Drug development is a process that costs billions of dollars a year, and most drugs fail in the testing phase. For the healthcare ecosystem and laboratories, the scenario represents a challenge that can now be tackled thanks to the use of artificial intelligence (AI) systems. At least that’s what Google wants.
The Mountain View company’s cloud division announced two new AI-based suites of solutions it will make available to biotech companies, pharmaceutical companies and public sector organizations to accelerate drug discovery and precision medicine.
Within the framework of the Bio-IT World Conference, Google Cloud presented the Target and Lead suite that aims to help scientists and researchers more clearly identify the function of amino acids and predict the structure of proteins, in the process Aspects of great relevance to develop new drugs.
That’s how Choiz works, a startup that combines telemedicine with telepharmacy to guarantee accurate diagnoses for its patients without shifts or lines.
Now, according to the National Center for Biotechnology Information (NIH), identifying a biological target involved in a disease that is viable for intervention by a drug is a process that can take up to 12 months. it takes time.
Most companies use techniques such as X-ray crystallography and nuclear magnetic resonance (NMR) to determine the 3D structures of proteins, although this is a method with a high rate of failure.
The promise of Google Cloud’s Target and Lead suite is to reduce the time and cost of both processes by rapidly predicting antibody structures, evaluating structure and function by amino acid mutagenesis, and accelerating de novo protein design.
In the final phase, Google’s new solution will allow biopharmaceutical companies to bring drugs to market faster and reduce their development costs due to efficient computer simulation (or in silico) simulation-based drug design.
Google maintains that this new solution is being used by major pharmaceutical companies like Pfizer as well as biotech companies like Cereval.
Google wants more drugs for less, thanks to AI
The second solution offered by Google is the Multiomics Suite, which aims to accelerate the discovery and interpretation processes of genomic data to help companies and organizations design precise treatments.
Genomic differences can affect susceptibility to certain diseases as well as how each individual responds to medication. The full potential of precision medicine can be unleashed by an increase in the diversity and amount of genomic knowledge.
However, the acquisition, storage, distribution, and analysis of this type of customized genomic data involves high costs that not all companies or organizations can afford, especially when the amount of this type of data, according to BMC Bioinformatics, exceeds 7 million copies every year. and doubles between 12 months.
Under this understanding, Google’s Multiomics suite promises researchers rapid, low-cost access to the genomic data needed to understand how genetic variations affect disease in order to develop appropriate and even personalized treatments. does.
Available globally from today, the Target and Lead Identification Suite and Multiomics Suite add to a growing range of AI-based tools that could be the key to alleviating the world’s drug shortage problem.
Currently, developing a new drug from a basic idea to the launch of a final product is a process that can take 12 to 15 years and cost more than $1,000 million, according to the British Journal of Pharmacology. AI can certainly accelerate this path and reduce costs.