Exposure-response modeling improves selection of radiation and radiosensitizer combinations
Artikel i vetenskaplig tidskrift, 2022

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.


Drug selection

Tumor growth model

Tumor Static Exposure

Treatment optimization


Tim Cardilin

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Joachim Almquist

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

AstraZeneca AB

Mats Jirstrand

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Astrid Zimmermann

Merck KGaA

Floriane Lignet

Merck KGaA

S. El Bawab

Merck KGaA

Johan Gabrielsson

MedDoor AB

Journal of Pharmacokinetics and Pharmacodynamics

1567-567X (ISSN) 1573-8744 (eISSN)

Vol. 49 2 167-178


Farmaceutisk vetenskap

Farmakologi och toxikologi

Sannolikhetsteori och statistik



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