Lantern Pharma Inc. (LTRN) Q1 2023 Earnings Call Transcript

Lantern Pharma Inc. (NASDAQ:LTRN) Q1 2023 Earnings Conference Call May 9, 2023 4:30 PM ET

Company Participants

Nicole Leber - Investor Relations Associate

Panna Sharma - Chief Executive Officer and President

David Margrave - Chief Financial Officer and Secretary

Conference Call Participants

Michael King - EF Hutton

Nicole Leber

Good afternoon, everyone. I'm Nicole Weber 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 will be 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 the 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 Securities 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.

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's CEO, Panna Sharma; and CFO, David Margave. Panna 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 Panna, and then we'll open up the call for Q&A.

I'd now like to turn the call over to Panna Sharma, President and CEO of Lantern Pharma. Panna, please go ahead.

Panna Sharma

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 of leveraging artificial intelligence, machine learning algorithms, biomarker, clinical, genomic, and drug response data to transform the cost, compress the timelines and derisk 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 LP-184 and LP-284. We've done this for both molecules in a fraction of the time and at 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 derisk 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 2 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 undermet 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 minds is Chat GPT, a very sophisticated model of language that's generative in 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, interaction 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, I 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 with the required level of personalization. That's why we're constantly on the hunt for new data, curating it, ingesting it, and 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, derisk 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. 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 submitted the -- we're about to submit the IND application for LP-184 to the U.S. Food and Drug Administration.

We anticipate submitting that this week. This is for a potential blockbuster therapy with $6 billion to $7 billion in annual sales, where we can use it both as a single agent or as a combination therapy. This Phase I clinical trial for LP-184 in genomically defined solid tumors 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 LP-284 and launching a first-in-human Phase I 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 the composition of matter patent for LP-284 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 comments, 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 low of accuracy, it compounds blood-brain barrier permeability. So our top 4 algorithms are not only highly accurate but also ultrafast 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 RADR collaboration, this one with one of the leaders in breast cancer, TTC Oncology to help advance their Phase II-ready drug candidate, TTC 352 in ER-positive breast cancers. This continues to prove and validate that, in fact, our AI platform RADR is valuable currency in dealmaking and in drug asset development.

We also continued to show fiscal discipline and ended the quarter with $51.5 million in cash, cash equivalents, and marketable securities, giving us a 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?

David Margrave

Thank you, Panna, 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 LP-300 Phase II trial and commence our Phase I trials for LP-184 and LP-284. 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 $0.36 per share compared to a net loss of $4.1 million or $0.38 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 1,095,46 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 the continued growth and evolution of our RADR 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 development 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 Panna for an update on some of our development programs. Panna?

Panna Sharma

Thank you, David. As we mentioned earlier in the call, this week, we'll be submitting our IND application to the FDA for LP-184 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 2-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 1A clinical testing that will be conducted by Lantern in the coming months. The clinical development of STAR001 in CNS cancers beyond the Phase 1A trial will be conducted exclusively by Starlight. But following that, Lantern will continue to advance LPU184'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 snus 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 have pointed out, we're accelerating the pace at which we are developing, validating our insights, and then leading those into potentially meaningful and breakthrough drug assets. We're very well positioned to 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 RADR to beyond 50 billion data points, we'll be to establish additional RADR-based collaborations, and also advance our ADC preclinical 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 LP-300 and launching LP-184 in the clinical setting. Now with that, I'd like to open up the call to any questions.

Question-and-Answer Session

A - Nicole Leber

Thank you, Panna. [Operator Instructions]. We have a question coming in here from an investor about our data and RADR. Where do we source our data and how do we validate and clean it?

David Margrave

That's a great question. So we get our data not only from public sources like TCGA and CCL and 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 in 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 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 renormalize and curate 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 where 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 as we've gone through tons of tagging when we give each of the data sets and data quality score. So we know what machine it comes from, the labs, where it was generated, how it was published.

It was published in duplicate, 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.

Nicole Leber

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

Michael King

Yes. I wanted just to ask you to speak about partnering strategies. I'm just wondering where your sweet spot or what you're seeing 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 RADR and other people's compounds?

Panna Sharma

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 are a higher priority for us than others. And so if we try to see if those things are out there and if they're available and if they've been manufactured, been tested if they come with biological data that then puts us ahead of the game. So yes, there are definitely certain areas.

And we're always open to learning new things. 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 RADRs. So if you look at the unique relationship we have with TTC, although we are helping them with the definition of 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 RADR-driven insights, we do try to always have that clause basically executable to try to get that asset through RADR.

In terms of licensing out, our goal is either Phase II or Phase III 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.

Nicole Leber

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

Panna Sharma

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 simple characteristics like weight and size and number of carbon rings, surface area of the carbon rings, whether it's in [indiscernible]. 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.

It's not just the characteristics, different algorithms, we prefer different characteristics. And then we also run an ensemble approach. If you look at therapeutic data comments, which I urge you to look at the top-performing algorithm right now is the Ensemble algorithm which means it's a mash-up of the 3 or 4 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.

Nicole Leber

Another one here from John. Regarding the ACR abstract on LP-184, does the related study suggest about using combination approaches with PARP inhibitors or other agents that disrupt damage repair pathways?

Panna Sharma

Can you put the question back up? Thanks. So regarding the ACR abstract, does the related studies suggest about using combinate with PARP inhibitors or other agents? Yes, I think PARP, because there are a number of PARPs that are approved, that's where we're actually actively exploring 184 and potentially 100 both the PARPi 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 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 has hinted at it will come out in the next set is that the PARP inhibitors, they keep the cancer cells from repairing themselves. There are great blockers of repair. And so that's what gives it its cancer cell kind of capability.

Now interestingly enough, LP-184 is a great breaker of DNA double strands. And so as it breaks to double strands, and then the PARPs are dosed and the PARP eyes keep the double strands from repairing. It's like a really perfect one to hit. And that's why we like the 184-plus-Parby combination. Could other double-strand breaking agents be used, perhaps like some topoisomerases, maybe some MMAEs, 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 Parby because of its complementary mechanisms, and we're also able to, we believe, change the dosage level significantly. So this is an area where we're very excited about.

Nicole Leber

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

Panna Sharma

Well, I wish that was 100% true. But yes, we have been frugal with our cash, but I'll let David kind of walk you through being burning between $3 million and $4 million roughly a quarter. But David, go ahead.

David Margrave

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 -- 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.

Nicole Leber

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.

Panna Sharma

Thank you.