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Engineered nanomaterials, such as nanoparticles, nanocomposites and chalcogenides, possess distinct characteristics as a result of their extremely small size, leading to significant advancements in industries such as healthcare, electronics and energy. Nanomaterials play a crucial role in cancer immunotherapy by greatly improving the effectiveness of several treatments, including immune checkpoint inhibitors, T cell transfer therapy, monoclonal antibodies and therapeutic vaccines. They achieve this by enhancing drug transport and increasing targeting accuracy. Although they have the ability to bring about significant changes, obstacles, such as potential toxicity, environmental effect and manufacturing complexity, impede their widespread use. In order to address these problems, it is crucial to use tactics such as thorough biocompatibility testing, environmentally friendly production procedures and modern manufacturing technology. This study examines the progress and difficulties in using synthetic nanomaterials for cancer immunotherapy and suggests ways to ensure their safe and efficient incorporation into medical treatments.

Microcapsules that release antigen-capturing nanoparticles (AC-NPs) with macrophage inflammatory protein-3 alpha (MIP-3$\alpha$) and anti-programmed death-1 (PD-1) antibody are developed, and these microcapsules have the ability to enhance immunoresponses through cross-priming of cluster of differentiation 8+ (CD8+) T cells by dendritic cells (DCs) in vivo in BALB/c mice.

Lipid protamine hyaluronic acid nanoparticles containing AC-NPs generated via nanoprecipitation of 4 mg/mL of polylactic-co-glycolic acid (PLGA), 1,000 ng/mL of MIP-3$\alpha$ and 400 $\mu$g of anti-PD-1 were mixed with 1 mL of 4.0% alginate and 3.0% of hyaluronate and then sprayed with 0.5 mM of ferrous chloride. These capsules were injected subcutaneously around LM17 tumor in the left hind legs of BALB/c mice. The tumors were exposed to a radiation dose of 10 or 20 Gy from 100 keV soft X-ray radiation. PLGA AC-NPs and MIP-3$\alpha$ were released in response to the radiation dose.

PLGA AC-NPs captured tumor-derived protein antigens are released by exposure to radiation, and these antigens were transported to DCs that were recruited and activated by MIP-3$\alpha$, intensifying the DC-associated cross-priming of CD8+ T cells. These treatments resulted in increased antitumor effect and reduced metastasis by abscopal effect. Our targeted immunotherapy may lead to better tumor therapy.

This study aimed to investigate the effect of the particles releasing chitosan upon exposure to radiation on inhibition of metastasis.

A 10 mL solution of water containing 0.2% weight/volume alginate, 0.1% hyaluronic acid, and 100-mg chitosan was sprayed into the vibrating solution through a stainless mesh filter (pore size: 0.8 $\mu$m) using an ultrasound disintegrator, thereby generating chitosan particles. Further, $1\times 10^{10}$ particles floating in 0.1 mL normal saline were subcutaneously injected around the 4TI cells-derived tumor in the left hind legs of six-week-old male C3He/N mice. Six hours after injection, tumors were exposed to 10 Gy or 20 Gy of 100-keV soft X-ray radiation. The release of chitosan was expressed as the frequency of ruptured chitosan particles 12 h after radiation. The antimetastatic effect was confirmed by a reduction in the number of metastatic pulmonary nodules 21 days after completion of treatment.

More than $56.3\pm 4.3$% of the chitosan particles released chitosan in response to radiation. The particles releasing chitosan had a prolonged antimetastatic effect when compared with the particles not releasing chitosan, thereby resulting in a significantly greater antimetastatic effect lasting for four weeks since the completion of treatment, in tumors treated with both 10 Gy and 20 Gy of radiation.

Hence, particlizing chitosan could be useful in reducing metastasis in irradiated tumors.

Hirsutella sinensis fungus (HSF) is an artificial substitute of the well-known medicine Cordyceps sinensis with similar beneficial effects in humans. We previously found that HSF can regulate immune function and inhibit tumor growth; however, the mechanisms involved in these effects were still unclear. Accordingly, in this study, we investigated the effects of HSF on immune cell subsets in the tumor microenvironment in mice. The results showed that HSF inhibited Lewis lung cancer growth, alleviated abnormalities in routine blood tests, and enhanced tumor-infiltrating T cells, particularly the proportion of effector CD8${}^{+}$ T cells. In addition, HSF also ameliorated the immune-suppressive microenvironment and decreased the proportions of regulatory T cell and myeloid-derived suppressor cell populations. To confirm the effects of HSF on promotion of effector CD8${}^{+}$ T-cell production, we further evaluated changes in postoperative metastasis following treatment with HSF. Indeed, orthotopic lung metastasis was significantly suppressed, and survival times were increased in HSF-treated mice. Taken together, our findings suggested that HSF inhibited Lewis lung cancer by enhancing the population of effective CD8${}^{+}$ T cells.

Traditional Chinese Medicine (TCM) has achieved high clinical efficacy in treating malignancies in recent years and is thus gradually becoming an important therapy for patients with advanced tumor for its benefits in reducing side effects and improving patients’ immune status. However, it has not been internationally recognized for cancer treatment because TCM’s anti-tumor mechanism is not fully elucidated, limiting its clinical application and international promotion. This review traced the mechanism of the TCM-mediated tumor cell death pathway and its effect on remodeling the tumor immune microenvironment, its direct impact on the microenvironment, its anti-tumor effect in combination with immunotherapy, and the current status of clinical application of TCM on tumor treatment. TCM can induce tumor cell death in many regulatory cell death (RCD) pathways, including apoptosis, autophagy, pyroptosis, necroptosis, and ferroptosis. In addition, TCM-induced cell death could increase the immune cells’ infiltration with an anti-tumor effect in the tumor tissue and elevate the proportion of these cells in the spleen or peripheral blood, enhancing the anti-tumor capacity of the tumor-bearing host. Moreover, TCM can directly affect immune function by increasing the population or activating the sub-type immune cells with an anti-tumor role. It was concluded that TCM could induce a pan-tumor death modality, remodeling the local TIME differently. It can also improve the systemic immune status of tumor-bearing hosts. This review aims to establish a theoretical basis for the clinical application of TCM in tumor treatment and to provide a reference for TCM’s potential in combination with immunotherapy in cancer treatment.

With the continuous advancements in modern medicine, significant progress has been made in the treatment of lung cancer. Current standard treatments, such as surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, have notably improved patient survival. However, the adverse effects associated with these therapies limit their use and impact the overall treatment process. Traditional Chinese medicine (TCM) has shown holistic, multi-target, and multi-level therapeutic effects. Numerous studies have highlighted the importance of TCM’s role in the comprehensive management of lung cancer, demonstrating its benefits in inhibiting tumor growth, reducing complications, mitigating side effects, and enhancing the efficacy of conventional treatments. Here, we review the main mechanisms of TCM in combating lung cancer, inducing cancer cell cycle arrest and apoptosis. These include inhibiting lung cancer cell growth and proliferation, inhibiting cancer cell invasion and metastasis, suppressing angiogenesis and epithelial–mesenchymal transition (EMT), and modulating antitumor inflammatory responses and immune evasion. This paper aims to summarize recent advancements in the application of TCM for lung cancer, emphasizing its unique advantages and distinctive features. In promoting the benefits of TCM, we seek to provide valuable insights for the integrated treatment of lung cancer.

Immunotherapy is one of the most recent approaches in cancer therapy. In this paper, a mathematical model of tumor-immune interaction with periodically pulsed immunotherapy, which is described by impulsive differential equations, is considered. The ODE system is turned into a discrete-time dynamical system for bifurcation analysis. A mathematical analysis is performed to determine the minimum dosage for successful treatment. An adaptive grid method is then developed to identify the fixed points and their bifurcations. The effects of continuous and pulsed treatment strategies are compared. The interindividual variability is studied by one-parameter and two-parameter bifurcation diagrams. Increasing the strength of the immune response improves the outcome of the treatment if the immune response is weak. However, it becomes a drawback when the strength of the immune response increases over a certain threshold. Multiple attractors exist so that a treatment may result in a tumor-free state, a large tumor, or a middle-sized tumor depending on initial conditions. It is believed that the numerical method proposed in this paper can be applied to a class of mathematical models of periodically pulsed drug therapies.

Surgery is the traditional method for treating cancers, but it often fails to cure patients for complex reasons so new therapeutic approaches that include both surgery and immunotherapy have recently been proposed. These have been shown to be effective, clinically, in inhibiting cancer cells while allowing retention of immunologic memory. This comprehensive strategy is guided by whether a population of tumour cells has or has not exceeded a threshold density. Conditions for successful control of tumours in an immune tumour system were modeled and the related dynamics were addressed. A mathematical model with state-dependent impulsive interventions is formulated to describe combinations of surgery with immunotherapy. By analyzing the properties of the Poincaré map, we examine the global dynamics of the immune tumour system with state-dependent feedback control, including the existence and stability of the semi-trivial order-1 periodic solution and the positive order-$k$ periodic solution. The main results showed that surgery alone can only control the tumour size below a certain level while there is no immunologic memory. If comprehensive therapy involving combining surgery with immunotherapy is considered, then not only can the cancers be controlled below a certain level, but the immune system can also retain its activity. The existence of positive order-$k$ periodic solutions implies that periodical therapy is needed to control the cancers. However, choosing the treatment frequency and the strength of the therapy remains challenging, and hence a strategy of individual-based therapy is suggested.

Tumor-immune interaction plays an important role in the tumor treatment. We analyze the stability of steady states in a diffusive tumor-immune model with response and proliferation delay $\tau$ of immune system where the immune cell has a probability $p$ in killing tumor cells. We find increasing time delay $\tau$ destabilizes the positive steady state and induces Hopf bifurcations. The criticality of Hopf bifurcation is investigated by deriving normal forms on the center manifold, then the direction of bifurcation and stability of bifurcating periodic solutions are determined. Using a group of parameters to simulate the system, stable periodic solutions are found near the Hopf bifurcation. The effect of killing probability $p$ on Hopf bifurcation values is also discussed.

In this paper, we study ultimate dynamics and derive tumor eradication conditions for the angiogenic switch model developed by Viger et al. This model describes the behavior and interactions between host ($x$); effector ($y$); tumor ($z$); endothelial ($w$) cell populations. Our approach is based on using the localization method of compact invariant sets and the LaSalle theorem. The ultimate upper bound for each cell population and ultimate lower bound for the effector cell population are found. These bounds describe a location of all bounded dynamics. We construct the domain bounded in $x$- and $z$-variables which contains the attracting set of the system. Further, we derive conditions imposed on the model parameters for the location of omega-limit sets in the plane $w=0$ (the case of a localized tumor). Next, we present conditions imposed on the model and treatment parameters for the location of omega-limit sets in the plane $z=0$ (the case of global tumor eradication). Various types of dynamics including the chaotic attractor and convergence dynamics are described. Numerical simulation illustrating tumor eradication theorems is fulfilled as well.

We investigate a mathematical population model of tumor-immune interactions. The populations involved are tumor cells, specific and non-specific immune cells, and concentrations of therapeutic treatments. We establish the existence of an optimal control for this model and provide necessary conditions for the optimal control triple for simultaneous application of chemotherapy, tumor infiltrating lymphocyte (TIL) therapy, and interleukin-2 (IL-2) treatment. We discuss numerical results for the combination of the chemo-immunotherapy regimens. We find that the qualitative nature of our results indicates that chemotherapy is the dominant intervention with TIL interacting in a complementary fashion with the chemotherapy. However, within the optimal control context, the interleukin-2 treatment does not become activated for the estimated parameter ranges.

The Kirschner-Panetta model describes the poblational competition between effector cells and tumor cells. We analize external changes in the parameters and mechanisms to obtain the decreasing of tumor cells. These variations were performed by three different ways: Oscillations, spikes with the natural frequency of the system, and spikes with Normal Distribution. It was observed that the amount of tumor cells decreases to zero if we change simultaneously the parameters properly.

In this paper, a new mathematical model of the interactions between a growing tumor and an immune system is presented by incorporating the danger model. The populations involved are tumor cells, CD8⁺ T-cells, natural killer cells (NK-cells), dendritic cells (DCs) and cytokine interleukin-12 (IL-12). A key feature of this work is the inclusion of the danger model into the dynamics of the immune system, which is rarely considered by previous works. Regarding the constructed mathematical model, both the location of equilibria and their stability properties are discussed, which are useful not only to gain a broad understanding of the specific system dynamics, but also to help guide the development of therapies. Moreover, numerical simulations of the system with chemotherapy and immunotherapy by using specific parameters are presented to illustrate that proper therapy is able to eliminate the entire tumor. In addition, we illustrate cases for which neither chemotherapy nor immunotherapy alone are able to control tumor growth, but a combination treatment is sufficient to eliminate the tumor cells.

In this paper, we investigate a mathematical model of pancreatic cancer, which extends the existing pancreatic cancer models with regulatory T cells (Tregs) and Treg inhibitory therapy. The model consists of tumor-immune interaction and immune suppression from Tregs. In the absence of treatments, we first characterize the system dynamics by locating equilibrium points and determining stability properties. Next, cytokine induced killer (CIK) immunotherapy is incorporated. Numerical simulations of prognostic results illustrate that the median overall survival associated with treatment can be prolonged approximately from 7 to 13 months, which is consistent with the clinical data. Furthermore, we consider cyclophosphamide (CTX) therapy as well as the combined therapy with CIK and CTX. Intensive simulation results suggest that both CTX therapy and the combined CIK/CTX therapy can reduce the number of Tregs and increase the overall survival (OS), but Tregs and tumor cells will gradually rise to equilibrium state as long as therapies are ceased.

To develop an anticancer drug, the mathematical models are nowadays indispensable because of complex immunological mechanisms defying with high experimentation costs as well as a large number of parameters. Based on immunological theories and vision of experimentation data, a simple and sufficient compartment model is designed that can accurately interpret and predict the effects of dendritic cell (DC)-based immunotherapy in accordance with experimentation data. The model includes effector cells, regulatory T cells, helper T cells, and DCs. A new key feature is the inclusion of immunotherapy with DCs matured with different materials. All the parameters of the model have been optimally obtained by fitting the experimental data using genetic algorithm. The proposed model has been used to predict a near-optimal pattern that minimizes tumor size after vaccination. This pattern has been validated by carrying out the associated in-vivo experimentation. The model recommends maturation materials and doses that activate a small amount of Treg in the early days and a large Th1/Treg ratio in the next days. The performance of the model compared with the previous study was shown to be superior, both qualitatively and quantitatively.

Based on experimental results of a mouse model provided in the literature, we develop a mathematical model by using system biology approach, aiming to investigate immunotherapy for 4T1 breast cancer. It is worth to mention that only 4 types of cells (tumor cells, CD8${}^{+}$ T cells, regular T cells (Tregs), and tumoricidal myeloid CD11b$+$Gr1dim cells) are quantitatively measured in experiments, which make the immunotherapy modelling more difficult since only limited system knowledge is available. To overcome the difficulty, the mathematical model is proposed by employing Evolutionary Computation to optimize the system parameters. Furthermore, with the mathematical model, analysis can be conducted to capture the inherent properties of the model, such as the number and stability of equilibria, and parameter sensitivity analysis, which disclose the nature of 4T1 breast cancer from a system biological perspective. Not limited to replication of experimental results, we further show that the mathematical model is in fact a prognostic immunotherapy model that can predict treatment outcomes of various cases; for instance, different combinations of drug delivery schedules. By virtue of computational convenience, it is relatively easy to intensively investigate most of the treatments that are impossible for animal models or clinical trials. In other words, a mathematical model based on system biology can provide meaningful reference when exploiting more effective treatment protocols.

Immunotherapy and targeted therapy are alternative treatments to differentiated thyroid cancer (DTC), which is usually treated with surgery and radioactive iodine. However, in advanced thyroid carcinomas, molecular alterations can cause a progressive loss of iodine sensitivity, thereby making cancer resistant to radioactive iodine-refractory (RAIR). In the treatment of cancer, tyrosine kinase inhibitors are administered to prevent the growth of cancer cells. One such inhibitor, lenvatinib, forms a targeted therapy for RAIR-DTC, while the immunotherapeutic pembrolizumab, a humanized antibody, prevents the binding of programmed cell death ligand 1 (PD-L1) to the PD-1 receptor. As one of the first studies on treatments for thyroid cancer with mathematical model involving immunotherapy and targeted therapy, we developed an ordinary differential system and tested variables such as concentration of lenvatinib and pembrolizumab, total cancer cells, and number of immune cells (i.e., T cells and natural killer cells). Analyzing local and global stability and the simulated action of drugs in patients with RAIR-DTC, revealed the combined effect of the targeted therapy with pembrolizumab. The scenarios obtained favor the combined therapy as the best treatment option, given its unrivaled ability to boost the immune system’s rate of eliminating tumor cells.

Immunotherapy has become a rapidly developing approach in the treatment of cancer. Cancer immunotherapy aims at promoting the immune system response to react against the tumor. In view of this, we develop a mathematical model for immune–tumor interplays with immunotherapeutic drug, and strategies for optimally administering treatment. The tumor–immune dynamics are given by a system of five coupled nonlinear ordinary differential equations which represent the interaction among tumor-specific CD4+T cells, tumor-specific CD8+T cells, tumor cells, dendritic cells and the immuno-stimulatory cytokine interleukin-2 (IL-2), extended through the addition of a control function describing the application of a dendritic cell vaccination. Dynamical behavior of the system is studied from the analytical as well as numerical points of view. The main aim is to investigate the treatment regimens which minimize the tumor cell burden and the toxicity of dendritic cell vaccination. Our numerical simulations demonstrate that the optimal treatment strategies using dendritic cell vaccination reduce the tumor cell burden and increase the cell count of CD4+T cells, CD8+T cells, dendritic cells and IL-2. The most influential parameters having significant impacts on the tumor cells are identified by employing the approach of global sensitivity analysis.

A number of lines of evidence suggest that immunotherapy with the cytokine interleukin-2 (IL-2) may boost the immune response to fight HIV infection. CD4⁺ T cells, the cells which orchestrate the immune response, are also the cells that become infected by the HIV virus. These cells use cytokines as signaling mechanisms for immune-response stimulation, growth and differentiation. Since CD4⁺ T cells are hampered due to HIV infection, normal signaling, and the resulting cascade, may not occur. Introduction of IL-2 into the system can restore or enhance these effects. We illustrate, through mathematical modeling, the effects of IL-2 treatment on an HIV-infected patient. With good comparison to existing clinical data, we can better understand what mechanisms of immune-viral dynamics are necessary to produce the typical disease dynamics.

This article deals about the new trends in biotechnology and pharmaceutical development in Taiwan.