Lantern Pharma Inc.

Good afternoon, everyone. I'm Nicole Lieber with Investor Relations here at Lantern Pharma. Welcome to our first quarter 2023 earnings call. I will be your host for today's call. As a reminder, this call is being recorded and all attendees are in a listen-only mode. We will open up the call for questions and answers after our management's presentation. A webcast replay of today's conference call will be available on our website at lanternpharma.com shortly after the call. We issued a press release after market closed today, summarizing our financial results and progress across the company for the first quarter ended March 31, 2023. A copy of this release is available through our website at lanternpharma.com, where you will also find a link to the slides that management will be referencing on today's call. I would like to remind everyone that remarks about future expectations, performance, estimates, and prospects constitute forward-looking statements for purposes of safe harbor provisions under the Private Security Litigation Reform Act of 1995. Lantern Pharma cautions that these forward-looking statements are subject to risks and uncertainties that may cause actual results to differ materially from those anticipated. A number of factors could cause actual results to differ materially from those indicated. by forward-looking statements, including the impact of the COVID-19 pandemic, results of clinical trials, and the impact of competition. Concerning factors that could cause actual results to differ materially from those in the forward-looking statements can be found in our annual report on Form 10-K for the year ended December 31, 2022, which is on file with the SEC and available on our website. Forward-looking statements made on this conference call are as of today, May 9, 2023, and Lantern Pharma does not intend to update any of these forward-looking statements to reflect events from circumstances that occur after today unless required by law. The webcast replay of the conference call and webinar will be available on Lantern's website. On today's webcast, we have Lantern Pharma CEO, Pana Sharma. and CFO David Margrave. Pana will start things off with an overview of Lantern's strategy and business model and highlight recent achievements in our operations, after which David will discuss our financial results. This will be followed by some concluding comments from Pana, and then we'll open up the call for Q&A. I'd now like to turn the call over to Pana Sharma, President and CEO of Lantern Pharma. Pana, please go ahead.

Thank you, Nicole. Good afternoon, everyone, and welcome to our first quarter 2023 earnings call, and company update. We've had a tremendously progressive and productive quarter. I want to tell you all about the corporate progress so far at Lantern Pharma. Lantern Pharma is at the leading edge, leveraging artificial intelligence, machine learning algorithms, biomarker clinical genomic and drug response data to transform the costs, compress the timelines, and de-risk oncology drug discovery and development. During the first quarter, we made many exciting and valuable advances to our platform and our pipeline of cancer therapies. Our team continues to be extremely focused on taking these insights for our new programs and driving them to meaningful clinical programs that can be launched in the coming months for both LP184 and LP284. We've done this for both molecules in a fraction of the time and in a fraction of the cost of traditional drug development. And what we're also practicing is the future of oncology therapy development, where data can be used to accelerate programs and de-risk the identification and progress of potentially life-changing medicines. My colleagues at Lantern and I are united in a core belief that a rise in sophisticated, highly scalable, analytical storage technologies, coupled with high-value data from cancer biology research and genomics, along with a decrease in the cost of AI computing, is allowing us to do two very important things, better model, understand, and predict cancer biology, and secondly, design and develop cancer drug programs at a fraction of the cost and at a fraction of the timeline that has traditionally been possible. This is true not only in cancers that have been well-researched and highly characterized, but also in cancers that have traditionally gone under-met or unresolved. As I know from talking with investors, analysts, and our partners, what's in everyone's mind right now, or at least many people's mind, is ChatGPT, a very sophisticated model of language that's generative and real-time and allows us to understand huge swaths of data. Everything on the internet, or even off of the internet, all in one place and interacts in a pretty compelling and interesting way with all of us, not only us as humans and end users, but also very importantly with us as researchers, developers, clinicians, and scientists. And this is being done at a scale that was never thought possible before and never at a level of precision, automation, and cost as today. The technologies and tools that make up those large language models, data automation and analytics, are the same approaches in AI technologies we believe that we are leveraging and deploying to create new drug programs and cancer biology insights at a cost and timeline that was unimaginable in the near past. Today at Lantern, we have at a highly massive scale, think highly massive scale, millions of simultaneous instances of competing algorithms that are working to help create correlations and relationships that would be far too complex and time-consuming for any team of humans to fully approach, let alone replicate with precision. Right now, we can understand and predict parts of these drug interactions with cancers, but not all of it as a complete system or with the required level of personalization. That's why we're constantly on the hunt for new data, curating it, ingesting it, evolving our systems. And we're evolving not only the underlying data and data sets and data tags, but also the algorithms. We're also now training the algorithms to update themselves and also finding methods in which we can allow the AI platform to find and ingest new data on its own. This is the future of developing cancer therapies, where data can be used to accelerate programs, de-risk the identification and progress of potentially life-changing medicines, and potentially find new patient groups or new patient classes that can have a big impact from our therapies or therapies of our partners. During the quarter, we made some very meaningful progress. Let me give you some of the highlights here. We dosed the first patient in the Phase II harmonic clinical trial, a study for the unique population of non-small cell lung cancer patients who are never smokers. And they make up about 15% to 20% of all lung cancer cases in the U.S. today. We're about to submit the IND application for LP184 to the U.S. Food and Drug Administration. We anticipate submitting that this week. This is for a potential blockbuster therapy with $6 to $7 billion in annual sales, where we can use it both as a single agent or as a combination therapy. This phase one clinical trial for LP184 in genomically defined solid tumors is will be launching in mid-2023 for patients with recurrent solid tumors, including brain cancers. We also plan on completing our IND-enabling studies for LP284 and launching a first-in-human phase one clinical trial in multiple non-Hodgkin's lymphomas. This is about a $1.2 billion indication, and this is targeted in the second half of this year. We also received notice of allowance from the USPTO for composition of matter patent for LP284 as well. This gives us exclusivity for this new molecule into 2039, 2014. We also developed an industry-leading series of AI algorithms. These are a series of algorithms that not only are now top-ranking, at the Therapeutic Data Commons, which is an industry consortium. But it helps solve one of the most challenging problems in brain cancer drug discovery, which is predicting with some level of accuracy a compound's blood-brain barrier permeability. So our top four algorithms are not only highly accurate, but also ultra-fast and scalable. We can run thousands of molecules at a level and scale that was not possible before on a daily basis. We also established an additional radar collaboration, this one with one of the leaders in breast cancer, TTC Oncology, to help advance their phase two ready drug candidate, TTC352, in ER-positive breast cancers. This continues to prove and validate that, in fact, our AI platform radar is valuable currency in deal-making and in drug asset development. We also continue to show fiscal discipline, and end of the quarter with $51.5 million in cash, cash equivalents, and marketable securities, giving us cash runway into 2025. Now, with those highlights behind us, let me turn the call over to our CFO, David Margrave, who will provide an overview of our first quarter financial results.

David? Thank you, Pana, and good afternoon, everyone. I'll now share some financial highlights. from our first quarter ended March 31, 2023. Our R&D expenses were $2.6 million for the first quarter of 2023, down slightly from $2.7 million in the first quarter of 2022. We see R&D expenses increasing in the second half of 2023 as we advance our LP300 Phase 2 trial and commence our phase one trials for LP184 and LP284. General and administrative expenses were $1.7 million for the first quarter of 2023, up slightly from $1.4 million in the prior year period. We recorded a net loss of $3.9 million for the first quarter of 2023, or 36 cents per share, compared to a net loss of $4.1 million or 38 cents per share for the first quarter of 2022. Offsetting the loss from operations in the first quarter of 2023 was interest income and other income net in the aggregate amount of $419,000. Interest income was approximately $134,000 for the first quarter of 2023 Other income net was approximately $285,000 for the first quarter of 2023 and reflected increases in dividend income of approximately $80,000, increases in unrealized gains on investments of approximately $207,000, and increases of approximately $136,000 in research and development tax incentives related to our Australia subsidiary. These were offset in part by increases in foreign currency losses of approximately $60,000. As of March 31, 2023, we had approximately 10.86 million shares of common stock outstanding and outstanding warrants to purchase approximately 177,998 shares and outstanding options to purchase approximately 1,095,046 shares. These warrants and options combined with our outstanding shares of common stock give us a total fully diluted shares outstanding of approximately 12.1 million shares as of March 31, 2023. Our cash position which includes cash equivalents and marketable securities at March 31, 2023, was $51.5 million. This balance is expected to carry us into 2025. Importantly, we believe our solid financial position will fuel continued growth and evolution of our radar AI platform, accelerate the development of our portfolio of targeted oncology drug candidates, and allow us to introduce additional targeted products and collaboration opportunities in a capital efficient manner. Our team continues to be very productive under a hybrid operating model. This hybrid model also removes geographic restrictions to our hiring initiatives, which gives us the ability to recruit extremely high caliber team members that otherwise might not be available. We currently have 23 employees who are primarily focused on leading and advancing our research and drug development efforts. We see this number expanding slightly in coming quarters as we add additional experienced and talented individuals to help advance our mission. I'll now turn the call back over to Pana for an update on some of our development programs. Pana? Thank you, David.

As we mentioned earlier in the call, this week we'll be submitting our IND application to the FDA for LP184's first in-human trial for advanced solid tumors and brain cancers. On average, we've been able to advance our newly developed drug programs from initial AI insights to first in-human clinical trials in two-plus years at a cost of around $1 million to $2 million per program, both metrics that are completely unheard of in oncology drug discovery. This breakthrough pace of development was most recently highlighted in Starlight Therapeutics, as it intends to pursue human clinical trials for multiple CNS indications starting in late 2023, building on prior IND-enabling studies and the upcoming Phase Ia clinical testing that will be conducted by Lantern in the coming months. The clinical development of STAR-001 in CNS cancers beyond The Phase 1A trial will be conducted exclusively by Starlight, but following that, Lantern will continue to advance LP184's preclinical and clinical development for non-CNS indications, including pancreatic, bladder, triple negative breast cancer, and other solid tumors that have DNA damage repair deficiencies. The formation of Starlight as a wholly-owned subsidiary allows Lantern to sharpen the focus on advancing Star001 through targeted clinical trials and dedicate increased time, resources, and personnel to progress one of the most promising drug candidates for CNS cancer patients in decades. We believe that by focusing our efforts via Starlight, we can accelerate and deepen our commitment to the CNS cancer patient community, while also creating the potential for meaningful additional upside for our investors. We'll always be looking for additional opportunities where the development needs and unique focus of certain programs or assets can be separated and developed in a more focused and perhaps more evolved manner. As we've pointed out, we're accelerating the pace at which we're developing and validating our insights, and then leading those into potentially meaningful and breakthrough drug assets. We're very well positioned to then partner these drug assets out with larger companies, and we'll begin exploring some of those licensing and partnership opportunities with biopharma companies this year. Other objectives for us this year will be to continue expanding radar to beyond 50 billion data points, will be to establish additional radar-based collaborations, and also advance our ADC development, both through advances in our platform, but also advances in exciting new preclinical compounds that we'll talk about later this year. At the same time, David pointed out in his review, our strong cash position is being carefully utilized to make meaningful progress in a disciplined manner. The most exciting, which are coming up, which is scaling to more patients for LP300 and launching LP184 in the clinical setting. Now, with that, I'd like to open up the call to any questions.

Thank you, Pana. Thank you. If you would like to ask a question, you can do so in one of two ways. You can either type your question in using the Q&A tool, or you can click on the raise hand tool to speak directly to management and I will allow you to speak. We have a question coming in here from an investor about our data and radar. Where do we source our data and how do we validate and clean it?

That's a great question. So we get our data not only from public sources like TCGA and CCLE and the NCI, but also from private sources such as collaborations that we have, our own sequencing and biomarker studies that we do routinely with all of our drug programs and with our CRO partners. We also get it from different studies that are posted at places like AACR, ASCO, et cetera. We also get them from historical drug programs and historical trials. So there's a number of different places, including our own proprietary data that we're generating, both preclinical and clinical data. And validating that data is interesting. Obviously, we run our studies in duplicate to make sure the data we are getting is reliable. We also know that a lot of historical data is not terribly reliable or has its own challenges. So, you know, we embarked a few years back on a massive cleanup of all of our NCI and CCLE and other historical TCGA-related data sources. And so we renormalized and recurated a lot of that data. We also threw a lot of it away. One of the very first things I did when I joined Lantern is about 30% of the data that we had at the time was tossed simply because it was not reliable enough or it came from cell lines that were unknown or dubious in nature. And these are pretty well known in the industry. So most people who use data either work around it, clean it, or duplicate it, which is pretty normal. But what we've done, which is unusual, is we've gone through tons of tagging when we give each of the data sets a data quality score. So we know what machine it comes from, what labs, where it was generated, how it was published, it was published in duplicate. You know, did we transform the data? So we have multiple ways that we not only curate, but then clean and normalize the data so that we can use it.

I see here Michael King is raising his hand. Michael, you should be able to speak.

Can you hear me?

Yes.

Hey, guys. I wanted just to ask you to speak about partnering strategies. I'm just wondering where your sweet spot or where you think your sweet spot is in terms of public, private, academic, et cetera, companies and other institutions to source further assets for the pipeline or Are you better, or maybe simultaneously, do deals based around radar and other people's compounds?

That's a great question. So I'm going to give you a lengthy answer, and we can talk more about it at your conference later this week, Michael. So in terms of ingest, new molecules, new ideas – we have ideas of what things that are a higher priority for us than others. And so we try to see if those things are out there and if they're available and if they've been manufactured, if they've been tested, if they come with biological data that then puts us ahead of the game. So, yes, there are definitely certain areas that, And we're always open to learning new things. You know, your questions are only going to be as good as the data you have. So, of course, we're always looking at new assets as well. But they all do go through radar. So if you look at the unique relationship we have with TTC, although we are helping them with the definition of TTC, The patients that are most likely to respond to their drug and also how their drug can be used in other indications, we also do have a clause in there that allows us to potentially license and co-develop the asset. So as we develop it into some meaningful radar-driven insights, we do try to always have that clause basically established. to try to get that asset through radar. In terms of licensing out, our goal is, you know, either phase two or phase three to license it out to bigger biopharma companies that will then take the asset, whatever molecular signatures that we have, et cetera, that make it meaningful and then put it into later stage trials. So hopefully that answers your question. Yep.

Thanks so much for taking it.

A few other questions coming in here, one from John Vandermosten. How are some of the characteristics that radar has identified in compounds that are able to cross the BBB?

That's a great detailed question. So, John, I'll be able to send you a white paper. And we look at probably somewhere in the range of between 4,000 and 5,000 different characteristics. Everything, you know, simple characteristics like weight and size and number of carbon rings, you know, surface area of the carbon rings, you know. whether it's an enantiomer. And there's like somewhere between 4,000 and 5,000 different characteristics. And we try to boil those down to the most important ones. But we use multiple algorithms. So it's not just the characteristics. Different algorithms prefer different characteristics. And then we also run an ensemble approach. If you look at therapeutic data commons, which I urge you to look at, the top performing algorithm right now is the ensemble algorithm. which means it's a mashup of the three or four other algorithms that all come right underneath it. And so these molecular fingerprints and these unique things about each chemical come from its structure, from its smiles, characteristics, and then we basically ingest all that and decide which of the few thousand are most important. Hopefully that answers your question.

Another one here from John. Regarding the AACR abstract on LP184, does the related study suggest about using combination approaches with PARP inhibitors or other agents that disrupt damage repair pathways?

So can you put the question back up? Thanks. Okay. So regarding the ACR abstract, does the related study suggest about using combination with PARP inhibitors or other agents? Yeah, I think PARP-I, because there are a number of PARP-Is that are approved, they're That's where we're actually actively exploring 184 and potentially 100, both with PARP-I drugs. There's a big opportunity because, number one, they're already approved, they're selling in the billions, and we know that there's dosage issues with PARP inhibitors. People become sensitive and there's some toxicity issues. So we're in discussions with some of the PARP-I investigators to look at a combination with 184. Now, one of the unique things that came out in the AACR abstract, maybe not as clear as we'd like, but definitely is hinted at, and it'll come out in the next set, is that the PARP inhibitors, they keep the molecules, sorry, the cancer cells from repairing themselves. They're great blockers of repair. And so that's what gives it its cancer cell kind of capability. Now, interestingly enough, LP184 is a great breaker of DNA double strands. And so as it breaks the double strands and then PARPIs are dosed and the PARPIs keep the double strands from repairing, it's like a really perfect one-two hit. And that's why I really like the 184 plus PARPI combination. Could other double strand breaking agents be used? Yes, perhaps like some topoisomerases. maybe some MMAs, but those have tremendous amounts of toxicity. And so you're going to get toxicity side effects from both those drugs that are just not good. That's what makes 184 more unique, especially when you use PARP-I because it's complementary mechanisms and we're also able to, we believe, change the dosage level significantly. So this is an area we're very excited about.

Another question coming in here. Our cash runway hasn't changed through our last few quarters. Can you expound on that a bit?

Well, I wish that was 100% true. We have been frugal with our cash, but I'll let David kind of walk you through. We've been burning between three and four million, roughly, of quarters. But David, go ahead.

Sure. As we described on the call, we have about $51.5 million in cash, cash equivalents and marketable securities at the end of the quarter. And we have been consistent and I think pretty solid in our forecasting in terms of where our cash would carry us. We've managed that very carefully. And I think the reality is compared to a large portion of our sector, we are in a very strong cash position. We're always looking for, you know, we're watching this cash position very carefully. We're looking for opportunities, and we'll continue to operate in a very fiscally disciplined manner.

Great. Thanks. I think that might be all the questions we have. For today, thank you everyone that joined us today, and we hope to see you soon.