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 for patient genetic profiling for robust drug response prediction.





  • The RADR process has 4 key components

  • 1 RADR-NET

    Patient data from clinical trials are analyzed to determine drug responses, clinical efficacy, and safety and closely matching tumor samples are obtained from clinical network.

  • 2 RADR-3DLab

    Lab-based genomic and drug sensitivity analysis of patient samples and disease-specific genetically modified tumor models.

  • 3 RADR-AI

    Our Al-based machine learning approach combines three automated modules. 1. data pre-processing, 2. feature selection, and 3. prediction.

  • 4 TRIALS and STRATIFICATION

    Biomarker panels are derived to select true responders for recruitment into clinical trials.

RADR™ at the Core

Our Al-based machine learning approach combines three automated modules that work sequentially to derive drug and tumor-specific complex biomarker panels. These three main modules include: data pre-processing, feature selection, and prediction.

(a) Data pre-processing includes data cleaning, transformation and normalization without compromising the original quality of data.
(b) Feature selection, RADR-AI performs proprietary gene filtering via biological, statistical and machine learning-based methods. Not all genes have equivalent relevance for response prediction and this method ensures that genes that do not contribute to outcome prediction are excluded from the output.
(c) Prediction AI-driven program reduces the initial number of genes (approximately 500) to a more manageable number, typically less than 50 potential predictive biomarkers.

RADR™ Overview Video

Let’s start a conversation about the future of drug development.

We are a biotech pharma at the intersection of Artificial Intelligence, genomics, and oncology drug development.

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