3 research outputs found

    Targeted EDTA Chelation Therapy with Albumin Nanoparticles to Reverse Arterial Calcification and Restore Vascular Health in Chronic Kidney Disease

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    Cardiovascular diseases (CVDs) are the leading cause of death globally. An estimated 17.9 million people died from CVDs in 2016, with ~840,000 of them in the United States alone. Traditional risk factors, such as smoking, hypertension, and diabetes, are well discussed. In recent years, chronic kidney disease (CKD) has emerged as a risk factor of equal importance. Patients with mild-to-moderate CKD are much more likely to develop and die from CVDs than progress to end-stage renal failure. Vascular calcification (VC), typical in aging, several genetic and metabolic disorders, is now recognized as a strong and independent predictor of cardiovascular events and mortality, not only in diabetic and CKD patients, even in the general population. VC is classified into two distinct types based on location in the vessel wall; intimal and medial. Elastin-associated medial arterial calcification (CKD) is more specific to CKD and contributes significantly to cardiovascular mortality in these patients. It is responsible for loss of vessel elasticity, increased arterial stiffness, increased pulse pressures and systolic blood pressure, and left ventricular hypertrophy ultimately causing arrhythmias and heart failure. Current clinical practice is mostly focused on prevention and retardation of VC progression. Unfortunately, most patients with CKD remain underdiagnosed, and those diagnosed have already heavily calcified vessels. As such, they are undertreated since preventative strategies no longer work at this stage. Unfortunately, there is no FDA-approved treatment available that reverses calcification in countless CKD patients. A treatment strategy which promotes resorption of calcified lesions, while simultaneously avoiding demineralization from normally calcified tissues (i.e., bones and teeth) remains an urgent health care need. Chelating agents bind to metal cations, can dissolve and wash away calcium deposits if delivered in close proximity to the calcification sites. This work was undertaken to see if we can develop targeted therapies to deliver chelating agents to vascular calcification sites. Amongst chelating agents known for their affinity to Calcium ions (Ca2+), we found that EDTA chelates Ca2+ from hydroxyapatite better than others. In our laboratory, we have developed a unique targeting mechanism by using nanoparticles to deliver chelating agents and other drugs to degraded elastin, a characteristic feature of VC. We take this approach forward in clinically relevant animal models of CKD. First, we tested the targeted nanoparticle-based EDTA chelation therapy in a rat model of adenine-induced renal failure. The targeted nanoparticles delivered EDTA at the sites of vascular calcification and reversed mineral deposition without any side effects. Furthermore, we validated the adenine-CKD model in mice to monitor MAC in vivo and explore the phenotypic and functional alterations associated with it. We were able to target our nanoparticles to calcified arteries in these mice. The mouse model will help us to test whether our EDTA chelation therapy tangibly improves arterial function by restoring vascular health. Lastly, we investigated the possibility of using an ex vivo organ culture model of VC as a simpler, and relatively easier model to assess if EDTA chelation therapy promotes vessel homeostasis. The work presented here represents another major step forward towards the development of targeted EDTA chelation therapy as an unconventional therapeutic approach to reverse pathological calcifications in CKD patients

    Data-driven spatio-temporal modelling of glioblastoma

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    Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarise the scope, drawbacks, and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. Finally, by providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks.Comment: 30 pages, 3 figures, 3 table
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