Advanced Non-linear Mathematical Model for the Prediction of the Activity of a Putative Anticancer Agent in Human-to-mouse Cancer Xenografts

Abstract

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.

Keywords: Pharmacokinetic (PK)–Pharmacodynamic (PD); TGI model parameters estimation; adaptive tumor growth short-term prediction; deep learning neural networks (DLNN); nonlinear optimization; pancreatic ductal adenocarcinoma (PDAC) xenograft; tumor growth inhibition (TGI) mathematical model; xenografted mice (PDX).