A preliminary cell kinetics model of thrombocytopenia after radioimmunotherapy

Thrombocytopenia is often the dose-limiting toxicity for radionuclide therapy. Prediction of platelet counts after therapy is important for treatment planning. Simple prediction methods based on linear correlation between radiation dose and blood count nadir have been insufficient because they have not considered time, because of the complicated hierarchical structure of the hematopoietic system in which platelets are not directly injured by low dose rate radiation and because of changing radiation dose rates to marrow with time. This study addresses these problems using a cell kinetics model.

The model consists of compartments for progenitor cells, megakaryocytes, platelets and stromal cells. A linear quadratic formula was used for progenitor cell survival. Stromal cells were described by a model based on a maximum likelihood estimate for cellular damage, repair and proliferation. Reported values for murine cellular turnover rates and radiosensitivity of progenitor cells were used in the model calculations. Experimental mice received 4 Gy of external beam radiation for tumor implantation and 12.4-23.3 MBq 67Cu-2-iminothiolane-BAT-Lym-1 (BAT = 6-[p-(bromoacetamido) benzyl]-1,4,8,11-tetra-azacyclotetradecane-N,N',N'',N'''-tetraacet ic acid) 19-30 days later. Blood counts were measured three times each week.

The model predicted the severity of thrombocytopenia, and the time of the nadir corresponded to measured values in mice. For a dose of 14.2 MBq 67Cu-2-iminothiolane-BAT-Lym-1 that induced a platelet nadir of 20% of baseline (Grade II), the model predicted that at least 20 days were needed before a second 14.2-MBq injection if a subsequent nadir of <10% of baseline (Grade IV) was to be avoided.

The nadir and duration of thrombocytopenia predicted by the model were similar to those observed in the mice. Predicted information could be useful for planning the dose and timing of fractionated radionuclide therapy. This model provides a stepping stone for future development of a predictive model for patients.