Today’s pharmaceutical companies face immense pressure to effectively deliver products that are safe, valuable to patients, and cost-effective. Life science companies are now being asked not just to develop new therapies, but to identify the patients who will most benefit from those treatments. Targeting the right patients for clinical trials is essential for passing the constantly rising bar for FDA approval. After approval, patients and payers are demanding real-world evidence of drug effectiveness and asking pharmaceutical companies to back that up with risk sharing agreements and value-based contracts.
Advances in artificial intelligence and new and emerging data sources now allow pharma companies to gain significant efficiencies in the targeting of new therapies and to demonstrate the value of late-stage clinical and newly approved products.
ClosedLoop.ai is an AI-based predictive analytics platform that uncovers insights in vast amounts of disparate healthcare data, including clinical trials, real world clinical and claims data, genomic, social, and environmental data. ClosedLoop can identify biomarkers that are predictive of treatment response, and select patient groups most likely to benefit from treatment. ClosedLoop.ai continues to get smarter as it absorbs more data - identifying new relationships and novel insights. ClosedLoop’s platform can be used to answer questions like:
BIOMARKERS: Which patients will have an increased success rate based on biological factors?
DRUG-COMBINATIONS: Which drug combinations are most likely to be successful?
SEGMENTATION: Which groups of patients respond differently to treatment?
STRATEGY: Which subpopulations should be included/excluded based off of predicted success rates?
RESPONSIVE: Which patients are responding to treatment?
EVENTS: Which patients are most likely to experience adverse reactions?
EFFECTIVENESS: How will clinical trial results translate into real world effectiveness?
VALUE: What improvement in outcomes will a new treatment generate over existing therapies?
SWITCHING: Which factors are most relevant in understanding which patients switch drugs?
MARKETING: Which physicians can we market to?
Ensure your data scientists are doing what they do best.
“Data scientists spend most of their work time (about 80%) doing what they least like to do: collecting existing datasets and organizing data. That leaves less than 20 percent of their time for creative tasks like mining data for patterns that lead to new research discoveries.”
- NIH Strategic Plan for Data Science