Abstract
A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.
Highlights
Radiotherapy is a cornerstone of modern oncology, and is frequently given in conjunction with chemical treatments to improve efficacy [1, 2]
We introduced the Tumor Static Exposure (TSE) concept—a model-based prediction of all combinations of radiation doses and radiosensitizer concentrations that result in tumor regression
The tumor model was calibrated to xenograft data using a nonlinear mixed-effects approach based on the first-order conditional estimation (FOCE) method in a computational framework developed at the Fraunhofer-Chalmers Research Centre for Industrial Mathematics (Gothenburg, Sweden) and implemented in Mathematica (Wolfram Research) [35]
Summary
Radiotherapy is a cornerstone of modern oncology, and is frequently given in conjunction with chemical treatments to improve efficacy [1, 2]. Radiosensitizers are a class of chemical agents designed to enhance the radiation effect, e.g. by interfering with the cell’s repair of radiation-. During preclinical development of novel compounds, including radiosensitizers, a central question is how to select the most promising compounds from a large number of candidates [4,5,6]. Proper assessment of radiation and radiosensitizer combinations requires studies of efficacy as well as toxicology and adverse effects [7]. Experimental studies must be supported by cheaper alternatives such as computer modeling and simulations [9, 10]
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