Our precision medicine drug development platform
RADR® , or Response Algorithm for Drug Positioning & Rescue, is Lantern’s proprietary integrated data analytics, experimental biology, and machine-learning-based platform. RADR® is used primarily to predict the potential response patients will have to Lantern’s drugs and to other drugs that are being reviewed and analyzed by Lantern. RADR™ is also being used to help define and develop combination strategies among drugs in development and those that are approved for a range of oncology indications. RADR® uses transcriptome data, genomic data and drug sensitivity data from a wide range of curated sources that are continually being analyzed, monitored and updated.
Our RADR® platform is core to our drug development approach for identifying the desired candidates to in-license and develop. Our RADR® platform is enabled through access to, and analysis of, a number of key datasets: (i) publicly available databases (ii) data from commercial clinical studies and trials and (iii) our proprietary data generated from ex vivo 3D tumor models specific to drug-tumor interactions. We incorporate automated supervised machine learning strategies along with big data analytics, statistics and systems biology to facilitate identification of new correlations of genetic biomarkers with drug activity. The value of the platform architecture is derived from its validation through the analysis of over 180 million oncology-specific clinical and preclinical data points, more than 120 drug-cancer interactions, and over 4,100 patient records from five data bases, one of which is our internal data base.
Our long-term objective is to collect and analyze over one billion oncology-specific clinical and preclinical data points to further enhance the prediction power of our RADR® platform. We currently have a roadmap to collect over 400 million data points by the end of 2020 and one billion data points by the end of 2021, in order to facilitate increased drug and cancer type-specific biomarker identification, discovery of new indications, and identification of additional drug candidates to build out our product pipeline.
RADR™ at the Core
Our Al-based machine learning approach combines four automated modules that work sequentially to derive drug and tumor-specific complex biomarker panels. These four main modules include: data pre-processing, feature selection, prediction and patient stratification.
Let’s start a conversation about the future of cancer drug development.
We are a biotech pharma at the intersection of Artificial Intelligence, genomics, and oncology drug development.