Artificial Intelligence (AI) is revolutionizing biotechnology, offering significant productivity gains and new opportunities for innovation. However, the industry must address data security concerns and other risks to reap the full benefits AIpotential for drug development and improved healthcare. Yuri Khodjamirian, Chief Investment Officer at Tema ETFs, discussed this and more in a recent episode of OPTO. Sessions.
AI has introduced innovations and improvements throughout the economy, and pharmaceutical companies are working to adopt technology to improve processes and outcomes. On May 21 for example, Sanofi (SNY) announced its partnership with OpenAI and Formation Bio to improve its drug development using AI.
The application of AI in the health field is not new, according to Khodjamirian. A 2021 report from the United States Food and Drug Administration noted that more than 100 applications had already been submitted that used some form of AI or machine learning in at least one of the drug discovery, research and clinical, advanced pharmaceutical manufacturing or post-marketing. security monitoring.
“Health care lends itself wonderfully to this,” Khodjamirian said. “If you think of AI as just a big statistical model that can ingest a lot of data and give you patterns… the biological system is perfect. There is a lot of data, petabytes, produced by many very complex interactions. When you apply modern tools to this, you can get a lot of information.
A boon for productivity
“Some people are as optimistic as saying, ‘This will be the step that moves biology from the realm of uncertainty, especially in areas like the brain, to what could be an engineering-type system.’ And it’s really exciting,” Khodjamirian said.
AI has also been instrumental in improving productivity in the sector. “The sector has a notoriously poor reputation in terms of productivity. People say it can cost up to $3 billion to bring a drug to market, and that’s why it’s so risky to invest in an industry.
But Khodjamirian noted that the sector’s productivity has improved since 2010, saying this trend will intensify with the application of AI. “It has been a great boon for the sector and it will continue to do so as well,” he predicts.
Data security concerns
The integration of AI In biotechnology has led to concerns about information and cybersecurity, given the personal and sensitive data associated with healthcare.
“We will need government intervention on this subject,” according to Khodjamirian. “Big pharmaceutical companies have databases on individuals in terms of their genetic makeup, and this needs to be very well regulated.
“I’m not going to speculate on how generalized AI is going to use this to its advantage or disadvantage, or whether something bad will come out of it,” he said, referring to centralized data repositories. “The risks are absolutely there… The data is going to have to be centralized somehow and used properly.”
However, data alone is not enough to ensure success. Khodjamirian emphasizes 23etMoi (ME), whose share price is down 75.8% over the past 12 months as of May 23. “Data and the ability to develop drugs and use that data is really where the power lies,” he explained.
Khodjamirian cited Denmark, which operates the Danish National Biobank with more than 25 million biological samples, as an example of spatial data centralization. Those wishing to obtain samples for research purposes must first go through a five-step process, including review by a research ethics committee and scientific council.
Rising
Biotech has been in a bear market since 2020, Khodjamirian said, “so valuations are extremely attractive. We’ve had a bit of a recovery since last summer, but it’s only just beginning.
However, the sector faces various risks. “If you look at biotechnology, you face every possible risk,” he says.
This requires a multi-tiered approach to risk management, starting with focusing on promising therapeutic areas to avoid high-risk or price-gouging sectors. Investing in companies with strong research and development teams and track records is crucial, as many biotech companies eventually go bankrupt.
The availability of financing also presents a critical risk. “It’s all well and good to have good science and data, but if you can’t get to the finish line in terms of funding and you’re short on money, you’re basically worthless.”
For Khodjamirian, this highlights the importance of seeking positive asymmetric risk-reward opportunities and misjudged outcomes rather than predicting clinical trial outcomes.
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