Lantern Pharma Inc. (LTRN) on Q4 2023 Results - Earnings Call Transcript
Operator: Good afternoon, and welcome to our Fourth Quarter and Year End 2023 Earnings Call. As a reminder, this call is being recorded and all attendees are in a listen-only mode. We will open 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 close today summarizing our financial results and progress across the company for the fourth quarter ended December 31, 2023. A copy of this release is available through our website @lanternpharma.com where you will also find a link to the slides management will be referencing on today’s call. We 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 results of clinical trials and the impact of competition. Additional information 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, 2023, which is on file with the SEC and available on our website. Forward-looking statements made on this conference call are as of today, March 18, 2024, 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 Margrave, 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 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: Hello, everyone, and thank you for joining us this afternoon to hear about our fourth quarter and fiscal year 2023 results and corporate progress. As many of you have heard me say in the past, computational and AI-driven approaches are increasing their presence and usage at both large and emerging pharma companies for all facets of drug discovery and development. Lantern's leadership in the innovative use of AI and machine learning to transform costs and timelines in the development of precision oncology therapies should yield significant returns for investors and patients as our industry matures and adopts an AI-centric and data-first approach to drug development. 2023 was a transformational year for Lantern Pharma across many measures. We launched multiple clinical trials for our using our AI-guided drug candidates. We had multiple peer-reviewed publications and posters for our drug candidates and for our radar AI platform. Our AI platform advanced, reaching over 60 billion data points last year and now is on the road to reaching over 100 billion this year. With significant and efficient expansion of our clinical infrastructure and operations team, this allows us to efficiently move our trials forward and maintain control over the data and operations of our clinical assets. We are also advancing a very exciting new company, Starlight Therapeutics, which is entirely focused on CNS cancers and an area where there has been no single approved therapy as monotherapy in nearly 18 years. We filed 11 patent applications last year across our drug candidates and our AI platform and we continue to show very strong focused fiscal discipline. Our team has accomplished a lot and is about 24 people today, small but focused and comprised of leaders at every level, high value contributors. They've made significant strides over the past quarter and throughout 2023 across all our programs and also with our AI platform radar. Radar which has guided the rapid and efficient development of three AI-guided drugs into clinical trials at a pace and cost that has traditionally been unheard of in our industry. Our team has been focused on executing our mission of transforming the oncology drug discovery and development process, especially as we bring our clinical stage drug candidates into human clinical trials. Two that are now in Phase I as part of our synthetic lethality franchise and one that is in Phase two. All of our clinical trials now have enrolled and dosed patients and we expect to have data to share with you later this year as enrollment progresses. Our team and many clinicians are particularly excited about and interested in the programs for our first in human drug candidates, LP-184 and LP-284, also our unique drug LP-300, which we in-licensed and rescued is aimed at never smokers, who've been impacted by non-small cell lung cancer but have failed other treatment options. Lung cancer among never smokers is a growing problem not only in the US but globally. And we've been successful in moving towards regulatory allowance for commencing our trial in Japan, Taiwan and South Korea where the incidence of non-small cell lung cancer among never smokers is nearly two and a half to three times that here in the US. We also continue to make significant progress on the launch of our clinical stage CNS and brain cancer focused subsidiary Starlight Therapeutics. This is a company that has been largely developed as a result of data, AI methods, computational approaches to optimize and maximize our insights about a molecule. About a year ago we announced the formation of the subsidiary. We recruited a CMO last quarter in the fourth quarter and now we're preparing to go into Phase Ib to clinical trials. We've also made major progress in developing the next major leg of our discovery efforts which we focused on drug conjugates including antibody drug conjugates. Specifically we've now engineered a cryptophycin linked antibody drug conjugate which we're developing in a highly efficient manner with our collaborative partners, academic partners in Germany. I'll talk a little bit more about that later in this call. Our progress across our preclinical assets and our clinical programs has been very focused, very efficient and was in large part guided by our AI platform's latest functionality, capability and modules. Modules like our antibody drug conjugate module. We released some exciting data earlier this quarter which we concluded as part of our collaboration with University of Belleville. But in particular in that collaboration we found that we had excellent control over the cysteine engineered proteins and that allowed us to have great control of the bio-conjugation process and we also saw very high potency in the picomolar range across six solid tumors, many of them with huge clinical need. In particular in medium expressing HER2 cancers which is a real unmet need in cancer care Additionally we continue to enhance and develop our AI platform for cancer drug development, Radar. Our platform we think is revolutionizing the way we model, predict and understand drug cancer interactions, enabling us to advance our newly developed drug programs from initial insights to first in human clinical trials in an average of less than two years and at a cost of under $2 million per program. It's a milestone unheard of in the realm of oncology drug discovery. Our leadership and the innovative use of AI and machine learning to transform costs and timelines has allowed us to bring three molecules to market with teams, costs and efficiency that is only beginning to make a massive impact. We think year over year we'll continue to make improvements and continue to refine and make our process more efficient, more precise and potentially even more powerful. During 2023 we achieved our goal of reaching 60 billion data points, growing that cancer focused data more in one year than we had in the prior three years. We expect these massive leaps, these increase in the pace to continue and our team expects to reach over 100 billion this year. And this data growth and data ingestion will be automated. It will free up our team to focus more on intelligent curation and analysis and also on creating upstream engineered data sets from the raw data to solve more specific problems that can make use of generative AI and generative models. This golden age of AI medicine is just beginning and it's being powered by large scale, highly available computing power, massive data storage and additionally it is being fed by health care, patient, cancer data, all of this data which is more widely available at an increasing levels of quality, higher than ever before. We believe that companies that harness these capabilities in biotech and more appropriately really the tech bio industry will become long term leaders that create massive value for patients, for investors and for our industry. Lantern Pharma is among the leaders in this transformation of the pace, risk and cost of oncology drug discovery and development. This transformation has the promise to not only make medicines faster, cheaper and with increased precision for patients but also to help change the direction of R&D productivity and output in the pharma industry. In the past three years, we have successfully developed and launched 11 additional programs, a testament to the agility, efficiency and groundbreaking nature of our approach. On average, these programs are advancing from initial AI insights to first in human clinical trials in just two and a half years and an average cost of $2 million per program, metrics unheard of in oncology drug discovery. In fact, in a recent study published by Drug Discovery Today, it was reported that nearly half of the 16 largest pharma companies had negative R&D productivity over the last 18 to 20 years, with Big Pharma collectively spending close to $3 billion per drug approval. These are startling figures and they serve as a stark reminder that the traditional model of Big Pharma R&D is not a sustainable or effective strategy and it is not the right approach to improve drug pricing or drug availability. With escalating economic and political pressures over drug prices, it's clear that our industry needs to rethink its approach fundamentally and we believe Big Pharma will increase adoption of AI and computational approaches, data-first approaches, to elevate above this major hurdle sitting in front of us Now these specific instances of value creation, along with the development of an entirely new company, which will be a clinical stage, Starlight, whose sole focus will be on these intractable CNS and brain cancers, demonstrates that Lantern continues to be at the forefront of a transformative approach to oncology drug discovery. We're reaching speeds and efficiency that we believe are setting new standards in developing cancer medicines. As we continue to accelerate the pace at which we're developing and validating insights that can lead to meaningful drug assets that we can partner, license, sell in the future, we believe that we're very well positioned to partner these drug assets out to larger companies. At the same time, our CFO David Margrave will cover shortly, we have a very strong cash position, approximately $41.3 million in cash, cash equivalents, and marketable securities. And we're carefully utilizing that to make meaningful progress in a disciplined manner. We're going after indications that are needed. We're going after studies that help validate insights and we're collecting data that will power our portfolio. We believe our approach is the future of developing cancer therapies, where data can be used to rapidly accelerate programs, de-risk the identification of cancer subtypes most likely to be responsive, use biomarker profiling to figure patient profiles and needs out earlier in the process, and progress these potentially life-changing medicines with economics that have not been seen in our industry. Now let's turn to some of the specific highlights of our financial results during the fourth quarter and for the year 2023. I'll now turn the call over to our CFO, David Margrave, who will provide an overview of our second quarter financial result of our fourth quarter financial results. David?
David Margrave: Thank you, Panna, and good afternoon, everyone. I'll now share some financial highlights from our fourth quarter and full year ended December 31, 2023. I'll start with a review of the fourth quarter. Our general and administrative expenses were approximately $1.3 million for the fourth quarter of 2023, down somewhat from approximately $1.6 million in the prior year period. R&D expenses were approximately $3.6 million for the fourth quarter of 2023, up from approximately $2.3 million in the fourth quarter of 2022. We recorded a net loss of approximately $4.2 million for the fourth quarter of 2023 or $0.39 per share compared to a net loss of approximately $3.4 million or $0.31 per share for the fourth quarter of 2022. For the full year 2023, our R&D expenses were approximately $11.9 million, up from approximately $8.6 million for 2022. This increase was primarily attributable to increases in research studies of approximately $2.98 million, increases in research and development payroll expenses of approximately $1.2 million and increases in consulting expenses of approximately $160,000. These increases were partially offset by decreases in product candidate manufacturing-related expenses of approximately $631,000 and decreases of approximately $459,000 in payments to Allarity Therapeutics. During the year ended December 31, '22, we released an escrow payment of approximately $459,000 to Allarity Therapeutics, and there was not a release of escrow payment amounts to Allarity during the year ended December 31, '23. Manufacturing-related expenses for the year ended December 31, '22 were also reduced by $935,000 as a result of a payment we received in July 2022 from one of our service providers in connection with the resolution of a difference of views regarding the service provider agreement. Our general and administrative expenses for 2023 were approximately $6.0 million, up slightly from $5.9 million for 2022. The increase was primarily attributable to increases in payroll and compensation expense and other professional fees. Our R&D expenses continue to exceed our G&A expenses by a strong margin, reflecting our focus on advancing our product candidates and pipeline. For the full year 2023, net loss was approximately $15.96 million or $1.47 per share compared to $14.3 million or $1.31 per share for 2022. Our loss from operations in the 2023 calendar year was partially offset by interest income and other income net, totaling approximately $1.9 million. Our cash position, which includes cash equivalents and marketable securities, was approximately $41.3 million as of December 31, 2023. We anticipate this balance will provide us with a cash runway into at least Q3 of 2025. Importantly, we believe our solid financial position will fuel 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 programs and collaboration opportunities in a capital-efficient manner. As of December 31, 2023, we had 10,721,192 shares of common stock outstanding, outstanding warrants to purchase 177,998 shares and outstanding options to purchase 1,091,196 shares. These warrants and options, combined with our outstanding shares of common stock, give us a total fully diluted shares, outstanding of approximately 11.99 million shares as of year-end 2023. In November of '23, we were able to reduce our outstanding share count through the purchase of 145,348 shares of Lantern common stock at a purchase price of $3.44 per share. Our team continues to be very productive under a hybrid operating model. We currently have 21 employees and 3 FTE consultants focused primarily 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 Panna for an update on some of our development programs. Panna?
Panna Sharma: Thank you, David. As we mentioned earlier in the call, one of the areas that we're very excited about is Starlight Therapeutics. We hired Dr. Marc Chamberlain, during the fourth quarter, and he's made excellent progress on advancing our clinical trial design in both adult and pediatric CNS cancers, and we expect to launch the initial adult trial during the second half of this year. This is incumbent on getting the type of safety and early efficacy signal from our current ongoing LP-184 trial, which is in Phase I and it's, at this point, over halfway enrolled. We will share more on the progress of this clinical trial in the coming weeks. Now Starlight's focus on CNS cancers came from initial screens to look at cancers that exhibited exquisite preclinical and in silico-based evidence of sensitivity to LP-184. It was essentially born from billions of data points and we had not yet gone to in vitro and in vivo observations. We naturally moved quickly to in vitro and in vivo observations as it was clear that the data was suggesting that GBM and actually several other brain cancers should be very sensitive given the genomic profile, given the interactome design and given the levels of DNA damage repair or PTGR1 we saw in those brain cancers. Let me share some background about the Starlight, which is 100% owned by Lantern, putting, of course, our shareholders. And we believe we'll have the potential to be another very positive impact on our investors as we monetize this unique asset, the patents and, of course, the insights. Starlight Therapeutics is targeted on several cancers, both adult and pediatric. The 5-year survival rate in many of these cancers is super low despite advances in cancer therapies. We think globally, there are over 500,000 patients that we can target. We have an Orphan Designation already for GBM and ATRT. We also have a Rare Pediatric Disease Designation. We have world class collaborators with Hopkins, UT Health San Antonio and the Children's Brain Tumor Network, which is one of our newer collaborators. Additionally, there are over 120 types of central nervous system in brain cancer. So it's a wide open area. Although 50% of them do tend to be GBM and other high-grid gliomas, and we will be enrolling some of those patients in the early Phase Ia study to determine maximum tolerated dose. There are many other brain cancers, both primary and secondary that Starlight has an option of going after. And we think this can be a pivotal drug startup 001 in the future of brain cancer therapies. Now let's talk a little bit about the trials that are planned for STAR-001. As I mentioned, the Phase I will be done by Lantern. The dosage and safety data obtained in the Phase I trial, which is now about halfway through, will be used to advance the indications for a future Phase Ib/II trial to be sponsored jointly by Lantern and our wholly owned subsidiary, Starlight Therapeutics. The markets we think globally are in excess of $5 billion, and this brings the total market for LP-184 indications, both in CNS and in other solid tumors to being in the range of about $10 billion to $12-plus billion. So you can see why we're particularly excited about this molecule, why we spent a lot of time understanding its molecular profile, understanding the triggers of patient response, understanding the indications where it'll be most sensitive and then also developing patents around combining this unique drug with other therapies. So this is one of the most well characterized molecules prior to even getting into the Phase I let alone now once we receive the Phase I data. We're very excited about this molecule, and we'll have data this year on the Phase I trial. And more importantly, we'll have data that allow us to go into combination trials and into CNS with Starlight Therapeutics. Let's go after another area that our team has been working on and this is the highly promising area of antibody drug conjugates. This is a very expensive area, which we believe we're going to crush the cost not only in early-stage development, perhaps but also in later stage development. It's a high-growth area for oncology. Earlier this year, we announced our advancements of the ADC program that we're working on in combination with the University of Bielefeld in Germany. Much of this work was accomplished in late '23 at Dr. Sewald's lab as part of the Magicbullet Consortium. We were able to take our cryptophycin antibody drug conjugate, and advanced it not only in proven synthesis and bioconjugation, but developed a preclinical proof of concept that it worked really well in an area of high unmet need, which is moderate HER2 expression. Our kill rates with this cryptophycin drug-payload averaged 80% across a number of cancer cell lines. And more importantly, we saw that it was about 10 times more potent than some of the existing ADCs that used a very common payload MMAE. This is a very, very efficient antitumor activity, and it was more importantly, gave us EC-50 values that means we're about 50% of the cells or the cancer cells of interest are killed in the picomolar to actually single-digit nanomolar range even in the more challenging cancers. We're now doing additional studies to develop and further validate these findings. And most importantly, do what we really think is most critical in these studies to obtain a deeper understanding of the genomic and biomarker correlates of payload efficacy. This is really one of the most important things is to understand what is driving that kind of response. How can we repeat it, how can we pinpoint it? And what other things do we need to be aware of as we go after these cancers? So again, we're taking a data first approach. We think this is going to save us a lot of time, energy and money. It can be an asset that we believe can be very licensable, partnerable or even spin out after we do Starlight. So again, we've got a lot of great assets that are following up to our existing clinical trial assets that are now in Phase II and Phase I. I also want to take some time on this call to update you on some critical informational updates. A major part of our business is to inform, educate and share with the general public and with the oncology community and with our stakeholders, details about our programs and efforts. It's an area that we want to be better at. It's an area we want to focus on. And we launched an effort that we're calling Webinar Wednesday. So our first webinar series will be rolled out. We're going to have a webinar as part of this effort, with Dr. Joseph Treat of Fox Chase Cancer Center. This will be on LP-300. Dr. Treat is a leading expert in lung malignancies, and he will be hosting our first webinar in the series in the coming month. This will be followed by a webinar on Starlight from our very own Dr. Marc Chamberlain. He's a tremendous resource of virtually human encyclopedia and store of knowledge about CNS and brain cancers, CNS trials, history of drugs in CNS and brain cancers, history of drug regimens, failed and successful across both pediatric and adult CNS cancers. We're very fortunate to have him and he'll be hosting our second webinar focused on Starlight, and that will be followed by another webinar about our LP-184 clinical trial, which again is about halfway enrolled, and that will be with Dr. Igor Astsaturov of Fox Chase and will focus heavily on pancreatic and other cancers, challenging tumors that seem to be very responsive to our drug candidate. In fact, it seems that the more aggressive these cancers are recurrent, they have higher levels of PTGR1. And that same markers actually drives the activity of the molecule. So inversely, the more aggressive and recurrent, the better our drug seems to have worked so far. And we're going to now obviously try to design future trials using the data from the Phase I and the data we have from our in silico and preclinical work. As I mentioned earlier in our call, this past quarter, our poster for AACR 2024 was selected. It focuses on a Phase Ia/Ib clinical trial of LP-284, and that will be presented by our very own Jianli Zhou on April 8, and it will focus on LP-284, which is a highly potent TP53 agnostic -- mutation-agnostic DNA damaging agent in refractory or relapsed lymphomas and other solid tumors. LP-284, as you know, we recently announced that we've dosed initial patients, and we expect to bring on many more sites and more patients in the coming quarter. RADR continues to advance in size, scope and capabilities and is also progressing, we believe, to becoming a standard for AI-driven drug development in oncology both for early-stage development and later-stage patient biomarker and combination therapy identification. RADR has now surpassed over 60 billion oncology-focused data points and is projected to reach well over 100 billion, we believe by the end of this year. The scope of RADR's data has broadened with the strategic focus on additional classes of compounds, including antibodies, checkpoint inhibitors and DNA damaging agents. Additionally, data from clinical studies, such as those being obtained from liquid biopsy and data from preclinical combination studies that aim to define drug interaction and optimal dosage are being incorporated into the data points and the data sets to Power RADR. These data points, the associated advancements in automation along with algorithms and code comprise a functional module in our platform. And we believe that we'll have over eight of these modules and all will help us advance and improve the speed, the precision and the efficiency of RADR's drug development kind of co-piloting capabilities. During the second quarter, we will host a Webinar Wednesday, discussing the near-term road map and the use cases for the AI platform RADR, which we believe, again, is the largest and most focused for oncology drug development. So 2023 was a pivotal year for us. Our insights are now entering into patient clinical trials. They started their journey to becoming meaningful therapies in cancer. Our collective efforts and dedication have fostered a transformational shift for our company, setting us on an exciting trajectory towards the future, where we're improving the lives of cancer patients with effective and more economically generated treatment options. By 2024, we have a lot of other exciting objectives. We expect 2024 to be a breakthrough year for Lantern and our programs. Specifically, we have -- we'd like to share kind of our top 10 milestones. We want to advance and expand our Phase I clinical trial for LP-184. We expect to accelerate enrollment in LP-284 in non-Hodgkin's lymphomas and some other responsive cancers. We will expand enrollment of Harmonic Trial into targeted sites in Asia, where the incidence of non-small cell lung cancer in never-smokers is about three times higher. We're going to explore licensing and partnership opportunities with biopharma companies, expand RADR's platform to over 100 billion data points and develop additional collaborations with biopharma companies, both large and small, that we'll be announcing. We also expect to progress Starlight Therapeutics towards a Phase Ib/II adult clinical trial and perhaps a Phase I pediatric trial by early next year or the end of this year. We also will further our ADC preclinical into IND development to support future partnering or a Phase I launch. We're going to develop combination programs for all three of our drugs with existing approved drugs. In fact, this is a big area of focus for our platform and for additional trials over the next couple of years. We're planning on growing and maturing our clinical operations capabilities and then most importantly, continue our disciplined fiscal and financial management. So we wanted to share those and we'll be providing updates routinely, both through webinars, roadshows, investor meetings and in press releases. We believe this is a great year to keep on communications with all of our interested parties very high. And in closing, I also want to express my gratitude to our team, our partners and our stakeholders for their unwavering support. Together, we're really lighting the way toward a brighter and better future in oncology and solving real-world problems with proprietary high-value AI solutions that enable rapid development of genomically targeted therapies. And at the same time, putting a path in place to alter the cost and time line in drug discovery. And we think this places us at the forefront of a new era of development in medicine what we -- what I like to call the emergence of a golden age in medicine due to AI. With that, I'd like to now open the call for any questions or clarifications.
Operator:
Panna Sharma: First question from John. A great question. He asked ADCs have been an important area of acquisition over the last year. And I have heard broadly that in general M&A conversations have picked up for life sciences, have you observed continued interest in ADCs from larger biopharmas?
Panna Sharma: I want to answer that question. Yes, John, we've seen interest from actually small -- midsize and larger biopharmas in ADCs, specifically in our cryptophycin ADC. Again, it's early, a lot of the M&A deals that we saw earlier this year and some in last year, we're in later stage ADC companies, many of them actually were already clinical. So it is exciting. There is, I think, not a lot of really unique assets in the ADCs. I mean I think most of the payloads, almost 70-plus percent of payloads are all the same. The designs tend to be very clumped together in terms of the category. So I think the novel target and plus perhaps a novel payload, with superior potency, especially in areas that are overlooked, could be of a lot of interest. So I think if you follow the data as opposed to a me-too approach, I think you're going to create something valuable. Great question. And as we get more data, we will explore licensing or partnering the asset out as early as possible.
Panna Sharma: Sure. We've another question from John. We hinted about our RADR platform moving now from five billion, I guess, a couple of years ago to 60 billion?
Panna Sharma: Yes. So the -- we're going to have a more detailed platform kind of view day, but the platform now has begun evolving to the point, where it can begin curating -- ingesting and curating data on its own. So we've gone through the process of what we call campaigns. So we've data ingestion campaigns, where we initially were doing this manually. And as we created kind of road maps or templates for how to ingest the data, and what the data structures are like, what the issues are like, we, of course, now train the AI to begin doing this for us. The AI now has learned a lot of the common data sets and common data conventions and common meta tagging. And so the AI is beginning to do the data ingestion. That's a big platform evolution. The AI is also beginning to parametrize all the algorithms and generate new algorithms. So our team can now take a step back as the platform basically starts growing in and on itself. And so we've also now started a process to do what we called engineered data that we're extracting from other data that people don't have access to. And so this kind of level-2 data actually is going to be making a lot of the insight creation, even more efficient and even more proprietary. And we'll talk about that when we talk about our platform. But yes, the platform has grown. It's kind of a different beast now than it was even a year and a half ago, and it will continue to evolve.
Panna Sharma: We've another question, this is about our buyback and plans for that. So David, do you want to talk a little bit about what we did last year?
David Margrave: Sure. Sure. Yes. This was not a -- it was not a buyback program. It was purchased from two holders, but we felt this was in the best interest of the company, accretive to shareholders and made sense. We purchased 145,348 shares at $3.44 a share for an aggregate of right around $500,000. And as we described earlier in the call, it's reduced our shares outstanding, which we believe is also beneficial to our holders.
Panna Sharma: Great. Thanks, David. Another question. The -- how are you -- about the timing for selecting a narrower Phase II indication?
Panna Sharma: I think, again, we allow data to guide the decision process. So as we get the data from the first set of patients, which is about 35 patients in the Phase Ia may go slightly over that. We'll see what the data suggests. We certainly have ideas based on our in silico findings, in our in vivo work, in our animal model work. And so hopefully, it will support or validate or nullify. But data is everything. So we'll see what the data suggests about the narrow indications. We think, clearly, we see that tumors with DNA damage repair deficiency seem to be very sensitive. So we think that will probably be one of the indications that it may be a Pan-tumor indication. We've also seen that tumors with high levels of PTGR1 above a certain threshold, roughly around 4.2 times, what's in a normal cell, also tend to be very sensitive. So if this continues to hold up throughout the trial, those are two very good kind of hallmarks of a characteristic for the indication. We may go after some targeted indications. If we see that things like pancreatic and triple-negative breast cancer are even more sensitive and we see that there's a clear need, and we think we can do a focused trial, they'll obviously bubble up to the top. So we'll see what the data suggests. And then we'll take a look at it commercially what is the most efficient way to bring the drug to market.
Panna Sharma: Next question is, do we intend to create further value by creating other companies?
Panna Sharma: Yes, that's a great question. We think Starlight is very unique because there's really no company that has focused 100% on a breakthrough new molecule for CNS indications. It's the reason we're able to get a lot of interest around it. We've had pharmas reach out about it. We've had biotechs reach out about it. The drug has some very good history in terms of its ability to be a proven mechanistic manner in which you can kill GBM cells, which is an alkylating agent. In fact, the only drug that seems to really work to kill off, our alkylators like nitro series and TMZ. I mean, everything else has had a middling to no effect. So we're in a good drug class. We're in a class that has a history of working. We're in a drug that seems to prefer cancer cells over any other type of cell. It seems to be much more bioavailable. And it seems to be -- have better blood-brain barrier penetrability than TMZ. It seems to be agnostic to MGMT, and it seems to work in several other brain cancers. So I think it was a very unique opportunity. We had the AI insights with a unique molecule. We're able to find not just one indication, but a family of kind of indications. And so it was really paramount that we launched this effort on its own and get it done further and deeper. If we see opportunities like that, we'll pursue them. We've to pursue them. I mean, I think ADCs could be like that. What we're seeing in terms of the early efforts, both on our antibody drug conjugate, but also another very exciting space we're looking at are fragments -- fragment bodies. And we can actually get even more precision against the antigen or the target of interest with what we call FDCs, fragment drug conjugates. And we've begun some very early exploratory work in that area with the cryptophycin and other picomolar agents. So again, we're trying to use the ADC module that we created to find targets and then to find something unique in those targets and then further classify both late-stage existing antibodies, but also some early stage where perhaps we can make the process compressed and cheaper and that fragment -- FDCs can hold that future. And then we're also trying to find the right agents that give us the right kind of DAR and improvement in kill rates. Like we said, the cryptophycin versus the MMAEs and others that just have a -- it's just -- it's a log better kill rate on the cancer cells. So it's -- if we think we can hold it up in a small portfolio of indications, again, it could be -- again, a very great spin-out idea or a partnering idea very early on.
Panna Sharma: Another question is around business development opportunities.
Panna Sharma: Yes. So we're exploring business development opportunities in three categories. Again, I don't like to really talk about deals until deals are really done. I don't think there's any point in getting people excited about pharma industry, everyone talks to everyone. So yes, we're in discussions with a lot of different companies. It doesn't mean that there may or may not be a deal. But let me walk through our deal ideas that we've that we're working on. Number one, we do expect to announce deals with other biotech companies where they use our platform, and we get certain rights to their drugs or development efforts. So we're using our platform as a currency to help those companies compress the time line or decrease the risk or increase the ideas around for their portfolio, and we get something in exchange for that. And that can be done with our platform. The second type of deal that we're beginning to explore and it's in fits and starts, but is with big tech companies. Big tech companies want to have unique platforms that they can offer in the cloud as a service to all their biopharma and academic and research groups. So now RADR can be taken into one of the big tech companies as a platform, and you can basically have RADR as a service. You can have any of these eight modules as a service, drug combination, blood-brain barrier penetrability, drug mechanism of action, hunting, ADC design. So any of these modules, and that's where we're trying to really create these unique modules. So again, that's very early. And the third one is traditional partnership, licensing, selling of an asset or program to big pharma. And I think the best way to excite big pharmas is data. So we'll power for the trials, we'll keep options open and share the data and results. And that -- again, we have 11, 12 programs now. So the likelihood of one of those -- two of those programs or even more getting licensed or spun out or sold off or partnered I think, continues to get higher and higher every quarter. Thank you for that question. So with that, I'd like to take a moment personally to thank everyone on our team for helping us prepare for these calls and prepare the information. We've got a lot of information out in the PR and also in the updated slides. We'll have a series of webinars throughout this year. We've the first three or four actually already kind of been programmed. And those Webinar Wednesdays, I definitely urge you guys to join. We're very excited about what the future holds for us. We've got a number of trials that are ongoing. We've got a number of exciting programs that we can bring to market or either spinouts or partnered out assets. And most importantly, the platform begins to grow. We're entering a new era of the AI platform. The AI platform now is beginning to grow itself. And more importantly, we're now creating new generative AI capabilities around molecular optimization and target selection, things that just didn't exist a year ago. So on all fronts, we're growing, we maintain fiscal discipline and we've got a significant amount of cash to continue executing our plan and reach milestones for partnering, selling, licensing on our portfolio. So thank you very much, and thank you for joining me this afternoon.
David Margrave: Thanks a lot.
Panna Sharma: Thank you, David.
