Probabilistic analysis of tumor growth inhibition models to Support trial design - Marcus Baaz; Tim Cardilin; Torbjörn Lundh; Mats Jirstrand (Journal of Theoretical Biology, 2024)

Exposure-response modeling improves selection of radiation and radiosensitizer combinations - Tim Cardilin; Joachim Almquist; Mats Jirstrand; Astrid Zimmermann; Floriane Lignet; Samer El Bawab; Johan Gabrielsson (Journal of Pharmacokinetics and Pharmacodynamics, 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.

Erratum to: Tumor Static Concentration Curves in Combination Therapy - Tim Cardilin; Joachim Almquist; Mats Jirstand; Alexandre Soselly; Christiane Amendt; Samer El Bawab; Johan Gabrielsson (The AAPS Journal, 2017)

Tumor Static Concentration Curves in Combination Therapy - Tim Cardilin; Joachim Almquist; Mats Jirstrand; Alexandre Sostelly; Christiane Amendt; Samer El Bawab; Johan Gabrielsson (The AAPS Journal, 2017)