RADR®, or Response Algorithm for Drug Positioning & Rescue, is Lantern’s proprietary integrated data analytics, experimental biology, biotechnology, 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 project 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 500 million oncology-specific clinical and preclinical data points, more than 144 drug-cancer interactions, and over 13,200 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. In July 2020, we announced that RADR® had surpassed 500 million data points, ahead of our initial projections for 2020. This has accelerated our path to collecting over one billion data points by the end of 2021, which will help 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 towards advances in experimental medicine.