Lantern Pharma Inc. has introduced a new artificial intelligence module designed to predict the effectiveness of cancer treatment combinations involving DNA-damaging agents and DNA damage response inhibitors. The technology integrates into the company's RADR platform, which supports AI-guided drug development, positioning Lantern Pharma as a developer of data-driven approaches to oncology drug development.
The module improves clinical success chances and aligns with growing industry efforts to personalize cancer treatments based on molecular and genetic profiles. This advancement represents a significant step in transforming the cost, pace, and timeline of oncology drug discovery and development through artificial intelligence and machine learning technologies.
Lantern Pharma's proprietary RADR platform leverages over 200 billion oncology-focused data points and a library of more than 200 advanced machine learning algorithms to address real-world problems in oncology drug development. The company has accelerated the development of its growing pipeline of therapies spanning multiple cancer indications, including both solid tumors and blood cancers.
The integration of this predictive technology into the RADR platform demonstrates the company's commitment to advancing precision medicine in oncology. By harnessing artificial intelligence with input from scientific advisors and collaborators, Lantern Pharma has achieved notable efficiency in drug development timelines and costs.
On average, Lantern's newly developed drug programs have advanced from initial AI insights to first-in-human clinical trials within 2-3 years and at approximately $2.5 million per program. This represents a substantial improvement over traditional drug development approaches, which typically require significantly more time and financial investment.
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This technological advancement in cancer treatment prediction has important implications for the oncology field, potentially leading to more effective treatment combinations and improved patient outcomes through personalized medicine approaches based on individual molecular profiles.
