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• W2055973980 abstract "Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DCA mechanistic understanding of a drug's biological effect can have significant implications for selection of clinical dosing schedule, combination partners, as well as biomarker selection. In this context, a question often faced from target validation to the end of Phase I is how much of a drug's antitumor effect is through altering cell-cycle progression or through the induction of cell death. Here we demonstrate the utility of a mathematical model of the cell-cycle for determining the effect of a drug on cell-cycle kinetics, as an objective and quantiative interpretation of DNA flow cytometry and cellular growth rate data. The model uses periodic measurement of DNA content as analyzed by flow cytometry to determine the percentage of cells in G0/G1, S, and G2/M phases of the cell cycle, as well as continuous readouts of cell confluence over the course of treatment to assess growth rates. The model is then fitted to the data to extract effective cell cycle transition rates demonstrating the effect of the drug on cell cycle progression.To demonstrate of the approach, we applied the model to data acquired during treatment of A375 melanoma cells with the investigational drug TAK-733, an allosteric MEK 1/2 inhibitor hypothesized to cause a G1 arrest. Rather than an arrest at G1, the model predicted that the G1-to-S transition rate was reduced, and that much of the change in cell cycle data was due to an increase in the S-to-G2/M and G2/M-to-G0/G1 transition rates. These model predictions were then tested with video microscopy and both predictions (increased time spent in G0/G1 as well as reduced time spent in mitosis) were supported by experimental data.The model was then used to test whether the cell cycle activity of the compound has any implications for optimization of dosing schedule. By varying the schedule of drug treatment in the simulation, we find that for the biologically relevant concentration range the compound is schedule independent (i.e. effect is proportional to AUC). This finding suggests that there is no need to maintain constant pathway inhibition to see efficacy in tumor volume reduction. To test this prediction, we compare the results with the outcome of experiments in an A375 xenograft model with different dosing schedules and verify directly that AUC was indeed a better predictor of efficacy than Cmin.With this example, we demonstrate that mathematical modeling can be used to aid in interpretation of cell cycle data, often generated to describe the cell killing activity of oncology compounds. In addition, the model provides a method of interpreting the mechanism of action for early development decision making around schedule selection. This work demonstrates an approach that can be extended to other compounds to provide an integrated measure of the total contribution of cell death and/or cell cycle arrest, to help provide a mechanistic rationale for schedule selection with single agent and combination therapies.Citation Format: Jerome Mettetal, Derek Blair, Esha Gangoli, Patrick Vincent, Jeff Ecsedy, Wen Chyi Shyu, Arijit Chakravarty. Mathematical model of the cell cycle to determine mechanism of action and optimize dosing schedule. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3411. doi:10.1158/1538-7445.AM2013-3411" @default.
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• W2055973980 date "2013-04-15" @default.
• W2055973980 modified "2023-09-25" @default.
• W2055973980 title "Abstract 3411: Mathematical model of the cell cycle to determine mechanism of action and optimize dosing schedule." @default.
• W2055973980 doi "https://doi.org/10.1158/1538-7445.am2013-3411" @default.
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