Panna. Yeah.
Hello, everybody. Good evening. I am going to be introducing Panna from Lantern Pharma here for this presentation.
All right, thank you. Sorry to keep you guys waiting, but let's go ahead and get started. I am going to be talking today about a pretty unique company, Lantern Pharma. We are advancing cancer drugs using our AI platform, and it is a platform that we started building several years ago. We will also talk about what the future of that platform holds in terms of spinning it out into a new entity in the future. We trade on Nasdaq, ticker is LTRN, and today I will be making some forward-looking statements. Like with most forward-looking statements, the actual results could differ significantly. I urge you guys to take a look at our disclosure, both on our website and as filed in our 10-K and on the SEC website.
What has our company and our platform accomplished since we focused our efforts on creating drug candidates using our AI? We have three molecules today in clinical trials that are going on. We have dosed over 100 patients. We have gotten 12 FDA designations ranging from Fast Track and Orphan to multiple rare pediatric disease designations. In fact, I believe one of our molecules has more rare pediatric disease designations than any other molecule out there. It has four of them. We have three additional trials that we have cleared recently to start, one in TNBC, triple-negative breast cancer, which is a terrible disease, one in bladder cancer, which just started in Europe under the EMA, and in pediatric brain cancers, that was just cleared with the FDA earlier this year. We have about 100 patents, both issued and pending.
For us, the cost that we will learn today to develop a new program and bring it to a human trial is $2 million-$3 million, about almost one-tenth to one-twentieth the cost of what it typically takes. The way we were able to achieve this was our AI platform. In fact, the platform was really central because it allowed us to ask questions about cancer and how to take that question or idea and transform it into a potential molecule, understand the mechanistic rationale behind that molecule, develop a biomarker signature, and then ultimately target a unique patient population. The one thing that is common across all of our programs is that they are going after a very precision group of patients, where there is a high need, a known rationale, and along with a biomarker. So every single step of our platform was developed to run and make decisions about real drugs.
It was not some theoretical AI data mumbo jumbo. First thing we wanted to do was gather all the potential data across cancer studies. We did that in our platform. We have well over 250 billion data points from cancer studies. We then decided we wanted to create algorithms, models to understand why certain things worked or did not work. Many of the top models out there in the industry are created by Lantern Pharma, including five of the top 12 on the Therapeutic Data Commons. Then we wanted to understand which drugs can be combined together or not combined. We want to understand which biomarkers would give us insight, and then ultimately, how does that translate into an actual patient need?
So again, we weren't just creating protein folding examples, and we weren't just doing a bunch of data arithmetic and math, like a lot of these new data discovery companies are doing. We were really focused on solving real fundamental, real-world drug problems. Our platform evolved. Initially, it was internal only. It was called RADR. We still use that platform today as the core machine learning platform. Then we decided I was at a conference, and another pharma executive said, "Can't you allow others to do what you guys are doing? What if we wanted to put in a biomarker or put in our molecule?" I thought, you know what, that really could be done. We raced for about a year to create a platform that we launched for the cancer community called withZeta.ai.
You can go there today and subscribe to it, we have a lot of subscribers, to basically do the full arc of development. Once we saw that was successful and scalable and highly functional, we decided we can do this not just for cancer, we can do it for almost any disease category. We created a new company called Open- Medicine AI that's 100% owned by Lantern. For those of you that want to participate in Open- Medicine AI as we roll that out, the best way to do that is to buy Lantern. It operates a platform with the goal of expanding it beyond oncology and opening it up to the entire world. Let's talk about our cost, because that's really part of our core differentiator, and very importantly, it's how we build. It's not just a marketing claim.
Industry norm is to spend about $20 million-$100 million to develop a new program. By new program, I don't mean the cost of the trial. I mean to go from an idea about a drug or a molecule or a target to actually having something to put into a human. That cost takes very easily $20 million. For us, it's $3 million. The number of years required can be 5-10. For us, it's two to three, and we've done this now repeatedly with our programs. Let's talk about what the data told us and then where that led to. In our program for LP-184, the data showed that SMARCB1 was really important. That was a data-driven insight that came from the AI. We use that to go into pediatric cancers that we never set out to do.
We didn't set out to be a pediatric cancer company, but we have four pediatric disease designations because that's what the data aimed at. Today, we have four designations, and we have an IND in pediatric cancer that was cleared. It told us that PTGR1 was the prime driver of this molecule, LP-184. What we did is we decided to select patients by that biomarker. Today we have a patent, but we also have a launch in a biomarker, the only dual biomarker in bladder cancer that just launched in Denmark using PTGR1 and DNA damage repair deficiency. Every one of these decisions that the AI and data- powered each led to successful clinical milestones for us. In drug LP-184, LP-284, and also in LP-300, which is our drug for never- smokers, it is really focused on a group of mutations called L858R.
We narrowed our trial to specifically focus on L858R mutations. Let's dig into our portfolio. We have six clinical paths. Each one of these can be worth hundreds of millions of dollars, and our goal is to sell these, of course, to larger biotech and pharma. LP-300 is in phase II. It's in the middle of a phase II program. It's enrolling in Taiwan and U.S. Again, we're focused on patients that have a very specific mutation called L858R, and that mutation shows some great promise. Those patients, unfortunately, don't do well with existing therapies, but with our drug, they extend life. LP-184, that's now launching in triple- negative breast cancer. We hope to have some patients this year, or early next year, but probably this year, and we're starting up two sites.
The bladder cancer trial, which is fully paid for, that's in Denmark, that's an investigator-initiated trial, and the only one that's using two biomarkers to select patients. We also have an ongoing trial in LP-284 in soft tissue sarcoma. We have a number of shots on goal. I think this is very important because, again, any of these programs can be licensed or sold or partnered off to pharma companies. In terms of going back to our signal, RADR gave us a very, very clear signal and we followed it, and then we kept following it because it was right. It told us SMARCB1 loss was important. It told us that this drug would have nanomolar potency. We then went to xenografts, and then we moved to actual mice, and these mice lived a lot longer.
We multiplied their, again, it's a mouse, but we were able to extend their life 4x or 5x. These were in very, very aggressive cancers. We said, "This actually has real validity." We put it into patient trials. We had about a 50% disease control rate in that trial in the adult phase I-A, and now we're cleared to also go into pediatric. We also found that our drug, LP-184, now called zirdafulven, had a very unique mechanism, and once that mechanism works, which is basically to switch on the drug, tumors can't repair the DNA. What we found is that the next trial will select based on that biomarker. TNBC, bladder, GBM, pediatric CNS, all these trials, we know that the drug is safe at that dose level, which we're starting at, it was 0.39 mg / kg.
We also know that there are other ways to make the drug even more active. The indications, again, we have Fast Track indications in TNBC and GBM, and in both of those very aggressive cancers, about 60%-70% of the patients have high levels of PTGR1. This is important because it shows clearly that there is a high chance that the drug will work on those patients and that they meet the right threshold. In TNBC, if you'll take a look at the red lines, you can see that our drug was able to destroy virtually every one of the TNBC models, including those that were resistant in the dark gray to PARP inhibitors. This is not like any other data we've seen in triple -negative breast cancer, that basically every one of the models was sensitive.
We also saw this drug worked really well in combination, as predicted by our AI. If you look at our drug, it is synergistic not only with PARP inhibitors, meaning that it works better together versus alone, but it also is very synergistic with IO checkpoint inhibitors. In the LP-300 trial, what we saw, and this is very important, this is the HARMONIC trial that is going on in both Asia and the U.S., we saw that patients so far have lived about 8.9 months longer, their progression-free survival. If you take a look at our tolerability profile versus what is used today, it is vastly superior. Our treatment- related AEs are significantly lower. There is no infusion- related reactions, rash, which is a major issue because that causes dermatologic stress, and a dermatologist is involved because of the use of the other option.
What we have also seen is that the longer someone gets this drug, the higher their PFS goes up. So right now, the number in red, 8.9, those people are given six cycles of our drug. We are moving that now to eight cycles, and so we think we will even move the PFS up even higher. Out of the 14 patients that were evaluated, you can see none of the patients the disease progressed, and 10 out of the 14, there is tumor shrink. So we had one complete response, many partial responses, and a lot of stable disease. No one progressed, and in some cases, these responses held for over two years. So we are enrolling the next set of 15 patients. We think the data will continue to be this good, perhaps even a little bit better, because remember, in the next group of patients, we are giving eight cycles.
We are moving from six cycles maximum to eight cycles, and we think, again, we will get pretty superior response in these patients. On the back of that data, we aim to go for an Accelerated Approval. We are enrolling again in Taiwan, and we are also beginning discussions with licensing partners regionally in Asia. Asia is important because in that population, there tends to be about double the rate, or even higher, of lung cancer patients who have not smoked that had the L858R mutation. Taiwan is where we are enrolling. Taiwan and Japan pretty much have led a lot of the early, what is called EGFR biology work also. In our drug for lymphoma, we also had a complete metabolic response in a patient, and this is very important also because this same drug also works in soft tissue sarcomas. We have three Orphan designations.
These are sister molecules, LP-184 and LP-284, but two very different cancers that they are going after, two very different mechanisms that turn them on, but all going after very unique classes of cancer. We also have four separate paths to get a priority review voucher, which as we know, can be worth $100 million- $200 million. We have been given a rare pediatric disease designation in ATRT, MRT, rhabdomyosarcoma, and hepatoblastoma, and we expect to launch the ATRT trial early next year under PNOC, and the majority of that trial will be paid for by the Pediatric Neuro-Oncology Consortium. That is 10 groups, including Stanford and many others, that will be working on that trial. The great thing about this drug in the pediatric setting is that it reaches the brain tumors, and when we give it with another drug, spironolactone, it actually becomes a lot more active.
It's about 3x - 6x times more active. If you look at the red, when we give our drug with spironolactone in brain tumors, it becomes a lot more potent. This molecule is organized entirely now around the brain, called STAR-001, and it's going into multiple trials. We've now been very productive. We have 12 FDA designations, and we've earned all of these one data package at a time, again, ranging going back a couple of years in pancreatic cancer and now most recently in soft tissue sarcoma. All this is data-driven and have been pointed to from our AI. Our AI is going to be our next spin-out. Our core business will continue to be to develop drugs in Lantern Pharma, but Open- Medicine AI will focus on serving lots of biotech and pharma companies, including also CROs and service providers.
People are using this platform today to make drugs. Over 100 drugs have been made now on the platform. We have several dozens of subscribers, and that's growing, and also actually banks and funds are using it to look at drug assets. If you opened up Nature last month in September 21st, there was an article that said, "AI co-scientists are revolutionizing how research is done," and that is core to Open- Medicine's entire approach. We were there at that problem even before the entire field got there, and we think artificial intelligence tools like what we've built are able to propose theories and do work faster. But of course, you still need humans and scientists. This is really why it's a co-scientist platform. Financially, our spending has been controlled. Our burn rate is about $3.5 million a quarter.
Last quarter, our spending fell 25%, while we still were cleared to start three new trials. R&D costs were down year-over-year, largely because we wrapped up one big trial, the LP-184 trial, which was in 60+ patients, and now we're starting up the new trial in TNBC. As of the end of the second quarter, we had about $7.4 million. Since then, we've done two raises, one for $4.4 million and one for $4 million. We've got cash well into next year, Q2 or Q3 of next year. What to watch for? LP-300 data, which we expect to have, regional licensing deals, which we hope to close next year, a first patient in their TNBC trial, multiple launches with both bladder and CNS, and then outside financing for the Open -Medicine AI, which is our AI asset.
We've got quite a few milestones and catalysts which we think will continue to show forward progress in the company. With that, I'll turn it over to any questions that we might have in the audience. Okay. Thank you, guys.