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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI | http://purl.tuc.gr/dl/dias/202E1739-77EE-4C41-A13B-B9A2613A442C | - |
| Identifier | https://doi.org/10.21873/anticanres.14521 | - |
| Identifier | https://ar.iiarjournals.org/content/40/9/5181 | - |
| Language | en | - |
| Extent | 9 pages | en |
| Title | Advanced non-linear mathematical model for the prediction of the activity of a putative anticancer agent in human-to-mouse cancer xenografts | en |
| Creator | Liliopoulos Sotirios | en |
| Creator | Λιλιοπουλος Σωτηριος | el |
| Creator | Stavrakakis Georgios | en |
| Creator | Σταυρακακης Γεωργιος | el |
| Creator | Dimas Konstantinos S. | en |
| Publisher | International Institute of Anticancer Research | en |
| Content Summary | Background/Aim: Mathematical models have long been considered as important tools in cancer biology and therapy. Herein, we present an advanced non-linear mathematical model that can predict accurately the effect of an anticancer agent on the growth of a solid tumor. Materials and Methods: Advanced non-linear mathematical optimization techniques and human-to-mouse experimental data were used to develop a tumor growth inhibition (TGI) estimation model. Results: Using this mathematical model, we could accurately predict the tumor mass in a human-to-mouse pancreatic ductal adenocarcinoma (PDAC) xenograft under gemcitabine treatment up to five time periods (points) ahead of the last treatment. Conclusion: The ability of the identified TGI dynamic model to perform satisfactory short-term predictions of the tumor growth for up to five time periods ahead was investigated, evaluated and validated for the first time. Such a prediction model could not only assist the pre-clinical testing of putative anticancer agents, but also the early modification of a chemotherapy schedule towards increased efficacy. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2021-11-18 | - |
| Date of Publication | 2020 | - |
| Subject | Pharmacokinetic (PK)–Pharmacodynamic (PD) | en |
| Subject | Tumor growth inhibition (TGI) mathematical model | en |
| Subject | Deep learning neural networks (DLNN) | en |
| Subject | Nonlinear optimization | en |
| Subject | TGI model parameters estimation | en |
| Subject | Adaptive tumor growth short-term prediction | en |
| Subject | Xenografted mice (PDX) | en |
| Subject | Pancreatic ductal adenocarcinoma (PDAC) xenograft | en |
| Bibliographic Citation | S. G. Liliopoulos, G. S. Stavrakakis and K. S. Dimas, “Advanced non-linear mathematical model for the prediction of the activity of a putative anticancer agent in human-to-mouse cancer xenografts,” Anticancer Res., vol. 40, no. 9, pp. 5181-5189, Sep. 2020. doi: 10.21873/anticanres.14521 | en |
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