Precision oncology therapeutics using A.I., genomics and machine learning
Lantern Pharma is a clinical stage oncology biotech that leverages Artificial Intelligence and Machine Learning to deeply understand genomic data.
Lantern in-licenses and develops therapies using genomic data, Machine Learning, and computational biology modeling to identify the patient groups most likely to respond to a therapy, and to clarify the potential underlying mechanism(s) of action.
We embrace nascent technology
Emerging technologies can help transform the pace and insight of oncology drug development. We currently leverage:
• Artificial Intelligence
• Machine Learning
• Cloud Computing
These techniques and methods have been robustly tested in many different industries and applying them to healthcare will help solve two of the central problems in cancer therapy:
• Stratifying patients into responders and non-responders, ultimately de-risking and streamlining clinical trials.
• Clarifying insight into the mechanism of action for drugs, resulting in improved molecular targeting.
Both of these problems have great potential to be improved through the application of A.I., which helps to shorten the drug development timeline and reduce costs.
We are continuously advancing our precision oncology therapeutics platform through partnerships with cloud computing firms, hospitals, clinical healthcare centers, investigator consortia, and tissue and data banks.
“Today we are able to understand the genomic or biomarker basis of why certain patients respond exquisitely to certain therapies in cancer, while others fail to respond at all. At the same time, we are now able to able to access the computing power and algorithms required to make sense of the clinical and scientific data that is available to us, and then propose precision focused trials trials that provide us with a compressed development timeline.”
“RADR associates massive molecular genomic data sets with drug sensitivity data. It identifies gene signatures that can then predict patient responses to cancer drugs. One of the strengths of RADR is that it applies a series of features that then reduce a large number of biomarkers to a smaller, more manageable set of gene signatures that can then be used in diagnostics. Because our approach is at the intersection of AI, genomics and drug development, we are a magnet for collaborations with academic institutions, biotechs and healthcare companies.”