- Lantern Pharma has introduced an AI-powered module within its RADR(R) platform to assess DDA-DDRi combination therapies.
- The module uses genomic, transcriptomic, and clinical data, to predict treatment synergy and patient response, and supported the design of Lantern’s FDA-cleared Phase 1B/2 trial in triple-negative breast cancer.
- A review of 221 clinical trials informed the development of the predictive algorithm.
- Non-PARP DDRi combinations with DNA-damaging agents showed strong outcomes in specific cancer subtypes.
- Lantern is evaluating licensing opportunities to commercialize the module for broader oncology use.
Lantern Pharma (NASDAQ: LTRN), a clinical-stage biotechnology company leveraging artificial intelligence and machine learning to redefine oncology drug development, has unveiled a new artificial intelligence module designed to predict the effectiveness of cancer treatment combinations involving DNA-damaging agents (“DDAs”) and DNA damage response inhibitors (“DDRis”). The technology is integrated into the company’s RADR(R) platform, which supports AI-guided drug development.
The launch comes at a time when the global market for combination cancer therapies is projected to surpass $50 billion by 2030, according to Lantern’s announcement (https://nnw.fm/GuuZy).
The new module draws on multi-omics data – genomic, transcriptomic, and clinical –and applies machine learning models to predict which DDA-DDRi combinations are likely to produce synergistic effects in specific patient subgroups. Lantern says this approach can help design more effective, less toxic treatments and reduce development timelines and costs by up to one-third.
Panna Sharma, Lantern’s CEO and president, said the AI module aims to streamline the development of tailored cancer therapies. “This AI-powered module is a transformative step in our mission to deliver personalized cancer treatments,” said Sharma. “By leveraging our RADR(R) platform to analyze complex multi-omics and clinical trial data, we identified optimal DDA-DDRi combinations that guided the development of our TNBC trial. We believe this approach could reduce combination therapy development timelines and costs by one-third compared to traditional methods.”
The system’s predictive capabilities are underpinned by a peer-reviewed study published in Frontiers in Oncology that examined 221 clinical trials involving DDA-DDRi regimens (https://nnw.fm/GEjpX). From these, 89 trials with interpretable outcomes were scored based on effectiveness, safety, and biomarker-driven responses. This data was then used to train the AI model, which classifies agents into 8 DDA and 14 DDRi subclasses.
Among the study’s findings, non-PARP DDRi combinations, especially those involving WEE1 inhibitors like adavosertib paired with platinum agents, showed an 80% positive outcome rate in interstrand cross-linker trials. This approach may hold particular promise for cancers with TP53 mutations, which are often difficult to treat.
The module also highlights how biomarker profiling can improve patient selection. For example, TP53 mutations and homologous recombination deficiency (“HRD”) signatures emerged as key predictors of drug response. In some regimens, reformulated drugs helped reduce toxicity. Liposomal doxorubicin, a variant with lower cardiotoxicity, was identified as a safer option for combination strategies.
Lantern has already applied the module’s insights to the design of its current clinical programs. Notably, the RADR(R) platform informed the company’s FDA-cleared Phase 1B/2 trial testing LP-184 and olaparib in triple-negative breast cancer (“TNBC”), a subtype with limited treatment options and high unmet need.
According to the company, the AI module’s architecture includes multiple specialized agents that aggregate data, classify compounds, identify biomarkers, and model outcomes. Its design allows it to evolve with new inputs, making it a continuously learning system.
Lantern is also exploring potential licensing agreements to bring the tool to a broader range of research institutions and biotech firms. The company believes the AI module could accelerate the pace of combination therapy development across various cancer types.
By embedding this technology into RADR(R), Lantern Pharma continues to position itself as a developer of data-driven approaches to oncology drug development. The module not only improves the chances of clinical success but also aligns with growing industry efforts to personalize cancer treatments based on molecular and genetic profiles.
For more information, visit the company’s website at www.LanternPharma.com.
NOTE TO INVESTORS: The latest news and updates relating to LTRN are available in the company’s newsroom at https://nnw.fm/LTRN
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