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spk00: Good afternoon and welcome to our third quarter 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 third quarter ended September 30th, 2023. A copy of this release is available through our website at 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, 2022, which is on file with the SEC and and available on our website. Forward-looking statements made on this conference call are as of today, November 8th, 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. Thank you so much for having me. This will be followed by some concluding comments from Pauna, and then we'll open the call for Q&A. I'd now like to turn the call over to Pauna Sharma, President and CEO of Lantern Pharma. Pauna, please go ahead.
spk01: Thank you. Hello, everyone, and thank you for joining us this afternoon to hear about our third quarter results and corporate progress. We made significant strides over this past quarter in executing our mission of transforming the oncology drug discovery and development process, especially now that we have all of our clinical stage drug candidates in human clinical trials that are active, two that are in phase one now and one that is in phase two. We also continue to make significant progress in the launch of our CNS and brain cancer-focused subsidiary, Starlight Therapeutics, and in developing the next major leg of our discovery and development efforts, which will be focused on drug conjugates, including antibody drug conjugates. Our team and many clinicians are particularly excited about the interesting first in human drug candidates, LP184 and LP284. Both of these candidates share a mechanism called synthetic lethality. During Q2, I was able to share the news that we launched LP184 into a phase one clinical trial for recurrent advanced solid tumors, especially those that are refractory to current standard of care therapies. This area is an area of especially critical need. During Q3, we launched the sister drug candidate, LP284, into a clinical trial for recurrent non-Hodgkin's lymphomas and also sarcomas. We also dosed the initial patient for LP184 this quarter. Additionally, we continue to enhance and develop our AI platform, Radar, Our AI platform is revolutionizing the way we model, predict, and understand drug cancer interactions, enabling us to advance our newly developed drug programs from initial insights, the first in human clinical trials, in an average of less than two and a half years, and at a cost of under $2 million per program. It's a milestone unheard of in the realm of oncology drug discovery. 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. Our 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 approach to drug development. The golden age of AI in medicine is just beginning and it is being powered by large scale, highly available computing, massive data storage, and additionally it is being fed by healthcare and patient data and cancer data, which is more widely available and at increasing levels of quality. Companies that can harness these capabilities in the biotech and tech bio industry and make them core to their business will be long term leaders that create massive value for patients, for investors and for our industry. We believe Lantern Pharma is among the leaders in this transformation of the pace, risk and cost of the development of cancer medicines. This transformation has a promise not only to make oncology medicines faster, cheaper, and with increased precision for patients and also for orphan and ultra-orphan groups, but also to help change the direction of R&D productivity and output in the pharma industry. In the past two years, we have successfully developed and launched 11 additional programs, a testament to the agility, efficiency, and groundbreaking nature of our approach. As compared to many other companies that are leveraging AI, our productivity and efficiency in a per dollar basis is unparalleled. On average, these programs are advancing from initial AI insights to first-in-human clinical trials at an unheard of cost and timeline. In fact, in a recent study published in Drug Discovery Today, it was reported that nearly half of the largest pharma companies had negative R&D productivity for the past 20 years. These startling figures serve as a stark reminder that the traditional model of big pharma R&D is just not sustainable, not effective, and is not the right approach to improve drug pricing or drug availability. With escalating economic and political pressures over drug pricing and the nature of drugs, it's clear that our industry needs to rethink its approach fundamentally, and we believe large pharma companies will increase the adoption of AI and computational approaches to elevate above this issue. These specific instances of value creation at Lantern, specifically in CNS and neuro-oncology, has allowed us to develop an entirely new company, Starlight Therapeutics, and we'll be providing more data later this year and early next year. We believe that company will be setting a new standard in cancer drug development in a category that hasn't seen a new drug candidate as monotherapy in almost 17 years. As we continue to accelerate the pace at which we're developing and validating insights, insights that can lead to meaningful drug assets, we are then positioning these drug assets after clinical trials to partner them out with larger biopharma companies. At the same time, as David will cover shortly, we have a strong ongoing cash position, approximately $45 million in cash and cash equivalents that is being carefully utilized to make meaningful progress on both our platform and our drug candidates into human clinical trials. We believe our approach is the future of developing cancer therapies, where data can be used to accelerate programs, de-risk the identification and progress of life-changing medicines, and provide insights into which patients are most suitable for trial. Now, turning to some of the specific highlights during the third quarter, We received FDA clearance of our IND application for LP284, a first in human clinical trial for refractory non-Hodgkin's lymphomas and sarcomas. We also dosed the first patient in our LP184 trial, which is for multiple advanced solid tumors. And we also expanded the number of sites in the U.S. for our LP300 non-small cell lung cancer trial for never smokers. We've also started the process of expansion into East Asian countries where the demographic for this patient population is twice that of the U.S., about 30 to 35% of non-small cell lung cancer patients are never smokers. We also developed an initial proof of concept and preclinical evidence for our novel cryptophycin-based antibody drug conjugate, And we plan to share broader data from the initial exciting efficacy and scientific benchmarks achieved with that drug candidate in January of 2024. We continue to advance radar, our AI platform across several dimensions, automation, data sets, an increasing number of modules specifically designed for oncology drug development. And very importantly, we had continued ongoing fiscal discipline as clearly evidenced by our burn rate and our balance of approximately $45 million in cash, cash equivalents, and marketable securities at the end of the third quarter. We believe this provides us with sufficient cash runway well into Q3 of 2025 or beyond, as David will talk about in our call in a few minutes. So at this point with the highlights behind us, I'll come back and talk in more details, but I'll turn the call over now to our CFO, David Margrave, who'll provide an overview for the second quarter financial results.
spk02: David. Thank you, Pana. And good afternoon, everyone. I will now share some financial highlights from our third quarter ended September 30, 2023. Our general and administrative expenses were approximately $1.3 million for the third quarter of 2023, down slightly from approximately $1.4 million in the prior year period. R&D expenses were approximately $2.2 million for the third quarter of 2023, up from approximately $0.7 million in the third quarter of 2022. A substantial portion of the R&D increase in 2023 relative to 2022 is related to a $935,000 payment received from a service provider in July 2022 to resolve a difference of views in the service provider agreement, which reduced our research and development expenses during the third quarter of 2022. The increase in Q3 2023 was also attributable to increases in product candidate manufacturing expenses, increases in research studies, and increases in payroll and compensation expenses. We recorded a net loss of approximately $3.2 million for the third quarter of 2023, or 29 cents per share compared to a net loss of approximately $2.3 million or 21 cents per share for the third quarter of 2022. Our loss from operations in the third quarter of 2023 was partially offset by interest income and other income net totaling approximately $362,000. our interest income and other income net increased by an aggregate of approximately $482,000 for the third quarter of 2023 compared to the third quarter of 2022. This increase was attributable to an increase in interest of approximately $194,000, increases in dividend income of approximately $152,000, and an increase in unrealized gains on investments of approximately $102,000. As of September 30, 2023, we had approximately 10.87 million shares of common stock outstanding, outstanding warrants to purchase approximately 177,998 shares, and outstanding options to purchase approximately 1.1 million 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 September 30, 2023. Our cash position, which includes cash equivalents and marketable securities was approximately $44.9 million as of September 30, 2023. We anticipate this balance will provide us with a cash one way into at least Q3 of 2025. Importantly, we believe our solid financial position will fuel continued growth and evolution of our radar AI platform, accelerate the development of our portfolio of targeted oncology drug candidates, and allow us to introduce additional targeted programs 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 has given us the ability to recruit extremely high caliber team members that otherwise might not be available. We currently have 21 employees 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 Pana for an update on some of our development programs. Pana.
spk01: Thank you, David. This past quarter, we launched another first in human phase one program with LP284, a novel synthetically lethal small molecule in refractory non-Hodgkin's lymphomas and sarcomas where there is a significant patient need for improved therapies. As I mentioned on our second quarter call, we had planned to launch this trial here in Q4, and we're on track to do that. So we've launched both 184 and 284, one quarter after another, which is what we had talked about earlier this year. Now, 284 can work effectively both as monotherapy or in combination with other standard of care agents. But finding these are as critically needed and important in cancer and can oftentimes take months or years of lab work. But computational approaches are increasing their ability to predict meaningful and clinically relevant combination regimens for cancer. And our team continues to increase the value of our platform in this regard. And it helps us sharpen the focus of our existing clinical drug candidates to very specific populations. With 284, we're able to understand that advanced non-Hodgkin's lymphoma cancer subtypes with DNA damage response deficiency, notably those with compromised functioning of the ATM gene, the ataxia gene, the ATM gene, can cause a tremendous amount of sensitivity to our drug agent. In the U.S., Europe, mantle cell double-hit and other high-grade B-cell lymphomas are diagnosed in about 16,000 to 20,000 patients each year and have an estimated annual market potential of about $3 billion to $4 billion. We also saw with this drug candidate that in PDX models of high-grade B-cell lymphomas, LP284 showed synergistic and significantly enhanced anti-cancer activity when used in combination with rituximab. In vivo PDX models, the synergy of rituximab with our drug LP284 was 63% more effective in destroying high-grade B-cell lymphomas than rituximab alone. When we put 284 on rituximab, we had 93% tumor growth inhibition, whereas rituximab alone only had 57%. So as many of you probably know, rituximab is a standard of care-approved therapy in a wide range of B-cell cancers and non-Hodgkin's lymphomas. We plan on releasing additional details on this data and on the set of results in the coming month. Nearly all mantle cell, DHL, and high-grade B-cell lymphoma patients relapse from the current standard of care agent And we believe there's an urgent and unmet need to introduce this drug, either as monotherapy or in combination, in the relapse and refractory setting for this patient group. Moving on to 184, we discussed that we dosed the first patient in the phase 1A clinical trial. It was the first in human phase 1 basket trial across multiple solid tumor indications. We think the market potential for this drug is quite significant. Since 20% to 25% of solid tumors have DNA damage repair deficiency, and the majority of them become refractory to existing standard of care therapies. This trial is anticipated to enroll patients that have relapsed refractory advanced solid tumors, such as pancreatic, GBM, triple negative breast cancer, lung, multiple other solid tumors, including GBM and brain cancers. The Lantern expects to continue phase one enrollment throughout the remainder of this year with additional sites and patients, and potentially finish the first few cohorts of patients and also have a number of major centers like Fox Chase and Johns Hopkins Medicine and USC also join the trial. The dosage and safety data obtained in this trial will be used to advance the central nervous system trials, central nervous system cancer trials for a future phase two to be sponsored by Lantern's wholly unsubsidiary Starlight Therapeutics. Globally, the aggregate annual market potential of LP184 target indication so far is an excess of $11 billion, consisting of about four and a half to five and a half plus for CNS cancers and well over 6 billion for solid tumors. So you can see why we're very excited about this drug as a potential blockbuster across multiple tumors. As we mentioned, our drug LP300 in a previous multi-center phase three clinical trial, a subset of never smokers and non-small cell lung cancer patients who received that drug with chemotherapy showed an increased overall survival of 91% and an increase in two-year survival of 125% respectively. This was compared to patients who only received the chemo double alone. Now we're doing that currently in a 90 patient phase two trial, and we continue to expand into multiple sites. Dr. Joseph Treat, who has been appointed the lead principal investigator of the harmonic study. Dr. Treat is a leading expert in lung malignancies, including non-small cell lung cancer and nervous smokers. Additionally, he has specific experience in many Asian collaborators where this population is at a significantly higher percentage So we're advancing the trial, the harmonic trial, into Asia, specifically Taiwan, Japan, and South Korea, where there's a higher incidence of never smokers in non-small cell lung cancer, about double or higher than that of patients in the U.S., 32 to 35 plus percent. And these are largely driven by pointed and subtle mutations in EGFR or other kinases. So as I mentioned earlier in our call, we'll be sharing our preliminary work in the ADC category. This work has been accomplished in the last six months at a very tremendous pace, but also at a very, very reasonable cost, largely driven by insights from our AI platform and through the good collaborations with our partners in Germany. We've had very good results with our initial ADC designs and believe that the cryptophycin link conjugate has picomolar IC50 values across multiple cancers. We've zeroed in on five or six. Again, we'll be talking more about the detailed update of this program and how this program will expand. We plan on that update for all the investors and analysts in January of 2024. This past quarter, we had two major presentations. Presentations like this are important because they help highlight why we're excited about the molecules and how we're de-risking them and how we're thinking about development. It also generates interest among the pharma community. So for 284, we presented at SOHO 2023. We talked about the drug having tremendous efficacy in B-cell cancers, which are normally non-Hodgkin's lymphomas that have what's called homologous repair deficiency. This allows us then after the phase one to perhaps zero in on HRD-focused B-cell cancers. For 184, we presented at a joint ASCO snow conference showing how the molecule is activated in certain high-expressing PTGR brain cancers, both adult and pediatric, and it completely inhibits or kills off the cancer cells. So again, we'll take that to not only pharma companies, but also to sites that have expressed an interest. And we believe these kind of posters and presentations and data are important to help us better understand where we can point the drug to get the most effective way to market it. Now, during this coming quarter, next week on November 17th, we'll continue driving awareness for our data and our platform. We'll be presenting at Society of Neuro-Oncology where our collaborator, Dr. John Lutera, will be showing how our drug candidate works across GBMs, independent of MGMT status. Dr. John Lutera is a director at the Brain Cancer Program at Johns Hopkins University. Now, the MGMT status is very important since about 50 to 60 plus percent of GBM cases have little or no methylation of MGMT. This means that they either don't respond or they stop responding to the current standard of care, temzolomide. And that's not only in GBM, but also many other high-grade gliomas and brain cancers. On December 1st, we'll be presenting at the Bengaluru Tech Summit in India on our AI platform and how we're building a future forward pharma labeled as Pharma 4.0. And then on December 5th in Boston and at the CNS Drug Delivery Summit, we'll be presenting on how we're leveraging our AI to accelerate the development of drug candidates for CNS and brain cancers, specifically how we believe we can save hundreds of thousands or millions of dollars through our algorithms, which can predict the blood-brain barrier permeability of any compound with 92% to 95% accuracy. Moving on to radar, we continue to advance the platform in size, scope, and capabilities, and we believe it continues progressing towards becoming a potential standard for AI-driven drug development in oncology for both early-stage development and later-stage patient biomarker stratification and combination therapy identifications. Radar has now surpassed 36 billion oncology-focused data points. We project to reach over 50 billion by the end of this year. And the scope of Radar's data has broadened with a strategic focus on additional classes of compounds, including antibodies, checkpoint inhibitors, and DNA damaging agents, and also additional data from clinical studies, such as from liquid biopsy and combination studies. So this data is really important because it helps us define drug interaction and optimal dosage. And we think those are very important future modules for the platform. Now, these data points and the associated advancements in automation on the platform, along with algorithms and code, comprise a functional module. and have advanced radar drug development capabilities. The key modules that are being advanced right now are for predicting patient response and identifying optimal combination regimens for immuno-oncology drugs, such as immune checkpoint inhibitors, which compromise well on close to 30 plus billion in sales, and also then predicting the blood-brain barrier permeability of any molecule. with 90 plus to 94 percent accuracy, and doing that at a scale and speed that allows the analysis of tens of thousands of compounds a day. We also are continuing to advance our ADC template or ADC module for generating drug conjugate templates for the next generation of the ADCs. We expect to have additional data and perhaps posters and papers out on that ADC module. These three modules exemplify the type of rapid and meaningful progress the RADAR platform is expected to make by the end of this year and over the next several quarters. And we think these can become really a hallmark, but almost a backbone for oncology drug development for many companies. And one of our primary focus during the second half of 2023 has been to further and accelerate the enrollment of the harmonic trial and also position ourselves within the patient advocacy community to drive improved awareness and enrollment in our trials for LP300, LP184, LP284. Now, we've had several events through the second quarter with groups from GBM Awareness, Lung Cancer Advocacy, and these have generated interest in our trials and are generating an enthusiastic groundswell of interest in participating with our drugs at specific trial sites. We also have an upcoming ATRT rally where our head of clinical development will be speaking for an ultra rare brain cancer, ATRT. And it's a pediatric indication that we plan on pursuing through Starlight, where our molecule has shown tremendous efficacy in preclinical models, specifically in ATRT, since that was an insight driven from our AI platform, proven in the lab, and now also allowing us to gain a rare pediatric disease voucher. So 2023 is shaping into being a pivotal year where our insights are now entering into patients and have 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 are improving the lives of cancer patients with effective and affordable targeted treatment options. In closing, I want to express my deep gratitude to our team, our partners, and our stakeholders for their unwavering support. Together, we're really lighting the way towards a brighter future in oncology and solving real-world problems with our proprietary AI platform that is enabling the rapid development of genomically targeted therapeutics. And these are the ones that will alter the costs and timelines in drug discovery and place us at the forefront of a new era in cancer therapy and cancer medicine. Now that I'd like to open up the call to some questions or clarifications, and I'll take questions from our audience now. Yeah, so we've got a couple of questions already teed up, which is great. We'll go first question from John. His question, I'll repeat it. As you move past the phase one trial for LP184, do you anticipate refining the indication, what will guide efforts to narrow it down? Well, that's a great question. We will be taking liquid biopsy from the patients in phase one, and obviously some other PK PD data as well. And we think that'll help us refine it. And since it is a basket trial, we do expect there to be a range of response. And that also will help us guide. Do higher levels of PTGR1 or does a bigger genomic signature for homologous repair deficiency or nucleotide excision deficiency mean a better response? Are we getting a muted response in certain cancers versus a higher response in others? But yeah, the phase one data, since it's a basket design, we allow all solid tumors that are refractory. We'll be obviously doing a lot of biomarker work on the... what's called FFPE slides, and also on liquid biopsy. So yeah, this will be very data heavy, even in phase one. Thank you. The second question, could you provide, you know, I'll read the question, it just came in. Could you provide guidance for when you expect to share initial data from harmonic phase one studies for LP184 and 284. So just for clarification, harmonic is in phase two. We expect to have perhaps some initial data in the first half of next year. But we expect that once we reach what's called 27 events, which we hope to reach by the end of next year, that we'll be able to give some good data. Now, of course, it could happen a lot sooner. And so we could have one or two readouts for harmonic next year. For the phase one, LP184, I expect that to be definitely in the first half of the year. And then, as I mentioned earlier, LP284 is about a quarter behind that. So I expect data in Q1, Q2, throughout the year, but definitely Q2, Q3, and Q4. Another question. Wow, we've got a lot of great questions. This is in regard to the ADC program. So in terms of the ADC program, we will be refining Some of the indications, we'll be sharing the data in January. So we'll talk a little bit about the timing for 2024. So we expect that IND application to be in 24, early 25. Really depends on how quickly we can manufacture and get a clarity on manufacturing at GMP level. That's going to be the key driver. We think we have a super potent molecule. We think there's no other design like it with a cryptofisin. So we believe that it's novel and can extend to many other cancers. For us, it's really going to come down to manufacturing it. And there are some things that we're looking at that'll potentially really shorten the manufacturing of the ADC, including, again, kind of stealing from the January, but we'll talk also about synthetic nanobodies that can take on the form and function of an antibody, but are easier and cheaper to manufacture. And so this might be one of the very first kind of synthetic fragment nanobody drug conjugate. Yeah, good question. So for 180, another question, 184 and 284. On 184 and 284, the question is, can you please, this is from another John, Can you please discuss the timing of the potential? Hello, John. Can you please discuss the timing of potential readouts? Yeah, so since 184 just started this past quarter, we have that designed in what's called cohorts of three. So you have three patients that you dose the first level to. And then if we see everything green light, we go to the second cohort as three. And we do these cohorts of three. We think at about cohort three and four, maybe five, we'll start seeing some really good signals. And we'll continue basically advancing the cohorts till we get to a maximum tolerated dose, which we expect to see sometime during Q1 and maybe early Q2. So we'll obviously be sharing that. And then same with 284. 284 is about a quarter behind that. But the cohort design for the first two cohorts in 284 are a little different. Those are designed as cohorts of one. And so we'll be able to get through the first two cohorts pretty quickly and then to cohort three, which is three patients. So again, in both, I think they're a quarter behind one another. Cohort, the 284 could speed up because we plan on some outbound activity with the lymphoma community. So maybe that'll help us. Again, these are fairly focused trials. We expect to see hopefully some signals as well, but I'm looking at Q2 and Q3 for those particular trials for 184 and 284. And if not earlier, then we'll start, obviously in parallel, some partnering discussions as well. Thank you. Okay, got some more questions via email that we'll be talking about. One is about Starlight. Yeah, so is our goal for Starlight, the reason we went after Starlight, is when we first started looking at where LP184 could be pointed at best, In our AI platform, we came back with a signal for CNS cancers. Now, we kind of weren't thinking about that because we thought other solid tumors would be exceptionally more sensitive. And we didn't know enough about the blood-brain barrier permeability of the molecule at the time, which is why we started creating the algorithms now, which are top in the world for that purpose. And so we realized once we got the data and signals for GBM that there's Dozens. And as I found out later, 120 plus other brain cancers. So we sucked in all the data as we possibly could. Like, you know, I think in one quarter we sucked in like almost a billion and a half or something. And these are wide, large-scale genomic biomarker drug sensitivity studies. We normalized that data as quickly as we could. And it wasn't just mutation data, it was also epigenomic data. And we discovered that there were another wide range, other brain cancers, secondary and primary, that the drug was active in. As we went to the lab to see if those in silico concepts made sense, many of them were right on. In fact, were even more sensitive. And so that led us to the insight that, wow, we have a lot of opportunity across a wide range of neuro-oncology. We shared that data with key thought leaders and KOLs. And we also published at Society of Neuro-Oncology in the last two years. And we got a lot of interest from pharma. And we realized that this is a massive market opportunity. You know, this is exactly what we want to do is we want to change the pace of finding these kinds of insights and then generating value for patients and value for investors and generating new medicines. And so the Starlight opportunity really took on a kind of a life of its own. It went from one indication to like five, six, seven indications, which obviously we can't do as one company. And since there's a lot of commercial need for patients, and there's a lot of pharma interest to develop a neuro-oncology franchise. We think that there's interest in a NUCO, and this could be one of the very first NUCOs that spun out directly as a result of AI-driven drug development. And so Starlight will get drug product potentially, obviously just from us. They'll have a license to pursue neuro-oncology indications. They won't have to worry about an AI platform and growing it. They won't have to worry about a lot of some of the fundamental drug product infrastructure and CMC questions. And so they'll be purely focused on execution in the CNS trials, which is great. And so we think we can make exceptional progress if we get a standalone management team focused on that and funded. So we're very excited about it. We get fairly excited. Very good and unique positive feedback. And for us, that's a Q1 event to raise some funding around that early part of next year, Q1, Q2, and then launch the phase two trial for multiple indications in GBM and potentially brain mets in Q2, Q3. So, yeah, very good question. I think there's one more. Yeah. Yeah. So a couple more questions have come in. I'll take these. So a question during the third quarter of 2023, Lantern filed four new patent applications relating to breast, liver, and blood cancers, and an additional application directed to the lyophilized formulation for these molecules. And where does our patent portfolio sit today? David, would you like to take that? Sure, sure.
spk02: We have an aggregate of over 95% issued patents and pending patent applications. We have a strong patent position in each of our lead product candidate areas. For LP300, we have claims extending into at least 2032. For LP184, we have claims extending into at least 2041. And for 284, we have claims extending into at least I think one very interesting thing we've seen with our radar platform is that it's also a great generator of new insights that you can then use to further expand your IP position. And we expect to continue that. So we are actively filing, as you saw, we described what we did in Q3. We will be actively filing and further building our IP position in coming quarters as well.
spk01: Thanks, David. Another question we have from Sean. Sean, thanks for your question. His question, what are your expectations around the pace of radar data accumulation in 2024 as you look beyond the 50 billion data points anticipated by end of 2023? That's a really good question. It's a very exciting question. Well, I think we'll have a talk sometime in January or maybe early February, specifically around our radar platform. But I think we'll probably pass 50 billion in the next month or two easily. And we're developing internal goals, but we're thinking kind of a three to four X increase for next year. So we're looking at, you know, we'll probably easily get to 100 billion. So we'll double that number, maybe get to 150 to 200 billion. A lot of this is going to depend on what the quality of data sets are. We have a couple of initiatives internally, and we'll talk about them later, around engineered data sets and extracting data from data that's already available. Second, we're also thinking about scraping, doing large scale automated scraping from the right kind of quality publications. And we're also looking at better and automated feeds from some of the existing more enlightened publications and systems that are doing machine readable data format, machine ready kind of data extractions like in through JSON files or other kind of configured configurations. So we have a number of things. I think we're going to crush that 50 billion number next year. I think it could be ordered at least two and maybe up to four times that. But yeah, it's a great question. And more importantly, it's not just the data. It's really the normalization and curation of the data. And then of course the algorithm so you can make sense of all that data. Thank you. Well, with that, I'd like to thank everyone for participating. I know we had a lot of really good questions and there being no further questions, we'd like to conclude today's call. Thank you.
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