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  • The advantage of antibody cocktails for targeted alpha therapy depends on specific activity.

The advantage of antibody cocktails for targeted alpha therapy depends on specific activity.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine (2014-10-29)
Jordan B Pasternack, Jason D Domogauer, Alisha Khullar, John M Akudugu, Roger W Howell
ABSTRACT

Nonuniform dose distributions among disseminated tumor cells can be a significant limiting factor in targeted α therapy. This study examines how cocktails of radiolabeled antibodies can be formulated to overcome this limitation. Cultured MDA-MB-231 human breast cancer cells were treated with different concentrations of a cocktail of 4 fluorochrome-conjugated monoclonal antibodies. The amount of each antibody bound to each cell was quantified using flow cytometry. A spreadsheet was developed to "arm" the antibodies with any desired radionuclide and specific activity, calculate the absorbed dose to each cell, and perform a Monte Carlo simulation of the surviving fraction of cells after exposure to cocktails of different antibody combinations. Simulations were performed for the α-particle emitters (211)At, (213)Bi, and (225)Ac. Activity delivered to the least labeled cell can be increased by 200%-400% with antibody cocktails, relative to the best-performing single antibody. Specific activity determined whether a cocktail or a single antibody achieved greater cell killing. With certain specific activities, cocktails outperformed single antibodies by a factor of up to 244. There was a profound difference (≤16 logs) in the surviving fraction when a uniform antibody distribution was assumed and compared with the experimentally observed nonuniform distribution. These findings suggest that targeted α therapy can be improved with customized radiolabeled antibody cocktails. Depending on the antibody combination and specific activity of the radiolabeled antibodies, cocktails can provide a substantial advantage in tumor cell killing. The methodology used in this analysis provides a foundation for pretreatment prediction of tumor cell survival in the context of personalized cancer therapy.

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