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A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition.

Bagheri N, Shiina M, Lauffenburger DA, Korn WM - PLoS Comput. Biol. (2011)

Bottom Line: Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies.Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study.We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
Oncolytic adenoviruses, such as ONYX-015, have been tested in clinical trials for currently untreatable tumors, but have yet to demonstrate adequate therapeutic efficacy. The extent to which viruses infect targeted cells determines the efficacy of this approach but many tumors down-regulate the Coxsackievirus and Adenovirus Receptor (CAR), rendering them less susceptible to infection. Disrupting MAPK pathway signaling by pharmacological inhibition of MEK up-regulates CAR expression, offering possible enhanced adenovirus infection. MEK inhibition, however, interferes with adenovirus replication due to resulting G1-phase cell cycle arrest. Therefore, enhanced efficacy will depend on treatment protocols that productively balance these competing effects. Predictive understanding of how to attain and enhance therapeutic efficacy of combinatorial treatment is difficult since the effects of MEK inhibitors, in conjunction with adenovirus/cell interactions, are complex nonlinear dynamic processes. We investigated combinatorial treatment strategies using a mathematical model that predicts the impact of MEK inhibition on tumor cell proliferation, ONYX-015 infection, and oncolysis. Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies. Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study. Further analysis revealed that a CAR-independent mechanism may be responsible for amplified virus production and cell death. We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis. Enhanced oncolytic therapy has the potential to dramatically improve non-surgical cancer treatment, especially in locally advanced or metastatic cases where treatment options remain limited.

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Simulated predictions point to unexpected treatment protocols.Simulated percent cell death (CD) is evaluated on day 8 as a function of the timing of MEK-inhibitor treatment initiation, timing of infection, and multiplicity of infection (MOI). Each Cartesian coordinate reflects an independent simulation or treatment/infection protocol. The timing of ONYX-015 infection is varied on the x-axis; the timing of CI1040 treatment initiation is varied on the y-axis. CI1040 removal by media change occurs 2 days post treatment irrespective of the timing of infection. MOI is held constant in each subplot. Percent cell death is defined as the complement of cell viability. Treatment and infection protocols that yield over 50% cell death are shown. Greater cell death is reflected by larger data points and an increasingly red color (see color bar). Empty data points depict protocols that fail to kill at least 50% of the cellular population.
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pcbi-1001085-g003: Simulated predictions point to unexpected treatment protocols.Simulated percent cell death (CD) is evaluated on day 8 as a function of the timing of MEK-inhibitor treatment initiation, timing of infection, and multiplicity of infection (MOI). Each Cartesian coordinate reflects an independent simulation or treatment/infection protocol. The timing of ONYX-015 infection is varied on the x-axis; the timing of CI1040 treatment initiation is varied on the y-axis. CI1040 removal by media change occurs 2 days post treatment irrespective of the timing of infection. MOI is held constant in each subplot. Percent cell death is defined as the complement of cell viability. Treatment and infection protocols that yield over 50% cell death are shown. Greater cell death is reflected by larger data points and an increasingly red color (see color bar). Empty data points depict protocols that fail to kill at least 50% of the cellular population.

Mentions: We interpolated intermediate parameter values and used the model to predict the extent of cell death as a function of the time of CI1040 treatment initiation, the time of ONYX-015 infection, and the MOI. We employed an exhaustive search algorithm to simulate the effect of various treatment and infection protocols. This algorithm systematically evaluated every possible sequence combination of drug treatment and infection conditions (within a defined interval), with the exception of media change, tw, which was set to occur 2 days after treatment. We varied CI1040 treatment initiation between days 0–3 and infection between days 0–7. The MOI was also varied between 0.1 and 10. We evaluated percent cell death on day 8 irrespective of the sequence protocol. In this context, percent cell death is defined as the complement of cell viability (the ratio of total cell density in a simulation consisting of treatment and infection, relative to total cell density in an independent simulation omitting infection). In Figure 3, we highlight drug treatment and infection protocols that yielded over 50% cell death on simulated day 8. (Please refer to Text S1 for additional MOI). Model simulations suggested that, at low MOI, the greatest efficacy of virus-mediated cell death results from MEK-inhibition that coincides with the time of infection. At higher MOI, our model predicted maximal cell killing when inhibitor treatment occurs at the time of, or soon after, infection.


A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition.

Bagheri N, Shiina M, Lauffenburger DA, Korn WM - PLoS Comput. Biol. (2011)

Simulated predictions point to unexpected treatment protocols.Simulated percent cell death (CD) is evaluated on day 8 as a function of the timing of MEK-inhibitor treatment initiation, timing of infection, and multiplicity of infection (MOI). Each Cartesian coordinate reflects an independent simulation or treatment/infection protocol. The timing of ONYX-015 infection is varied on the x-axis; the timing of CI1040 treatment initiation is varied on the y-axis. CI1040 removal by media change occurs 2 days post treatment irrespective of the timing of infection. MOI is held constant in each subplot. Percent cell death is defined as the complement of cell viability. Treatment and infection protocols that yield over 50% cell death are shown. Greater cell death is reflected by larger data points and an increasingly red color (see color bar). Empty data points depict protocols that fail to kill at least 50% of the cellular population.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3040662&req=5

pcbi-1001085-g003: Simulated predictions point to unexpected treatment protocols.Simulated percent cell death (CD) is evaluated on day 8 as a function of the timing of MEK-inhibitor treatment initiation, timing of infection, and multiplicity of infection (MOI). Each Cartesian coordinate reflects an independent simulation or treatment/infection protocol. The timing of ONYX-015 infection is varied on the x-axis; the timing of CI1040 treatment initiation is varied on the y-axis. CI1040 removal by media change occurs 2 days post treatment irrespective of the timing of infection. MOI is held constant in each subplot. Percent cell death is defined as the complement of cell viability. Treatment and infection protocols that yield over 50% cell death are shown. Greater cell death is reflected by larger data points and an increasingly red color (see color bar). Empty data points depict protocols that fail to kill at least 50% of the cellular population.
Mentions: We interpolated intermediate parameter values and used the model to predict the extent of cell death as a function of the time of CI1040 treatment initiation, the time of ONYX-015 infection, and the MOI. We employed an exhaustive search algorithm to simulate the effect of various treatment and infection protocols. This algorithm systematically evaluated every possible sequence combination of drug treatment and infection conditions (within a defined interval), with the exception of media change, tw, which was set to occur 2 days after treatment. We varied CI1040 treatment initiation between days 0–3 and infection between days 0–7. The MOI was also varied between 0.1 and 10. We evaluated percent cell death on day 8 irrespective of the sequence protocol. In this context, percent cell death is defined as the complement of cell viability (the ratio of total cell density in a simulation consisting of treatment and infection, relative to total cell density in an independent simulation omitting infection). In Figure 3, we highlight drug treatment and infection protocols that yielded over 50% cell death on simulated day 8. (Please refer to Text S1 for additional MOI). Model simulations suggested that, at low MOI, the greatest efficacy of virus-mediated cell death results from MEK-inhibition that coincides with the time of infection. At higher MOI, our model predicted maximal cell killing when inhibitor treatment occurs at the time of, or soon after, infection.

Bottom Line: Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies.Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study.We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
Oncolytic adenoviruses, such as ONYX-015, have been tested in clinical trials for currently untreatable tumors, but have yet to demonstrate adequate therapeutic efficacy. The extent to which viruses infect targeted cells determines the efficacy of this approach but many tumors down-regulate the Coxsackievirus and Adenovirus Receptor (CAR), rendering them less susceptible to infection. Disrupting MAPK pathway signaling by pharmacological inhibition of MEK up-regulates CAR expression, offering possible enhanced adenovirus infection. MEK inhibition, however, interferes with adenovirus replication due to resulting G1-phase cell cycle arrest. Therefore, enhanced efficacy will depend on treatment protocols that productively balance these competing effects. Predictive understanding of how to attain and enhance therapeutic efficacy of combinatorial treatment is difficult since the effects of MEK inhibitors, in conjunction with adenovirus/cell interactions, are complex nonlinear dynamic processes. We investigated combinatorial treatment strategies using a mathematical model that predicts the impact of MEK inhibition on tumor cell proliferation, ONYX-015 infection, and oncolysis. Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies. Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study. Further analysis revealed that a CAR-independent mechanism may be responsible for amplified virus production and cell death. We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis. Enhanced oncolytic therapy has the potential to dramatically improve non-surgical cancer treatment, especially in locally advanced or metastatic cases where treatment options remain limited.

Show MeSH
Related in: MedlinePlus