Lantern Pharma Inc. (LTRN) Q3 2020 Earnings Report, Transcript and Summary

Good afternoon and welcome to Lantern Pharma’s Third Quarter 2020 Conference Call. As a reminder, this call is being recorded and all participants are in listen-only mode. We will open the call for questions-and-answers after the presentation. I would now like to introduce your host for today's conference, Marek Ciszewski with Lantern Pharma. Marek, please go ahead.

Thank you, Chloe. And thank you for joining us for Lantern Pharma’s third quarter 2020 conference call. On the call today are Panna Sharma, Lantern's President and CEO; and David Margrave, Lantern’s CFO. A press release was issued this afternoon with our third quarter financial results that we will be discussing here today. Following the Safe Harbor statement, Panna will provide an overview of business highlights, after which David will share quarterly financial results. Panna will then offer concluding comments, after which we will open this call to questions. Please also note that we have provided a link on the IR website to the slides that we will be referencing in today's call. I would also like to remind everyone that remarks about future expectations, plans and prospects continue to 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. There are a number of important factors that could cause our actual results to differ materially from those indicated by forward-looking statements, such as the impact of COVID-19 pandemic, the results of our clinical trials, and the impact of competition. Additional information concerning factors that could cause actual results to differ materially from those in forward-looking statements can be found in the risk factors section of our final prospectus dated June 10, 2020, for our initial public offering, that is unfiled with the Securities and Exchange Commission. Any forward-looking statements made on this conference call speak only as of today's date, Thursday, October 29, 2020, and Lantern Pharma does not intend to update any of those forward-looking statements to reflect events or circumstances that occur after today. A webcast replay of the conference call will be available on Lantern Pharma website. With that, I'd like to turn the call over to President and CEO, Panna Sharma, for his opening comments. Please go ahead, Panna.

Thank you, Marek. And, good afternoon to everyone on the call today. Thank you for joining us for our third quarter 2020 conference call. As many of you know, a few moments ago, we issued a press release at 4 p.m., highlighting both the operational and financial results for our third quarter. We'll be reviewing those in some detail on this call, and also spending time on questions that investors and analysts will have after our prepared remarks. For those investors that are new to Lantern story, we are an oncology biotech that leverages the power of artificial intelligence and machine learning to both rescue and develop oncology therapies. We're one of the few AI-based biotechs that has multiple clinical stage programs and also a rapidly growing proprietary platform for accelerating our understanding, modeling and prediction the patient and tumor response to cancer therapies. This is a very powerful tool for the development of targeted cancer drugs. In this regard, we are a very unique company at the forefront of the data and machine enabled transformation that's happening today in drug development and drug discovery. Our team has been working very hard this past quarter to in advancing our collaboration, developing meaningful lab data, advancing our manufacturing, onboarding team -- new team members, both employees and consultants, while also getting major new milestones for our platform. Shortly after we began trading in June, we announced that our proprietary AI platform for precision oncology drug development RADR surpassed 450 million data points. This was roughly six months ahead of our previous plan. And during our last earnings call, on July 29th, I indicated that we should reach 1 billion before the end of the year. I'm very pleased to announce another even more important milestone that we've actually now crossed over 1 billion data points, 1.1 billion actually, and expect to cross the 3 billion data point mark during 2021. This is several months ahead of our schedule. And these data sets have been curated specifically for our drug development program, but also for oncology drug development, and drug response prediction. Our team has made tremendous progress getting to this billion data point milestone, and more importantly, in selecting, cleaning, curating and tagging the data. And the data is only making our engine now more efficient and more powerful. This will allow our Company to develop cancer therapies and better understand where and how certain compounds work with even greater precision, reduced risk, and also now at a much, much more rapid pace. Beyond merely the share amount of data, the quality and relevance of the data also continue to grow exponentially, because we're feeding data back into the system from our own experiments and from the lab results of our collaborations and partnerships. Our RADR AI platform stands at the core of our business model, alongside our targeted and accelerated drug development path. The growth in the quantity and quality of our data sets is an important driver of the value of our franchise. RADR’s growing genomic drug sensitivity and patient outcome datasets, combined with our AI and machine learning algorithms, enables us to streamline the drug development process, while also identifying two very important things. What are the mechanisms that drive activity or sensitivity to the drug? And what are the patient types that will benefit from our targeted oncology therapies? This at the core is really essential to oncology drug development. And we are confident that power of RADR will enable us to add one additional derisked genetically defined or biomarkerly defined program to our pipeline every 12 to 18 months. During our last earnings call, I mentioned that we are today all experiencing and living in the beginnings of a golden age of artificial intelligence, an era where the availability of relevant data, computing power, cloud resources, on-demand sequencing, talent, and acceleration of AI and large scale data analytics, along with algorithms have aligned to make highly responsive machine-driven approaches to solving complex and sometimes unknown problems a reality. This is especially true in drug development. And we're harnessing the trends and capabilities of this golden age to accelerate our pipeline and drive meaningful results for investors and most importantly for cancer patients. For those of you that are just beginning to follow Lantern and our story, our pipeline of small molecule oncology assets includes new compounds that we have identified or we have developed through our biomarker discovery efforts as well as potential therapies that have extensive prior clinical experience that we acquired after previous owners’ abandoned development efforts, following late stage failures. Our RADR AI platform underpins each of these efforts. And as the quantity of quality RADR goes above and beyond 1 billion data points and continues to grow, we’re confident that the value of our ability to transform drug development and develop an enduring business model will rise. During the third quarter, we entered into two very important collaborations, both for LP-184, which is a DNA-damaging agent with nanomolar potency. It's a preclinical asset that we're aggressively working on getting into clinical trials. Collaboration such as these are core to our strategy to leverage leading cancer centers to help us develop insights, generate data, and eventually help serve as clinical trial sites for our clinical trials for the drugs. Our collaborations that we announced during the past quarter were with Fox Chase Cancer Center for pancreatic cancer, and with Georgetown University, in prostate cancer. Let me talk a little bit about each of these collaborations and what we expect from each. In Georgetown, we launched the next phase of collaboration for LP-184. The first stage of the joint research activities began in the fourth quarter of 2019, prior to us being public, and it was there that we generated very compelling evidence of the efficacy of LP-184 in solid tumors that overexpress PTGR1. The antitumor activity of the drug was actually linked in a dose dependent fashion to the overexpression of PTGR1. And now, we will be further validating this in the ongoing second stage of the collaboration with Georgetown in very-specific subtypes of prostate cancer. And this research is also expected to help guide the development of a signature that correlates to increased sensitivity to the drug, both for metastatic hormone-refractory prostate cancers and potentially cancers that are DNA-damage repair gene deficient. This is an important and very-underserved market. The next phase of the collaboration research program with Georgetown will focus on a larger set of PDS models and help pinpoint the specific mechanism of action and seek confirmatory validation of the role PTGR1 as both a gene and potentially as an enzyme or protein has in driving the DNA damage repair pathways that make the drug highly potent to these cancers. The research will also focus on completing the acquisition of detailed genomic information in prostate cancers, which will involve work in animal models, as well as in cell lines that have been edited to under and over express key driver gene. The goal of Phase 2 of this collaboration is to create a biologically relevant, robust gene signature that can be used in our clinical trials. The objective is to personalize prostate cancer treatment and allow patients to experience the benefits of a personalized targeted program that ultimately shows great antitumor activity in their cancer. Ultimately, we believe Lantern's AI-driven approach could save millions of dollars in drug development costs, while significantly accelerating the path to commercialization for this asset. The lead investigator at Georgetown is Dr. Partha Banerjee, a world renowned expert in molecular oncology and in prostate cancers. The Company also initiated a collaboration and research agreement with Fox Chase Cancer Center in Philadelphia with further development of LP-184 in pancreatic cancer. This collaboration advances the targeted use of LP-184 in genetically defined subtypes of pancreatic cancer, and again, to develop a biologically relevant, robust gene signature that we can use in the upcoming clinical trials. We believe that LP-184 if successful could provide pancreatic cancer patients a personalized therapy option that has the potential to improve survival. The research of Fox Chase will be led by Dr. Igor Astsaturov and internationally recognized researcher in GI cancers in the molecular therapeutics program at Fox Chase, where he specializes in investigating signaling pathways. And these are pathways that typically inform the choice of biomarkers in innovative therapy and therapy combinations for clinical trials. We expect initial early results in Q1 from this collaboration, but actually, we're already beginning to see data from that collaboration that's informing our thoughts on the best use of LP-184. During our last call, we talked about how LP-184 was also demonstrating high nanomolar potency and the ability to cross the blood brain barrier. This is a work that we did prior to going public, and we had very solid evidence of LP-184 crossing the blood brain barrier that was done in-silico and from prior annulments. We think now what we’ve done this past quarter is we moved ahead the further validation in the wet lab and actual models on two important fronts. We validated that the in-silico generated hypothesis about LP-184 crossing the blood brain barrier is valid in real world biology, which we did by using a 3D model that replicated the biology of glioblastoma. And by doing so, we demonstrated gathered data that LP-184 would have a blood brain barrier permeability equivalent to the current standard of care temozolomide, and other drugs that are being used in GBM patients. We also validated that LP-184 does not compromise cell viability, while it crosses the blood brain barrier, leaving neuronal cells intact and functional. As you may know, less than 10% of drugs make it through this very important first conflict. And we believe that the validation and data places us in a great position to develop our next collaboration to further develop LP-184 in GBM, which we believe has several hundred million dollars in opportunity in the U.S. alone, in cancer therapy sales. This data that we are getting -- that's genomically targeted and biomarker driven, allows us to pursue a transformational drug development strategy, one that we can identify, rescue or develop drugs, and advance or small molecule drug candidates for what we believe is a fraction of the time and cost associated with traditional cancer drug development, which tends to be highly serial and also very much driven by large scale lab experiments, and usually trials that are not very targeted. We believe that our dual approach to developing de novo biomarker guided drug candidates but also rescuing historical drug candidates, using AI and using our growing AI platform is really an emblematic of a new era of drug discovery where the continuous advances in genomics and computational biology are being used to derisk programs and bring them to market in a more meaningful time and cost perspective. Together, our current portfolio of drug candidates and our AI platform has the potential to provide we believe multiple value-enhancing milestones for shareholders over the coming year, including the potential for our most mature asset, LP-100, which is partnered with Allarity Therapeutics for that drug to be partnered with other pharma companies, and that asset also can receive significant economic participation. That's currently in a Phase 2 trial for metastatic hormone refractory prostate cancer. And later this quarter, we'll be having a joint scientific and development committee meeting with Allarity, and we should be in a position to provide updates on the program and progress on that front as well. Now, I'll hand the call over to David Margrave, Lantern’s CFO, for a review of our third quarter financial results. David?

Thank you, Panna, and good afternoon, everybody. As Panna stated earlier, we've been very busy this first full quarter after our mid-June IPO, both in advancing our platform and portfolio and in establishing meaningful collaborations that will aid in driving our programs to the clinic. I'm proud of our focused, highly confident and growing team who all share the passion and vision we have to transform oncology drug development. Turning to our financial review details. As of September 30, 2020, we had working capital of approximately $21.7 million, primarily driven by the net proceeds of our IPO that closed on June 15, 2020. For the third quarter ended September 30, 2020, we reported a net loss of $1.7 million versus a net loss of $669,652 in the third quarter of 2019. General and administrative expenses increased $659,468 or 149% from $441,251 for the three months ended September 30, 2019 to $1,100,719 for the three months ended September 30, 2020. The increase was primarily attributable to increases in labor expense of approximately $177,000, increases in business development expense of approximately $72,000, and corporate insurance expense increases of approximately $451,000. This was partially offset by a decrease in travel and relocation expense of approximately $45,000. Research and development expenses increased $372,368 or 163% from $228,401 for the three months ended September 30, 2019 to $600,769 for the three months ended September 30, 2020. The increase was primarily attributable to increases in research study expenses of approximately $85,000, increases in product candidate manufacturing related expenses of approximately $74,000, and an increase in research and development employee associated expenses of approximately $206,000. We expect to be increasing our spend on R&D as we further advance our portfolio and move towards commencement of additional clinical trials and research studies. Currently, we have nine full time employees, two part time employees, and four consultants, the substantial portion of whom are focused on leading and advancing our drug development, biology and data science efforts. Our cash position of $20.8 million as of quarter-end gives us a strong financial platform that we anticipate will allow us to support and fuel our business model and growth strategy through at least mid-2022. Thank you. And I'd now like to hand things back to Panna.

Thank you, David. I have a few more comments before we open up for Q&A. As David highlighted, our team has been very hard at work, not only on making additional progress in development of our existing programs, but also strategically focusing on new opportunities that we are uncovering, or that we can create in collaboration with others. These opportunities are largely being driven by large scale data analytics and also by looking at how our programs -- sorry, how our therapies can be used in combination with other approved therapies. This is something that a company of our size and scale, without the cloud and without AI, could not have done. So, we believe we're really at the forefront of a new type of productivity and drug development. And in this context, we will continue building a portfolio of quality, potentially high-value small molecules oncology drug candidates that we can develop or that we can partner with large biotech and pharma to develop. We believe that each of these candidates that we pursue can potentially be partnered for pivotal registration directed trials, and provide a defined path for significant value creation for our shareholders. We are focused on establishing Lantern Pharma as the leading AI-driven drug discovery and development franchise, with a focus on oncology, a company that can deliver enduring and significant value for our shareholders. There are potentially thousands of discarded or otherwise deprioritized therapeutic candidates across the industry and academia. And again, we aim to bring at least one of these into our pipeline every 12 to 18 months, either as a monotherapy program or a combination program. We are laser-focused on achieving our existing milestones, and marching our programs into the clinic so that we can make an impact on cancer patients. To emphasize our point, we currently for a company of our size have four programs in active development, including two drugs in clinical trials for Phase 2, one with our partner, Allarity Therapeutics. And more importantly, our business model has already validated to this active partnership, because this partnership is driven by enrolling patients using a genomic signature for that trial. That's for the earliest asset. We also believe that now our significantly strengthened balance sheet following our IPO, and are significantly increased RADR platform, which is now over 1.1 billion data points, will deliver meaningful value and actually accelerate our ability to drive insights. We look forward to sharing the progress with you in feature updates, both with our drug portfolio, and also our RADR platform. And we'll continue expanding both of these through collaborations as well. And with that, I'd now like to open the call up for questions.

[Operator Instructions] And we'll move first to Kyle Bauser with Colliers. Please go ahead.

Good evening. Thanks for the update and for taking my question here. So, you've clearly significantly exceeded your goal for number of RADR data points now at 1.1 billion plus going to 3 billion by the end of next year. How should we think about how this translates to the pipeline or the power of the platform? I think, you mentioned you were in the past currently exploring about 8 to 10 different opportunities. With 3 billion data points that mean you'll be able to kind of ramp the pipeline faster, or does it mean you'll be able to have a better prediction model, a smarter platform? I'm just trying to understand the incremental benefit of additional data points when you're already over 1 billion? Thank you.

Great question. Thank you for that question, Kyle. So, I also think you hit the nail on the head there. Both are very important in getting de-risking an asset and selecting right assets. So, as you get more data points, your ability to have a precision signature increases. And also as we feed data back into that precision signature, our ability to understand how that tumor works or doesn't work also increases. So, it's really de-risking the program by having a more precise signature, because now we have surveyed a larger landscape of tumors and tumor responses. So, that's really one of the most important drivers. And to get that precision signature earlier can mean hundreds of thousands or maybe even millions of dollars. And that's one of the big drivers for that. Again, as we have over 1.1 billion data points, I think it's important for everyone to know, not all cancers are even close to the same sort of liver cancer versus a glioblastoma versus a prostate cancer, very, very different biologies. And so, we've tried to enrich as we get to the roadmap to billion in cancers that we're focused on. But, there's still several areas and several cancers where we believe our program can be enriched further. And so, the biology of every tumor is so distinct that you really can't have enough data. And so, as we get more data, we're currently heavily driven by RNA data, transcriptome data, and also DNA data. But as we grow, we'll also be enriching for an enzyme data, antibody data, antigen data. And so, as you grow other types of multi-omics information, it'll give us the ability to better understand immuno-oncology drugs, better understand large molecules, biologics. And so, it really makes the platform a lot more powerful. So, precision is key thing. The second is to develop new opportunities. As I mentioned, there are definitely many tumor types where we feel like we can continue enriching to get a better understanding of how that tumor reacts to different drug classes. And so, as we look at new opportunities, so for example, it could be a drug that failed in a tubulin class or a drug that failed in EGFR class for HER2 cancers or for breast cancer or in gastric cancer, but perhaps it works in a different subtype. And do we have enough meaningful data to reach that conclusion? And so, as we get more data from studies and from data sets, and from real world evidence, we can start generating insights that will give us ideas about new opportunities. And just like I mentioned, our 184 glioblastoma, that was largely initially in-silico observation that we've been validated. And so, uncovering these new opportunities can mean, obviously, much more than our existing market cap for investors. So, if then cover an opportunity that ends up resulting in a $250 million or $500 million or $1 billion exit or sale of an asset for a certain indication, that's very important. And if we can uncover that months earlier or years earlier, that is really meaningful. So, there's the two things that getting more data allow us to do. Also, as we get more data, the third thing which is central for our mid-term to longer term strategy is making ourselves more valuable to larger biotech and pharma customers, and to pursue collaborations with them. So, I think those are three things that we try to achieve as we increase the share amount of data is can we get greater precision, can we uncover new opportunities, and can we become more attractive in terms of partnering with other larger pharma and biotech companies.

On the expense side, so R&D and SG&A came in better than what we were modeling. How should we think about the burn rate going forward? And how might the headcount change over the coming month? I think, you said that your cash can get you to mid-2022. But, is that under the current overhead levels or increasing headcount and OpEx?

Sure. As we move towards commencing study for LP-300 and IND-enabling studies, progressing towards our clinical trial for LP-184, you'll see our R&D expense continue to increase. So, we see it increasing from what it was for this quarter. And then, I'll turn it back to Panna to discuss the aspect of your question related to headcount.

So, I think, we have made some increases in headcount, namely in our data science team and also a new biologists. And but I think most of our work, as you know, is outsourced. So, we're really kind of an asset-light type strategy. We prefer to outsource to global experts and collaborators where possible, so that we can scale up and scale down the experiments and studies as needed. So, I think, we'll make selective growth on our team. But, I think it's pretty much in line with kind of how you already modeled it, Kyle, actually. So, I think, there'll be some growth in R&D, as David pointed out, and there'll be more externalization to CROs. But again, our strategy mainly is one to kind of keep the infrastructure fairly light and leverage experts wherever possible to advance our studies.

[Operator Instructions] We'll move next to John Vandermosten with Zacks Small Cap. Please go ahead.

Good evening, Panna and David.

Hey John. How are you doing?

Hey. Pretty good. Let me start off with something building off what Kyle asked about the data points. So, you've accumulated quite a number of new data points since last quarterly update. How much of that -- or what proportion of that could be attributed to refining the current programs in the portfolio and what proportion is looking at new candidates?

It's a good question. I don't know if we've cut the data exactly that way, but the vast majority is looking at new categories. Yes. That's a good question. A lot of what we enriched this past period is we have really streamlined our ability to access data sets, especially real world data sets and integrate them into our platform. So, access clean, meta-tags, a lot of that process has been increasingly more and more automated I would say, then it is more sophisticated with it. So, that's why we've had the massive increase. We really wanted to get to 1 billion before the fall, which was the team is very focused on. And as we've done that, we really tried to focus on certain cancer types. So, there are definitely cancers that we're more interested in, because we're already developing like GBM, for example, we enriched heavily, heavily in GBM. We also enriched heavily in a few other cancers, like prostate and liver and non-small cell lung. So, basically where we have programs, we did a lot of enrichment of the data. As we did that, there are a couple of opportunities that we're looking at that we were selectively increasing, but we didn't really cut the data that way. The way we look at the data, when we look at data sets is by drug class or by study type or by site of tumor, because that's obviously where a lot of it is published. And then, we also look at existing trials or studies if people are looking at certain mechanisms. But yes, that's a good tag to think about and a lot of times it'll support our existing drugs, but also uncover new drugs. So, I mean, sometimes there are data sets that kind of point to both things.

Okay. Yes. That's interesting, the way that you look at that. And how has RADR helped you, as you develop more data points, how have you refined the way you look at those and evolve your curation? Because I think in the past you've mentioned that there's certain sorts of data that are more useful than others and some that is a little bit less useful. So, how has that process been refined as you've accumulated more and more?

One thing that we've done and we're still in the process of refining it are the meta-tags themselves, and I don't know if people talk a lot about it. But I mean, I think there's certainly ways to tag data. We have, believe it or not -- I looked at it recently, I think we have 34 different tags on our data, not counting like where the data is specifically located on what server and what volume and all that, forget the actual physical location, time that it was ingested, where it was ingested from, for getting those tags over 30 different tags. And so, that's something that we've gotten more -- I would say, more sophisticated about. And so, that'll allow us to ask and answer more questions in a more automated way. And in fact, we're actually thinking about having a detailed kind of Analyst Investor Day where we have people, we want people to kind of look to platform does for us on a day to day basis, and they're showing people different kind of use cases. So, that's something that we're thinking about, like here is how we look at a certain tumor, here's how we look at a certain drug type, here's how we look at a certain combination. So, there's a number of these things that we're actually thinking about to kind of open up how we look at drug development. So, those are all great, but over that -- because of the collaborations with the Fox Chase and Georgetown, and another one that we plan on, this will take up a lot of the Q4 time with our RADR, so we won't be ingesting as many external new data. So, I think, we are targeting getting to about 1.5 billion by the end of the year, which is, if you look at it just a level of pace, it's a little bit slower pace than it was, these past few months, which is kind of on purpose, because we're focusing the time now on two things, our existing collaborations, and supporting that really with getting data -- crunching free data, to better understand how we can make sense of the biology coming out of those. And then second, we actually have -- now that we have over 1 billion, 1.1 billion data points, another big is, how do we actually crunch through that data in a way that's efficient. So, now we're coming up with like limits to memory limits and size limits, and so how to paralyze faster and how to use more nodes and some of those kind of fundamental processing things. Because one of the metrics for our RADR platform was to be able to do things three to four times faster than we were doing them earlier this year. So, that's also part of getting to these precision signatures faster. Because if we can do that, we can know whether a drug is going to work or not work, and we can also understand is there really a new opportunity. And you always hear about those cases where a company uncovers well, this drug actually really works here best, but they uncovered it two years, too late, and when they're out of cash, and then goes to someone else. And so, those are the kind of things that AI allows us to potentially avoid. And we think those are meaningful. And so, they uncover those things faster and cheaper and consistently and better that makes for a lot of upside for our investors.

[Operator Instructions] And it does appear there are no further questions at this time.

Okay. Thank you. So, with that, I'd like to kind of conclude our call, and questions were great. And we look forward to having follow-on conversations about the progress we've made this past quarter and also kind of what we can expect over the next few quarters. So, thank you again to our investors and to the analysts, and also for listening to our story. Thank you.

Thanks, everyone.

This concludes today's program. Thank you for your participation. You may disconnect at any time.