Abstract
Oncolytic virus therapy (OVT) is emerging as a potent alternative to conventional cancer treatments by employing engineered viruses that selectively infect and lyse tumor cells while sparing normal tissues. Although mathematical models have been developed to elucidate the dynamics of OVT and inform personalized therapies, they are often specific to certain organisms. Mathematical models tailored to more recently developed animal models of OVT, such as zebrafish, are not yet available. Here, we introduce the first mathematical model of OVT trained on zebrafish data from published studies to bridge the gap. We explore a variety of mathematical model structures and perform parameter estimation and model selection. The selected model effectively captures the observed tumor dynamics, i.e. delayed tumor shrinkage, and provides valuable insights into the underlying mechanisms of OVT in zebrafish. Our work establishes the groundwork for advancing experimental studies in zebrafish, contributing to the design of more effective cancer treatment strategies in the future.
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Abstract
Oncolytic virus therapy (OVT) is emerging as a potent alternative to conventional cancer treatments by employing engineered viruses that selectively infect and lyse tumor cells while sparing normal tissues. Although mathematical models have been developed to elucidate the dynamics of OVT and inform personalized therapies, they are often specific to certain organisms. Mathematical models tailored to more recently developed animal models of OVT, such as zebrafish, are not yet available. Here, we introduce the first mathematical model of OVT trained on zebrafish data from published studies to bridge the gap. We explore a variety of mathematical model structures and perform parameter estimation and model selection. The selected model effectively captures the observed tumor dynamics, i.e. delayed tumor shrinkage, and provides valuable insights into the underlying mechanisms of OVT in zebrafish. Our work establishes the groundwork for advancing experimental studies in zebrafish, contributing to the design of more effective cancer treatment strategies in the future.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
(e-mail: yuhong.liu{at}uni-bonn.de, dilan.pathirana{at}uni-bonn.de, jan.hasenauer{at}uni-bonn.de).
⋆ The authors acknowledge funding by the Deutsche Forschungsge-meinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC 2047—390685813 (Hausdorff Center for Mathematics), EXC 2151—390873048 (ImmunoSensation2)), and under project ID 432325352 – SFB 1454 (Metaflammation), by the European Union via ERC grant INTEGRATE, grant agreement number 101126146, and by the University of Bonn (via the Schlegel Professorship of Prof. Dr. Jan Hasenauer)
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