Predictive Oncology Inc. (NASDAQ:POAI) Q1 2023 Earnings Call Transcript May 15, 2023

Operator: Good day, and thank you for standing by. Welcome to the Predictive Oncology Q1 2023 Earnings Conference Call. [Operator Instructions] 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 Garmont, Investor Relations. Mr. Garmont, please go ahead.

Glenn Garmont: 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 Vennare, 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 your 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, 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 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 Vennare, our Chief Executive Officer. Raymond?

Raymond Vennare: Glenn, 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 recognize 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 PeDAL and our computational research engine called CORE, C-O-R-E, which, by the way, was developed by [indiscernible] computational biologists 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 Trilab assets, our assets, those being PeDAL and Core, together with our own wet lab capabilities in a CLIA certified facility allowed for both in-silico computer modeling and bench level laboratory experimentation, by predicting clinical success or failure early in the drug discovery process and by introducing the very human element of heterogeneity or 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 exciting 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 unsuccessful outcome of very large extremely expensive and highly speculative clinical trial to determine if the 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 appear 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 Predictive 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 $1 billion 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 PeDAL platform. PeDAL is not just one thing, PeDAL is several interrelated things functioning in unison. PeDAL is the application of artificial intelligence as a tool that is directed by rigorous scientific experimentation conducted a laboratory, which is informed by the most significant resource at our disposal, a biobank of more than 150,000 a 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 of 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 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 that 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 PeDAL 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 analysis 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 misunderstood 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 or 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 PeDAL has been scientifically and technically tested, validated and verified. PeDAL is able to predict with 92% accuracy whether a tumor sample respond to a certain drug compound or not. And so doing Pat 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. PeDAL is a decision support tool. It answers scientific questions and clarify scientific decision make. A very significant milestone that occurred during the first quarter this year was the announcement that we have partnered with Cancer Research Horizon CRH.

CRH is the world’s largest private funder of Cancer Research. I want to repeat this point to emphasize the significance of this collaboration. CRH is the single largest private funder of cancer research in the world. CRH has chosen to partner with Predictive Oncology to utilize our PeDAL platform to identify the most suitable population of patients, the 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’ve 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 and depth and impact of cancer research horizons on drug discovery and development globally. Here are a few statistics. CRH has access to a network of more than 4,000 of the world’s leading cancer researchers. They spent 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 spinout companies have secured investments in excess of $2.8 million. 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 Cvergenx to develop the first ever genomic-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 radio sensitizers and radio protectors for biopharma and industry.

Cvergenx 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 for the development of an entirely new class of drugs. We mentioned this on our last call, but it certainly bears repeating. Cvergenx is a spinout of the market Cancer Center, where the Cvergenx precision genomics platform is currently being used in a Phase II 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 has 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 and improving clinical outcomes of cancer patients treated with radiation therapy. So in essence, Predictive Oncology and Cvergenx have entered into scientific collaboration but leverage the computational capacity of our PeDAL platform and the diagnostic capabilities of the Cvergenx precision genomics radiation platform.

The impact of these two technologies they 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 genomic-based artificial intelligence approach to identify novel radio protectors is viewed as a significant benefit to aerospace in general and the astronaut core, 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 PeDAL 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 and self-interaction chromatography, which we refer to as HFC 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. Reinhardt 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, Christoph’s experience, obviously, is both timely and critical in light of the strategic direction in which Predictive Oncology is now moving. Christoph joins Dr. Marc Malandro, and Dr. Robert Murphy on the Scientific Advisory Board. Marc 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 Partner at Carnegie Mellon University and led the development of our core machine learning technology that powers Predictive Oncology’s PeDAL 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 Scientific Advisory Boards. The names of those advisers 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 and the prelaunch 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 adviser 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?

Bob Myers: Thank you, Raymond. We ended the first quarter 2023 with cash and cash equivalents of $18.6 million as compared to $22.1 million as of December 31, 2022. In addition, we have 1.8 million outstanding warrants that represent an additional source of capital. We have no debt, so our balance sheet is very strong. As of March 31, 2023, shareholders’ equity stood at $18.6 million as compared to $21.8 million as of December 31, 2022. We recorded first quarter 2023 revenue of $0.2 million as compared to $0.3 million for the first quarter of 2022. Our gross margin in the first quarter 2023 was 50% as compared to 65% for the first quarter of 2022. Operating expenses were $3.6 million in the first quarter of 2023 as compared to $3.6 million for the first quarter of 2022.

Net cash used in operating activities during the first quarter of 2023 was $3.4 million as compared to $3.1 million for the comparable period in 2022. Net cash used in investing activities during the first quarter of 2023 was $0.1 million as compared to $0.1 million for the comparable period in 2022. Net cash provided in financing activities during the first quarter of 2023 was zero as compared to $1 million for the comparable period 2022. That concludes our financial summary. You can find additional details in our 8-K containing our earnings press release as well as our 10-Q, which is on file with the SEC and also available on our website. So with that, I’m going to turn the call over to the operator for Q&A. And as a reminder, Dr. Pamela Bush, our Chief Business Officer, is also available for this segment of the call.

Q&A Session

Operator: [Operator Instructions] Our first question comes from Michael Broadbent, a private investor.

Operator: The next question comes from Rob Antonia [ph], Private Investor.

Operator: [Operator Instructions] There are no further questions at this time. One second. Our next question comes from Michael Broadbent, a Private Investor.

Operator: Thank you. There are no further questions at this time. I would like to turn the floor back over to Raymond Vennare, CEO, for closing comments.

Raymond Vennare: Thank you very much. I appreciate it. So that is this conversation has concluded the call for today, but we hope that you take away from this call that we’re really very motivated and frankly, very excited about the direction we’re going. I know that the current investors have been very patient, if not impatient with some things, but very patient waiting for us to sort of break out of gate here. So everything you heard today is basically setting the company up for the next year, for the next year to two years. And obviously, many of you who are on the phone right now on the call right now, some of you have called me personally called this personally. And you know that you can always reach out and ask more specific questions.

I know this isn’t perfect on you to do that. but we will continue to keep you informed. We’ll continue to make announcements. I know that everyone wants to hear good PR and no one wants to give more — give TR more than we do. And so we look forward to doing that. So thank you all for your support, and I look forward to our second quarter update in August, and wish you all a good day. Thank you.

Operator: This concludes today’s teleconference. You may disconnect your lines at this time. Thank you for your participation.