Recent Developments in Oncology Immunotherapy, Adverse Effects Part 2

Safe optimal control of cancer using a Control Barrier Function technique

This paper addresses the problem of designing a safe and optimal control strategy for typical cancer using the Control Barrier Function (CBF) technique. Cancer is a complex and highly dynamic disease characterized by uncontrolled cell growth and proliferation. By formulating the cancer dynamics as a control system, this study introduces a CBF-based controller that guides the cancerous tissue towards safe and controlled behaviors. The controller is designed to simultaneously optimize treatment efficacy and patient safety. The methodology involves modeling the cancer growth dynamics, incorporating relevant biological constraints, and designing the CBF-based controller to regulate the tumor's evolution within acceptable bounds. Simulation results demonstrate the effectiveness of the CBF-based strategy in achieving safe and optimal cancer control. The controller showcases the ability to drive the cancerous tissue towards desired states while respecting predefined safety constraints.

Studying the importance of regulatory T cells in chemoimmunotherapy mathematical modeling and proposing new approaches for developing a mathematical dynamic of cancer

Studying the mathematical dynamics of cancer has gained the attention of bioengineers in the past three decades. Different kinds of modelling considering various aspects of treatment have been proposed. In this paper, the key role of Regulatory T cells is discussed and a model in ordinary differential equation (ODE) form is proposed by adding this state to the system dynamics considering chemoimmunotherapy treatment. Regulatory T cells are considered as one of the main tumor cells' tactics to deceive the body's immune system. The improved model is verified mathematically and biologically and fits all criteria in both fields. The results show that entering Regulatory T cells state on cancer mathematical modelling for simulating body cells for chemoimmunotherapy provides a way to identify critical cases more carefully, which a simplified model is unable to accomplish. This point emphasizes the fact that this state must be present in cancer modelling to anticipate immune response more accurately. The advanced system fixed points are obtained by the Newton method and bifurcation diagrams are derived and discussed. New features and remarks are proposed during the journey of developing more accurate models that have the best fit with laboratory data. The sensitivity chart of the model is illustrated and novel aspects of discussions are made with the aim of personalizing a model for a patient and identifying critical conditions based on the chart before any treatment begins. This point enables physicians to determine whether critical conditions have occurred for a patient in a specific treatment or not.

Optimal control design for drug delivery of immunotherapy in chemoimmunotherapy treatment

BACKGROUND AND OBJECTIVE

There are various approaches to control a mathematical dynamic of cancer, each of which is suitable for a special goal. Optimal control is considered as an applicable method to calculate the minimum necessary drug delivery in such systems.

METHODS

In this paper, a mathematical dynamic of cancer is proposed considering tumor cells, natural killer cells, CD8+T cells, circulating lymphocytes, IL-2 cytokine and Regulatory T cells as the system states, and chemotherapy, IL-2 and activated CD8+T cells injection rate as the control signals. After verifying the proposed mathematical model, the importance of the drug delivery timing and the effect of cancer cells initial condition are discussed. Afterwards, an optimal control is designed by defining a proper cost function with the goal of minimizing the number of tumor cells, and two immunotherapy drug amounts during treatment CONCLUSIONS: Results show that inappropriate injection of immunotherapy time schedule and the number of initial conditions of cancer cells might result in chemoimmunotherapy failure and auxiliary treatment must be prescribed to decrease tumor size before any treatment takes place. The obtained optimal control signals show that with lower amount of drug delivery and a suitable drug injection time schedule, tumor cells can be eliminated while a fixed immunotherapy time schedule protocol fails with larger amount of drug injection. This conclusion can be utilized with the aim of personalizing drug delivery and designing more accurate clinical trials based on the improved model simulations in order to save cost and time.

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2+) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2+ breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2+ breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2+ breast cancer management, we also review mathematical models pertaining to the dynamics of HER2+ breast cancer and immune checkpoint inhibitors.

Adverse Events of Oncologic Immunotherapy and Their Management

Over the past two decades, immunotherapy has emerged as a promising treatment option for patients with cancer. However, newer versions of immunotherapy, such as checkpoint inhibitors, may be associated with unusual adverse effects (AEs) that can range in severity from mild to life-threatening. Unlike common AEs of conventional chemotherapy, which have a predictable nadir or cyclic pattern after administration, AEs of these newer immunotherapies are variable, depending on the type of immunotherapy, route of administration, and mechanism of action. The onset and resolution of these AEs may be present at any time, during administration of treatment, a few weeks after administration of treatment, or several months after completion of treatment. Therefore, improving outcomes in patients undergoing oncologic immunotherapy requires oncology nurses' knowledge and understanding of various immunotherapy agents, as well as early recognition and management of potential AEs, especially AEs associated with checkpoint inhibitors and other therapies that manipulate T-cell activation causing autoimmune toxicity. This article draws upon current evidence from systematic reviews, meta-analyses, and expert consensus guidelines to provide a brief overview of common immunotherapies used in cancer and management of their associated AEs.