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.