Drug development is notoriously failure-prone. Only one in every ten drug candidates that enter human trials eventually goes onto the market. Turning a promising molecule into a useful medicine typically takes ten to 15 years after its discovery. These challenging economics mean that the cost of developing each successful drug is roughly $2.8bn. And because medicines ultimately come off-patent, the drive to find the next blockbuster is relentless.
Enter generative AI, which the pharma industry is adopting at a terrific rate. By ingesting and analysing vast biological data sets, AI tools can identify promising target proteins and then suggest novel molecules that could latch onto those drug targets. They can sift through libraries of data to predict the potency and toxicity of candidates, before a single test tube is touched AI can also help with trials, analysing health records to find the patients most likely to respond to novel treatments. Though it is still early days, the signs are promising. ai could lead to more efficient drug discovery, better medicines and more competition in the industry.
Dig deeper
ai-designed molecules show an 80-90% success rate in early-stage safety trials, compared with a historical average of just 40-65%. It will be years before it becomes clear whether success rates rise in later-stage trials, too. But even if they do not, one model suggests that early-stage improvements alone could increase the success rate across the entire pipeline from 5-10% to 9-18%. The industry is also wringing efficiencies out of its business using AI, in areas from clinical documentation to HR. McKinsey reckons that if AI is fully utilised by the pharma industry—no doubt with its consultants’ assistance—it could provide a boost worth $60bn-110bn annually.
The hope is that improvements in the technology will push up the success rate even further. Sophisticated new models for understanding tricky bits of biology are emerging at a rapid pace. A few years ago an AI model called AlphaFold solved the problem of figuring out the structure of proteins. More complex puzzles, such as how cell membranes function, are likely to be cracked at some point.
The technology is already changing how the pharma industry works. A new generation of AI-native biotech startups—particularly in America and China—is emerging. Pharma companies are increasingly forming alliances with AI-biotech firms, as well as with technology giants including Amazon, Google, Microsoft and Nvidia. And those big tech firms have their own ambitions in health. Isomorphic Labs, a spin-out from Google DeepMind, is trying to design entirely new therapeutic molecules from scratch inside a computer. Nvidia, too, has a generative-AI platform for drug discovery. Both firms are signing deals to offer design services to pharma companies. And in October Nvidia teamed up with Eli Lilly, the world’s most valuable drugmaker, to build the pharma industry’s most powerful supercomputer.
All this means that some of the value of drug discovery may be captured by tech giants. For now, pharma firms have many clear advantages, including heaps of data, scientists who know the field and long experience of shepherding new drugs through a maze of regulation. Over time, though, as parts of biology become more of a computational problem that can be solved with technology, such advantages could be eroded. Pharma firms may need to buy in ai expertise in the same way that they buy early-stage assets from biotech firms today.
As drug discovery becomes more efficient, governments will need to turn their attention to other potential bottlenecks in the system, such as regulation and trials. America’s Food and Drug Administration and the European Medicines Agency are themselves starting to use ai to screen the mountains of data they receive. As the number of drug candidates rises, faster regulatory reviews will be needed to avoid a logjam. Governments could also do more to encourage the sharing of patient data with AI companies in privacy-preserving ways so that AI models—and drug discovery—can improve.
Time to get AI-pilled
Patents, too, will need rethinking. Today, long patent lives let pharma firms recoup the investments they make, encouraging them to undertake the risky business of drug discovery. Yet if the costs and riskiness of innovation fall dramatically, then patent terms (which typically provide 10-15 years of market exclusivity) may need to become shorter. ai brings good news for drug innovation. But to ensure that it benefits both the makers and takers of new drugs, the industry and its regulators will need to adjust to this new reality.
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