Lantern Pharma Inc. announced that in vivo data highlighting the enhanced efficacy of Lantern's drug candidate LP-184 in glioblastoma (GBM) were published in Clinical Cancer Research, a journal of the American Association for Cancer Research. LP-184 is a unique small molecule with low nanomolar activity and favorable CNS penetration. This approach is about more than just developing new treatments, it's about making them more targeted, more effective, and ultimately doing all of this more efficiently.

This publication demonstrates ability to deliver on these aspirations and introduce new therapeutic programs in areas where there is significant unmet patient need. A Phase 1 clinical trial (NCT05933265) evaluating LP-184 in patients with advanced solid tumors is underway. The single arm multicenter trial is assessing the safety and tolerability of escalating doses of LP-184 to determine the maximum tolerated dose (MTD) and the recommended Phase 2 dose (RP2D) in patients with advanced solid tumors and recurrent high-grade gliomas, including GBM.

The anti-tumor potential of LP-184 has been demonstrated across an extensive number of in-vitro and in-vivo cancer models, including pancreatic, prostate, lung, triple-negative breast cancer (TNBC), glioblastoma (GBM), brain metastases, and ATRT. In addition to LP-184's promise as a single agent, its antitumor potency has the potential to be enhanced when used in combination with existing FDA-approved agents and other treatment modalities including spironolactone, PARP inhibitors, and radiation therapy. Results validating LP-184's anti-tumor potential have been published at leading conferences and journals including, the American Association for Cancer Research (AACR) annual meeting, Clinical Cancer Research, an AACR journal, the Society for Neuro-Oncology annual meeting, the San Antonio Breast Cancer Symposium, and the Frontiers in Drug Discovery Journal.

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 indication focused program. lead development programs include two Phase 2 clinical programs and recently initiated Phase 1 clinical programs for two additional product candidates with potential in multiple important cancer indications.

These forward-looking statements include, among other things, statements relating to: future events or future financial performance; the potential advantages of RADR®? 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 antibody drug conjugate (ADC). estimates regarding the development timing for 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; estimates regarding patient populations, potential markets and potential market sizes; sales estimates for drug candidates and plans to discover and develop drug candidates and to maximize their commercial potential by advancing such drug candidates themselves or in collaboration with others. There are a number of important factors that could cause the actual results to differ materially from those indicated by the forward-looking statements, such as (i) the risk that the research and the research of collaborators may not be successful, the risk that none of product candidates has not be successful, the risk of product candidates has been successful, the risk that none the product candidates has been successful, and the risk that none of its product candidates has been successful, but not be successful, but not be successful.