In the early stages, 97% of drug development fails.
The discovery and development of a new cancer drug is a long, costly, and high-risk process.
The estimated time to develop a new cancer drug exceeds 10 years, costs 2 billion EUR or more, and yet only 4% of the new molecular entities that are created from preclinical research will become an approved drug.
And remember that cancer is the second leading cause of death worldwide, with more than 18 million new cases and almost 10 million deaths per year.
Therefore, there is a pressing need to reduce the failure rate in the discovery and development process of new cancer drugs.
Looking at the entire drug discovery and development process, at CancerAppy, we particularly focus on the very early stages of the discovery and development of new therapeutic targets in cancer.
Why?
Because the highest level of risk pertains precisely to the early stages, from target identification to pre-clinical development, where failures rates may amount up to 97%.
Our solution combines AI powered data with, deep AI knowledge, adn deep scientific knowledge to develop an “end to end” platform that enable researchers and scientists to identify novel therapeutic proteins, validate novel targets, and eventually discover new cancer drugs with enhanced speed, accuracy, and success.
We can say Cancerappy platform is a failure reduce machine
What is the impact of our platform?
The therapeutic targets we discovered and validated using our platform have proven that our platform substantially reduces time and resources in the target validation stage, from 4 years down to just 2.
We are currently handling more than 3 million compounds, more than 25K genes, 400 different tumor types, and a massive amount of cell lines,
So from the beginning until now, our solution has reduced uncertainty when considering the number of inhibitors identified, and our prediction score has increased massively, from initially 8% to a 24% success rate in target validation.
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