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?
