Lantern Pharma Inc. announced a substantial increase in the power and capabilities of RADR(R) focused on improving the drug development process for immune checkpoint inhibitors (ICIs). Additionally, recent clinical trial failures reveal headwinds to the desired expansion of ICIs for a broader range of cancers and patient groups. Currently, there are over 5,200 ongoing clinical trials involving ICIs, many of these lacking adequate biomarker strategies or guidance from AI enabled approaches to optimize the selection of patient responder populations.

This capability will be powered by tens of billions of new data points from immunotherapy and checkpoint inhibitor studies that Lantern has begun to add to its RADR(R) platform. Lantern plans to deploy its new RADR(R) ICI predictive module with biopharma partners and to identify potential comparative strategies for LP-184 and LP-284, the first of Lantern's drug candidates developed internally with the assistance of the RADR(R) AI platform. By harnessing the power of AI and with input from world-class scientific advisors and collaborators, have accelerated the development of growing pipeline of therapies including eleven cancer indications and an antibody-drug conjugate (ADC) program.

On average, newly developed drug programs have been advanced from initial AI insights to first-in-human clinical trials in 2-3 years and at approximately $1.0-2.0 million per program. lead development programs include two Phase 2 clinical programs and multiple upcoming Phase 1 clinical trials anticipated for 2023. These forward-looking statements include, among other things, statements relating to: future events or future financial performance; the potential advantages of RADR(R) platform in identifying drug candidates and patient populations that are likely to respond to a drug candidate; strategic plans to advance the development of drug candidates and ADC development program; expectations and estimates regarding clinical trial timing and patient enrollment; research and development efforts of internal drug discovery programs and the utilization of RADR®?

platform to streamline the drug development process; intention to leverage artificial intelligence, machine learning and biomarker data to streamline and transform the pace, risk and cost of oncology drug discovery and development and to identify patient populations that would likely respond to a drug candidate; sales estimates for drug candidates and plans to discover and develop drug candidates and to maximize their commercial potential by advancing such drug candidates ourselves or in collaboration with others. There are a number of important factors that could cause our actual results to differ materially from those indicated by the forward-looking statements, such as (i) the risk that our research and the research of our collaborators may not be successful, the risk that none of our product candidates has received FDA marketing approval, and we may not be able to successfully initiate, conduct, or conclude clinical testing for or obtain marketing approval for our product candidates, (iii) the risk that no drug product based on our proprietary RADR(R®? AI platform has received FDA marketing approval or otherwise been incorporated into a commercial product, and (iv) those other factors set forth in the Risk Factors section.