AI in R&D for life sciences is transforming drug targeting, patient recruitment, and regulatory compliance. Advancements in AI-enhanced protein structure prediction are revolutionizing the industry. The impact of AI on drug discovery, clinical trials, and regulatory functions is discussed, highlighting the potential for innovation and efficiency in the pharmaceutical field.
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Quick takeaways
AI-enhanced protein structure prediction is revolutionizing drug targeting in life sciences R&D.
AI expedites regulatory compliance in pharmaceutical R&D by automating manual documentation processes.
Deep dives
Challenges in R&D in Pharma
R&D productivity in pharmaceuticals has stagnated for over a decade, with low success rates and internal rates of return. The industry faces pressure to improve productivity amidst the complexity of biology and the uncertainty of drug outcomes. The urgency brought about by COVID has accelerated digital transformations, revealing the potential for AI to drive innovation.
AI Revolutionizing Drug Discovery
AI offers the opportunity to redefine disease using multimodal data, enabling the identification of more homogenous patient segments with specific biological drivers. Tools like alpha fold enhance protein structure prediction, aiding in drug discovery by prioritizing candidate drugs and suggesting potential therapeutic targets. The goal is to improve productivity by leveraging AI to generate hypotheses and streamline drug discovery processes.
AI's Impact on Clinical Trials and Regulatory Compliance
In clinical trials, AI addresses patient recruitment challenges by optimizing protocol development and improving patient targeting strategies. AI-enabled protocol feasibility tools simulate patient recruitment scenarios, minimizing delays caused by narrow inclusion criteria. Generative AI transforms regulatory functions by automating manual documentation processes and enhancing translation quality to ensure compliance and accelerate regulatory intelligence.
Today’s guest is Andrew Bolt, Partner at Deloitte. He joins Emerj CEO and Head of Research Daniel Faggella on today’s podcast to talk about challenges in R&D for life sciences. While productivity has remained stagnant for the last 10-15 years, advances in AI-enhanced protein structure prediction are revolutionizing drug targeting and the very infrastructure of recruiting patients for clinical trials. Later, the two postulate on the ways AI can expedite regulatory compliance in pharmaceutical R&D in similar ways. Find out more about sponsored content and how to engage with the Emerj audience at emerj.com/ad1.
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