Lantern Pharma Inc. (LTRN) on Q3 2021 Results - Earnings Call Transcript
Nicole Leber: We'll get started here. Good afternoon. And welcome to the Lantern Pharma's Third Quarter 2021 Earnings Call. Theme bought to you today as a Zoom Webinar and Conference Call. As a reminder, this call is being recorded and all the attendees are in a listen-only mode. I am Nicole Leber with Finance and Administration at Lantern Pharma and I will be your host for today's call. I will be joined by our CEO, CFO and CSO. We will open the call for questions-and-answers after the presentation which will be managed by our CEO, Panna Sharma; recent quarters by our CSO, Kishor Bhatia and our CFO, David Margrave. A press release was issued today after market closed, with Lantern's third quarter 2021 financial results that management will be discussing during the call today as well as providing a corporate update. Following the Safe Harbor statement, Panna will provide an overview of Lantern's highlights, after which, David will overview Lantern's quarterly financial results and Dr. Bhatia will provide an update on our R&D sector. Panna will then offer completing comments, after which we will answer questions. You may also note that we've provided a lease on the Investor Relations website for the slide that management we referenced in today's earnings call and webinar. I would also like to remind everyone that remarks about future expectations, claims 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 versus the impact of COVID-19 pandemic, 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 quarterly report on Form 10-Q for the third quarter of 2021 and in the Risk Factors section in our annual report on Form 10-K for the year ended December 31, 2020. Both of these documents are on file with the SEC and available on our website. Forward-looking statements made on this conference call take only as of today, Monday, November 1, 2021, the Lantern Pharma does not intend to update any of these forward-looking statements to reflect events, from circumstances that occur after day unless required by law. The webcast replay of the conference call and webinar will be available on the Lantern website. And with that, I'd like to turn the call over to Panna Sharma, President and CEO of Lantern Pharma. Panna, please go ahead.
Panna Sharma: Thank you, Nicole. Good afternoon, everyone and welcome to our third quarter earnings call. Thank you for joining us today. We're also live training this call for a Zoom webinar which many of you investors have asked for and feel that this format can bring you more information or insight about our business. Thank you for your feedback and request and bear in mind, we'll always work in improving assets of our operations, both internally but also our communications with investors externally. This past quarter, the third quarter, has been a very exciting and very busy quarter for Lantern. We've made meaningful progress on multiple fronts, clinically, operationally and our RADR platform which many of you read about this morning and also with our ongoing discovery efforts. I'd like to highlight that we have exceeded our growth expectations that we set out earlier this year regarding our proprietary AI platform, RADR. We just surpassed 10 billion data points this past month and this represents a 10x increase in the number of data points since November of last year and approximately a 37-fold increase since our June 20 IPO. The increase in data points which many of you have asked about in the past, provides management an important long-term advantage. First, it accelerates our drug development time lines by giving us ideas about communications or the feasibility of certain locations. It allows us to uncover new therapeutic opportunities and potentially in license new compounds will find new uses for our existing portfolio. It also allows us to develop insights into how we can create combination therapy programs with our drugs and existing approved therapeutics. And we'll talk a little bit about that today as well. And most importantly, it expands our ability to collaborate with additional biopharma partners. We believe that the platform has gotten to the stage where it can be used not only for our existing portfolio but for many other drug development portfolios in oncology. Later uses vast amounts of data from the transcriptome, from the genome, from the discretion data, methylome data, from drug sensitivity data from a wide range of curated sources, both human, animal cell line, PDX, 3D steroid and even cell line. All these data are analyzed, monitored, scored and constantly updated. And ultimately, the goal is to reduce the cost, reduce the risk and accelerate the time line to get drugs developed by uncovering mechanistic insights on drug-tumor interactions and developing companion diagnostic biomarker signatures. Biomarker signatures are essential in derisking drug development and, as I'll talk about later, have proven to increase the likelihood of bringing a drug to market on an average of 5x higher than those developed without such signatures. Ultimately, our goal is to potentially benefit and select patients that have the best option for our drug therapies or our combination therapies that we uncover. By corresponding with this growth in data points, we also focused our resources and the technology on a very important area which is the growth and constant improving evolution of our library of algorithms. Algorithms are critical because they are constantly giving us new ways to correlate the data, automatically sift through the data and, more importantly, give us new ways to rapidly identify correlations we may or may not know about that are critical to making decisions in cancer drug development. They also allow us to rapidly identify rare cancer subtypes that may have gone unknown or unnoticed or misunderstood and they provide insight into potential drug target interactions. They also can help uncover patient groups that can respond to specific drugs, not only at one time but over the course of their treatment. Now with such an incredible assortment of algorithms and as it grows, what we've done is also embedded in machine learning development operations environment. This is very important because it allows us to then pick and choose and select and compare different algorithms in how they perform or algorithms being used together. When algorithms are used together, this is called an ensemble-based approach where we use multiple algos and methods and we can rapidly and with higher accuracy, understand what the response is going to look like in a patient or a group of patients to our drug or drug candidates. All of this is critical because it helps define and develop the strategy to bring this drug to market and develop companion -- sorry, combination strategies that we think have a higher chance of approval. We plan on continuing further data expansion. Many of you have always asked me how much data is enough? The answer is really there is no enough in data. 10.4 billion data points is a wonderful and very meaningful milestone for our team but there are tens of billions of additional data points to collect hundreds of additional cancers to further explore and additional data sets being generated globally every single month. Our job is to bring in these data sets, score them, understand them and, more importantly, do this in an automated way where we can evolve RADR but, mostly now, turn our attention to the library of algorithms that we're evolving. Lantern will continue to augment the 10.4 billion data points. But in a very specific set of areas. One area that we looked at last quarter was in hematologic cancers. A major chunk of our growth came from blood cancers because this of our own focus on blood cancers going into 2022. Additionally, this coming year, we'll be focusing on immuno-oncology-related studies and trials. Data from these studies and trials will include antigen, immunomics and protein data and also robust multi-omic analysis that's out there. As many of you know, there's a wealth of methods and algorithms already in development for IO drug response prediction and I/O drug response combination creation. Our team will review many of these. They'll improve them, they'll incorporate them and this will make our platform even stronger. We think this will be a long-term strategic advantage as we deepen our capabilities in two very important areas: antibody drug conjugate development and combination therapies using IO agents. We already have significant reason to believe that certain IO agents, especially those that show high sensitivity in TMB high -- TMB meaning tumor mutation burden high, as marked in certain cancers -- and they may have the potential to work synergistically with either 184 or 284, especially in intractable and more challenging tumors. Turning my attention out of the biomarker signature; we think this is of real importance. But in a recent multiyear study done by Professor Dr. Jason Parker from University of Toronto, he and his colleagues reviewed over 10,000 clinical trials from 1998 to 2017 in 4 solid tumors across 745 drug programs and they showed that biomarker-based trials had success rates that were 4 to 12x higher than those that did not use biomarkers. Actually, Dr. Parker's team concluded that the inclusion of biomarker status is a covariant significantly improved the fit of his Markov models that they use to describe the drug trajectories through the clinical trial testing stages. So the hazard ratios on the Markov models reveal the likelihood of a drug approval with biomarkers had an average of 5x increase across all 4 solid tumors. And it was 12x, 8x and 7x, respectively, in breast cancer, melanoma and non-small cell lung cancer, all diseases that are highly related and driven now by biomarker analysis. Markov models, even with exploratory biomarkers, outperform Markov models with no biomarkers by a major factor. So his team's conclusion, well, first of all, we just know this is one of the first systematic statistical review is done but had -- we showed clear evidence that biomarkers clearly increased clinical trial success rates in multiple indications in oncology. And very importantly, exploratory biomarkers long before they are properly validated in many labs appear to improve success rates in the drug development process. This supports one very important thing early in aggressive adoption of biomarker-based signatures and biomarker-derived signatures in oncology clinical trials. This is a hallmark of our development process. This further encourages us that utilizing our RADR platform with our drug candidates and also independently across cancer drugs to drive biomarker signatures has a unique potential in addressing the $200 billion global oncology drug market and it has a long-term place in the future of cancer drug development and discovery. We've witnessed firsthand now that the growing industry interest and solutions that innovate the development of precision therapies and combination therapies and reduce the risk and cost. We believe that these kinds of solutions will pave the road -- this kind of appetite for solutions will pave the road to new partnerships and ultimately, greater investor value. We remain committed to achieving our goal of building the world's largest AI platform for precision oncology drug development. We believe we're significantly on our way there already. Our goal next year is to get to over 20 billion data points, deepen our focus on blood cancers, add several additional rare cancers and add valuable data from that will aid in IO and ADC development. We believe that our AI platform will be pivotal in uncovering potential new therapeutic opportunities and also opportunities both internally and with third-party collaborators. Now, getting into our drug candidates. During the quarter, we reported positive preclinical data for LP-184 in pancreatic cancer and GBM, glioblastoma multiforme brain cancer. We also advanced LP-300 toward a Phase II clinical trial for nervous smokers in non-small cell lung cancer. We began assessment of the next phase of our LP-100 program in metastatic castration-resistant prostate cancer and potentially other cancers that we'll talk about later and we prepared LP-284 for further development in blood cancers as we have some exciting data coming out later this quarter. And also 284 in -- sorry, LP 284 next year in 2022 will be a significant area of focus. Now first, as a result of the encouraging results in 184 in GBM and pancreatic cancer. We're granted orphan drug designation. Now receiving orphan drug designation gives us several benefits which many of you know about, market exclusivity for seven years, eligibility for tax credits for qualified clinical trials done here in the U.S., waiver of marketing registration application fees, reduced annual product fees and assistance in the clinical product call as well as review, hopefully, in an expedited manner. These are all massively important because they reduce our burden of development and they give us increased commercial protection. Two very important areas that investors should look for as positive early validation and we've accomplished both in this past quarter. We continue to look at orphan designation as an important milestone but also validation of our novel AI-driven approach. We also submitted an abstract with Dr. Igor Astrosoft and established NCI-funded physician scientist. He's also a co-leader of the Marvin and Kanchana Greenberg Pancreatic Cancer Institute at Fox Chase Cancer Center and that was accepted for presentation at the AACR Virtual Conference in pancreatic cancer. We released data about the abstract and about the work that was designed that showed the efficacy of 184 in multiple mice models and it showed increased efficacy potentially as a synthetically lethal agent in pancreatic cancers that also harbored some kind of DNA damage repair deficiency. And our Chief Scientific Officer, Kishor Bhatia, will talk about that later. This is an area that's particularly unique and we're continuing further development because it provides us a road map for prioritizing additional cancers where we can potentially develop first-in-class solutions or show significant improvement over the existing standard of care outcomes. LP-184 also showed potentially best-in-class efficacy in pancreatic cancer with a unique mechanism of action. The study that we did observe that LP-184 not only had very good effect in pancreatic cancers but also in pancreatic cancers that were resistant to standard of care drugs. We also showed, very importantly, using CRISPR and gene editing, that the biomarker that we had predicted through RADR does actually directly link to the antitumor activity of 184. This is PTGR1 where we saw not only just really exquisite activity once PTGR1 was there but we saw really no activity. It's almost black-and-white scenario if you look at the charts. And we believe we can exploit this biomarker mechanism in various tumors beyond pancreatic cancer in the future and very importantly, again, take a biomarker-driven approach to selecting and developing the trials. We are now in discussions in the design, the first-in-human clinical studies for 184 in collaboration with Fox Chase and other KOLs in the pancreatic cancer treatment landscape. We plan on -- as Dr. Bath will tell you, we've initiated IND-enabling studies. And those will then inform and guide or Phase I in human trials next year once we finish the IND application. I'm also very pleased to announce that we'll be hosting a virtual KOL event on -- for LP-184 in pancreatic cancer with Dr. Eager Astrosoft and Kishor Bhatia on November 18, World Pancreatic Cancer Day. We'll announce the details of this event later this week. We also reported very importantly and another what we feel is a multi-billion-dollar global indication against intractable cancer GBM, glioblastoma. LP-184 was able to significantly improve survival in animal models in a statistically meaningful way. This study was done with Kennedy Krieger and Johns Hopkins University. And results of this study are expected to guide the clinical application and focus of this drug candidate. Now our next phase, as we expand in the next phase of the work with Johns Hopkins and Kennedy Krieger, is to look at a very important observation. And that is that we've looked at in silico that LP-184 can be an effective treatment in glioblastoma which we now have seen in the lab and we've proven that in mice and we plan on taking this into humans next year. But one important observation is that we think that it can be an effective treatment in GBM regardless of MGMT status, a DNA repair enzyme that gives you not only a status of the cancer but potentially actually gives information about it's ability to respond to TMZ. We believe that this has significant potential to provide a much-needed alternative to the standard of care drug, temzolimide, especially in GBMs that overexpress MGMT which is about 50% of GBMs. So this is a major population that needs a new drug choice. These patients that are overexpressing MGMT are generally unresponsive to TMZ and the new therapy options. So development of an agent with efficacy in GBM regardless of MGMT status would be an important advancement towards addressing this critical gap and we believe is a molecular pathway that can be exploited elsewhere. Our current in silico analysis actually shows that LP-184 should work regardless of the status. But actually, interestingly enough, it actually shows increased sensitivity in many MGMT cancers. Now, we also plan to launching additional studies for the ADC program in Q4 and we expect to have data during the second quarter of 2022 for our ADC program and in a few specific designations. So unlike conventional cytotoxic agents or chemotherapy or even some targeted therapies that can damage healthy cells or have toxic side effects, ADCs are targeted medicines that can deliver the chemotherapy or target a molecular agent to a very specific cancer cell through the connecting the ADC and the right molecule is a little bit of a science and to the linkers. Now we've been reworking this year on perfecting this. And we believe we can take advantage of the high potency of our molecule and the superior specificity of some of the antibodies that we've selected. We believe our ADC program represents a huge market opportunity as two out of the four largest oncology licensing deals last year were an ADC assets. And it's clear that ADCs will continue to be a critical part of the therapeutic armamentarium against cancer and it's an area where our AI platform is only becoming more relevant and more powerful. Turning now to LP-300 candidate; we've entered into a strategic collaboration with Deep Lens. It's a digital health care company enabled on one thing, that's faster recruitment of the best-suited cancer patients that meet our protocol. So we're leveraging their AI clinical trial technology, Viper, to basically create a unique end-to-end solution where we're using AI to basically develop the drug. But then we're using AI to actually find the patients. So their technology is able to come through thousands and thousands of records like trading medical and health records to understand the criteria that allow us to match the right patients. In our case, never smokers with non-small cell lung cancer that are chemo-naive and or relapsing from TKI. Now we have experienced some supply chain and sourcing issues caused initially by COVID-19, then eventually seeking into global shipment and equipment availability and then sourcing some backup equipment. So we're delayed in finalizing our manufacturing but we are planning to launch a 90-patient Phase II clinical trial in the U.S. in the near future, meaning later this quarter, early Q1 and in non-small cell lung cancer focused on the never smoker population. There are no other trials focused in this population and we believe we have a unique protocol with a very clear selection list for the patients. So we're now planning on enrolling about 20 sites in the U.S. and we believe we, can select 4 to 5 patients at each of these sites, never smokers and fairly quickly. Our existing AI platform allows us to drug outcomes and the Viper platform allows us to use AI to find the appropriate patients and proactively suggest patients that can match to our treatment. We think this will reduce the time line next year and, more importantly, reduce the cost. We'll always in turn to really exciting collaborations with like-minded companies or with companies that are leaders in their industry. One such as Code Ocean, their leading computational research environment for sharing scientific discoveries, so not on the patient side but on the internal operations side, to very different areas. But Code Ocean allows us to share scientific discoveries in a more secure, more transferable manner and allows us to do it internally and externally with our network of collaborators. It allows us to manage our external data and our code with simple ease in a much more cost-efficient environment and it already takes our RADR platform and adds new efficiencies in terms of development time and cost. So we think this powers our platform for faster or more collaborative discoveries, not only internally as we have distributed teams but also with our collaborators at major research institutions. As evidenced by our progress this quarter, again not only clinically but also across any number of measures, we remain very excited and committed. We've announced some exciting and positive data as well and we believe that these advancements are the types of advancements that are needed to change the cost and the risk associated with cancer therapy development. I'm also pleased this quarter that H.C. Wainwright Investment Bank has initiated research coverage on Lantern and we remain committed to growing awareness of Lantern within the investment community, especially with the number of upcoming milestones that we have. We have a lot of progress in our ATRT program which, for sure, we'll talk about; progress in the IND-enabling studies for our ADC program; and also we'll have results in 184 in a number of indications, including pancreatic bladder and GBM in the coming months. So we have a lot of data to share. But also, we have continued to show good financial discipline. And I'll ask David Margrave, our CFO, to provide an overview of our third quarter financial results. David?
David Margrave: Thank you, Panna and good afternoon, everyone. I will share some of the financial highlights from our third quarter of 2021 ended September 30, 2021. We had a net loss of approximately $4.1 million or $0.36 per share for the quarter ended September 30, 2021 and compared to a net loss of $1.7 million or $0.27 per share for the quarter ended September 30, 2020. And research and development expenses were approximately $2.96 million for the third quarter of 2021 and compared to approximately $0.6 million for the third quarter of 2020. The increase was primarily attributable to increased manufacturing-related expenses and expenditures to advance and expand the company's product portfolio. General and administrative expenses were approximately $1.2 million for the third quarter of 2021 and compared to approximately $1.1 million for the third quarter of 2020. The nominal increase was primarily attributable to increased business and corporate development expenses legal and patent-related fees and general and administrative-related stock option expenses. Our R&D expenses for the third quarter of 2021 were approximately 2.5x the amount of our G&A expenses for the same third quarter 2021 time period. This reflects our continued focus on advancing and expanding our product pipeline. Our team continues to be very productive, especially as we migrate to a hybrid work environment. We currently have 16 employees who are primarily focused on leading and advancing our drug development biology and data science efforts. We see this number expanding slightly in coming quarters as we add additional high-caliber multifaceted individuals to help advance our mission. To date, we believe we have effectively managed the impact of the COVID-19 pandemic on our operations. Recently, the timing of manufacturing for our LP-300 and LP-184 candidates has been impacted by supply chain delivery issues, as Panna mentioned which has extended the time to launch our planned Phase II clinical trial for LP-300 and extended the time to commence IND-enabling studies for LP-184. Nevertheless, we are making continued progress despite these hurdles. As of September 30, 2021, we had approximately 11.2 million shares of common stock outstanding. This includes approximately 4.9 million shares that were issued in our January 2021 follow-on offering. At September 30, 2021, we also had outstanding warrants to purchase 298,204 shares and outstanding options to purchase 801,588 shares. These warrants and options, combined with our outstanding shares of common stock, give us a total fully diluted shares outstanding of approximately 12.3 million shares as of September 30, 2021. Our cash position which includes cash equivalents and marketable securities at September 30, 2021, was $73.8 million. This balance is expected to carry us into 2025. 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 products and collaboration opportunities in a capital-efficient manner. Thank you. And I'll now, hand the call back to Panna. Panna?
Panna Sharma: David, thank you very much. I'd like to now invite our Chief Scientific Officer, Dr. Kishor Bhatia, to provide some detail on the growing data and excitement on some of our early stage programs. Also, we'll be sharing data for the first time in several of our programs and some insights during this call. So Kishor, please go ahead.
Kishor Bhatia: Thank you, Panna. I'm excited to report the initiation of our IND-enabling studies for LP-184. Our first animal dog and rat toxicity and those range finding studies are expected to begin in the next couple of weeks. These studies are projected to be completed by April, paving the way for us to take LP-184 to the clinic. We continue in addition to build on more evidence supporting the uniqueness of our molecule, LP-184. Moving forward with data from the previous quarter that showed efficacy in pancreatic cancers, we now have direct evidence of enhanced efficacy in pancreatic cancers with specific mutations that affect the transcription couple nucleotide excision repair pathway. The transcription couple nucleotide excision operate is a specialized pathway that cells used to repair DNA debt from damage their blocks transcription. Our drug exhibits highly heightened sensitivity to cancers that have damage in such genes that are part of the nucleotide excision repair pathway. An example of the correlation between the TC NER pathway is provided in this slide. What you can see in this slide is that if a cell is a wild type, that is, it has no mutations in any of these pathways, depicted by the blue horizontal line on the top, the cells survive well. If there are mutations in specific genes in the pathway and here, we have interrogated three genes that are part of this pathway, PD, ERCC1 and CSP, the presence of LP-184 in these cells make LP-184 synthetically lethal to these cells and these cells done. Very recently, using a CRISPR-based ERCC 4, again, ERCC 4 is a part of the NER pathway, using a CRISPR-based RCC for cell line xenograft model, we demonstrated a doubling of the efficacy of LP-184 in these tumors. So these data works quite well with other data that I'm going to show you in the next slide which we obtained in prostate cancer models. And here, what we have done is we have taken prostate cancer models and down-regulated BRCA2 by using SHR a day. So these cells are slightly different than the sums that I was showing you earlier. Here, we have created homologous recombination deficient cells versus previously I was showing you nucleotide excision repair deficient cells. Nonetheless, in seller homologous recombination decision, as you can see in the left-hand panel, the down regulation of BRCA2 by such RNA increases the potency of LP-184 tenfold. This is significantly greater than the effect olaparib has on these cells where it increases roughly about 1.7 fold. The uniqueness of LP-184 lies in these two aspects, not only does it target cells that have nucleotide excision repair deficiency but it also target cells that have homologous combination of deficiency. This dual targeting of pathways by LP-184 is very unique and does not like either with known alkaletic agents or known PARP inhibitors. We are very excited because this allows us to utilize LP-184 for a wide variety of tumor systems. During this quarter, we further strengthened LP-184 positioning for a very rare central nervous system tumor called atypical teratoid/rhabtoid tumor. We got a sense that this tumor would be an indication from our RADR data. It suggested to us that LP-184 would be very sensitive in those cell lines that have a deficiency of SMARC. Now ATRT happens to be tumor that is driven by a dilution of SMARCB1 which is a chromatic protein. We followed these clues and were benefited by this amazing data. And as you can see in the graph, in the panel on the left which shows you tumor growth in presence or absence of LP-184 in mice implanted with ATRT, the blue line shows you how the tumor would grow without the drug. And the red and the green lines show you the regression of the tumor when LP-184 was injected either as a dose, at a very, very low dose, of 2 milligrams per kg or 4 milligrams per kg. The right-hand panel basically gives you a sense of the size of the tumor attains when it is not treated, roughly to about 1.5 to 2 centimeters. And in mice that are treated, there is barely any tumor much less than 0.2 centimeters. Our GPM story also continues to gather strength. And at this time, I'm very excited to import that when we look at LP-184 and compare it with TMZ, for each of the criteria that can be used to score the bioavailability, the CNS bioavailability of the drug, LP-184 compares quite well with TMZ, either we use admetSAR2 in silico analysis or we do a 3D cell culture permeability assay or directly go into the mice, inject LP-184 and ask what amount of LP-184 reaches the brain, each of those criteria give us confidence that LP-184 would be an excellent candidate for CNS bioavailability. And knowing that it affects orthotropic models of GBM, we are very excited to move ahead with the LP-184 in the clinical areas of GBM. Obviously, these data allowed us to move forward and obtain ODD designation, orphan drug designation, for GBM and the previous data, I discussed allotted orphan drug designation for the pancreatic cancers. But the data that I showed you about ATRT has now allowed us also to move forward and apply for orphan drug designation as well as pediatric rare disease drug designation for ATRT indications. During this quarter, we have further delineated based upon in vitro studies some very exciting combinations of LP-184. Again, this is driven by a lot of clues we get both from our wet lab studies as well as from RADR. And basically, we have identified approved compounds that we can combine LP-184 and allow LP-184 to be here to affect tumors, even tumors that don't have any mutations, in the same way as if the tumors were NER efficient. Our molecule LP-184 continues to progress through a better clinical understanding of these combinations. I will now turn to our new molecule, 284 and we are obtaining additional indications where we can fill unmet clinical gaps. In the next month, we will begin an advanced collaboration with a well-known hemato-oncologist from Duke University where we will begin animal studies on a range of hematological cancers, including mantle cell lymphoma and diffuse large B-cell lymphomas and others in order to further fine-tune and define the most interesting cancers that we will proceed with 284. This will give us greater confirmation of the subtypes of the blood cancers that RADR and premium lab studies up predict efficacy model. Additionally, this data will also provide us safety and efficacy and dosing studies, allowing us to move quite rapidly in the 284 program. During this past quarter, results from our studies were accepted at several scientific conferences, including the AACR Pancreatic Cancer Conference where we presented data from the pancreatic cancer. We will be presenting data on the glioblastoma at the Society of Neuro-Oncology Conference later on in November. And our 284 data will be presented at ASH early in December. Panna?
Panna Sharma: Thank you, Kishor. Before I open it up to questions, I'd like to provide a brief recap and also discuss some of the anticipated milestones. As you know, we're very confident in the launch of multiple human clinical trials over the next 12 months for 184 and 300 and 100. We're also looking at the ongoing growth of our RADR platform as well as committed to bringing the ADC programs further along through IND-enabling studies. We also believe that with our network of strategic collaborators, that will be adding additional KOLs and collaborators to, that we'll be able to generate positive new data and, more importantly, generate new programs through licensing opportunities, both with RADR and also our existing drug portfolio. We believe that 2022 will be a fairly transformational year for Lantern. We do expect that the platform will grow, that the trials will initiate and most importantly, we will continue growing our very experienced team that is committed to bringing these drugs and, more importantly, doing it faster and cheaper. So with that, I'd like to now open up the call to any questions.
Unidentified Company Representative: Thank you, Panna. We received some questions from analysts. Our first question comes from Kyle Bauser with Colliers. What are your new goals for RADR in terms of number of data points and what sort of visual demonstration might we see during the upcoming Investor Day?
Panna Sharma: Thanks, Kyle from Colliers. Great question. We had set out to reach initially when we came into 2021 with 1.2 billion data points, we thought we'd get to 5 or 6 by the end of the year. We updated that in the middle of the year to 8% to 10%. Now that we're at 10.4%, we're thinking next year, we'll get to 20, maybe slightly north of that. But we're also looking at new types of data, as I mentioned and we're also looking at the complexity of our algorithms. That's one of the big areas of focus is the machine learning development environment. So in terms of numbers, I believe we'll be at 20 plus next year and maybe even faster. In terms of the -- we do have some really exciting visualizations of the output of the platform. We call these RADR insights internally. We routinely review these during our team meetings. We expect to have an Analyst Day. We're trying to figure out the right time and the right kind of environment for that. So sometime in December or January, where we'll showcase many of these RADR insights and also a peak at the kind of development environment. So Yes, we'll have some looks at what RADR looks like and feels like but also in terms of what is the data and what are the nature of algorithms. That's probably the most important thing. So thank you for that question.
Unidentified Company Representative: Next question. Are you still evaluating new partnerships that you can take equity stakes in? And how is he actually a therapeutics partnership progressing?
Panna Sharma: Great question. One of the most important reasons for making the $10 billion announcement was to showcase that we continue to develop the platform for much wider use than just our own. And so we think that now is the right time for us to even be more aggressive. We've had very good experience with our first partner, Actuate Therapeutics. It's given us a great template to think about how to approach additional partnerships with both emerging companies. And we've had actually some interactions with some bigger biopharmas that gives us very good ideas on the process that we need. So part of today's announcement was really to get on to people's RADR, so to speak, that we're going to be much more aggressive in seeking RADR-driven biopharma collaborations where we can take equity or milestones as part of our growth.
Unidentified Company Representative: The next question is how much faster do you anticipate enrolling the Phase II trial now that you've inked the collaboration agreement with Deep Lens?
Panna Sharma: That's a great question. I know that David probably won't want me to commit to any number. But I can tell you what we learned in the process which we believe is a template for what we're trying to do with Deep Lens. So we spoke to many people in the biopharma industry as part of our reference checks for not only the Deep Lens technology but other patient recruitment technologies that sift through patient data. And we found that the people who use the Deep Lens technology, we're able to talk about enrollment rates that were 2x higher, faster than they expected. So if you imagine an 18-month enrollment, you're looking at 9 months, 12-month enrollment, looking at 6 or 7 months. So we're hoping that we can be in that same ballpark. But to be honest, since we're going after probably a group that has less competition which is the never smokers, I think once people know about it, I think there is a very high likelihood if they meet all the clinical criteria that we could see very good acceleration because of that. Thank you.
Unidentified Company Representative: Our next questions come from Michael King with H.C. Wainwright. What day is Lantern presenting the poster on ASH? Or what will be the topic?
Panna Sharma: That's a good question from H.C. Wainwright. Thank you, Michael and Riner. We're not allowed yet to give the date and title. Is that right, Kishor? That's still under embargo, I think, until the end of this week. So at the end of this week, we'll be lifting that and talking about the presentation. Thank you.
Unidentified Company Representative: Next question. Any progress on resuming the LP-100 clinical trial after the buyback?
Panna Sharma: Yes, great question. So we have some very good promising data in the 100 trial, not only about the median overall survival improvement that we've seen in the nine patients that were dosed with LP-100 but we also have seen publications now that support the role of LP-100 in DNA damage repair pathway deficient tumors like bladder cancer -- namely bladder cancer, actually, with ERCC23 mutations. We're thinking about some modifications to the trial to allow us to attack both groups of patients but also to refine and simplify the signature that was used to potentially guide their enrollment. So we're in the process now of doing the modifications but also now exploring bringing the drug substance in the trial to the U.S. for some of the indications, including in bladder. So we'll have more timing on that, I think, in Q1 of next year.
Unidentified Company Representative: Last question from me. Any progress on the trial for LP-300.
Panna Sharma: Yes. So I think we're hoping to submit the protocol and the final manufacturing dossiers separately. But after under advisement and because of the manufacturing delays, it's been advised that we submit both together. So we're hoping that over the next 30, 45 days, we'll have clarity on the submission but we've already gone ahead and beginning to look at clinical trial sites and the types of patients. So really, the supply chain issues around manufacturing have really held up the final submission to the FDA. And so I'm hoping that happens during this quarter or early January at the latest.
Unidentified Company Representative: Thank you. Our next question comes from John with Goldman Sachs. How will Code Ocean be used to improve the R&D progress?
Panna Sharma: That's a great question. So Code Ocean is, again, a development environment that containerizes the code, the data and the environment that you use, whether it be R, Python or something else and it puts it in a secure package and, more importantly, time stamps what you're doing. So if we can go back and we can take a look at who is doing, or what's doing what, whether it be internal or externally, people who don't have to manage the creation of like pipelines, the instantaneous security that might be required to go from one institution to another. So a lot of these things are simplified. So with collaborators, we expect a pretty significant increase in the efficiency of doing work. Internally, we also expect efficiencies that allow our team to focus more on the code and the analysis and less on the management of the development environment. And so this container-type approach, I think, increases one of the most important things that's needed in machine learning which is reproducibility. Reproducibility can be very challenging, to know what the algorithm was, exactly what language was going on, what the input data were, what the hyperparameters were or were not and what was the raw data that was used ultimately. So all that now is handled in an automated fashion. So it reduces the IT burden and staffing burden on us and it also reduces the expense. So it's an important development internal operations type of collaboration. But we do think it's a competitive advantage. We don't see many of our peers having a sophisticated or as mature as a development environment for machine learning as we have.
Unidentified Company Representative: Thank you. This now concludes our question-and-answer session. I will turn the call back to Panna for closing remarks. Panna?
Panna Sharma: Thank you, Natalya. Thank you for the great questions. So as we mentioned, we're very excited for the outlook for Lantern. We think we'll make significant progress next year and throughout this quarter. We've got a lot of data coming out this quarter at the Society of Neuro-Oncology at ASH with our ATRT program. And we believe that this data and the progress will translate into biopharma deals where we can partner for license our portfolio for several hundred million or billions. And ultimately, that's really the way to generate value for our investors is to take our programs and to partner or sell or license them out. So we look forward to providing further updates as the developments unfold and also to meeting many of you in person in the coming months or in 2022. So thank you all. And with that, I'll turn it over to our host, Nicole in Finance.
Nicole Leber: This concludes today's earnings call and webinar. We look forward to hosting you on our next corporate webinar which will be on Thursday, November 18 on World Pancreatic Cancer Day. This webinar will be co-hosted by Dr. Igura Saturav from Fox Chase Cancer Center and Kishor Bhatia from Lantern Pharma. We will release additional details in a press release later this week. Thank you so much, everyone, for joining and have a great rest of your evening.
David Margrave: Thank you.
Panna Sharma: Thanks.
