Lantern Pharma Inc. Common Stock Investor update

Good afternoon, everybody. We are going to go ahead and get started on our call and webinar here in a few minutes. We are going to give everyone some time to log in. We are increasing the number of attendees. I have got my colleagues from Lantern and Open Medicine also on, Dr. Kishor Bhatia and Reid Bender. They will introduce themselves a little bit later in this call. As we give everyone some time to join in, I am going to remind everyone that we will be making forward-looking statements, and the presentation I am making today will contain forward-looking statements, and that I urge you guys to read our full disclosure that is in compliance with Section 27A of the Securities Act of 1933, and that we may or may not update statements that we make today, and these statements will include potentially issues such as anticipation, revenue, product roadmaps, estimates, et cetera.

You can also access our annual and quarterly reports under the Investor SEC Filings tab of our website, and also at sec.gov. All forward-looking statements in this presentation represent our judgment as of today, the date, September 23rd, and we disclaim any obligation to update any forward-looking statements to conform to actual results or changes in our expectations. With that, I am going to go ahead and get started. Thank you guys again for joining us. I have got my colleagues, Dr. Kishor Bhatia and Reid Bender on as well, and they will help me with questions. I urge you guys to type in your questions.

We will be doing a demo talking about Open Medicine AI, where we are headed, the feedback that we have been getting so far, and also talking a little bit about how we see the roadmap evolving, both in terms of usage and in terms of growth of this amazing platform. Again, I urge you guys to type in your questions, or raise your hand when we get to the Q&A, and we will try to work through as many questions as we can today. Okay. Can everyone see the presentation?

Great. We are going to talk about Open Medicine. It is a platform that has really been built internally by drug developers for the broader drug development community. Our vision for Open Medicine is really to become like a Bloomberg, but for medicine, for everyone involved, whether they be developer, service provider, investigator, individual, power groups, and large pharma companies. Our vision was initially to accelerate our own drug development because we had to. We were a small, emerging company. What we have found, which is really quite exciting and amazing, is that the entire world realizes the power of AI and has moved to very similar models. We have spun out or are spinning out Open Medicine into its own entity, which we accomplished earlier this year, and we have transferred certain assets and IP and capabilities into this new multi-agentic platform.

Again, I made the forward-looking statements earlier in the call, so I will not go through those. Let us talk a little bit about what we are going to cover. I am going to give you a view of the market size and how we view the market, which is a little bit different than how a lot of other companies and people approach the market. It is a large market, no doubt. As a former analyst myself and a consultant, I really try to size the opportunity bottom up and in terms of what is really addressable, where is the revenue, new or disruption coming from, and how will this actually shift to companies like ours? I am going to give feedback from customers.

I am going to talk about the roadmap toward the end, types of partnerships that we have gotten interest in and seem to be pursuing, and also a little bit on, of course, the demo, because that is worth more than any webinar alone, is actually seeing the differentiated capabilities in real time. Again, we are going to allot about 45 minutes. We are going to try to end our discussion and demo around 1:00, and then leave 10 to 15 minutes for Q&A. Like I said earlier in the discussion, when we first started Open Medicine, back when I got to the company at the end of 2018, early 2019, we were not setting to build an AI platform company.

We were really building a drug development company that was using an AI platform, an industrialized AI platform, not one that you take off the shelf and put away, but something that was always on, always evolving, and really an AI-native drug development company is how we viewed ourselves. Our focus was really singular. Can we get cancer drugs faster and with less capital? We built the tools, and we built the methodologies, and we built the approach because that is what we needed. That was the way that we could do it. We built this on the back of clinical programs. At the time, we had no clinical programs. They were all preclinical. In fact, some of these concepts did not exist. The indications, the Fast Track designations, LP-284 as a molecule, all were ideas.

Today, we have LP-184 that is going into phase I-B, phase II trials, multiple trials, as well as has four Rare Pediatric Disease Designations and two Fast Track Designations and multiple Orphan Drug Designations. We have LP-300 that is in a phase II trial that has seen some great results so far in a very targeted population called L858. Mutation in non-small cell lung cancer, and we have also developed a whole new drug, LP-284, which is a stereoisomer of 184, which was optimized using our platform, and we brought it to GMP quality, launched at phase I, have three Orphan Drug Designations for that drug. Just yesterday, we talked about a new patent for that as well, and we have seen some good responses in the trial. We also focused on CNS cancers and developed a new subsidiary to focus on these devastating cancers, both in pediatric and adult brain cancers.

We have, we think, a library of amazing work. But the most important thing is that all the molecules that we set out to develop not only showed themselves to be tolerable and got to a meaningful dosage that is therapeutically relevant in trials, but are actually seeing results, mechanistic results that were thought about using our AI and data-driven approach. We started this AI layer, this machine learning layer, to support these programs. Initially, RADR, which was a machine learning platform, which has a lot of accolades, hundreds of ML algorithms, 200 billion plus data points. But again, focus for us and for our collaborators. It was internally focused and continues to be used internally. It is a team effort. You have got to bring in people who are data scientists and molecular biologists and other multidisciplinary people.

It was really there to compress what traditionally takes place in early development that can take two, three, four, five years, and try to compress that to one or two years. As we saw that succeed, we thought, well, could we do something even more aggressive? Could we build this into a natural language system that you can prompt without the use of a data engineer, without the use of a multidisciplinary team, and still access a lot of the information and algorithms and set them off in an automated fashion? At the time, as natural language processing, ChatGPTs, and LLMs were growing, we thought, could we create this for rare cancers? Could we create an AI for good, where we take all of our knowledge, guardrail the architecture, and focus it on the entire litany of hundreds of rare cancers?

Initially, it actually started as an experiment internally with the team. We said, "Wouldn't it be great if we could take a biomarker or mechanism and just fully address every single cancer and see where it made most sense and automatically rank it and do all the complicated pathway analysis that we do, and then actually verify it both at the RNA and protein level, and then have it come back to us with the results?" That is great. It is not impossible to do, but it could take days or weeks to do that, enrich for pathways that are both known and maybe not known, enrich with your own proprietary data, look at results, look at different bioinformatic analysis, guardrail with existing published literature, and iterate. We thought there is got to be a better way, a more automated way, and that is why we created withZeta.

Our learnings in withZeta were fantastic, and Reid will talk a lot about the architecture later on. Each layer that we built was built to support and guide the programs. Kishor will talk about how we've now also used withZeta to develop whole new programs. We have an entire library of new molecules that we haven't even really talked about, which we'll be talking about later this year. Eventually we said the same architecture we can use not just in rare cancers, but we can use it for other therapeutic development purposes. We can expand it to go downstream. We can expand it to go into new disease categories. We can actually now use a subscription and freemium model. There's no reason we have to go door to door to every pharma and spend months convincing them.

Let's just open this platform up and let's disrupt the way drugs are being developed. Again, each layer for us was built to run, influence, guide, and validate the work that we've already done in the past. This is very important. We're not approaching this as some kind of data and AI agent building exercise. We're really approaching this as can we empower people to do drug development the way we would do it faster, cheaper, highly parallel, and at a level and pace that hasn't been done before. Where do we sit with Open Medicine? We see Open Medicine as a wonderful complement to our core drug discovery and development business in cancer. It allows our team to focus on a major new category and our investors to participate in the upside of this AI revolution.