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Quantitative Pharmacokinetics of Radiolabeled Monoclonal Antibodies for Imaging and Therapy in Patients

  • Gerald L. DeNardo
  • Sally J. DeNardo
  • Daniel J. Macey
  • Stanley L. Mills
Part of the NATO ASI Series book series (NSSA, volume 152)

Abstract

Antibodies can be used to carry radionuclides to tumors for in vivo diagnosis and treatment. Monoclonal antibody reagents have virtually replaced the heterosera of earlier days. When suitably radiolabeled, these antibodies provide potential for imaging and treatment of cancer. The potential of radioimmunoimaging and radioimmunotherapy of cancer based upon monoclonal antibodies has excited the scientific community. These developments provide a unique opportunity for radionuclide imaging based on the specificity of monoclonal antibodies and therapy based on treatment planning techniques analogous to those used in external beam and radionuclide sealed source radiation therapy. Our goal has been to develop a comprehensive treatment planning system for clinical cancer therapy with radiolabeled monoclonal antibodies against cancer-associated antigens. We expect the system to provide estimates of the radiation dose distributions associated with various choices of radionuclide and targeting molecule. These approaches are now feasible because of advances in quantitative radionuclide imaging. Tracer techniques can now be implemented by advanced equipment for quantitative radionuclide imaging and strengthened by dynamic modeling of the physiological parameters which govern radionuclide distribution, and hence radiation dose distribution (1). We believe that quantitative radionuclide imaging is necessary to efficiently and accurately make decisions that can influence the efficacy of radioimmunoimaging and radioimmunotherapy.

Keywords

Radiation Dosimetry Scatter Fraction Scintillation Camera Pharmacokinetic Information Septal Penetration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1988

Authors and Affiliations

  • Gerald L. DeNardo
    • 1
    • 2
    • 3
  • Sally J. DeNardo
    • 1
    • 2
    • 3
  • Daniel J. Macey
    • 1
    • 2
    • 3
  • Stanley L. Mills
    • 1
    • 2
    • 3
  1. 1.Department of RadiologyUniversity of CaliforniaDavisUSA
  2. 2.Department of Internal MedicineUniversity of CaliforniaDavisUSA
  3. 3.Medical CenterSacramentoUSA

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