| |
By Rupak K. Banerjee, University of Cincinnati, Cincinnati,
OH; Cynthia Sung, Human Genome Sciences, Inc., Rockville, MD; Peter M.
Bungay and Robert L. Dedrick, National Institutes of Health, Bethesda,
MD; and William W. van Osdol, ALZA Corporation, Mountain View, CA
View the pdf of this article
( Please click on each thumbnail to view a larger image.)
Absorption of monoclonal antibody (top) and its binding
with tumor antigens (bottom) in a spherical tumor. At tstarting, the mAb
has penetrated into the normal tissue and is beginning to enter the tumor
and bind with the Ag. After 12 hours, the level of mAb in the normal tissue
is maximum, and binding inside the tumor is nearly complete. After 24
hours, the level of mAb in the normal tissue and tumor has reduced, and
the binding to Ag in the tumor is complete.
A joint imaging and therapy study has been
performed that examines a new technique
for delivering systemically-administered drugs
to tumors embedded in normal tissue. The simple,
antibody-based protocol labels a monoclonal
antibody (mAb) with a radionuclide. The radiolabeled
mAb is injected into the bloodstream of
the patient. Carried to the site by the blood plasma,
the mAb diffuses into the interstitial volume
of the nodule, where it targets and binds with tumor
antigens (Ag). Excess mAb diffuses out of the tumor
nodule and is removed from the surrounding normal
tissue by the lymphatic system capillaries.
A 3D FiDAP simulation of this process was performed
using a spherical tumor nodule immersed
in a region of normal tissue. Species transport equations
were solved to track the transient concentrations
of the mAb, the tumor Ag, and the mAb-Ag
complex, created after the mAb binds with the
Ag inside the tumor region. Sources and sinks in
the species equations included both diffusion (into
and out of the tumor) and reaction (inside the
tumor). The results have helped explain how diffusive
gradients in the normal tissue affect mAb
percolation (spread and consumption) in the tumor.
One of the goals of the FiDAP simulations was
to test the degree of approximation in earlier models
that were limited to the tumor nodule alone.
Based on the choice of boundary conditions on
the tumor surface, the earlier models either overor
under-predicted the mAb percolation in the
tumor. The FiDAP results clearly showed that a
simulation that incorporates mAb transport in both
the normal and tumor tissue is needed for the most
accurate prediction of percolation time.1
FiDAP was also used to explore the influence
of mAb diffusivity and dose on the time course
of mAb distribution in the tumor. Calculations were
performed using the experimental values of mAb
diffusivity measured by Clauss and Jain2 and Berk
et al.3, the latter being larger by a factor of about
thirty. When the low diffusivity values were used,
the time needed for the mAb to reach the center
of the tumor nodule and attain its maximum
average concentration was significantly longer
than when the high diffusivity values were used.
Lower diffusivities were also associated with lower
peak levels of mAb.
If the higher mAb diffusivity measurements are
more accurate, this has important implications for
imaging and therapy. High mAb diffusivity may
produce higher tumor concentrations at earlier times
than low diffusivities, but the mAb concentration
in the surrounding normal tissue is higher as well,
making detection more difficult. In addition, at later
times, mAb efflux from the tumor is more rapid,
potentially compromising therapy. The question
also arises whether higher diffusivity permits the
use of lower antibody doses. This possibility must
be weighed carefully, because absorption in organs
of high capacity places limits on how much the
dose can effectively be reduced. Though more
research is needed, it is clear that these studies have
shed new light on how antibodies are absorbed
for the detection and treatment of tumors.
References:
- R.K. Banerjee, C. Sung, P.M. Bungay, R.L. Dedrick and W.W. van Osdol,
Annals of Biomedical Engineering, 30, p. 828-839, 2002.
- M.A. Clauss and R.K. Jain, Cancer Res. 50, p. 3487- 3492, 1990.
- D.A. Berk, F. Yuan, M. Leunig and R.K. Jain, Proc. Natl. Acad. Sci.
USA. 94, p. 1785-1790, 1997.
|
|
|
