Lantern Pharma develops precision oncology therapies using artificial intelligence, machine learning, and genomics to enhance the efficiency and accuracy of drug discovery and development. The company addresses the high costs and lengthy timelines associated with oncology drug development by utilizing a data-driven platform that analyzes over 100 billion data points to identify effective treatment candidates.
Funding
$60M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Oncology drug development faces challenges including high costs, lengthy timelines, and low success rates in clinical trials. Traditional methods struggle to efficiently identify which patients will respond to specific therapies, leading to wasted resources and delayed access to effective treatments.
Solution
Lantern Pharma is developing precision oncology therapeutics by leveraging artificial intelligence (AI), machine learning (ML), and genomics to improve drug discovery and development. Their AI-powered RADR® platform analyzes extensive genomic and clinical data to identify biomarker signatures that predict patient response to specific drugs. This approach aims to de-risk clinical trials by stratifying patient populations, developing companion diagnostics, and increasing the likelihood of successful FDA approval while reducing costs and timelines. Lantern Pharma's portfolio includes multiple drug candidates targeting various cancer indications, with some candidates already in clinical trials.
Target Audience
The primary target audience includes oncology researchers, pharmaceutical companies, and clinicians involved in drug development and patient care, particularly those focused on precision medicine and biomarker-driven therapies.
Features
- RADR® platform: An AI-powered drug development platform designed exclusively for oncology therapeutics.
- Biomarker discovery: Identifies biomarker signatures to predict patient response to specific therapies.
- Patient stratification: Accurately stratifies patient populations into responders and non-responders.
- Companion diagnostics development: Develops diagnostics to identify patients most likely to benefit from specific treatments.
- Drug repurposing: Identifies new uses for existing drugs based on genomic data.
- Clinical trial de-risking: Aims to increase the success rate of clinical trials by selecting the right patients.
- Portfolio of drug candidates: Includes multiple drug candidates in various stages of development, targeting a range of cancer indications.