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Bio-effect model applied to 131I radioimmunotherapy of refractory non-Hodgkin’s lymphoma

  • Peter L. Roberson
  • Hanan Amro
  • Scott J. Wilderman
  • Anca M. Avram
  • Mark S. Kaminski
  • Matthew J. Schipper
  • Yuni K. Dewaraja
Original Article

Abstract

Purpose

Improved data collection methods have improved absorbed dose estimation by tracking activity distributions and tumor extent at multiple time points, allowing individualized absorbed dose estimation. Treatment with tositumomab and 131I-tositumomab anti-CD20 radioimmunotherapy (BEXXAR) yields a cold antibody antitumor response (cold protein effect) and a radiation response. Biologically effective contributions, including the cold protein effect, are included in an equivalent biological effect model that was fit to patient data.

Methods

Fifty-seven tumors in 19 patients were followed using 6 single proton emission computed tomography (SPECT)/CT studies, 3 each post tracer (5 mCi) and therapy (∼100 mCi) injections with tositumomab and 131I-tositumomab. Both injections used identical antibody mass, a flood dose of 450 mg plus 35 mg of 131I tagged antibody. The SPECT/CT data were used to calculate absorbed dose rate distributions and tumor and whole-body time-activity curves, yielding a space-time dependent absorbed dose rate description for each tumor. Tumor volume outlines on CT were used to derive the time dependence of tumor size for tracer and therapy time points. A combination of an equivalent biological effect model and an inactivated cell clearance model was used to fit absorbed dose sensitivity and cold effect sensitivity parameters to tumor shrinkage data, from which equivalent therapy values were calculated.

Results

Patient responses were categorized into three groups: standard radiation sensitivity with no cold effect (7 patients), standard radiation sensitivity with cold effect (11 patients), and high radiation sensitivity with cold effect (1 patient).

Conclusion

Fit parameters can be used to categorize patient response, implying a potential predictive capability.

Keywords

Tumor volume Equivalent biological effect Tumor response Radiolabeled antibody therapy Non-Hodgkin’s lymphoma 

Notes

Acknowledgments

This work was supported by grant 2R01 EB001994 awarded by the National Institute of Health, US Department of Health and Human Services. The authors thank Ken Koral, Ph.D. for many useful discussions.

Supplementary material

259_2010_1699_MOESM1_ESM.doc (103 kb)
(DOC 103 kb)

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Peter L. Roberson
    • 1
  • Hanan Amro
    • 1
  • Scott J. Wilderman
    • 2
  • Anca M. Avram
    • 2
  • Mark S. Kaminski
    • 3
  • Matthew J. Schipper
    • 1
  • Yuni K. Dewaraja
    • 2
  1. 1.Department of Radiation OncologyUniversity of MichiganAnn ArborUSA
  2. 2.Department of RadiologyUniversity of Michigan Medical CenterAnn ArborUSA
  3. 3.Division of Hematology and Oncology, Department of Internal MedicineUniversity of Michigan Medical CenterAnn ArborUSA

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