Advanced Non-linear Mathematical Model for the Prediction of the Activity of a Putative Anticancer Agent in Human-to-mouse Cancer Xenografts
| Autor: | Konstantinos Dimas, George S. Stavrakakis, Sotirios G. Liliopoulos |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Cancer Research
Pancreatic ductal adenocarcinoma Antineoplastic Agents TGI model parameters estimation Mice Pharmacokinetic (PK)–Pharmacodynamic (PD) Deep learning neural networks (DLNN) Pancreatic ductal adenocarcinoma (PDAC) xenograft medicine Animals Humans Tumor growth Cancer biology Solid tumor Cell Proliferation business.industry Xenografted mice (PDX) Cancer General Medicine Models Theoretical medicine.disease Xenograft Model Antitumor Assays Gemcitabine Pancreatic Neoplasms Adaptive tumor growth short-term prediction Disease Models Animal Nonlinear Dynamics Oncology Nonlinear optimization Cancer research Tumor growth inhibition business Algorithms Tumor growth inhibition (TGI) mathematical model Carcinoma Pancreatic Ductal medicine.drug |
| Zdroj: | Anticancer Research. 40:5181-5189 |
| ISSN: | 1791-7530 0250-7005 |
| DOI: | 10.21873/anticanres.14521 |
| Popis: | Summarization: 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. Presented on: Anticancer Research |
| Databáze: | OpenAIRE |
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