BioAI
Key Takeaway:
In 2024, health spending is set to exceed 10% of global GDP, with pharmaceutical sales expected to surpass $1.6 trillion, a significant portion of which will come from the U.S. and China. AI is crucial in this sector, notably revolutionizing drug discovery by enhancing the speed and accuracy of research, and is projected to expand the drug discovery market to nearly $5 billion by 2028. Additionally, AI advancements, like Google Cloud’s new AI solutions, are driving progress in small-molecule drug development and complex biological decoding. As are innovations like organoid intelligence and brain-computer interfaces. However, AI in biotech faces challenges such as data limitations, ethical concerns, and privacy issues, which could inhibit its growth.
Trend Type: Technology
Sub-trends: AI drug race, Scientific AI, Synthetic Biology, The brain as a tech battleground
According to the Economist, in 2024, health spending will account for more than 10% of global GDP, and pharmaceutical sales will surpass $1.6trn worldwide – America accounting for over a third and China for a tenth. In both health and pharmaceutical dynamics, AI plays a pivotal role. First, it disrupts the drug discovery market in both go-to-market and innovation, accelerates research by optimizing experiments, emphasizes impactful targets, and enables virtual screening, leading to faster failure and diversified testing. Overall, the global size of AI in the drug discovery market in terms of revenue was estimated to be worth just under $1B in 2023 and is poised to reach almost 5B in 2028, growing at a CAGR of 40.2% from 2023 until 2028. The top 3 players in the CBI Pharma AI Readiness Index have all linked up with AI-focused drug discovery startups.
AI in Drug Discovery
AI has been essential in the small-molecule drug development landscape, which is now central to the pharmaceutical industry. As a reference, from 2013 to 2022, nearly 2 in 3 drugs approved by the FDA were small molecules. AI is boosting the speed and precision of small-molecule drug discovery, rapidly growing over the last 12 months, with candidates approaching late-stage trials. In addition, the “decoding” of complex biology is becoming possible with AI’s ability to process large amounts of visual data and recognize patterns in images. It is being used to identify the disease signature (e.g., capture images of live mRNA biology from millions of diseased and healthy cells and have an AI neural network trained to recognize the differences) and gene therapy (by harnessing the power of genetic engineering, AI-gene therapies can correct or replace faulty genes within individual cells). Last year, Google Cloud launched two new AI-powered solutions that aim to help biotech and pharmaceutical companies accelerate drug discovery and advance precision medicine, stating that both Big Pharma’s Pfizer and biotech companies Cerevel Therapeutics and Colossal Biosciences are using the solutions.
The transformative impact of AI in science is also exemplified by DeepMind’s AlphaFold, which has significantly accelerated the discovery of protein structures, and Gnome, which has identified over 380,000 stable crystal structures. These advancements represent a paradigm shift in scientific discovery, with AI technologies enabling faster, more cost-effective research processes that have yet to impact their application fields fully.
Organoid Intelligence
On the other hand, there’s a shift to more innovative approaches. In 2023, scientists formed a new field called Organoid intelligence (OI) – joining up computer science and biology to develop biological computing using 3D cultures of human brain cells (or brain organoids) and brain-machine interface technologies. And because brain activity data can now be coupled with AI to ‘read minds’ with eerie accuracy, investors are betting on brain-computer interfaces (or BCIs) with human trials kicking off. The USD 1.86 billion 2022 global Brain Computer Interfaces market is estimated to reach USD 8.37 billion by 2032, mainly from integrating BCIs into healthcare systems for diagnostics, treatment, and rehabilitation of various diseases, including personalized medicine. But as the brain-computer interface landscape matures, privacy debates will reach a new fever pitch. Data privacy media mentions were at an all-time high last year, but tech that can capture even superficial information on inner thoughts will intensify scrutiny.
However, it’s important to acknowledge the potential limitations and ethical concerns of AI. The depth, dimensionality, and scale of data are sometimes too limited for AI to fully characterize diseases. There may also be challenges related to missing metadata on cell culture or assay conditions. Moreover, the use of AI raises other questions, particularly regarding data privacy, bias, and transparency in decision-making. These factors are expected to restrain the growth of the AI market in the coming years, and it’s crucial for industry professionals, investors, and policymakers to be aware of these issues.
Use Cases
Synthetic biology: Bluebird announced in August 2022 that the FDA approved its one-time therapy that treats the underlying genetic cause of beta thalassemia, a blood disorder that would otherwise require red-blood cell transfusions.
AI drug race: In 2023, a team of researchers from Baidu Research approved its one-tim has developed an AI algorithm that can rapidly design highly stable COVID-19 mRNA vaccine sequences that were previously unattainable.
The brain as a tech battleground : While AI has the ability to crunch huge amounts of data in a short span of time, it still falls behind when it comes to finding an energy-efficient way to make complex decisions. In 2023, researchers from John Hopkins University proposed that 3D cell structures that mimic brain functions can be used to create biocomputers.