Background:
We have developed echogenic immunoliposomes (Ab-ELIP) as a means of highlighting atheroma at different stages of atherogenesis. We have devised methods to measure conjugated antibody (Ab) binding affinity (targeting efficiency, TE) in order to assess and predict Ab-ELIP imaging enhancement and therapeutic efficacy. We have now constructed an algorithm for calculating binding force from the evaluation data and have tested it relative to ultrasound (US) image enhancement results.
Hypothesis:
One or more Ab-ELIP binding force parameters is predictive of ultrasound imaging enhancement of atheroma in vivo.
Methods:
Ab-ELIP were prepared by conjugating specific MAbs to ELIP. Conjugation efficiency (CE) was then determined by a quantitative immunoblot assay and particle enumeration with a Beckman-Coulter Multisizer to yield CE in molecules Ab/liposome. Conjugated Ab affinity (K
D
and K
assoc
) was derived from ELISA data and used to generate specific TE (CE x relative binding area) and functional avidity (K
assoc
x specific TE). The functional avidity was then converted to free energy of association using the equation of state (ΔG = -RT
ln
K). Using specific TE, ΔG in kcal/mole was converted to binding energy in erg/liposome, from which binding forces, E
b
, in dyne/liposome were derived. Based on specific TE and Ab molecules per m
2
, binding force/liposome was converted to Pascal (10 dyne/cm
2
). Finally, dyne/liposome was converted to piconewton (pN)/molecule, a common measure of binding force.
Results:
Using rabbit and miniswine atherosclerotic models, we previously demonstrated US imaging enhancement of atheroma by MAbs specific for ICAM-1, α
v
β
3
-integrin and VCAM-1 conjugated to ELIP. Percent image enhancement correlated (p < 0.05) with binding force in Pascal, but not in dyne/liposome, indicating the importance of binding area to TE. Binding force in pN/molecule ranged from 311 to 385, which agrees well with the published results of others.
Conclusions:
We have discovered a binding force parameter calculated from CE and TE data that is predictive of Ab-ELIP targeting performance in vivo. The next step, to demonstrate a similar correlation with therapeutic efficacy, will then provide an important tool for clinical Ab-ELIP optimization.
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