Targeting SARS-CoV-2 (COVID-19) methyltransferases (nsp14 and nsp10-nsp16 complex) toward developing small molecule antiviral therapeutics - Part 2

COVID-19 pandemic has brought the world to standstill with more than 55 million people infected to-date and more than 1.34 million mortality so far. It has literally brought the health care systems in many countries to the breaking point, if not beyond. The economic consequences have been devastating with millions of people out of work. We are taking a novel approach by focusing on two SARS-CoV2 (COVID-19) methyltransferases that are essential for viral replication. Both enzymes (nsp14 and nsp16) are druggable.

Detoxification of oil sands process-affected water

Alberta’s oil sands tailings ponds contain approximately one billion m3of oil sands process-affected water (OSPW). This water is toxic and must be treated in order to comply with environmental regulations. In this project, Metabolik’s team will conduct two sequential field trials in small, contained environments, where they will identify, and assess the impact of the key environmental parameters such as dissolved oxygen, pH, osmolarity and temperature on the ability of the strains to degrade the toxins in tailings pond water.

Modulating HMGB1 in COVID-19-associated inflammatory response

The project addresses urgent and clinically-relevant questions related to COVID-19, which causes in some patients life-threatening respiratory distress, septic shock and organ failures. Patients in intensive care units were found to have significantly higher levels of high mobility group box 1 (HMGB1) than patients with milder symptoms. HMGB1 is a protein normally found in the cell nucleus that is released outside the cell under inflammatory conditions such as viral infections.

Standardization and optimization of saliva sample processing for SARS-CoV-2 detection without nucleic acid purification

The SARS-CoV-2 outbreak, which started in Dec. 2019, has so far not been contained due to unpreparedness and unsuccessful development of antiviral drugs against SARS-CoV-2. In response to this pandemic, we propose development of a diagnostic assay based on saliva samples. We will also standardize virus collection procedure and inactivation steps to reduce the turnaround time of the results. We have the required expertise of working with virology techniques, molecular biology and diagnostic assay development.

Epigenetic Regulators of Anticancer Drug Response

The effectiveness of cancer drugs depends on several factors which are governed by the genetic and ‘epigenetic’ code of cancer cells. The epigenetic code comprises those heritable modifications that bookmark DNA and DNA-associated proteins to guide the expression of genetic attributes without changing the DNA sequence. This epigenetic code is written, read, and erased by a group of proteins known as epigenetic regulators.

Development of microneedle array patch for COVID-19 vaccine delivery

COVID-19 is a global pandemic with no effective therapeutic and preventive agents. Given the high infectivity of the SARS-CoV-2 and severity of the disease, vaccines are urgently needed to tackle the infection of this novel coronavirus. Thus far, various types of vaccine candidates, in different stages of preclinical testing and clinical trials, are being explored, including RNA- and DNA-based, vectored and attenuated virus, and recombinant proteins. However, possible adverse effects and limitations exist.

Perioperative Opioid Usage Quality Improvement [CDTS-PDF2]

Our aim is to use machine-learning to improve treatment of post-surgical pain in children and adults. Most people addicted to opioids were initially exposed through the treatment of pain from trauma and/or surgery. The opioid crisis is reaching the pediatric population, in whom effective post-surgical pain management, with less reliance on prescription of opioids, is more important than ever. Recent advances in machine-learning, combined with approaches to patient-oriented research, provide significant prospects for a learning health system.

Perioperative Opioid Usage Quality Improvement [CDTS-PDF1]

Our aim is to use machine-learning to improve treatment of post-surgical pain in children and adults. Most people addicted to opioids were initially exposed through the treatment of pain from trauma and/or surgery. The opioid crisis is reaching the pediatric population, in whom effective post-surgical pain management, with less reliance on prescription of opioids, is more important than ever. Recent advances in machine-learning, combined with approaches to patient-oriented research, provide significant prospects for a learning health system.

Next-Generation Precision Medicine Solutions – Diagnostics

As personalized medicine approaches aim to tailor treatments to individuals, improvements are needed in the detection of existing biomarkers and genomic, epigenomic, and proteomic changes that occur during disease development. This would have potential impact on medication selection and targeted therapy, reduce adverse effects, improve cost effectiveness, and shift the goal of medicine from reactive to preventative clinical decision making1.

Application of multi-omics and pharmacological studies to discover potential new therapeutics for COVID-19

COVID-19 is the largest pandemic of the 21st century, affecting over 6.6 million individuals and claiming over 391,000 lives worldwide as of June 4, 2020. It is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV2), which uses a receptor to gain entry into the host and cause active infection. This project involves the expertise of PROOF Centre and Professors Don Sin and Pascal Bernatchez.

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