Furin and the adaptive mutation of SARS-COV2: A computational framework

SARS-2 virus has reached its most harmful mutated form and has damaged the world's economy, integrity, health system and peace to a limit. An open problem is to address the release of antibodies after the infection and after getting the individuals vaccinated against the virus. The viral fusion process is linked with the furin enzyme and the adaptation is linked with the mutation, called D614G mutation. The cell-protein studies are extremely challenging. We have developed a mathematical model to address the process at the cell-protein level and the delay is linked with this biological process. Genetic algorithm is used to approximate the parametric values. The mathematical model proposed during this research consists of virus concentration, the infected cells count at different stages and the effect of interferon. To improve the understanding of this model of SARS-CoV2 infection process, the action of interferon (IFN) is quantified using a variable for the non-linear mathematical model, that is based on a degradation parameter gamma. This parameter is responsible for the delay in the dynamics of this viral action. We emphasize that this delay responds to the evasion by SARS-CoV2 via antagonizing IFN production, inhibiting IFN signaling and improving viral IFN resistance. We have provided videos to explain the modeling scheme

Modeling the crossover behavior of the bacterial infection with the COVID-19 epidemics

To explore the crossover linkage of the bacterial infections resulting from the viral infection, within the host body, a computational framework is developed. It analyzes the additional pathogenic effect of Streptococcus pneumonia, one of the bacteria that can trigger the super-infection mechanism in the COVID-19 syndrome and the physiological effects of innate immunity for the control or eradication of this bacterial infection. The computational framework, in a novel manner, takes into account the action of pro-inflammatory and anti-inflammatory cytokines in response to the function of macrophages. A hypothetical model is created and is transformed to a system of non-dimensional mathematical equations. The dynamics of three main parameters (macrophages sensitivity κ, sensitivity to cytokines η and bacterial sensitivity ϵ), analyzes a “threshold value” termed as the basic reproduction number R0 which is based on a sub-model of the inflammatory state. Piece-wise differentiation approach is used and dynamical analysis for the inflammatory response of macrophages is studied in detail. The results shows that the inflamatory response, with high probability in bacterial super-infection, is concomitant with the COVID-19 infection. The mechanism of action of the anti-inflammatory cytokines is discussed during this research and it is observed that these cytokines do not prevent inflammation chronic, but only reduce its level while increasing the activation threshold of macrophages. The results of the model quantifies the probable deficit of the biological mechanisms linked with the anti-inflammatory cytokines. The numerical results shows that for such mechanisms, a minimal action of the pathogens is strongly amplified, resulting in the “chronicity” of the inflammatory process.Taif University, Taif, Saudi Arabi

Forecasting the action of CAR-T cells against SARS-corona virus-II infection with branching process

The CAR-T cells are the genetically engineered T cells, designed to work specifically for the virus antigens (or other antigens, such as tumour specific antigens). The CAR-T cells work as the living drug and thus provides an adoptive immunotherapy strategy. The novel corona virus treatment and control designs are still under clinical trials. One of such techniques is the injection of CAR-T cells to fight against the COVID-19 infection. In this manuscript, the hypothesis is based on the CAR-T cells, that are suitably engineered towards SARS-2 viral antigen, by the N protein. The N protein binds to the SARS-2 viral RNA and is found in abundance in this virus, thus for the engineered cell research, this protein sequence is chosen as a potential target. The use of the sub-population of T-reg cells is also outlined. Mathematical modeling of such complex line of action can help to understand the dynamics. The modeling approach is inspired from the probabilistic rules, including the branching process, the Moran process and kinetic models. The Moran processes are well recognized in the fields of artificial intelligence and data science. The model depicts the infectious axis “virus—CAR-T cells—memory cells”. The theoretical analysis provides a positive therapeutic action; the delay in viral production may have a significant impact on the early stages of infection. Although it is necessary to carefully evaluate the possible side effects of therapy. This work introduces the possibility of hypothesizing an antiviral use by CAR-T cells

Computational model to explore the endocrine response to trastuzumab action in HER-2/neu positive breast cancer

Breast cancer is a very frequent type of cancer and much attention is paid to therapy with considerable efforts both in the pharmacological and clinical fields.The present work aims to create a non-linear dynamic model of action of the drug Trastuzumab against HER-2 + breast cancer, mainly considering its action of ADCP (antibody-dependent phagocytosis) killing of cancer cells. The model, while also considering the other therapeutic effects induced by Trastuzumab, shows how the action of this monoclonal antibody in the induction of ADCP through the action of macrophages, is strictly connected to the formation of a multi-complex “Trastuzumab -HER-2 - macrophage” that shows a prolonged action over time, responsible for the increase in the Overall Survivor (OS) parameter reported in various. The model shows the correlation between the various therapeutic effects and the killing action of cancer cells through the variation of the dynamic fluctuation of the representative ”c” parameter

Modeling and simulation of the "IL-36 cytokine" and CAR-T cells interplay in cancer onset

Background: CAR-T cells are chimeric antigen receptor (CAR)-T cells; they are target-specific engineered cells on tumor cells and produce T cell-mediated antitumor responses. CAR-T cell therapy is the "first-line" therapy in immunotherapy for the treatment of highly clonal neoplasms such as lymphoma and leukemia. This adoptive therapy is currently being studied and tested even in the case of solid tumors such as osteosarcoma since, precisely for this type of tumor, the use of immune checkpoint inhibitors remained disappointing. Although CAR-T is a promising therapeutic technique, there are therapeutic limits linked to the persistence of these cells and to the tumor's immune escape. CAR-T cell engineering techniques are allowed to express interleukin IL-36, and seem to be much more efficient in antitumoral action. IL-36 is involved in the long-term antitumor action, allowing CAR-T cells to be more efficient in their antitumor action due to a "cross-talk" action between the "IL-36/dendritic cells" axis and the adaptive immunity. Methods: This analysis makes the model useful for evaluating cell dynamics in the case of tumor relapses or specific understanding of the action of CAR-T cells in certain types of tumor. The model proposed here seeks to quantify the action and interaction between the three fundamental elements of this antitumor activity induced by this type of adoptive immunotherapy: IL-36, "armored" CAR-T cells (i.e., engineered to produce IL-36) and the tumor cell population, focusing exclusively on the action of this interleukin and on the antitumor consequences of the so modified CAR-T cells. Mathematical model was developed and numerical simulations were carried out during this research. The development of the model with stability analysis by conditions of Routh-Hurwitz shows how IL-36 makes CAR-T cells more efficient and persistent over time and more effective in the antitumoral treatment, making therapy more effective against the "solid tumor". Findings: Primary malignant bone tumors are quite rare (about 3% of all tumors) and the vast majority consist of osteosarcomas and Ewing's sarcoma and, approximately, the 20% of patients undergo metastasis situations that is the most likely cause of death. Interpretation: In bone tumor like osteosarcoma, there is a variation of the cellular mechanical characteristics that can influence the efficacy of chemotherapy and increase the metastatic capacity; an approach related to adoptive immunotherapy with CAR-T cells may be a possible solution because this type of therapy is not influenced by the biomechanics of cancer cells which show peculiar characteristics

Dynamical analysis of the delayed immune response to cancer

In the antitumor response, cytotoxic CD8  + T cells and the functional organization of CD4  + T cells play an important role. During this research, a mathematical model is built. In this model, the ability of CD4  + T cells, to trigger the tumor cell population control, is analyzed. The cytokine IFN-gamma is used as a mediator and it is linked with the antitumor action. The computational model simulates the cellular interactions. With the aid of this model, one can visualize the future development in this field, based on recent data. A therapeutic scheme can be proposed, where CD4  + T cells play a “leading role.” Thus the proposed mathematical model can prove to be useful in the field of computational oncology. In this article, we have worked on the dynamical analysis of the developed mathematical model. Furthermore, we have worked on numerical simulations with the aid of smart programming tools for nonlinear systems. The parametric values are evaluated with the aid of the Markov chain Monte Carlo (MCMC) approximation algorithm. Therefore, the present article illustrates the functional dynamics that occur between CD4  + T cells, cytotoxic CD8  + T cells, and cancer, and provides deeper evaluation of complex personalized treatment strategy