Predictive Oncology’s approach to market
Predictive Oncology has launched a breakthrough solution at a pivotal time in drug discovery that enables pharmaceutical and biotech companies to expedite drug development and speed time to market. The company’s novel platform technology valued at $400M+ has the power to impact a billion-dollar biopharmaceutical landscape in a way that has never been done before.
Pairing the largest privately held biobank of more than 150,000 tumor samples with artificial intelligence, Predictive Oncology is able to maximize the potential of drug discovery with its PeDAL™ platform by efficiently screening compounds over thousands of highly characterized patient tumor samples. This enables drug developers to more confidently predict which molecules may -- and may not -- be successful drug candidates which ultimately leads to greater chances of commercial success.
Bringing in the human element sooner
Predictive Oncology provides resources currently unavailable to the biopharma industry in the early phase of drug discovery. While they rely on data, blood samples, cell lines, tissue samples and/or animal testing, we are able to bring the human element forward sooner prior to clinical trials with real tumor samples. This means we can test how hundreds of tumor samples respond to a given drug to see how patients will respond. To date, cost and time have prohibited drug developers from testing drug response in a representative sample of the diverse human population prior to clinical development. Predictive Oncology is creating a paradigm shift in drug development by advancing this step forward. Our biobank is then powered by our proprietary AI platform, called PeDAL, which can efficiently predict which drugs will generate a response in a diverse tumor sample collection.
This platform, called PeDAL, pairs AI with our biobank of actual tumors from patients. We combine our technology with an iterative series of wet lab experiments to accurately inform the types of tumors that will respond best to the drug molecules being examined, some new and some repurposed. At the end of the day, this powerful technology helps researchers at pharmaceutical and biotech companies select better drug-tumor-type combinations more efficiently and cost-effectively. Ultimately, this leads to a higher probability of success. In fact, PeDAL can predict if a cancer tumor sample will respond to a certain drug compound with 92% accuracy.
Behind it all is our machine learning technology, called CoRE®, which was developed by two professors from Carnegie Mellon University (CMU), one of whom founded CMU’s machine learning program. This proprietary technology serves as the engine behind our PeDAL platform and makes millions of computations that help narrow down which molecules will be most successful against the tumors (and which ones won’t) with speed, accuracy and efficiency. The predictive model gets more accurate at each stage of the process. These predictions can help pharmaceutical companies achieve a higher probability of success.
Collaborating with our human engine of scientists, researchers and technologists, we also supply the brainpower to help advance the efforts of drug development in our CLIA-certified lab. Predictive Oncology supports our partners at the pharmaceutical and biotech companies with a one-stop-shop solution for their oncology drug discovery process.
In addition to leveraging the PeDAL platform for drug discovery, the platform provides an opportunity to reassess or repurpose drugs through new evaluation across additional tumor types.
Moving along the continuum
And it doesn’t stop there. In addition to the flagship technology of our PeDAL platform that powers this process, we offer a suite of other products and technologies along the drug development continuum, from early discovery to clinical trials.
Advancing the discovery with 3D tumor models
Whether coming to us to start this process at the beginning of discovery or jumping in once the promising molecules are identified, we offer the ability to test drug response with our 3D tumor models. Our novel approach to growing 3D tumors in the lab mimics a tumor in the patient’s own body. The models allow the use of real patient biology in the drug development process. These models are capable of maintaining long-term cell survival, providing a valuable drug testing platform to the pharmaceutical and biotech companies. We currently have validated models for multiple myeloma, breast and pancreatic cancer, as well as liver and lung cancer in development, and are extending the use of our tumor-specific models to offer testing of immuno-oncology therapies.
Paving the way to clinical trials
With our new in-house Good Manufacturing Practices (GMP) lab, Predictive Oncology is able to provide interconnected services in the drug formulation and manufacturing process up to clinical trials. In addition to partnering on formulation development solutions and solubility studies, we offer innovation with protein production and endotoxin removal that promote accuracy and rapid optimization.
Some of our formulation products, like our automated High throughput Self-interaction Chromatography technology platform, can help companies develop formulations within 3 months (versus 1+ years for competitors), with just one technician (instead of an entire team). This is also one area where we offer solutions beyond oncology.
Inspiring treatments is our mission
We are inspired by our mission and driven to propel Predictive Oncology into the drug development landscape in a powerful way to provide cancer patients with needed and effective treatments.
Evolving as one Predictive Oncology
We have a leadership team and board of directors that understand the drug development landscape and they have the vision to bring together a group of products and services that are uniquely positioned in the marketplace. This collection of companies has evolved to one company with many solutions across the drug-development continuum. Note that while we still report our quarterly and annual filings to reflect those segments, we bring a united front under Predictive Oncology.
How do these products and services align into each solution area?
AI + Biobank: The solution to pair our proprietary AI technology with the world’s largest biobank of actual tissue samples and the science in our wet lab to pharmaceutical companies. This site in Pittsburgh is home to our CLIA-certified lab and is the hub of collaboration for the PeDAL platform.
3D Modeling: As pharmaceutical and biotech companies proceed in the drug development process; our 3D tumor models allow for further development and screening of drug molecules.
GMP Services: Home to our GMP lab, this area allows us to expand our offerings up to the clinical trial stage. This segment includes soluble and stable formulations for proteins including vaccines, antibodies, large and small proteins, protein complexes, and endotoxin detection and removal.
Predictive Oncology will continue to offer it’s revolutionary, FDA-approved STREAMWAY® System through its segment, Skyline Medical. This revolutionary product line is an automated, direct-to-drain system that’s changing the way healthcare facilities collect and dispose of potentially infectious waste fluid.
Understanding the marketplace
The drug development landscape
The average length of time to bring a drug to market is 12 years. Cancer patients, clinicians and pharmaceutical companies all believe that this is too long.
95% of all drugs fail. The success rate for drug development can be less than 5%.
A 2% increase in the speed of preclinical and phase-one drug development could equate to an additional $50 billion in market potential for the biopharma industry.
Oncology drug development
“Only 8% of all cancer drugs that enter phase 1 clinical trials are ultimately approved by the U.S. Food and Drug Administration (FDA),” according to the Pharmaceutical Manufacturers Association.
Of the nearly 1,500 cancer drugs selected for clinical trials over the last 20 years, only 115 were approved. In a world where that process costs nearly $650 million on average for a single cancer drug, a 92% rate of failure means billions of dollars and thousands of development and testing hours wasted.
The number of cancer drugs entering pre-clinical and clinical trials is on the rise. In 2020, 23% of all clinical trials were oncology drug trials, according to ClinTrials.gov.
Artificial intelligence in drug discovery
The global AI in drug discovery market is expected to reach $1.1 billion this year, with oncology at 20% of revenue share, according to McKinsey. Expectations by the end of 2022 are anticipated to reach $1.1 billion.
The AI-fueled drug pipeline has been expanding at an annual rate of almost 40%.
AI can accelerate the time to identify preclinical candidates up to 3x faster, between 12-18 months, compared to the three to ﬁve years typically required. This is where Predictive Oncology comes into play.