Thank you, Glenn, and good morning, everyone. Thank you for joining us today. I would like to begin this morning with an update on a truly groundbreaking study that we recently completed with UPMC Magee-Womens Hospital in Pittsburgh. This was a retrospective multiyear study to determine if we could leverage our artificial intelligence capabilities to build multi-omic machine learning models that would be better than clinical data alone in predicting both short- and long-term survival outcomes among ovarian cancer patients. Ovarian cancer represents a significant unmet need in oncology with epithelial ovarian cancer being the deadliest of all gynecologic malignancies. While these cancers are sensitive to frontline chemotherapy in approximately 75% of the cases, these women will ultimately experience disease relapse in an equal percentage, which is incurable.
Outside of primary chemotherapy, there is no universal treatment decision path to determine the agent, sequence and timing of the standard of care for chemotherapy agents. The Magee study was designed to identify key features that drive overall survival endpoints. It included data from 235 ovarian cancer patients from 2010 through 2016, a broad array of inputs, including patient data, whole exome sequencing, whole transcriptome sequencing, drug response profile and digital pathology profiles were used to train the 160 models that we included in the study.
We are very pleased to report that we were able to deliver strong predictive models with high levels of accuracy and our machine learning capabilities demonstrated the ability to identify prognostic subgroups within an ovarian cancer patient population. Further validating the significance of these study results, we announced a few weeks ago that an abstract detailing the study has been accepted for presentation at a very prestigious American Society of Clinical Oncology Annual Meeting, better known as ASCO, which is being held May 31 through June 4 in Chicago. The presentation, which will be delivered by Dr. Brian Orr, gynecologic oncologist at the Hollings Cancer Center, Assistant Professor at the Medical University of South Carolina and lead investigator of the study is scheduled for Monday, June 3 at 9 a.m. Central Time.
As we stated last quarter, the potential implications for the Magee and other clinical decision-makers are significant as these models can be used as an important decision support tool to better tailor therapies to individual patients and positively affect overall survival. The implications for Predictive Oncology extend beyond that, however, we believe there are many opportunities to utilize this information for purposes other than clinical utility. The possibility does exist to leverage these data to develop digital pathology applications and new predictive models for other cancer types. And other such application would be to drive the design of more efficient and effective clinical trials. Also, with the ability to identify novel biomarkers that are correlated with survival, we can leverage this information to become more directly involved in drug discovery itself in addition to acting as a partner to others to expedite drug discovery. This has formed in my vision, as you know, for the company.
The successful results of this ovarian cancer study not only clinically validate our ability to successfully predict outcomes, they serve as a sort of proof-of-concept that supports further work towards that goal. With these compelling results in hand, we are accelerating our drug rescue, drug repurposing and drug combination initiatives and more fully leveraging our artificial intelligence, machine learning and wet lab capabilities to evaluate the drug response of hundreds of diverse patient tumors against hundreds of drugs in a fraction of the time and at a fraction of the loss of valuable samples.
Turning now to another recent development. Last month, we announced the collaboration with Fujifilm to co-market our EndoPrep sample treatment technology, together with Fujifilm's PYROSTAR bacterial endotoxin detection reagent to reduce protein interference and bacterial endotoxin testing of biopharmaceutical products. For those interested in learning more, the first joint webinar will be held on Wednesday, May 29, at 10:00 a.m. Eastern Time. Endotoxins also known as lipopolysaccharides LPS are components of the outer cell membrane of gram-negative bacteria and are released when intact bacteria are disrupted. Sub-nanogram levels of endotoxins can trigger immune responses such as inflammation and fever in patients, even leading to systemic shock and death.
Endotoxins are highly resistant to sterilization processes and accurate detection and removal of endotoxins in biopharmaceuticals are required before entering animal trials or human clinical trials. PYROSTAR is widely considered to be the best detection system for measuring endotoxin levels in biologic products. When paired with EndoPrep, they can accurately detect residual endotoxins in the presence of interfering glucans and reduced interference of most biologic products with detection assay.
In a proof-of-concept study, we achieved reproducible and accurate measurements of endotoxin in the presence of specific interfering proteins in biologics. The study results indicate that 3 or 4 tested biologics went from not failing in the 50% to 200% detection of challenge endotoxin to falling in the 50% to 200% detection range as required by the FDA for clinical testing. In addition to demonstrating the versatility of our biologics technology, this collaboration will allow Predictive Oncology to make a significant positive impact on drug safety. We are very pleased to have the opportunity to work with Fujifilm on this project as they are a clear industry leader in the development of endotoxin solutions for injectable pharmaceuticals and biological products.
We also announced recently that we are making meaningful progress with FluGen in the development of a first-of-its-kind intranasal flu vaccine. This project is part of a $6.2 million Phase IIb grant awarded by the United States Department of Defense. Pursuant to this agreement, we are utilizing our formulations expertise to help FluGen develop its M2SR vaccine that is soluble and stable in a refrigerated state, which is a vital part in the drug development process. Most importantly, this would also address the need for a longer vaccine shelf life to support global distribution, including remote locations.
Unlike the standard of care flu vaccines, M2SR stimulates mucosal, humeral and cellular immunity. In an unprecedented challenge trial, M2SR demonstrated protection against infection and illness across 7 years of virus DRiPs, and M2SR induces a durable antibody response with potential to cover an entire flu season beyond. M2SR also has shown activity as a vaccine vector for other respiratory vaccines in infectious diseases, including a COVID-19 flu combination.
With our proprietary HSC technology and artificial intelligence platform that efficiently analyzes more than 4,000 different drug formulation combinations using FDA-approved excipients. We are able to find the optimal formulation tailored to the final product's application in only 3 to 6 months using as little as 20 micrograms of protein. Our novel design of experiments is a critical component currently being utilized for the development of FluGen's flu vaccine. This is exactly the kind of innovation that we strive to be part of, and we look forward to the continued development of this groundbreaking advancement in the vaccine field.
Moreover, as I mentioned in our last update, we have recently announced the development of our latest stem cell technology breakthrough, a novel protein expression method for G protein-coupled receptors, GPCRs and other membrane protein classes. This capability supports drug discovery for a variety of diseases, including aggressive cancers.
Turning now to other collaborations we have discussed in the past. Let me give you a brief update on Cvergenx as well as the most recent submission to the center for the advancement of science and space. You may recall that last February, we announced the collaboration with Cvergenx to develop the first ever genomics-based approach to precision radiation therapy and drug discovery using artificial intelligence. The objective of this collaboration is to leverage and maximize the combined power of Predictive Oncology's expertise in artificial intelligence and Cvergenx's proficiency in biomarker development to identify novel radioprotector and radiosensitizer drugs.
Over the past year, we have made significant progress, having now evaluated trained or developed models to predict changes in radio sensitivity for more than 3,000 different drug exposures as well as using well-established gene expression databases. These findings form the basis of an NIH SBIR Phase 1 grant to screen vast libraries of compounds to accelerate the potential development of drugs, drug combinations or repurpose drugs, sensitize or protect human subjects from the effects of radiation. The significance of identifying of these radiosensitizers and radioprotectors extends well beyond drug repurposing, however. Using these models, for example, we would be able to proactively screen workers in the nuclear energy industry and military and in the clinical setting, optimize the planning and treatment of patients receiving radiotherapy.
So our work with Cvergenx has potentially broad utility across a number of important applications. And in the process, we have been able to expand those data sets, which may be leveraged in several important ways with respect to commercialization. First, to screen individuals for radiation sensitivity or resistance to optimize the clinical effect of radiotherapy. Second, to screen for interactions between sensitive or resistant patient tumor samples and therapeutic compounds. And third, to identify combined or developed novel or repurpose radioprotective or radiosensitizing drugs. These are not isolated developments, with synergistic activities that have created new and more interesting opportunities, which has led to collaborations with Merck & Company, OCMS and Redwire Space.
And now I would like to turn this call over to Josh Blacher, our Chief Financial Officer. Josh?