Backward Hopf bifurcation in a mathematical model for oncolytic virotherapy with the infection delay and innate immune effects

Age-structure model for oncolytic virotherapy

A non-local delay differential system was developed by age-infected consideration to understand the interaction between tumor cells and oncolytic virus. The critical threshold value [Formula: see text] was obtained that dominates the dynamical behaviors of our model. By strongly [Formula: see text]-persistent and Comparison theorem, the global stability and uniformly strongly persistent were investigated. Our global sensitivity analyses indicate that [Formula: see text] is most sensitive to virus clearance rate [Formula: see text] while is least sensitive to viral life cycle [Formula: see text]. Finally, our model was used to fit with experimental data which indicates that viral life cycle [Formula: see text] plays a key role in data fitting. And the trained model shows perfect prediction in the testing data set with the coefficient of determination [Formula: see text].

Natural Killer Cells Recruitment in Oncolytic Virotherapy: A Mathematical Model

In this paper, we investigate how natural killer (NK) cell recruitment to the tumor microenvironment (TME) affects oncolytic virotherapy. NK cells play a major role against viral infections. They are, however, known to induce early viral clearance of oncolytic viruses, which hinders the overall efficacy of oncolytic virotherapy. Here, we formulate and analyze a simple mathematical model of the dynamics of the tumor, OV and NK cells using currently available preclinical information. The aim of this study is to characterize conditions under which the synergistic balance between OV-induced NK responses and required viral cytopathicity may or may not result in a successful treatment. In this study, we found that NK cell recruitment to the TME must take place neither too early nor too late in the course of OV infection so that treatment will be successful. NK cell responses are most influential at either early (partly because of rapid response of NK cells to viral infections or antigens) or later (partly because of antitumoral ability of NK cells) stages of oncolytic virotherapy. The model also predicts that: (a) an NK cell response augments oncolytic virotherapy only if viral cytopathicity is weak; (b) the recruitment of NK cells modulates tumor growth; and (c) the depletion of activated NK cells within the TME enhances the probability of tumor escape in oncolytic virotherapy. Taken together, our model results demonstrate that OV infection is crucial, not just to cytoreduce tumor burden, but also to induce the stronger NK cell response necessary to achieve complete or at least partial tumor remission. Furthermore, our modeling framework supports combination therapies involving NK cells and OV which are currently used in oncolytic immunovirotherapy to treat several cancer types.