Nonlinear observer output-feedback MPC treatment scheduling for HIV

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Significant Unresolved Questions and Opportunities for Bioengineering in Understanding and Treating COVID-19 Disease Progression

COVID-19 is a disease that manifests itself in a multitude of ways across a wide range of tissues. Many factors are involved, and though impressive strides have been made in studying this novel disease in a very short time, there is still a great deal that is unknown about how the virus functions. Clinical data has been crucial for providing information on COVID-19 progression and determining risk factors. However, the mechanisms leading to the multi-tissue pathology are yet to be fully established. Although insights from SARS-CoV-1 and MERS-CoV have been valuable, it is clear that SARS-CoV-2 is different and merits its own extensive studies. In this review, we highlight unresolved questions surrounding this virus including the temporal immune dynamics, infection of non-pulmonary tissue, early life exposure, and the role of circadian rhythms. Risk factors such as sex and exposure to pollutants are also explored followed by a discussion of ways in which bioengineering approaches can be employed to help understand COVID-19. The use of sophisticated in vitro models can be employed to interrogate intercellular interactions and also to tease apart effects of the virus itself from the resulting immune response. Additionally, spatiotemporal information can be gleaned from these models to learn more about the dynamics of the virus and COVID-19 progression. Application of advanced tissue and organ system models into COVID-19 research can result in more nuanced insight into the mechanisms underlying this condition and elucidate strategies to combat its effects.

Oncolytic virus therapy benefits from control theory

Oncolytic virus therapy aims to eradicate tumours using viruses which only infect and destroy targeted tumour cells. It is urgent to improve understanding and outcomes of this promising cancer treatment because oncolytic virus therapy could provide sensible solutions for many patients with cancer. Recently, mathematical modelling of oncolytic virus therapy was used to study different treatment protocols for treating breast cancer cells with genetically engineered adenoviruses. Indeed, it is currently challenging to elucidate the number, the schedule, and the dosage of viral injections to achieve tumour regression at a desired level and within a desired time frame. Here, we apply control theory to this model to advance the analysis of oncolytic virus therapy. The control analysis of the model suggests that at least three viral injections are required to control and reduce the tumour from any initial size to a therapeutic target. In addition, we present an impulsive control strategy with an integral action and a state feedback control which achieves tumour regression for different schedule of injections. When oncolytic virus therapy is evaluated in silico using this feedback control of the tumour, the controller automatically tunes the dose of viral injections to improve tumour regression and to provide some robustness to uncertainty in biological rates. Feedback control shows the potential to deliver efficient and personalized dose of viral injections to achieve tumour regression better than the ones obtained by former protocols. The control strategy has been evaluated in silico with parameters that represent five nude mice from a previous experimental work. Together, our findings suggest theoretical and practical benefits by applying control theory to oncolytic virus therapy.

Robust adaptive Lyapunov-based control of hepatitis B infection

A new robust adaptive controller is developed for the control of the hepatitis B virus (HBV) infection inside the body. The non-linear HBV model has three state variables: uninfected cells, infected cells and free viruses. A control law is designed for the antiviral therapy such that the volume of infected cells and the volume of free viruses are decreased to their desired values which are zero. One control input represents the efficiency of drug therapy in inhibiting viral production and the other control input represents the efficiency of drug therapy in blocking new infection. The proposed controller ensures the stability and robust performance in the presence of parametric and non-parametric uncertainties (and/or bounded disturbances). The global stability and tracking convergence of the process are investigated by employing the Lyapunov theorem. The performance of the proposed controller is evaluated using simulations by considering different levels of uncertainties. Based on the obtained results, the proposed strategy can achieve its desired objectives with different cases of uncertainties.

Modeling Kick-Kill Strategies toward HIV Cure

Although combinatorial antiretroviral therapy (cART) potently suppresses the virus, a sterile or functional cure still remains one of the greatest therapeutic challenges worldwide. Reservoirs are infected cells that can maintain HIV persistence for several years in patients with optimal cART, which is a leading obstacle to eradicate the virus. Despite the significant progress that has been made in our understanding of the diversity of cells that promote HIV persistence, many aspects that are critical to the development of effective therapeutic approaches able to purge the latent CD4+ T cell reservoir are poorly understood. Simultaneous purging strategies known as “kick-kill” have been pointed out as promising therapeutic approaches to eliminate the viral reservoir. However, long-term outcomes of purging strategies as well as the effect on the HIV reservoir are still largely fragmented. In this context, mathematical modeling can provide a rationale not only to evaluate the impact on the HIV reservoir but also to facilitate the formulation of hypotheses about potential therapeutic strategies. This review aims to discuss briefly the most recent mathematical modeling contributions, harnessing our knowledge toward the uncharted territory of HIV eradication. In addition, problems associated with current models are discussed, in particular, mathematical models consider only T cell responses but HIV control may also depend on other cell responses as well as chemokines and cytokines dynamics.

Abnormality detection in noisy biosignals

Although great strides have been achieved in computer-aided diagnosis (CAD) research, a major remaining problem is the ability to perform well under the presence of significant noise. In this work, we propose a mechanism to find instances of potential interest in time series for further analysis. Adaptive Kalman filters are employed in parallel among different feature axes. Lung sounds recorded in noisy conditions are used as an example application, with spectro-temporal feature extraction to capture the complex variabilities in sound. We demonstrate that both disease indicators and distortion events can be detected, reducing long time series signals into a sparse set of relevant events.

Combining pharmacological therapy and vaccination in Chronic Myeloid Leukemia via model predictive control

This paper describes a simulation study which aims at optimizing the therapy for the control of Chronic Myeloid Leukemia according to the following objectives: the reduction of the administered drug and vaccine amounts, the establishment of a auto-immune response and the long-term control of disease without reducing the effective of therapy with respect to the full treatment. A therapy optimization method is developed defining and solving a Model Predictive Control algorithm, preceded by an accurate Initial Guess search based on Monte-Carlo like approach. Simulation results show that the suggested procedure achieves the proposed goals.

Optimal antiviral switching to minimize resistance risk in HIV therapy

The development of resistant strains of HIV is the most significant barrier to effective long-term treatment of HIV infection. The most common causes of resistance development are patient noncompliance and pre-existence of resistant strains. In this paper, methods of antiviral regimen switching are developed that minimize the risk of pre-existing resistant virus emerging during therapy switches necessitated by virological failure. Two distinct cases are considered; a single previous virological failure and multiple virological failures. These methods use optimal control approaches on experimentally verified mathematical models of HIV strain competition and statistical models of resistance risk. It is shown that, theoretically, order-of-magnitude reduction in risk can be achieved, and multiple previous virological failures enable greater success of these methods in reducing the risk of subsequent treatment failures.