Lantern Pharma Reports Complete Metabolic Response in Aggressive Lymphoma Patient with AI-Discovered Drug LP-284

Lantern Pharma has reported a confirmed complete metabolic response in a patient with aggressive lymphoma treated with its investigational drug LP-284, marking a significant milestone for the company's artificial intelligence-driven oncology platform. The 41-year-old patient with Grade 3 non-germinal center B-cell diffuse large B-cell lymphoma achieved the response after failing four prior treatment regimens, including CAR-T and bispecific antibody therapies. This outcome supports the drug's synthetic lethal mechanism and suggests potential for addressing a critical treatment gap for patients who have exhausted immunotherapy options.

The response occurred after just two 28-day cycles of LP-284 treatment in the ongoing Phase 1 trial, with data presented at the 25th Annual Lymphoma, Leukemia & Myeloma Congress in New York City. The company's RADR AI platform, which leverages over 200 billion oncology-focused data points and more than 200 machine learning algorithms, was instrumental in discovering and developing LP-284. Lantern CEO Panna Sharma stated that the results validate the company's AI-driven approach and highlight LP-284's dual potential as both a monotherapy and in combination with FDA-approved agents such as rituximab.

The company noted LP-284's favorable early safety profile, multiple FDA Orphan Drug Designations, and strong intellectual property protection through 2039. Additional information about the company's developments is available at https://ibn.fm/LTRN. The achievement represents a significant validation of artificial intelligence in drug discovery, particularly in oncology where treatment resistance remains a major challenge. Lantern's growing pipeline of therapies spans multiple cancer indications including both solid tumors and blood cancers, with an estimated combined annual market potential exceeding $15 billion.

The company's antibody-drug conjugate program and multiple clinical trials, including a Phase 2 program, demonstrate the broad application of their AI platform in accelerating oncology drug development. This development is particularly important as it shows that AI can identify promising drug candidates for patients who have exhausted conventional treatment options, potentially opening new avenues for treating resistant cancers. The success with LP-284 suggests that AI-driven approaches could significantly reduce the time and cost of bringing new oncology therapies to market while addressing unmet medical needs in cancer treatment.