Advanced non-linear mathematical model for the prediction of the activity of a putative anticancer agent in human-to-mouse cancer xenograftsAdvanced non-linear mathematical model for the prediction of the activity of a putative anticancer agent in human-to-mouse cancer xenografts Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2021-11-182020enBackground/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.http://creativecommons.org/licenses/by/4.0/Anticancer Research4095181-5189 Liliopoulos Sotirios Λιλιοπουλος Σωτηριος Stavrakakis Georgios Σταυρακακης Γεωργιος Dimas Konstantinos S. International Institute of Anticancer Research Pharmacokinetic (PK)–Pharmacodynamic (PD) Tumor growth inhibition (TGI) mathematical model Deep learning neural networks (DLNN) Nonlinear optimization TGI model parameters estimation Adaptive tumor growth short-term prediction Xenografted mice (PDX) Pancreatic ductal adenocarcinoma (PDAC) xenograft