Nordic Life Science 1
COGNIZANT ADVERTORIAL AGENTIC AI: Revolutionize l
ife sciences from R&D to commercialization WITH ITS ABILITY to both generate insights and act on them autonomously, agentic AI is poised to revolutionize the process of developing new therapies and bringing them to market. Across every stage of the value chain – from R&D to manufacturing to commercialization – agentic AI is already having a significant impact. W HAT IS agentic AI? Traditional AI systems create models designed to analyze data sets – from a large number of databases across a digital network – to generate predictions for human operatives to consider and act upon. Recent evolution in AI technology has resulted in the creation of AI agents, able not only to generate recommendations but to act on them autonomously, without human intervention. Agentic AI possesses multiple agents but harnesses them in a unique way that allows them to perform even more complex analytical tasks. Where multi-agent AI models use an array of agents to generate predictions, agentic AI employs agents to act autonomously. Endowed with greater context understanding than the alternative, agentic AI can analyze complex situations, weigh potential 1 REDEFINING DRUG R&D Drug discovery: Agents can accelerate the identification of potential drug targets by autonomously analyzing vast datasets of biological information, including genomic, proteomic and metabolomic data. Agents can be designed to quickly explore how targets interact within complex biological pathoutcomes, and make autonomous decisions or recommendations based on their programmed objectives and learned knowledge. The applications of agentic AI Agentic AI has the potential to integrate digital systems across all departments functioning at every stage of the product lifecycle. This means that each discrete business function can share knowledge and learn from each other to enhance efficiency across the entire lifespan of the product. Here are some key examples of how agentic AI can be harnessed across the product lifecycle: ways before any in vivo or in vitro testing has taken place. Transfers into clinical manufacturing: AI agents can be used to review and analyze data gathered during the discovery and research process to identify efficiencies that can accelerate the transfer of projects into clinical manufacturing ahead of clinical trials. They can be used to determine effective small-batch production techniques, devise efficient supply lines for raw materials and draw up and action streamlined implementation timelines for transfers to be completed. Right-first-time regulatory submissions: The solution can compare draft submissions with previous filings and external regulatory intelligence to help develop content with the greatest likelihood of approval success. Regulatory change impact assessments: With agentic AI, companies can enhance the evaluation of external regulatory intelligence to develop impact assessments in the event of changes to regulations or standards.