Drug Designing: Open Access
Open Access

Role of Oncolytic Viruses in Tumor Reduction for Cancer Therapy

Jing Qiu^{* *}Correspondence: Jing Qiu, Department of Anesthesiology, Sichuan University, Chengdu, China, Email:

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Description

Chemovirotherapy, a novel approach combining chemotherapy and virotherapy, offers a potential solution for improving cancer treatment by addressing key challenges associated with chemotherapy, such as its toxicity and the development of drug resistance. In this approach, Oncolytic Viruses (OVs) are injected into the patient’s bloodstream, where they specifically target tumor cells, leaving healthy cells unharmed. This process not only aids in shrinking the tumor but also stimulates the immune system, further enhancing the overall effectiveness of chemotherapy. By integrating oncolytic virotherapy into chemotherapy, the side effects of chemotherapy are reduced and resistance is overcome, leading to a more effective and targeted treatment approach.

To maximize the benefits of chemovirotherapy, a robust optimal control strategy is essential. This strategy aims to regulate the tumor's density and ensure that tumor cells decrease to a stable condition over time. The treatment plan must be responsive to input disturbances and parametric uncertainties, ensuring that tumor mass is minimized while limiting the delivery of both the drug and the virus. A mathematical model using Ordinary Differential Equations (ODEs) was developed to represent the interactions between avascular tumor cells, immune cells, and the treatment agents. This model serves as the foundation for designing controllers that manage uncertainties in the system and optimize the treatment process.

Two types of controllers h-infinity and Dual Controller (DK) iteration controllers are implemented using both continuous and discrete procedures. The goal is to manage the system’s response under uncertain conditions, ensuring optimal treatment while minimizing risks and side effects. Simulations of closed-loop control systems indicate that discrete injections of treatment agents are more effective and realistic than continuous infusion methods, providing a more controlled and manageable approach in practice. Cancer, a leading cause of death worldwide, has been a focus of research for over a century. Chemotherapy has long been a common treatment for various cancer types, but it faces significant challenges due to its toxic side effects and the development of resistance by cancer cells. To improve chemotherapy’s effectiveness, researchers have focused on optimizing key treatment variables, such as the duration, frequency and dosage of drugs.

Chemovirotherapy combines chemotherapy with oncolytic virotherapy, a novel approach that has shown substantial potential in clinical trials. During chemovirotherapy, oncolytic viruses are injected into the patient’s bloodstream, where they infect tumor cells and trigger an immune response, while sparing healthy cells. This dual action not only targets the cancer directly but also activates the immune system to further combat the tumor. Clinical trials involving chemovirotherapy have demonstrated improved treatment outcomes across a range of cancer types, including gliomas, lung cancer and breast cancer.

Mathematical modeling has become an essential tool in understanding and improving chemovirotherapy. Various models, including Partial Differential Equations (PDEs) and ODEs, have been used to study the dynamics of chemovirotherapy. These models account for factors such as the interaction between infected and uninfected tumor cells, drug concentrations and the presence of free viruses. The original PDE model focused on tumor cell spread and the effects of drug and virus treatment, while later ODE models integrated the impact of immune cells on the therapeutic process. More recent advancements include Delay Differential Equations (DDEs), which account for time delays in the body's response to drug and virus treatments.

To identify optimal cancer treatment strategies, several solution methods have been proposed, including analytical, approximated and heuristic approaches. Analytical solutions aim to minimize treatment plans using closed-form expressions, though they can become complex for practical use. In these cases, approximate and heuristic solutions offer simpler alternatives for solving complex problems.

Conclusion

The goal of this research is to design a robust, optimal chemovirotherapy strategy that effectively uses limited drug and virus dosages to reduce tumor density. The proposed control methods, including h-infinity and DK iteration controllers, aim to optimize the treatment under uncertain conditions. By handling input disturbances and parametric uncertainties, the control system ensures more reliable and effective treatment. The research investigates optimal treatment conditions, such as drug and virus dosages, treatment frequency and start time, while accounting for uncertainties to ensure the best possible therapeutic outcome.