Predictive Oncology Inc.

and thank you for standing by. Welcome to the Predictive Oncology Q1 2023 Earnings Conference Call. At this time, all participants are on a listen-only mode. After the speaker's presentation, there will be an opportunity to ask your questions. Please be advised that today's conference is being recorded. And now, I would like to hand over the conference to your speaker today, Mr. Glenn Guermont, Investor Relations. Mr. Garmont, please go ahead.

Welcome and thank you, everyone, for dialing in to the Predictive Oncology Q1 2023 earnings call. First, you will hear from our Chief Executive Officer and Chairman of the Board, Raymond Venary. Then our Chief Financial Officer, Bob Myers, will review our financials. Finally, Dr. Pamela Bush, our Chief Business Officer, will join Raymond and Bob to answer any questions that you may have. Certain matters discussed during this call contain forward-looking statements. These forward-looking statements reflect our current expectations and projections about future events and are subject to substantial risks, uncertainties, and assumptions about our operations and the investments we make. All statements other than statements of historical facts included in the call regarding our strategy, future operations, future financial position, future revenue, and financial performance projected costs, prospects, plans, and objectives of management are forward-looking statements. The words anticipate, believe, estimate, expect, intend, may, plan, would, target, and similar expressions are intended to identify forward-looking statements, although not all forward-looking statements contain these identifying words. Our actual future performance may materially differ from that contemplated by the forward-looking statements, as a result of a variety of factors, including, among other things, factors discussed under the heading Risk Factors in our filings with the SEC. Except as expressly required by law, the company disclaims any intent or obligation to update these forward-looking statements. And now, I would like to turn the call over to Raymond Venary, our Chief Executive Officer. Raymond?

Dwayne, thank you very much, and good afternoon, everyone. So as many of you already know, when I accepted the opportunity to serve as Chief Executive Officer of Predictive Oncology in November of last year, I did so because I recognized the enormous potential of this company's intellectual capital and the compelling suite of assets that are truly unique among companies that are currently applying artificial intelligence to drug discovery. These assets not only include our proprietary patient-centric discovery platform called PETL and our computational research engine called Core, C-O-R-E, which, by the way, was developed by a brilliant computational biologist at the Center for Computational Biology at Carnegie Mellon University, but also our comprehensive biobank of more than 150,000 heterogeneous tumor samples and more than 200,000 pathology slides, all of which are currently being digitized and curated for both clinical utility and drug discovery. And I should say that the word heterogeneous is a very important term. Even though many patients are diagnosed with the same type of cancer, each of those tumors respond differently to particular cancer therapies. Each tumor and every response is unique. Understanding that heterogeneity is invaluable along the continuum of drug discovery through drug development. These dry lab assets, our assets, those being pedal and core, together with our own wet lab capabilities in a CLIA-certified facility, allow for both in silico computer modeling and bench-level laboratory experimentations. By predicting clinical success or failure early in the drug discovery process, and by introducing the very human element of heterogeneity far earlier than previously thought possible, we offer biopharmaceutical companies and drug developers a persuasive and multifaceted value proposition that includes, first, the mitigation of clinical risk at the most critical stage of drug discovery. Second, by identifying and validating drug targets sooner, thereby avoiding unnecessary trials that are likely to fail in their development, the potential exists not only to significantly reduce the cost of drug discovery, but potentially to expand the drug development pipeline. And third, by accelerating early stage discovery, replenishing pipeline, and truncating clinical development, the patent life of these drugs post-commercialization is extended and expanded. Prior to the application of artificial intelligence to the methodology of drug discovery, drug developers relied entirely on the successful outcome of very large, extremely expensive, and highly speculative clinical trials to determine if a tumor type will respond to a certain compound. As we all know, the failure rate across the industry is staggering. It is estimated that as many as 95% of drug candidates that enter clinical trials will fail and never be developed. Our value proposition is this. By utilizing our pedal platform, our partners essentially have the ability to peer into the future of drug response and to confidently anticipate clinical validation with much lower financial risk and therefore a much greater likelihood of commercial success. We have subsequently repositioned Predictable Oncology as a science-driven company that is enabled by artificial intelligence. And given this important distinction, we have been able to differentiate ourselves in the market. I am pleased with the progress that we are making, which in my view seems to indicate that what we do and how we do it is resonating with commercial drug developers as well as academic and research institutions. As we continue down this path, we are earning visibility and credibility, which is both telling and rewarding. And as momentum grows, we are very well positioned to participate in the billion-dollar artificial intelligence drug discovery market that is projected to grow annually at a compound rate of more than 30%. For the benefit of those who may be new to our story, please allow me to step back for a moment and give you a high-level overview of our PETL platform. PETL is not just one thing. PETL is several interrelated things functioning in unison. PETL is the application of artificial intelligence as a tool that is directed by rigorous scientific experimentation conducted in our laboratory, which is informed by the most significant resource at our disposal, a biobank of more than 150,000 tumor samples. These three things comprise the PEDAL platform. And to be clear, when I say artificial intelligence, I am referring specifically to active learning or machine learning. which is also a key differentiator for us. A simple way to think about artificial intelligence is this. AI is a process that mimics human thought. It is programmed to match the intelligence and capabilities of human thinking. That is to reason artificially, hence the term artificial intelligence. Machine learning, on the other hand, actually has the capacity to make predictions or decisions based upon data. It is a very sophisticated form of statistical analysis. It makes predictions based upon information that humans, which in our case are scientists, have provided. It is supervised, not programmed. It is active, not static. The more data or information that we feed into the system, the more the system is able to make accurate predictions based upon that data. It is something that learns by iteration, hence the term, machine learning. Our PETL platform is essentially supervised machine learning driven by scientific experimentation conducted in our own laboratory and informed by a biobank of 150,000 tumor samples. We conduct laboratory experiments to validate those in silico analyses to prove or disprove the viability of drug targets scientifically not just statistically. This is our value proposition. This is what sets us apart from all other AI drug discovery companies. The significance of this methodology, I believe, continues to be misunderstood or perhaps underappreciated by analysts, investors, and the market in general. That we are not an artificial intelligence company but that we utilize our own proprietary machine learning capabilities in a way that cannot be duplicated elsewhere, precisely because it is primarily driven by scientific rigor, not just algorithmic programming. It is informed by testing actual heterogeneous human tumor samples retrieved from our own biobank and validated experimentally in our own laboratory. Lastly, it is equally important to understand that PETL has been scientifically and technically tested, validated, and verified. PETL is able to predict with 92% accuracy whether a tumor sample responds to a certain drug compound or not. And in so doing, PETL is able to inform the selection of drug-tumor type combinations for subsequent in vitro testing and facilitating go-no-go decisions before investing in costly and time-consuming later stage in vivo human trials. PETL is a decision support tool. It answers scientific questions and clarifies scientific decision-making. A very significant milestone that occurred during the first quarter of this year was the announcement that we have partnered with Cancer Research Horizon, CRH. DRH is the world's largest private funder of cancer research. I want to repeat this point to emphasize the significance of this collaboration. DRH is the single largest private funder of cancer research in the world. DRH has chosen to partner with Predictive Oncology to utilize our pedal platform to identify the most suitable population of patients with a further development of their compounds. Why? Because we can preemptively screen for patient heterogeneity before ever conducting a single clinical trial. And to be even more specific, we have partnered with CRH to evaluate preclinically certain drug inhibitors for the purpose of determining which cancer types and patient populations are most likely to respond to treatment with these compounds. To give you an idea of the breadth, depth, and impact of cancer research horizons on drug discovery and development globally, here are a few statistics. DRH has access to a network of more than 4,000 of the world's leading cancer researchers. They spend upwards of $370 million annually on cancer research. They generate an excess of $647 million in revenue from royalties and intellectual property alone. They have provided critical support that has launched 11 cancer therapies currently on the market. They are currently sponsoring an additional 160 compounds. Their portfolio of spin-out companies have secured investments in excess of $2.8 billion. So while we were not able to disclose the specific compounds or mention a specific dollar value related to this relationship when it was first announced in March, we can say that this partnership provides for substantial development milestones and sales-related royalty payments over time. Through this one strategic relationship, we have exponentially expanded our reach into the oncologic drug development community and, by virtue of the collaboration itself, have the potential to impact the most critical stage of early drug discovery in a way that has never been done before. In February, we also announced a partnership with Civergenics to develop the first-ever genomics-based approach to precision radiation therapy utilizing artificial intelligence, which we believe has the potential to revolutionize the field of radiation oncology. Today, radiation therapy or RT is prescribed and delivered using a one-size-fits-all approach where all tumors are treated empirically, if not uniformly. We believe that the next and most significant paradigm shift in the field of radiation oncology will come from exploiting tumor genomics to personalize and optimize the radiation therapy prescription dose and to identify drug targets for the development of radiosynthetizers and radioprotectors for biopharma and industry. T-VIRGENIC's precision genomics radiation therapy platform provides the first clinically validated approach to optimizing the radiation therapy prescription dose for each individual patients. It is personalized medicine. If we take this one step further and we apply artificial intelligence and machine learning to the equation, the possibility exists to not only pursue the personalization and optimization of radiation therapy prescription dose, but potentially to discover medicinal radio sensitizers and radio protectors, which might lead to the repurposing of existing compounds or the development of an entirely new class of drugs. We mentioned this in our last call, but it certainly bears repeating. Cevergenics is a spin-out of the Moffitt Cancer Center, where the Cevergenics precision genomics platform is currently being used in a phase two prospective clinical trial for triple negative breast cancer. This is the first ever genomics approach to precision radiation therapy. This is not hypothetical. and it is clinically actionable. To put this in context, there are approximately 1.9 million newly diagnosed cancer patients in the United States every year. And potentially, more than 1 million of those patients may be treated with radiotherapy. If the overall survival rate of those patients treated with radiotherapy is improved by just 4%, That translates into 40,000 lives, which is almost equivalent to eradicating breast cancer. Needless to say, we are very excited about potentially playing a role in improving clinical outcomes of cancer patients treated with radiation therapy. So, in essence, predictive oncology and C-vergenics have entered into a scientific collaboration to leverage the computational capacity of our pedal platform and the diagnostic capabilities of the Ceregenics Precision Genomics Radiation Platform. The impact of these two technologies extend well beyond clinical utility to include drug discovery, drug repurposing, and screening for radio sensitivity or radio resistance. Based upon initial conversations with NASA, for example, The ability to develop the first ever genomics-based artificial intelligence approach to identify novel radio protectors is viewed as a significant benefit to aerospace in general and the astronaut corps in particular. This potentially extends to other government agencies or industries, including the Department of Defense and nuclear energy. Keep in mind that predictive oncology provides services along the entire continuum of drug discovery through drug development. While the PETL platform, for instance, focuses exclusively on drug discovery, our formulations and solubility capabilities address drug development. So, in addition to existing contracts with biotech and biopharma companies, predictive oncology recently announced a new collaboration with FluGen, Inc., on a next generation vaccine related to respiratory diseases. And in addition, we are finalizing two quite novel formulation proposals for pharmaceutical companies, both of which will likely begin before the end of the third quarter. Most recently, we announced the extension of a contract with Integra Therapeutics, a very well respected leader in the development of next generation gene writing tools to advance gene therapies. Through this collaboration, predictive oncology will utilize our proprietary high-throughput self-interaction chromatography, which we refer to as HSC, to rapidly and accurately measure biomolecular interactions that assist pharmaceutical and biotech companies in the workflow and process of drug development. Taken together, we believe that These initial collaborations speak to the broad applicability of our suite of technologies. Before I turn the call over to Bob, I would like to conclude by reviewing some rather noteworthy additions to our Scientific Advisory Board and Board of Directors, as well as the formation of a Business Advisory Board. Beginning with our Scientific Advisory Board, we are pleased to welcome Christoph Reinhardt PhD, MBA to our team. Christoph brings vast experience in oncology, translational research, drug development, and innovation. For more than a decade, Dr. Reinhart worked at Eli Lilly, where he was responsible for strategy and implementation of translational research for its portfolio of oncology assets and biomarkers. As acting chief scientific officer for cell phenomics, He now plays an instrumental role in determining what types of novel drugs and drug combinations might be beneficial to future cancer patients with solid tumors. So this experience, Christophe's experience, obviously is both timely and critical in light of the strategic direction in which predictive oncology is now moving. Christophe joins Dr. Mark Malandro and Dr. Robert Murphy on the Scientific Advisory Board. Mark Malandro is Vice President of Operations for Science at the Chan Zuckerberg Initiative and a very well-respected expert in genomics, molecular biology, biochemistry, and bioengineering. Robert Murphy is a pioneer in the field of machine learning and biological analytics. He was founding head of the Computational Biology Department at Carnegie Mellon University and led the development of our core machine learning technology that powers Predictive Oncology's Petal platform. These industry thought leaders comprise what I consider to be a world-class scientific advisory board, and individually and together, we are already benefiting from their insights and contributions. We have also convened a business advisory board, which, like the scientific advisory board, will be comprised of relevant business leaders and key thought leaders that will work directly with senior management, but also interact with the board of directors and the scientific advisory boards. The names of those advisors will be announced in the coming weeks. And lastly, we recently announced that Veena Rao, PhD, MBA, has also joined the board of directors of predictive oncology. Dr. Rao is a pharma, biotech, and digital health veteran with extensive experience launching products and building commercial organizations in the pre-launch and early launch phases. Dr. Rao currently serves as Chief Business Officer of Portal Instruments, where she leads the identification, evaluation, and negotiation of partnership opportunities for that company and heads a team of science and business professionals to guide the company's short-term and long-term commercial strategies. Dr. Rao will replace David Smith, who is stepping down as director, but will remain as an advisor to the board and serve as lead corporate counsel for the company. At this point, I will turn the call over to Bob Myers, our CFO. Bob?