From 15th to 18th of May, our CEO and CMO will be attending various international events in Hong Kong introducing the latest advances of our company. They will attend Asia Summit on Global Health, Invest in Honk Kong #investHK, and different meeting with local business Angels.
Our CEO Luis Martín Ezama will be attending the #Meet2win23 event 11th and 12th of May, exposing the characteristics of our pipeline of new cancer drugs. We are developing first-in-class antibodies and small molecules against solid tumors.
April 20th. We are going to attend the 19th Annual Anglonordic Life Science Conference.
Meet us to discuss face to face about our latest works: CA04 first-in-class monoclonal antibody, CA05 first-in-class kinase inhibitor and CA07 novel PROTAC.
We can’t wait to hear from you.
Cancerappy, together with UCLM, has been awarded the grant from the CCP21 program for the development of nanomedicines (nanoPROTACs), with a total budget of €757,000 and a project duration of 3 years.
Artificial intelligence (AI) has the potential to revolutionize the drug discovery process by accelerating the identification of novel drug candidates and reducing the time and cost of drug development. In recent years, there has been a surge of interest in applying AI techniques such as machine learning, deep learning, and natural language processing to various stages of drug discovery, from target identification and lead optimization to clinical trial design.
One of the most promising applications of AI in drug discovery is the identification of new drug targets. Traditional methods for identifying drug targets rely on empirical observation and trial-and-error approaches, which can be time-consuming and expensive. With the help of AI, researchers can analyze large volumes of biological data to identify new drug targets and predict the efficacy of potential drugs.
Another area where AI has shown promise is in the optimization of lead compounds. By analyzing chemical and biological data, AI algorithms can predict the effectiveness of different chemical compounds and identify the most promising candidates for further testing.
AI is also being used to accelerate the clinical trial process by identifying patient populations most likely to respond to a particular drug and predicting potential drug interactions and side effects. This can help to improve the efficiency of clinical trials and reduce the risk of adverse events.
At Cancerappy, we have used our developments in artificial intelligence as a driving force for the discovery and validation of new targets, as well as for the identification and optimization of new compounds with the potential to become drugs that reach the clinic.
However, there are also some challenges associated with using AI in drug discovery. One of the biggest challenges is the lack of large, high-quality datasets that are necessary to train and validate AI algorithms. Additionally, there are concerns around data privacy and intellectual property rights, as well as the need for regulatory oversight to ensure the safety and efficacy of AI-generated drug candidates.
Definitely, the application of AI in drug discovery has the potential to transform the pharmaceutical industry by accelerating drug development and reducing costs. While there are still some challenges to overcome, the rapid progress in AI technology and the growing availability of high-quality data make it likely that we will see more AI-generated drugs in the future.
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.
Share the story of our progress with your colleagues and friends and inspire more scientists to contribute to this exciting field.