Radiobiological modelling of the treatment of leukaemia by total body irradiation

Purpose: Total body irradiation (TBI) has been used as part of the conditio ning regimen before bone marrow transplantation or stem cell re-infusion fo r more than 30 years. A wide variety of regimens have been used, and no sin gle one has emerged as the best. Experimental evidence suggests a diversity of radiosensitivities of leukaemia cells in culture, which may correlate w ith a significant variation of leukaemic cell radiosensitivities between pa tients. The purpose of this project was to compute leukaemic cell killing b y different schedules and determine whether a 'best treatment' could be dev ised for individual patients. Methods: We have developed a mathematical model for leukaemic cell killing by alternative TBI schedules, applied to a patient population with diverse leukaemic radiosensitivities. We considered 13 schedules in clinical use, a nd 14 theoretical schedules calculated (by the linear-quadratic model) to b e iso-effective for risk of radiation pneumonitis. When each schedule of tr eatment is applied to the patient population, a distribution of leukaemic c ell kills (log cell kill values) can be obtained for that schedule. The leu kaemic kill distribution was also computed for optimized individual schedul ing, each individual being treated by the schedule that was most effective for that patient. Using available data on the clinically observed dose resp onse relationship for acute myeloid leukaemia, the model was extended to pr ovide leukaemia cure probabilities for each of the schedules and for the in dividualized strategy. Results: The computer simulations show that each schedule, applied to the t reatment of a radiobiologically diverse patient population, results in a br oad distribution of leukaemic log kill values, with a mean of 3-5 for most schedules (i.e. 10(-3)-10(-5) surviving fraction or leukaemic cells), and a broad variation (1-10 log kill) amongst patients. The distributions genera ted by the various schedules were found to be overlapping, implying that ma ny of the schedules would be difficult to distinguish reliably in clinical trials. Individualized optimum treatment is possible if radiobiological par ameters are known for each patient and would improve the leukaemic log kill distribution by about 1 log on average, corresponding to an increase of le ukaemia cure probability of several percent overall. For some individual pa tients, however, optimal scheduling could make a large difference to treatm ent outcome. Conclusions: The use of many different clinical treatment schedules may be continuing because outcomes are similar when these diverse schedules are ap plied to unselected patient populations. The measurement of individual leuk aemic cell radiosensitivity would allow individualized scheduling, which co uld result in modest increases in overall curability, but substantial impro vements in survival or duration of remission for individual patients. (C) 2 001 Elsevier Science Ireland Ltd. All rights reserved.