Greetings and welcome to the Predictive Oncology Fourth Quarter 2023 Earnings Call. At this time all participants are in a listen-only mode. A brief question-and-answer session will follow the formal presentation. [Operator Instructions]. As a reminder this conference is being recorded.

It is now my pleasure to introduce your host, Glenn Garmont, LifeSci Partners. Thank you Glenn, you may begin..

Welcome, and thank you, everyone, for dialing into the Predictive Oncology full year 2023 financial results call. First, you'll hear from our Chief Executive Officer and Chairman of the Board, Raymond Vennare; then our Chief Financial Officer, Josh Blacher, will review our financials.

Certain matters discussed on 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 Vennare, Chief Executive Officer.

Raymond?.

Thank you, Glenn. And good morning, everyone. It has now been slightly more than 12 months since we essentially restructured, repositioned, and implemented our new strategic vision for predictive oncology. And I have to say that I'm very pleased with the progress that we've made in a relatively short period of time.

We have not only identified previously unutilized assets within the company, but we have further bolstered one of our most significant and valuable assets, our intellectual property and trade secrets. Sometimes progress is invisible.

Trying on this unique set of integrated assets and capabilities, we have now begun to articulate what we believe is a compelling offering for accelerating oncologic drug discovery and development on a global scale. 2023 was a difficult but important year for us. It was transitional and necessary. Rebuilding is never easy.

It was also a year of learning and responding to that knowledge. It has enabled us to incorporate valuable commercial lessons from our first year in the market to further refine our business development initiatives and set the stage for accelerating growth moving forward.

As reminder, and particularly for those who may be new to our story, Predictive Oncology is unique in the evolving artificial intelligence in drug discovery market.

We are the only company to our knowledge that is able to bring together cutting edge artificial intelligence and active learning technology with a proprietary biobank of more than 150,000 tumor samples, more than 25 years of longitudinal patient specific drug response data, a CLIA certified wet lab and more than 200,000 pathology slides, which are currently being digitized and incorporated into our PEDAL platform.

We can add to that reservoir of proprietary assets more than 40,000 formalin fixed paraffin embedded blocks of human tissue, and an expanding portfolio of intellectual property.

This platform, which is more than just an artificial intelligence platform, but its scientific methodology generates efficient, highly accurate predictions of how tumors respond to drugs, and supports those in silico predictions with clear validated in vitro experimentation.

Our biobank allows us to introduce patients and tumor heterogeneity into the earliest phases of drug development, which enables our partners to gain critical insight into the prioritization and future development of potential drugs that have greater or lesser degrees of clinical success well before human clinical trials are initiated.

Having technically, scientifically, and commercially validated PEDAL platform, we have clearly demonstrated that we are able to predict if a tumor will respond to a given drug with 92% accuracy.

The implication for drug developers is that they can mitigate the risk of failure and expedite the probability of success in ways that have never been seen before. It has been well documented that drug development is inherently expensive and risky, particularly in the area of oncology.

Over the past 20 years, in fact, there have been approximately 1500 drugs selected for clinical trials, only 115 of which were ultimately approved for clinical use. In other words, less than 8% of all cancer drugs that enter Phase 1 trials are actually approved by the FDA.

By harnessing the power of our PEDAL platform, drug developers are able to save many millions of dollars and thousands of man hours, pursuing the development of drug candidates that we predict are most likely to succeed in the clinic, while avoiding the risk of the dancing candidates, which we may have predicted, might fail in later stages of clinical trials.

With PEDAL, we are also able to generate proprietary data that can aid our drug development partners in training novel AI, broad tumor response models.

While others in our industry have certainly attempted to replicate what we have already developed, and currently own, they simply cannot duplicate the comprehensive historical archive of tumor samples, drug response data, and domain expertise that we possess.

This speaks volumes about the importance of our unique ability to integrate data from multiple sources into a single solution that can successfully introduce tumor heterogeneity into the early drug discovery.

Moreover, we believe our biobank and large repository of drug response data gives us an enduring competitive advantage that will take years if not decades to reproduce.

Although we don't discuss this as much as we could, the fundamental significance of owning and controlling the entire continuum of biological samples, historical drug response data, computational capacity, anthology reference sets and wet lab experimentation means that we are in control of our own destiny with respect to the use of these assets.

Ultimately, if not inevitably, we will not only be in a position to identify drug candidates ourselves, but to license those candidates directly to Big Pharma. I would now like to provide an update on some of our more notable engagements from 2023 beginning with cancer research horizons.

Recall that we have been working with CRH, the world's largest private funder of cancer research to evaluate three of their preclinical glutaminase inhibitor drug compounds to determine which cancer types and patient populations are most likely to respond to treatment with those compounds.

In only a matter of a few weeks of actual lab time, we were able to deliver the results of this first campaign, which was able to identify subpopulations of patients that were more responsive to their compounds across four different tumor types, colon, liver, lung, and ovary.

Armed with this information, CRH is now in a position to prioritize the development of the strongest candidates on the ideal tumor types and in the process gave significant time and resources by pursuing only those targets that are most likely to succeed in the clinic.

The next step here might be to use a sampling of the subpopulations of interest, presumably responders in one or two of the tumor types of interest to perform more extensive molecular sequencing, including DNA, or whole exome and or RNA or whole transcriptome.

This would provide much more information for the purpose of better defining responsive patient tumor samples. From our perspective, this engagement with CRH provides crucial third party validation of our PEDAL technology and serves as an important reference point as we engage with other leading cancer drug discovery and drug development companies.

Turning now to UPMC Magee-Women’s Hospital, we announced last quarter that we successfully completed a multi-year retrospective study, build multi-omic machine learning models that could predict overall short and long term survival in ovarian cancer.

Ovarian cancer treatment represents an unmet need in oncology, with epithelial COVID [ph] ovarian cancer being the deadliest of all kinds of cologic 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 chemotherapeutic agents. The Magee Study included data from 235 ovarian cancer patients, and was designed to identify key features that drive overall survival endpoints.

We are very pleased to say 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.

We continue to engage with Magee on additional methods of incorporating these models into clinical practice.

One potential application would be the development of an AI driven decision support tool, leveraging the features that we identified as having prognostic importance for the model to tailor therapies for individual patients and positively affect overall survival.

Another such application would be to drive more efficient and effective clinical trial designs. While these discussions are ongoing, we are in parallel, utilizing this information for purposes other than clinical utility.

The possibility certainly does exist to reevaluate this data to develop biomarker leads, digital pathology applications, and new predictive models for other cancer types, as well as models for drug rescue, drug repurposing, and drug combinations.

Leaning heavily on this evidence in fact, we recently submitted a proposal to the RK Mellon Foundation to further enrich the data sets necessary for modeling, including sequencing activities, aggregation and curation of clinical information especially outcome data, and the digitization and futurization of more than 200,000 pathology slides currently in our possession, delivering significant value and unique insights to highly regarded collaborators such as Magee and through them gaining access to drug developers and CROs who can benefit from our models is a key factor in our business and corporate development efforts moving forward.

Our work with both CRH and Magee confirm without question, our ability to aggregate, analyze, and model multi-omic data to successfully predict patient responses to certain drug targets across heterogeneous patient populations.

Again, it is the patient heterogeneity that is incorporated into our models that truly sets us apart from others in the industry, both new and established AI players, as patient heterogeneity is a major reason why oncology drugs fail in later stages of clinical development. Turning now to other collaborations that we have discussed in the past.

Let me give you a brief update on Cvergenx, FluGen and Fuji [ph], 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 a collaboration with Cvergenx develop the first ever genomic spaced approach to precision radiation therapy and drug discovery using artificial intelligence.

The objective of this collaboration was to leverage and maximize the combined power of Predictive Oncology’s expertise in artificial intelligence and Cvergenx proficiency in biomarker development to identify novel radio protector 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 3000 different drug exposures using well established gene expression databases.

These findings form the basis of an NIH SBIR Phase 1 grant, screen vast libraries of compounds to accelerate the potential development of drugs, drug combinations, or repurposed drugs to sensitize or protect human subjects from the effects of radiation.

The significance of identifying these radio sensitizers and radio protectors extend well beyond drug repurposing however. Using these models, for example, we can proactively screen workers in the nuclear energy industry and in the military and in a 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 datasets, 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 efficiency of radiotherapy. Second, screening for interactions between sensitive or resistant patient tumor samples and therapeutic compounds. And third, to identify, combine, or develop novel or repurpose radio protective or radio sensitizing drugs.

These are not isolated elements, but synergistic activities that created new and more interesting opportunities. For example, as you might remember, our Biologics Group is led by Dr. Larry DeLucas, former astronaut and Senior Scientist for the space station.

Because of that experience, and given his relationship with NASA, we recently submitted a proposal to the Center for the Advancement of Science and Space to use our technology on the space station.

Larry and his team have now developed a novel membrane protein expression system to produce significant quantities of biologically active G protein coupled receptors, also known as GPCRs, and other membrane protein classes.

The reason for this is that the microgravity environment in space can improve cell differentiation and increase the rate of cell growth and size which enhances the yield of expressed GPCRs. The more practical and immediate opportunity, however, is that this concept has also led to collaborations with Merck & Company, OCMS, and Red Wire Space.

As a result of these developments, we have just filed intellectual property or novel method and system for expression and purification of G protein coupled receptors. When issued, we will be in a position to broadly out license this technology to biopharma for numerous drug development applications.

You may remember from our last call that Predictive Oncology and FluGen entered into a collaboration to develop a novel first of its kind flu vaccine to market as part of a multi-million dollar project funded by the Department of Defense for which a Phase 2B grant has been submitted to the NIH.

I'm pleased to say that this grant has now been awarded, the details of which will be disclosed in the coming weeks pending approval by the Department of Defense. Predictive Oncology will play a critical role in making FluGen’s vaccine more stable and sustainable in a refrigerated state, which is a crucial requirement in the drug development process.

And lastly, Predictive Oncology and Fuji [ph] will in the next few weeks announce their intention to co-market our endotoxin detection and treatment technologies as a novel solution for injectable pharmaceuticals and biological products.

According to a recent report by Future Market Insights, the $6 billion injectable drug market is expected to grow by 5.8%, reaching the projected value of $10 billion by 2034. Before turning the call over to Josh to review the financials, I would like to recap recent conference activity.

During the initial months of 2024 we had a meaningful presence at several investor conferences geared toward raising awareness of our company among high quality institutional healthcare investors, as well as potential collaborators.

These include Biotech Showcase in San Francisco, The Bio CEO and Investor Conference in New York, the 2024 New Cancer Oncology Conference in New Orleans, during which I sat on a panel discussing the topic of precision medicine. And finally, the H.C. Wainwright First Annual Artificial Intelligence Based Drug Discovery and Development Conference.

We have more such conference appearances planned for the balance of 2024 and we view these events as a critical element of our effort to create long-term value for both our partners and our shareholders. At this point, I will turn the call over to Josh Blacher. Josh..

Thank you, Raymond. We concluded the fourth quarter of 2023 with 8.7 million in cash and cash equivalents compared to 22.1 million as of December 31, 2022, and 8.3 million in stockholders equity compared to 21.8 million as of December 31, 2022.

Our net loss per share in 2023 declined approximately 50% to $3.48 for basic and diluted share, $6.98 per basic and diluted share for 2022. The company recorded revenue of 1.8 million in 2023, compared to 1.5 million in 2022. Revenues for the years ended December 31, 2023 and December 31, 2022 were primarily derived from our Eagan operating segment.

The Eagan operating segment contributed 1.135 million and 1.064 million for the years ended December 31, 2023 and December 31, 2022 respectively, while the Pittsburgh operating segment contributed 493,000 and 359,000 respectively. Operation expenses primarily consist of expenses related to product development, prototyping, and testing.

Operation expenses decreased by 142,000 to 3.7 million in 2023, compared to 3.8 million in 2022. The decrease in operations expenses in 2023 is primarily due to approximately 132,000 lower research and development expenses related to office closures and approximately 52,000 lower related to staff expenses.

These decreases were offset by higher cloud computing expenses associated with our Pittsburgh operating segments. Net cash used in operating activities was 13.2 million in 2023 compared to net cash use of 12.4 million in 2022.

Cash used in operating activities increased in 2023, primarily due to cash operating losses as well as changes in working capital, including decreases in accrued expenses in contract liabilities, offset by an increase in accounts payable. Net cash used in investing activities was 302,000 in 2023 compared to 476,000 in 2022.

Cash used in investing activities decreased in 2023 primarily due to a decrease in the acquisition of property and equipment. Net cash provided by financing activities was 149,000 in 2023 compared to 67,000 in 2022.

Cash provided by financing activities in 2023 was primarily related to proceeds refinancing insurance premiums over the insured period with a short-term note payable, while cash provided in 2022 was primarily proceeds from the issuance of common stock and warrants.

The company incurred net losses of 14.0 million and 25.7 million for the years ended December 31, 2023 and December 31, 2022. That concludes the financial overview. We will now open the call for questions. Operator..

Operator:.

Thank you all very much again for participating in this call.

I just want to reiterate the fact that we are we actually are very pleased with our progress since we implemented our new strategic vision for the company a little over a year ago, and acting on the information that we have learned, accelerating our traction in the marketplace with many of the companies that I mentioned in my remarks.

So if there are no other questions, I want to thank you all for participating. I thank you for your continued support. And I thank you for remaining involved in the company and we look forward to reporting again soon. Thank you..

This concludes today's conference. You may disconnect your lines at this time. Thank you for your participation..