PRECLINICAL RADIOIMMUNOTHERAPY WITH AN INTERNALIZING MAB

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The ability of antibodies to target tumors in vivo and provide reduction in tumor burden has been well documented, but many of the issues pertaining to the selection of an optimal radionuclide-MAb conjugate remain unresolved. The aim of this project is to examine important variables of radioimmunotherapy (RAIT) for the treatment of cancer which relate to the ability of a MAb to internalize into antigen bearing cells. RS7-3G11 is the primary antibody to be used in this project, and has been selected due to a number of promising characteristics. Our preliminary results indicate that RS7-3G11 demonstrates: a) a high frequency of antigen expression on a wide variety of tumor types, especially lung, bladder, breast, cervical, ovarian, prostate, and stomach cancers, with limited expression on normal human tissue; b) the ability to localize to tumor in an animal model; c) the ability to provide reduction in tumor burden in vivo when conjugated to 131I; and d) the ability to rapidly internalize into antigen-bearing cells. The ability to internalize into target cells was not seen with many other MAbs possessing the other positive characteristics, and is a promising property of RS7-3G11 which will enable us to utilize RS7-3G11 in comparative studies on the importance of antibody internalization in RAIT. The proposal is divided into areas of evaluation which are designed to study the selection of radionuclide-MAb conjugate providing the best tumoricidal activity covering (a) choice of radionuclide and labeling technology in relationship to whether or not an antibody internalizes, and (b) evaluations of the biological effects of RAIT with these conjugates and the cellular processing of the radionuclide-MAb conjugates. These studies are designed taking into account the descriptions of the uptake and distribution of antibodies to solid tumors which have been generated using mathematical modeling, and we will assess whether the results we obtain experimentally conform to the predictions of the models.