Biopharmaceutical companies are under increased pressure to deliver products that are not only effective and safe, but are also cost-effective and of proven value to patients.
The cost to bring a drug to market is approaching $3 billion and takes nearly a decade. That’s for a drug that successfully makes it to FDA approval. Nearly nine out of ten drugs fail to make it through the rigorous clinical trial process to market. Adding to biopharma’s list of challenges is the growing demand for innovative, risk and outcome-based contract models that link cost to the value of the drug.
Life sciences companies need to find a way to meet the demands of an increasing number of value-based frameworks and produce evidence earlier and earlier in the drug development process to be successful and differentiate their products.
To remain competitive, companies are looking for innovative approaches that drive the creation of new therapies and demonstrate the value of late-stage clinical and newly marketed products.
GNS’s AI platform, REFS™ transforms vast amounts of disparate data, including clinical trials, real world data, claims, laboratory, genetic, genomic and others to accelerate the discovery of what works for whom and why. Unlike traditional AI platforms, REFS analyzes data sets beyond correlation, instead discovering cause and effect relationships within the data. Our technology gets smarter over times as it ingests additional data, revealing unforeseen relationships and novel insights. REFS works by unraveling the complexity of biology, discovers biomarkers and subsets of patients who respond to specific drugs, and reveal disease pathways.
Allows biopharma companies to run a complete trial dataset and quickly identify predictive biomarkers to select patients with a high probability of treatment benefit in clinical trials. It also prioritizes prognostic biomarkers to identify disease drivers.
Leverage real-world data to provide insights into how a drug works in the real world (outside of the controlled clinical trial environment) both during clinical trials and post launch to accelerate development, expand indications and provide evidence for lines of therapy.
Enables data scientists and biostatisticians to work with our expert causal machine learning team to identify targeted goals and develop appropriate models to build and perform simulations on multiple analysis models.
Causal Machine Learning for Life Sciences
GNS products, services and platforms provides benefits at every stage of the drug lifecycle
- Accelerate the development of novel drugs and companion diagnostics
- Identify biomarkers
- Improve probability of success
- Discover new and novel targets
- Uncover drug-drug combination therapy
- Discover mechanism of response/non-response to drive combination therapy strategies
- Accelerate patient enrollment in clinical trials and reduce trial costs
- Discover responding subpopulations in phase II to drive inclusion/exclusion criteria in phase III trials
- Use RWD to conduct in silico clinical trials post launch for label expansion, new indications, lines of therapy
- Optimize order of therapy
- Maximize the value of therapies in the framework of payers, providers, and patients
- Increase drug persistency/lifetime value
- Uncover additional indications