Lantern Pharma (LTRN) investor relations material

Introduction and platform overview

• withZeta.ai is an AI-driven platform designed to accelerate oncology drug discovery, especially for rare cancers, by synthesizing complex scientific data and generating real-time insights.

• The platform is positioned as a scalable, subscription-based business with significant commercial potential.

• It originated as an internal tool to optimize indication selection and drug combinations, leading to multiple orphan and rare pediatric disease designations.

• withZeta covers over 438 curated cancers, integrating bioinformatics, disease progression, biomarkers, and trial outcomes.

• Rare cancers, though individually uncommon, account for about 30% of annual cancer deaths globally.

Key features and capabilities

• Multi-agent, multi-tool architecture supports diverse user needs, from scientists to students and patients.

• Offers multiple research modes: investigator (deep dive), explorer (quick, conversational), and reporter (summarized reports).

• Users can select different scientist personas (e.g., medicinal chemist, clinical trial strategist) to tailor research workflows.

• Knowledge graphs are dynamically generated, visualizing relationships among genes, diseases, and drugs, and can be exported for collaboration.

• Integrates proprietary and external databases, including PubMed, NCI Thesaurus, FDA, and clinical trial data.

Real-time demonstration and workflow

• Demonstrated querying rare pediatric blood cancers, highlighting genes, diseases, and emerging trends.

• Showcased rapid literature review, pathway analysis, and ranking of Menin Inhibitors in pediatric blood cancers.

• Designed a novel Menin Inhibitor in real time, iterating through molecular structures and optimizing for drug-likeness and combination potential.

• Generated a detailed development budget and timeline for the new molecule, compressing months of work into minutes.

• Provided insights into failed clinical trials, identifying systematic issues such as trial design flaws and enrollment barriers.