Mathematical model predicts anti-adhesion-antibiotic-debridement combination therapies can clear an antibiotic resistant infection

Combination Therapy in Alzheimer’s Disease: Is It Time?

Alzheimer’s disease (AD) is the most common cause of dementia globally. There is increasing evidence showing AD has no single pathogenic mechanism, and thus treatment approaches focusing only on one mechanism are unlikely to be meaningfully effective. With only one potentially disease modifying treatment approved, targeting amyloid-β (Aβ), AD is underserved regarding effective drug treatments. Combining multiple drugs or designing treatments that target multiple pathways could be an effective therapeutic approach. Considering the distinction between added and combination therapies, one can conclude that most trials fall under the category of added therapies. For combination therapy to have an actual impact on the course of AD, it is likely necessary to target multiple mechanisms including but not limited to Aβ and tau pathology. Several challenges have to be addressed regarding combination therapy, including choosing the correct agents, the best time and stage of AD to intervene, designing and providing proper protocols for clinical trials. This can be achieved by a cooperation between the pharmaceutical industry, academia, private research centers, philanthropic institutions, and the regulatory bodies. Based on all the available information, the success of combination therapy to tackle complicated disorders such as cancer, and the blueprint already laid out on how to implement combination therapy and overcome its challenges, an argument can be made that the field has to move cautiously but quickly toward designing new clinical trials, further exploring the pathological mechanisms of AD, and re-examining the previous studies with combination therapies so that effective treatments for AD may be finally found.

Lung Cancer Radiotherapy: Simulation and Analysis Based on a Multicomponent Mathematical Model

Background

Lung cancer has been one of the most deadly illnesses all over the world, and radiotherapy can be an effective approach for treating lung cancer. Now, mathematical model has been extended to many biomedical fields to give a hand for analysis, evaluation, prediction, and optimization.

Methods

In this paper, we propose a multicomponent mathematical model for simulating the lung cancer growth as well as radiotherapy treatment for lung cancer. The model is digitalized and coded for computer simulation, and the model parameters are fitted with many research and clinical data to provide accordant results along with the growth of lung cancer cells in vitro.

Results

Some typical radiotherapy plans such as stereotactic body radiotherapy, conventional fractional radiotherapy, and accelerated hypofractionated radiotherapy are simulated, analyzed, and discussed. The results show that our mathematical model can perform the basic work for analysis and evaluation of the radiotherapy plan.

Conclusion

It will be expected that in the near future, mathematical model will be a valuable tool for optimization in personalized medical treatment.

To give or not to give antibiotics is not the only question

In a 1945 Nobel Lecture, Sir Alexander Fleming warned against the overuse of antibiotics, particularly in response to public pressure. In the subsequent decades, evidence has shown that bacteria can become resistant to almost any available molecule. One key question is how the emergence and dissemination of resistant bacteria or resistance genes can be delayed. Although some clinicians remain sceptical, in this Personal View, we argue that the prescription of fewer antibiotics and shorter treatment duration is just as effective as longer regimens that remain the current guideline. Additionally, we discuss the fact that shorter antibiotic treatments exert less selective pressure on microorganisms, preventing the development of resistance. By contrast, longer treatments associated with a strong selective pressure favour the emergence of resistant clones within commensal organisms. We also emphasise that more studies are needed to identify the optimal duration of antibiotic therapy for common infections, which is important for making changes to the current guidelines, and to identify clinical biomarkers to guide antibiotic treatment in both hospital and ambulatory settings.

Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times

Mass production and use of antibiotics has led to the rise of resistant bacteria, a problem possibly exacerbated by inappropriate and non-optimal application. Antibiotic treatment often follows fixed-dose regimens, with a standard dose of antibiotic administered equally spaced in time. But are such fixed-dose regimens optimal or can alternative regimens be designed to increase efficacy? Yet, few mathematical models have aimed to identify optimal treatments based on biological data of infections inside a living host. In addition, assumptions to make the mathematical models analytically tractable limit the search space of possible treatment regimens (e.g. to fixed-dose treatments). Here, we aimed to address these limitations by using experiments in a Galleria mellonella (insect) model of bacterial infection to create a fully parametrised mathematical model of a systemic Vibrio infection. We successfully validated this model with biological experiments, including treatments unseen by the mathematical model. Then, by applying artificial intelligence, this model was used to determine optimal antibiotic dosage regimens to treat the host to maximise survival while minimising total antibiotic used. As expected, host survival increased as total quantity of antibiotic applied during the course of treatment increased. However, many of the optimal regimens tended to follow a large initial ‘loading’ dose followed by doses of incremental reductions in antibiotic quantity (dose ‘tapering’). Moreover, application of the entire antibiotic in a single dose at the start of treatment was never optimal, except when the total quantity of antibiotic was very low. Importantly, the range of optimal regimens identified was broad enough to allow the antibiotic prescriber to choose a regimen based on additional criteria or preferences. Our findings demonstrate the utility of an insect host to model antibiotic therapies in vivo and the approach lays a foundation for future regimen optimisation for patient and societal benefits.

Research into optimal antibiotic use to improve efficacy is far behind that of cancer care, where personalised treatment is common. The integration of mathematical models with biological observations offers hope to optimise antibiotic use, and in this present study an in vivo insect model of systemic Vibrio infection was used for the first time to determine critical model parameters for optimal antibiotic treatment. By this approach, the optimal regimens tended to result from a large initial ‘loading’ dose followed by subsequent doses of incremental reductions in antibiotic quantity (dose ‘tapering’). The approach and findings of this study opens a new avenue towards optimal application of our precious antibiotic arsenal and may lead to more effective treatment regimens for patients, thus reducing the health and economic burdens associated with bacterial infections. Importantly, it can be argued that until we understand how to use a single antibiotic optimally, it is unlikely we will identify optimal ways to use multiple antibiotics simultaneously.

Mathematical Modelling for the Role of CD4+T Cells in Tumor-Immune Interactions

Mathematical modelling has been used to study tumor-immune cell interaction. Some models were proposed to examine the effect of circulating lymphocytes, natural killer cells, and CD8+T cells, but they neglected the role of CD4+T cells. Other models were constructed to study the role of CD4+T cells but did not consider the role of other immune cells. In this study, we propose a mathematical model, in the form of a system of nonlinear ordinary differential equations, that predicts the interaction between tumor cells and natural killer cells, CD4+T cells, CD8+T cells, and circulating lymphocytes with or without immunotherapy and/or chemotherapy. This system is stiff, and the Runge-Kutta method failed to solve it. Consequently, the "Adams predictor-corrector" method is used. The results reveal that the patient's immune system can overcome small tumors; however, if the tumor is large, adoptive therapy with CD4+T cells can be an alternative to both CD8+T cell therapy and cytokines in some cases. Moreover, CD4+T cell therapy could replace chemotherapy depending upon tumor size. Even if a combination of chemotherapy and immunotherapy is necessary, using CD4+T cell therapy can better reduce the dose of the associated chemotherapy compared to using combined CD8+T cells and cytokine therapy. Stability analysis is performed for the studied patients. It has been found that all equilibrium points are unstable, and a condition for preventing tumor recurrence after treatment has been deduced. Finally, a bifurcation analysis is performed to study the effect of varying system parameters on the stability, and bifurcation points are specified. New equilibrium points are created or demolished at some bifurcation points, and stability is changed at some others. Hence, for systems turning to be stable, tumors can be eradicated without the possibility of recurrence. The proposed mathematical model provides a valuable tool for designing patients' treatment intervention strategies.