Model-based assessment of combination therapies – ranking of radiosensitizing agents in oncology
Abstract Background To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method fo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-023-10899-y |
| _version_ | 1827952676097228800 |
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| author | Marcus Baaz Tim Cardilin Floriane Lignet Astrid Zimmermann Samer El Bawab Johan Gabrielsson Mats Jirstrand |
| author_facet | Marcus Baaz Tim Cardilin Floriane Lignet Astrid Zimmermann Samer El Bawab Johan Gabrielsson Mats Jirstrand |
| author_sort | Marcus Baaz |
| collection | DOAJ |
| description | Abstract Background To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data. Methods We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves. Results The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 $$\mu \mathrm{g}/\mathrm{mL}$$ μ g / mL of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication. Conclusions A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process. |
| first_indexed | 2024-04-09T14:02:27Z |
| format | Article |
| id | doaj.art-a6f9721279f44bcf9f33ddd27b842889 |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-04-09T14:02:27Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-a6f9721279f44bcf9f33ddd27b8428892023-05-07T11:15:50ZengBMCBMC Cancer1471-24072023-05-0123111910.1186/s12885-023-10899-yModel-based assessment of combination therapies – ranking of radiosensitizing agents in oncologyMarcus Baaz0Tim Cardilin1Floriane Lignet2Astrid Zimmermann3Samer El Bawab4Johan Gabrielsson5Mats Jirstrand6Fraunhofer-Chalmers Research Centre for Industrial MathematicsFraunhofer-Chalmers Research Centre for Industrial MathematicsTranslational Medicine, Quantitative Pharmacology, Merck Healthcare KGaATranslation Innovation Platform Oncology, Merck Healthcare KGaATranslational Medicine, Quantitative Pharmacology, Merck Healthcare KGaAMeddoor ABFraunhofer-Chalmers Research Centre for Industrial MathematicsAbstract Background To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data. Methods We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves. Results The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 $$\mu \mathrm{g}/\mathrm{mL}$$ μ g / mL of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication. Conclusions A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.https://doi.org/10.1186/s12885-023-10899-yRadiation therapyCombination therapyTumor static exposureNon-linear mixed effectsInter-study variability |
| spellingShingle | Marcus Baaz Tim Cardilin Floriane Lignet Astrid Zimmermann Samer El Bawab Johan Gabrielsson Mats Jirstrand Model-based assessment of combination therapies – ranking of radiosensitizing agents in oncology BMC Cancer Radiation therapy Combination therapy Tumor static exposure Non-linear mixed effects Inter-study variability |
| title | Model-based assessment of combination therapies – ranking of radiosensitizing agents in oncology |
| title_full | Model-based assessment of combination therapies – ranking of radiosensitizing agents in oncology |
| title_fullStr | Model-based assessment of combination therapies – ranking of radiosensitizing agents in oncology |
| title_full_unstemmed | Model-based assessment of combination therapies – ranking of radiosensitizing agents in oncology |
| title_short | Model-based assessment of combination therapies – ranking of radiosensitizing agents in oncology |
| title_sort | model based assessment of combination therapies ranking of radiosensitizing agents in oncology |
| topic | Radiation therapy Combination therapy Tumor static exposure Non-linear mixed effects Inter-study variability |
| url | https://doi.org/10.1186/s12885-023-10899-y |
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