AI for drug development

The traditional process of drug development was costly as well as time-consuming. However, artificial intelligence-powered platforms have now made the process easy by enabling biotech companies to use vast data to identify patient response markers quickly and develop new drugs cheaply and efficiently, writes Morgan Stanley.

In recent years, advancements in technology have made it easier to obtain and store reams of digital patient data, resulting in rich troves of medical imaging, genomic data, health records and other patient information. These can be mined by AI platforms to facilitate the development of drugs faster and with more chance of success even in the early stages of creation.

“Predictive diagnostics, enhanced by data, present a significant near-term opportunity for the life sciences industry,” says Tejas Savant, who covers life science tools and diagnostics at Morgan Stanley Research.

For biotech companies, it can often take a blockbuster drug discovery just to break even. The average investment needed to bring a new drug to market is abound $1 bn, while the actual cost of research and development may be as high as $2.5 bn per marketed therapy.

In other words, savings from AI could offer significant value. But with the high risks involved in creating biologically feasible treatments and the limited history of the technology platforms involved, investors will need to see solid evidence for real-world use cases for AI-enabled drug development.

An AI drug development platform could generate significant revenue growth through partnerships, assuming modest annual increases in AI investment within biopharma research and development budgets. Morgan Stanley says that modest improvements in early-stage drug development success rates enabled using AI and machine learning could lead to an additional 50 novel therapies over a 10-year period, which could translate to a more than $50 bn opportunity.

View the complete insight here.