Itziar Irurzun-Arana (1,2), Thomas O. McDonald (2,3,4), Iñaki F. Trocóniz (1) and Franziska Michor (2,3,4).
(1) Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain. (2) Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA. (3) Department of Biostatistics and Computational Biology, Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02115, USA. (4) Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Objectives: The identification of drug administration schedules to avoid the emergence of resistance is a major challenge in cancer research. Here we propose a computational strategy to explore the effects of pharmacokinetics and drug interactions in evolutionary models of cancer progression and emergence of resistance, with the ultimate goal of identifying optimum dosing strategies.
Methods: Our approach combines stochastic evolutionary models of heterogeneous tumor cell populations with pharmacokinetics and drug-drug interaction models. This approach is made up of a cell-level description of the changes in sensitive and resistant cells over time and in response to treatment in the form of a stochastic model known as “multi-type branching process” [1]. A branching process is a stochastic model of cell division, mutations events and cell death used to describe the growth and composition of tumor cell populations. In this model, sensitive cells accumulate mutations at a given rate per cell division, generating new clones harboring specific drug-resistance mechanisms. The birth and death rates of each cell type are affected by the varying drug concentrations; therefore accounting for pharmacokinetic processes is also crucial. In order to simulate pharmacokinetic models we included the mrgsolve R package and we provide the codes of the most commonly used pharmacokinetic models to ease the degree of competency needed to perform simulations of these models. To assess drug interaction effects on the different rates of the model, we used non-parametric methods. This forms a multiscale description of drug metabolism and cancer evolution [2].
Results: In this work we present an R package called ACESO (A Cancer Evolution Simulation Optimizer) which incorporates a model that consist on a multi-scale description of how a heterogeneous cell population evolve over time depending on the drug administration schedule. This tool can then be used to search through different possible drug administration strategies to identify the one that is predicted to be best, for instance because it minimizes the risk of resistance or the expected number of cancer cells over time. Using ACESO, the different strategies can also be tested for robustness due to variability in pharmacokinetic parameters among patients, variable growth and death rates of sensitive and resistant cells as well as different compositions of the tumor at the start of therapy. We demonstrate the use of this tool using publicly available data from the Harvard Medical School Library of Integrated Network-based Cellular Signatures (LINCS) Database [3].
Conclusions: We present an accessible tool called ACESO to explore the dynamic evolution of heterogeneous tumor cell populations while taking pharmacokinetic and drug interaction effects into account to rationally identify optimum treatment administration strategies. This work represents a crucial step towards making clinically relevant predictions since it incorporates the most important aspects governing treatment response and cancer evolution.
References:
[1]Foo and Michor, J. Theor. Biol. 2010.
[2]Chakrabarti and Michor, Cancer Research. 2017.
[3] http://lincs.hms.harvard.edu/db/
Reference: PAGE 28 (2019) Abstr 9004 [www.page-meeting.org/?abstract=9004]
Poster: Methodology - New Modelling Approaches