Radiobiological modeling and clinical trials.

PURPOSE: Standard clinical trial designs can lead to restrictive conclusions: the "best recommended treatments" based on trial results, although generally applicable to patient populations, do not necessarily apply to individual patients. In theory, radiobiological modeling, coupled with reliable predictive assays, can be used to rationalize the selection of patients for particular schedules in trials.
MATERIALS AND METHODS: Linear-quadratic modeling of radiotherapy can be used to simulate a clinical trial. This is achieved by random sampling techniques where the key radiobiological parameters (alpha, beta, T(pot) and clonogen number) are selected from known or expected ranges. Clinical trial design in radiotherapy may be improved by formal radiobiological assessment designed to estimate the likely changes in tumor cure probability (TCP) and the likely normal tissue biologically effective dose (BED). Modeling may also be used to rationalize the allocation of patients to a test or standard schedule or for individual optimization of a treatment schedule. Such approaches depend on there being reliable predictive assays of the radiobiological parameters in individual patients. The influence of variations in predictive assay accuracy on the improved outcomes are assessed.
RESULTS: Clinical trials, which have been preceded by modeling simulation, offer potentially substantial improvements in the results of cancer treatment by radiotherapy. These exceed the usual gains found in standard clinical trials.
CONCLUSION: Future preclinical trial design should include modeling assessments that indicate how best to structure the trial.