentration from an ROI positioned at the known tumor location.
• Each tumor TAC was used as an input to the distributed model. Model equations
were solved to generate estimates of tumor antigen density and vascularity.
A simulation study incorporating a variety of in vivo, ex vivo, and in vitro experimentally
determined parameters was used to guide design of a clinical PET/CT first-in-human
protocol. The information learned in this study was coupled with practical patient and
site specifications to develop a clinically viable PET/CT imaging protocol. Initial image
data are expected in Q2 2014 and will be analyzed using the image processing
methods and mechanistic model described here.
References
[1] D. Cvet, et. al. “In vitro and in vivo investigation of the novel, first-in-class Guanylyl Cyclase C
(GCC) targeted 68Ga labeled heat stable peptide MLN6907 ([68Ga]MLN6907) for tumor imaging”,
Abstract 4949, AACR 2014
[2] G. M. Thurber, S. C. Zajcic, K. D. Wittrup, “Theoretic criteria for antibody penetration into solid
tumors and micrometastases”, Journal of Nuclear Medicine, vol. 48, no. 6, pp. 995-9, 2007.
[3] W. P. Segars, G. Sturgeon, S. Mendonca, J. Grimes, and B. M. W. Tsui, “4D XCAT phantom for
multimodality imaging research,” Medical Physics, vol. 37, no. 9, pp. 4902–4915, Sep. 2010.
Acknowledgments
The authors sincerely thank their collaborators at Mediso, Kft., for providing simulated
list mode data and access to an online reconstruction engine and Dr. W. Paul Segars
at Duke University for providing access to the XCAT phantom.