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…

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.…

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…

[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.