The cell, fundamental biological unit of the human organism, is the site of millions of chemical interactions. In some cases, these cellular mechanisms can be disrupted (viruses, chemical compounds, genetic mutations) and lead the cell to a pathological state that causes various diseases. DeepLife is developing a technological solution capable of identifying and precisely reproducing the mechanisms of these pathologies at the molecular level in order to offer the most effective drugs.
From the complexity of developing new drugs to the birth of DeepLife
Today, designing a new drug is a very long process. In general, it takes 10 to 15 years before a possible marketing.
To achieve this result, it is necessary to determine which molecule (or “ligand”) could have an activating or inhibiting action on the target (or “receptor”) involved in the disease. This identification of the potentially effective molecule and its therapeutic target is a crucial step in the development of a new drug.
Unfortunately, the complexity and the large number of chemical interactions occurring at the cellular level slow down this quest for new drugs. And very often the methods used in the selection of molecules likely to be effective in the treatment of a given disease and the identification of their therapeutic targets turn out to be slow, costly and not very fruitful.
Result, today, only 1% of diseases have a treatment approved by the FDA (Food & Drug Administration), the administration responsible for the marketing of drugs in the United States.
From this observation was born DeepLife, which, through its patented and innovative technology, is reshuffling the cards of pharmaceutical research.
A solution using AI for the interpretation of biomolecular data
DeepLife, founded in 2019, specializing in digital biotechnology, has set itself the task of accelerating the discovery of new drugs through artificial intelligence.
But how could this technology help in the process of developing new drugs? It all starts at the cellular level! In a sick person, cells are taken from the pathological organ or tissue in order to measure its activity in vitro. Millions of cellular samples are then obtained using different methods for measuring molecular activity (DNA, RNA, proteins, etc.).
Using algorithms created by DeepLife, all this data is automatically aggregated and harmonized to produce digital twins of healthy or diseased cells.
Beyond the need to collect as much data to feed its artificial intelligence algorithms, there is also a statistical interest. Indeed, the absolute truth does not exist in biology, much research is done by differential analysis between a healthy state (control) and a pathological state. The data collected by DeepLife therefore makes it possible to significantly increase the statistical relevance of the studies by going from a few dozen samples compared to several million. Thus, the digital twins reproduce the structure of the cell studied and all the chemical interactions that occur there. It then becomes possible to test in silico novel drug combinations with strong therapeutic potential. Compounds that best meet the efficacy and safety criteria will then be taken to clinical trials as drug candidates.
The ambition to be a major player in scientific research
DeepLife saves significant time and money in the drug discovery process, by enabling biologists to identify therapeutic targets faster. Thanks to its technological platform, DeepLife reinforces the quality of research processes: a guarantee of reliability for patients who will have access to future drugs.
Furthermore, the use of digital twins of cells is also extremely useful for the repositioning of drugs, offering the possibility of reusing existing ones for new therapeutic indications.
Finally, the interest of the research carried out by DeepLife does not stop there, since the optimal use of biomolecular data also strengthens our knowledge, still very incomplete, of complex cellular mechanisms. Valuable knowledge that will eventually make it possible to improve personalized care for millions of patients.
To find the latest news from DeepLife, go directly to their Linkedin page.
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The editorial staff of Le Figaro did not participate in the production of this article.