Lantern Pharma Unveils AI Module for Predicting Cancer Treatment Efficacy

Lantern Pharma has unveiled an AI-powered module to predict the efficacy and safety of combination cancer regimens using DNA-damaging agents and DNA repair inhibitors. The module has guided FDA clearance for a Phase 1B/2 trial in triple-negative breast cancer and is expected to reduce development timelines and costs. The combination cancer therapy market is projected to exceed $50 billion by 2030, growing at 8.5% annually.

Lantern Pharma Inc. (NASDAQ: LTRN) has 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 (DDRi) in clinical cancer treatment. This module represents a significant advancement in precision oncology, enabling faster, more cost-effective development of tailored therapeutic regimens.

The module, trained on 221 clinical trials, will initially focus on tailored combinations of DNA damaging agents and DNA repair inhibitors. It addresses a market projected to exceed $50 billion by 2030, growing at a CAGR of 8.5% [1]. Over 60% of cancer patients received DNA damaging agents or DNA repair inhibitors as part of their clinical treatment, making this module particularly relevant.

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. The module integrates genomic, transcriptomic, and clinical data to predict synergistic drug interactions, optimize therapeutic outcomes, and identify biomarker-defined patient subpopulations likely to respond to specific combinations.

Key insights from the study powering the AI module include the promise of non-PARP DDRi combinations, particularly WEE1 inhibitors like adavosertib with platinum agents, which showed an 80% positive outcome rate in interstrand cross-linker trials. Biomarkers such as TP53 mutations and HRD signatures were critical predictors of response, enabling patient stratification to maximize efficacy. The use of novel formulations like liposomal doxorubicin in combination regimens also reduced cardiotoxicity, providing a safer backbone for combination strategies.

The module’s multi-agentic framework integrates specialized AI agents for data aggregation, drug classification, predictive modeling, biomarker identification, and optimization, creating a dynamic system that is planned to evolve along with new data. Lantern Pharma is exploring licensing and commercialization opportunities to expand the application of this technology, further revolutionizing combination therapy development.

References:
[1] https://www.biospace.com/press-releases/lantern-pharma-unveils-groundbreaking-ai-powered-module-to-predict-activity-and-efficacy-of-combination-regimens-in-clinical-cancer-treatment