Efforts to use artificial intelligence to discover drugs have been underway for a decade, but industry watchers predict a tipping point is coming for investors, who have been looking for ways to determine how AI drug developers should be valued. AI and machine learning offer the potential to accelerate the search for new therapies by more quickly identifying compounds to treat disease. There is also the promise of making the phases of clinical trials more efficient by improving patient enrollment and processing information quickly as information comes in from studies. More tangible evidence of these capabilities is now being demonstrated. A prominent example has been the effort to fight Covid-19, which forced biotech and pharmaceutical companies to bring all their capabilities to the effort to discover vaccines and treatments in record time. Lidia Fonseca, chief digital and technology officer at Pfizer, has discussed the role the pandemic has played in accelerating digital advances during several conference appearances over the past year. “We believe that Covid-19 has advanced these trends by up to five years,” Fonseca said in a virtual chat with McKinsey in January. “It’s not so much that they’re new technologies, more that we’re applying them at scale.” Key points for investors According to Deloitte’s latest estimates, developing a new drug can cost $2 billion. Artificial intelligence and machine learning promise to reduce this cost by reducing development times and increasing success rates. More advanced algorithms, increased computing power, and richer data sets are leading to further progress. While most biotech and pharmaceutical companies use AI and machine learning tools, companies native to the space are about to reach an inflection point that will help investors value these companies. The Boston Consulting Group said in March that early AI drug developers have identified more than 150 small-molecule drugs, with at least 15 already in clinical trials. The capabilities that will come when quantum computing is widely adopted are unimaginable now, Fonseca added. But even with today’s supercomputing power, Pfizer is able to use modeling and simulation to screen millions of compounds for potential drug targets. Pfizer has said that the development of Paxlovid, an oral treatment for Covid, in four months was helped by the deployment of various machine learning techniques. “A big convergence” A “big convergence” is underway across the sector, according to Julia Angeles, portfolio manager of the Baillie Gifford Health Innovation Fund. “It’s not just one technology that comes into play. It’s actually a combination of technologies,” Angeles said. In an interview, he detailed a number of improvements that have come with the advanced algorithms used to power machine learning, the richness of data sets that can be examined for insights, and the effectiveness of the computing power that it takes to bring it all together. . But the critical change is the scale at which it’s being made, Angeles said. “A lot more companies can do that,” he said. “We have much more relevant data for mine biology, and we have much more powerful computers to do it much more effectively and much faster than we have in the past.” A key component has been a sharp drop in the cost of sequencing genomic data over the past 10 years, resulting in a wealth of patient information that can be combined with other types of electronic health records. Separately, the release last year of the AlphaFold2 source code by DeepMind, the UK-based AI company owned by Alphabet, has helped visualize the protein’s structure, which would also to help development in this area in the coming years. So far, technological progress has led to a wave of small molecule drugs created by AI-native drug discovery companies. Reviewing public records, Boston Consulting Group has identified more than 150 small-molecule drugs, with at least 15 already in clinical trials, from leading companies in the space. BCG said the pipeline is growing nearly 40% annually. “Do these work in the clinic? We’ll have to wait and see. Hopefully they do. Because if they do, if they work as well as the drugs discovered in humans, that would be very exciting,” said Chris Meier, director . director and partner at BCG. “If the success rate comes back much better, then of course it will be very exciting because suddenly we have something that is better than humans. We don’t know yet,” he said. Expected upgrades to a number of drug candidates over the next 12 to 18 months were one of the key reasons why Morgan Stanley analysts said they expect the industry is about to reach a tipping point. inflection In a research note published in late June, Morgan Stanley said readings from early clinical work will help the market assign a value to AI-native drug stocks. The report said investors in the past have debated whether the group should carry the valuation of a technology platform or a biotech company. In fact, the business models of these companies can vary. Some are more akin to the software-as-a-service model, where companies offer machine learning capabilities to partners for a fee. But many are also developing their own solo projects and have partnerships with pharmaceutical companies, where they will receive payments and royalties as the compounds meet targets and are commercialized. The value of failing fast According to Deloitte’s latest estimates, it can cost $2 billion to develop a new drug. This number represents the vast majority of compounds that are studied but fail in early clinical trials. Success rates can be less than 5% and development times can span a decade or more. Morgan Stanley analysts estimate that a roughly 2% improvement in the pace of preclinical and phase 1 development could lead the industry to generate about 50 new therapies over the next 10 years. That could equate to about $50 billion in net present value for the biopharma industry, they said. One of the key ways AI drug research can save money is by identifying which molecules are most and least likely to succeed early in the research cycle. By doing this, the cost of failure is greatly reduced. Robert Burns, CEO of HC Wainwright, said Schrodinger has described a 10-month period to identify a development candidate, while Exscientia has put its average time at around 12 months. In comparison, traditional drug discovery can take three to five years. “That’s important, especially as you know, a lot of these major pharma and biotech companies are all trying to pursue very similar goals,” Burns said. Speed can not only save you money, it can provide a competitive advantage. Despite the promise of these companies, stocks have fallen sharply along with the rest of the biotech sector. Most are now trading below their IPO prices. The Baillie Gifford Health Innovation Fund reflects this trend. It’s down more than 26% year to date, but has gained nearly 7% so far this month, according to FactSet. Within the AI-first space, Angeles owns Exscientia and Recursion Pharmaceuticals, though neither is among the fund’s top holdings. Exscientia shares are down 39% year to date and trade 45% below their debut price last September. The company has partnerships with the Bill & Melinda Gates Foundation, Bayer, Sanofi, Bristol-Myers Squibb and others. The immunotherapy oncology drug, EXS-21546, is Exscientia’s most advanced compound. It is in phase 1b/2 trials to test the drug in patients with solid tumors. Recursion Pharmaceuticals has lost about 45% of its value since its IPO in April 2021. It is very focused on using imaging technology to discover drug targets, and much of its focus has been focused on rare diseases. It has partnerships with Bayer, Roche and Takeda, and is already in a phase 2 clinical trial to treat cerebral cavernous malformations, a disorder of blood vessels in the brain, which can lead to seizures and fatal bleeding in the brain. Burns has a buy rating on Relay Therapeutics, which is down about 35% so far this year and is trading just below its $20 IPO price. The company has several breast cancer treatments in the pipeline, and data on its lead compound, RLY-4008, should be released later this year. Its partners include Roche and Genentech. On Thursday, Relay said it had sufficient funding to support its operating plan through at least 2025. As of June 30, its cash and investments totaled about $838 million, compared with $958 million at the end of 2021. Schrodinger reported that it has $513 million in cash, cash equivalents, restricted cash and marketable securities as of June 30, down from $529 million as of March 31. At the end of its first quarter, Exscientia had about $719.8 million in cash, while Recursion’s was $591.1 million as of March 31. Until these companies provide updates on these programs, the investment case hinges on the potential value of the companies’ platforms. Once investors can see the progress being made in clinical trials, there will be more confidence. “I think there really needs to be some kind of validation here,” Burns said.
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