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Advanced non-linear mathematical model for the prediction of the activity of a putative anticancer agent in human-to-mouse cancer xenografts

Liliopoulos Sotirios, Stavrakakis Georgios, Dimas Konstantinos S.

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URIhttp://purl.tuc.gr/dl/dias/202E1739-77EE-4C41-A13B-B9A2613A442C-
Identifierhttps://doi.org/10.21873/anticanres.14521-
Identifierhttps://ar.iiarjournals.org/content/40/9/5181-
Languageen-
Extent9 pagesen
TitleAdvanced non-linear mathematical model for the prediction of the activity of a putative anticancer agent in human-to-mouse cancer xenograftsen
CreatorLiliopoulos Sotiriosen
CreatorΛιλιοπουλος Σωτηριοςel
CreatorStavrakakis Georgiosen
CreatorΣταυρακακης Γεωργιοςel
CreatorDimas Konstantinos S.en
PublisherInternational Institute of Anticancer Researchen
Content SummaryBackground/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 ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2021-11-18-
Date of Publication2020-
SubjectPharmacokinetic (PK)–Pharmacodynamic (PD)en
SubjectTumor growth inhibition (TGI) mathematical modelen
SubjectDeep learning neural networks (DLNN)en
SubjectNonlinear optimizationen
SubjectTGI model parameters estimationen
SubjectAdaptive tumor growth short-term predictionen
SubjectXenografted mice (PDX)en
SubjectPancreatic ductal adenocarcinoma (PDAC) xenograften
Bibliographic CitationS. 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.14521en

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