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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.

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/202E1739-77EE-4C41-A13B-B9A2613A442C-
Αναγνωριστικόhttps://doi.org/10.21873/anticanres.14521-
Αναγνωριστικόhttps://ar.iiarjournals.org/content/40/9/5181-
Γλώσσαen-
Μέγεθος9 pagesen
ΤίτλοςAdvanced non-linear mathematical model for the prediction of the activity of a putative anticancer agent in human-to-mouse cancer xenograftsen
ΔημιουργόςLiliopoulos Sotiriosen
ΔημιουργόςΛιλιοπουλος Σωτηριοςel
ΔημιουργόςStavrakakis Georgiosen
ΔημιουργόςΣταυρακακης Γεωργιοςel
ΔημιουργόςDimas Konstantinos S.en
ΕκδότηςInternational Institute of Anticancer Researchen
ΠερίληψηBackground/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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2021-11-18-
Ημερομηνία Δημοσίευσης2020-
Θεματική ΚατηγορίαPharmacokinetic (PK)–Pharmacodynamic (PD)en
Θεματική ΚατηγορίαTumor growth inhibition (TGI) mathematical modelen
Θεματική ΚατηγορίαDeep learning neural networks (DLNN)en
Θεματική ΚατηγορίαNonlinear optimizationen
Θεματική ΚατηγορίαTGI model parameters estimationen
Θεματική ΚατηγορίαAdaptive tumor growth short-term predictionen
Θεματική ΚατηγορίαXenografted mice (PDX)en
Θεματική ΚατηγορίαPancreatic ductal adenocarcinoma (PDAC) xenograften
Βιβλιογραφική ΑναφοράS. 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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