Lantern Pharma Launches AI Module Trained On 221 Clinical Trials To Predict Efficacy Of DNA-Damaging Agent Combinations; Platform Already Guided FDA-Cleared Phase 1B/2 Trial In Triple-Negative Breast Cancer

Author: Benzinga Newsdesk | July 15, 2025 09:42am

• The AI module, trained on 221 clinical trials, will be incorporated as part of Lantern's AI platform, RADR ®, and will initially focus on tailored combinations of DNA damaging agents and DNA repair inhibitors
• Addresses $50+ billion combination cancer therapy market projected to grow 8.5% annually through 2030
• Over 60% of cancer patients received DNA damaging agents or DNA repair inhibitors as part of their clinical treatment and unique AI-powered module will focus on predicting the efficacy, safety and biomarker signatures associated with those potential treatments
• Platform successfully guided FDA-cleared Phase 1B/2 trial design for LP-184 + olaparib in triple-negative breast cancer

Lantern Pharma Inc. (NASDAQ:LTRN), a pioneering artificial intelligence (AI) company transforming oncology drug discovery and development, today announced the launch of an innovative AI-powered module within its proprietary RADR® platform, designed to predict the activity and efficacy of combination regimens involving DNA-damaging agents (DDAs) and DNA damage response inhibitors (DDRis) in clinical cancer treatment. With the global market for combination cancer therapies projected to exceed $50 billion by 2030, growing at a CAGR of 8.5%, this module represents a significant advancement in precision oncology, enabling faster, more cost-effective development of tailored therapeutic regimens. Leveraging this AI-driven framework, Lantern Pharma has successfully architected and achieved FDA clearance for a Phase 1B/2 clinical trial in triple-negative breast cancer (TNBC), focusing on a novel DDA-DDRi combination regimen with promising preclinical efficacy.

In a peer-reviewed study published in Frontiers in Oncology, Clinical outcomes of DNA-damaging agents and DNA damage response inhibitors combinations in cancer: a data-driven review, Lantern Pharma researchers systematically analyzed 221 DDA-DDRi combination-arm clinical trials, involving 22 DDAs and 46 DDRis, to develop this module. The study categorized DDAs into eight subclasses (e.g., alkylating agents, interstrand cross-linkers) and DDRis into 14 subclasses (e.g., PARP, ATR, WEE1 inhibitors). From these, 89 trials with interpretable outcomes were scored for clinical effectiveness, safety, and biomarker-driven responses, providing a robust dataset to train the AI module.1

Transforming Cancer Combination Therapy Development

The new AI module represents a paradigm shift in precision oncology, leveraging machine learning to predict which drug combinations will be most effective for specific patient populations while minimizing toxicity risks. This data-driven approach has already demonstrated its value by successfully guiding the design of Lantern's FDA-cleared Phase 1B/2 clinical trial combining LP-184 with olaparib in triple-negative breast cancer (TNBC).

Posted In: LTRN