The life sciences industry is right to be optimistic about the potential of generative AI. Biotech startups are already testing AI-generated drugs in clinical trials with human patients. Researchers have estimated that AI-powered drug discovery could drive as much as $50 billion in economic value over the next decade.
As the CEO of Dotmatics, a software company that builds technology for pharmaceutical scientists and researchers, I’m excited for anything that promises to reduce the time and cost of getting new drugs to market and ultimately decreasing the costs of therapies for patients.
However, when it comes to AI, this is no Cambrian moment. Like previous and transformative technological advances before it, the march toward an AI-supported future of drug discovery will necessarily be deliberate, incremental and marked with ups and downs.
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We’re already seeing setbacks: a schizophrenia drug discovered with AI recently failed two Phase 3 clinical trials. It may be years until the costs and timelines of drug discovery decrease measurably, particularly because some estimates are that more than 20% of the cost is from clinical trials which are necessarily manual.
And I worry that once the shine wears off of AI, interest from those outside of the lab will dissipate. Investors, governments and journalists will play key roles in funding, regulating and publicizing how AI is changing drug discovery.
So, I’m laying out my case for paying attention — and staying optimistic — as the life sciences industry does the hard work to make the promise of AI a reality.
Long seen as a laggard among industries, life sciences is finally catching up in the race to digitally transform.
Pharma companies have access to scalable and cost-effective infrastructure and tooling for managing massive amounts of data, particularly as they adopt a more efficient approach to building databases for electronic data capture (EDC). The traditional approach for this sort of database build takes around 12 to 16 weeks.
Perhaps just as crucially, the life sciences ecosystem is finally aligned on the importance of digitization. In February 2020, digital leaders surveyed by McKinsey reported that their biggest hurdle to convincing their companies to transform was a "lack of leadership support." But today, after the shock of the global coronavirus pandemic, that hurdle barely rates.
Strategic alignment and executive leadership are only the first steps. Pharma still faces significant challenges in making AI useful — namely, a tsunami of data and complex new treatment modalities.
Advanced research techniques produce ever-larger amounts of information. Genomics research is expected to generate between twp and 40 exabytes of data within the next decade. (An exabyte is one billion gigabytes, so that’s about 8.3 million iPhones’ (128 GB size) worth of storage.) And the velocity of the growth of that data is only increasing.
This detailed data offers tremendous long-term value for drug development, though it poses short-term challenges. Pharma companies must learn to harness it for AI to be effective in the lab. It’s not just a matter of buying the right technology — organizations also need to ensure their data governance practices.
This includes designing data collection protocols with future reuse in mind. The R&D process for new treatment modalities, such as monoclonal antibodies, mRNA vaccines, and gene editing is costlier and riskier than for traditional drugs.
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Life sciences companies must be able to use the knowledge their researchers gain from abandoned targets and clinical failures to make ongoing development of new treatments cost-effective.
All of this work, from upgrading technology to analyzing the results of failed clinical trials, is slow and arduous because of the manual work involved. Frankly, it’s going to be a grind.
But that’s what makes progress meaningful. By investing in the platforms and processes that enable the practical use of AI in the lab, Pharma companies are building the foundations for a future in which scientists develop treatments quickly and cost-effectively. Each new drug candidate, whether it succeeds in clinical trials or not, represents a step toward better health and quality of life for people with both common or rare diseases.
Keep in mind that the introduction of ChatGPT wasn’t a Cambrian moment either. The idea of large language models dates back to the 1960s. Computer scientists and chip designers worked quietly and diligently for decades to make the release of ChatGPT possible. Along the way, they delivered advances in data storage and processing that have transformed how we live and work.
Pharma’s march toward successful application of AI will be punctuated with the same small wins that add up to transformative change. Hardworking scientists and researchers should acknowledge and celebrate this incremental progress — and so should the rest of us.
