28TWENTYFOURSEVENBIOPHARMA Issue 2 / June 2025 BIOTECH Artificial intelligence, innovation in cell and gene therapies, and evolving investment models. Never has the biotechnology landscape experienced so many breakthroughs that have such an influential stake in shaping the future of medicine and patient care. These key themes are sure to dominate future discussions, none more so than at this year’s Bio USA convention in Boston, MA , where the stage is set for industry leaders, researchers, investors, and policymakers to unveil groundbreaking research converge, forge partnerships, and collectively shape the future trajectory of biotechnology. AI’s role in biotech R&D The conversations begin with Artificial Intelligence (AI), a technology with transformative capabilities across industries. From target identification to clinical trial optimization, AI is arguably playing its most influential role in biotech research and development, accelerating timelines, reducing costs, and significantly increasing the probability of success. AI’s power is evident in identifying novel drug targets, traditionally a painstaking process. The technology’s ability to mine vast amounts of biological data to uncover potential targets that might otherwise go unnoticed is revolutionising this aspect of drug discovery. Analysis appears to back this up. US-based consulting company, Coherent Solutions highlights that, “Such an approach allows researchers to zero in on the most promising opportunities faster and accelerate the drug development process.” Besides identification, AI is rewriting the process of evaluating drug-target interactions and analysing disease mechanisms with unparalleled precision. Coherent Solutions adds that this is leading to “more effective drugs, ensuring that compounds are not only innovative but also highly specific to the target.” But where else is AI making its presence felt? The time-consuming and error-prone job of recruiting patients for clinical trials may be the answer. Machine learning models have been developed that can analyse huge numbers of Electronic Health Records (EHRs), identifying eligible participants quickly and with high accuracy. Indeed, AI tools like TrialGPT are already showing
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