Literature DB >> 19449978

Chemotherapy may be delivered based on an integrated view of tumour dynamics.

B Ribba1, B You, M Tod, P Girard, B Tranchand, V Trillet-Lenoir, G Freyer.   

Abstract

The aim here was to explore the potential of pharmacokinetic (PK)/pharmacodynamic (PD) and physiopathological parameters in explaining the primary effects of an anti-cancer treatment that targets cells in a specific cell cycle phase. The authors applied a theoretical multi-scale disease model of tumour growth that integrates cancer processes at the cellular and tissue scales. The mathematical model at the cell level relies on a dynamic description of cell cycle regulation while the model at the tissue level is based on fluid mechanics considerations. Simulations show that the number of target cells oscillates as the tumour grows after a first cycle of chemotherapy. Both treatment effect and tumour growth processes drive these oscillations. Nonetheless, results indicate that parameters related to physiopathological processes may have greater relevance than classical drug-related parameters in determining the efficacy of a chemotherapy treatment protocol. Physiopathological parameters, in particular those related to cell cycle regulation, may be integrated in PK/PD models aimed at optimising the delivery of phase-specific cytotoxic treatments.

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Year:  2009        PMID: 19449978     DOI: 10.1049/iet-syb.2008.0104

Source DB:  PubMed          Journal:  IET Syst Biol        ISSN: 1751-8849            Impact factor:   1.615


  3 in total

1.  Tumor growth rate determines the timing of optimal chronomodulated treatment schedules.

Authors:  Samuel Bernard; Branka Cajavec Bernard; Francis Lévi; Hanspeter Herzel
Journal:  PLoS Comput Biol       Date:  2010-03-19       Impact factor: 4.475

2.  Pharmacodynamic Modeling of Cell Cycle Effects for Gemcitabine and Trabectedin Combinations in Pancreatic Cancer Cells.

Authors:  Xin Miao; Gilbert Koch; Sihem Ait-Oudhia; Robert M Straubinger; William J Jusko
Journal:  Front Pharmacol       Date:  2016-11-15       Impact factor: 5.810

3.  Efficient prediction of drug-drug interaction using deep learning models.

Authors:  Prashant Kumar Shukla; Piyush Kumar Shukla; Poonam Sharma; Paresh Rawat; Jashwant Samar; Rahul Moriwal; Manjit Kaur
Journal:  IET Syst Biol       Date:  2020-08       Impact factor: 1.615

  3 in total

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