This technology predicts outcomes of pharmaceutical use based on data from previous patients to identify the best course of action, the major goal of pharmacogenomics. Many classes of disease have large percentages of patients who do not respond to medications, represented wasted time, money, and patient wellness. Understanding pharmacogenomics will help save tens of millions of dollars in ineffective drug administration for diseases ranging from diabetes to cancer. It has proven extremely difficult to determine which patients will respond best to which treatments. This technology uses a novel statistical approach to predict—within minutes—the likely outcome of pharmaceutical administration to a patient based on their molecular and clinical data to guide physicians in optimal healthcare decision making.
A General Approach to Pharmacogenomics
Predicting from genomic data how a patient will respond to treatment in complex diseases is an open problem with few success stories. This technology has risen to the challenge, besting the leading competitors in two different pharmacogenomics settings: the 2013 DREAM Breast Cancer Challenge and the 2014 DREAM Rheumatoid Arthritis Responder Challenge. This represents a meteoric rise in the effectiveness of pharmacogenomics prediction capability. The technology does this by matching a patient with the most similar observations from previous patients and using a statistical model to predict the new patient’s outcome based on the weighted outcomes of all previous patients. This allows a generalized approach to pharmacogenomics, the only of its kind.
- A general approach toward predicting patient responses to drug or other treatment.
- The only tool available which is applicable across a wide range of diseases.
- Incredibly rapid, producing results in minutes.