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Lantern Pharma Inc. (NASDAQ:LTRN), a leader in AI-driven cancer drug discovery and development, announced a series of important milestones related to the development, size, and advancement of RADR® -- its proprietary AI platform focused on transforming the cost, pace, and timeline of oncology drug development.
"Every data point we add to RADR® further advances our goal of building the most complete, largest, and most powerful AI platform for oncology drug development. This unparalleled growth in data points provides us with greater and potentially more accurate insights into areas of cancer treatment that have insofar seen little to no progress, while also giving us a solid and cost-advantaged starting point to transform that," said Panna Sharma, CEO and President of Lantern Pharma. "RADR® has now surpassed 60 billion data points, making innovation in developing cancer therapies potentially more precise, powerful, and comprehensive. Additionally, we continue to automate key areas of the growth in our data collection and curation, leading us to have more successful and larger data expansion campaigns. We expect that the RADR® platform will advance to over 100 billion data points this year, giving us a unique and unparalleled ability aimed at guiding drug development in a wide range of adult and pediatric cancers that need improved therapies."
Lantern plans to continue the expansion and growth of RADR® data with an increasingly automated, machine learning enabled process, that allows the collection, tagging, and curation of datasets from proprietary, collaborative, and public sources in a highly efficient manner. Lantern also expects that a meaningful amount of the new data will come from immuno-oncology (IO) studies, and IO clinical trials as well as from proprietary analysis aimed at molecular feature extraction from hundreds-of-thousands of molecules (both FDA approved and those under development). Large-scale data expansion efforts were initially begun for RADR® in early 2019 when the platform had under 20 million data points, and grew to nearly 300 million data points by mid-2020 (at the time of the Lantern's IPO) and today have grown beyond 60 billion – a 200x increase since the IPO and a nearly 3,000-fold increase since the start of the data-growth campaigns. This strategy has allowed data from thousands of previously siloed sources to be analyzed in a more comprehensive, complete, and productive manner and has aided in the development of new indications for LP-184 and the development of LP-284 in a highly compressed and cost-effective manner while also leading to several conference posters, and scientific publications by Lantern Pharma and our collaborators.
The current data-growth campaigns, which plan on the addition of antigen, immune-response, and protein data, are also enabling a more robust and powerful multi-omic analysis that is positioned to guide the use of LP-184, LP-284, and other similar synthetically lethal agents in combination with standard-of-care checkpoint inhibitors. These large-scale, machine-learning driven analyses can be critical in future efforts where AI can contribute more efficiently to drug development efforts by automatically creating its own models and testing combinations of drugs not previously being considered, including in rare and hard-to-treat oncology indications where conventional therapies have failed to show any measurable improvement or where patients often will develop resistance to these therapies and require new approaches.
"With every new piece of useful data, RADR® becomes more capable of creating and testing against statistically meaningful models that can help us to identify potential treatments in areas of unmet need that can make a difference and lead to positive outcomes for patients," said Sharma. "With the growing set of data points RADR® is able to test hundreds of combinations of drugs against these models we did not have before, and quickly determine whether certain compounds deserve additional attention from our efforts or the efforts of our collaborators. Additionally, we can uncover new correlations that may have gone underappreciated or have been challenging to uncover without the support of powerful AI approaches. This expansion of data within RADR® provides the potential to identify and predict pathways of resistance early in drug development and therapeutic avenues to mitigate or circumvent these challenges."
"RADR's growth has also deepened our capabilities in novel ADC development and has allowed us greater capability in predicting combination therapy approaches using our own drug candidates as well as existing approved immuno-oncology therapies," said Sharma. "Previously this type of work in identifying and analyzing new cancer therapies and new uses for existing cancer therapies has been costly, slow and often lacked correlations with real-world outcomes. While studying the impact of compounds and therapeutic combinations previously took years, RADR® has reduced many aspects of this process to mere months. Because the drug development timeline already takes between 10 and 12 years to successfully complete, being able to reduce that by even half would have the potential to not only change the industry but also outcomes for millions of people waiting on cancer therapies and cures."
Posted In: LTRN
