Summary
Exposure bracketing is a widely used strategy in clinical pharmacology to optimize dosing for targeted patient populations. In many programs, these brackets must be extrapolated from prior human and animal studies to define the minimum and maximum safe and efficacious dose ranges for a given therapy and treatment paradigm.
Traditionally, this requires a labor-intensive and time-consuming review of internal and external studies to manually extract the necessary PK/PD data. Recent advances in artificial intelligence (AI) now provide a compelling alternative: automating the ingestion of large volumes of unstructured data from reports, presentations, and publications and transforming them into analysis-ready datasets.
Sign up today for our live webinar on January 21, 2026 for an in-depth discussion on how emerging AI techniques, such as prompt chaining, can streamline and scale data aggregation in clinical pharmacology workflows.
What you’ll learn
- How structured AI workflows can model agentic AI behavior in a deterministic and transparent manner
- How LLM prompt chaining can convert unstructured study content into usable datasets for exposure bracketing
- Best practices for implementing AI-driven data extraction pipelines while maintaining data integrity and accuracy
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Director of Product, Certara
Sean McGee is currently the Director of Product at Certara, working within the Certara artificial intelligence (AI) group. Throughout his career, Mr. McGee has supported the strategy and go-to-market motions of various software technologies, including Benchling’s laboratory informatics platform and the AI and molecular modeling and simulation offerings for Dassault Systèmes BIOVIA brand. In his role with Certara, Mr. McGee guides the development of new AI-focused use cases which maximize the benefits of the Certara AI and broader company portfolio.
Mr. McGee completed his Master of Science at the University of Notre Dame exploring the scientific and commercial applications of medical devices designed to aid in the identification of child abuse.
Data Science and AI Solutions Architect
Ian Kerman (M.S. in Biology, M.S. in Computer Science) is a data science and AI solutions architect at Certara. With over 15 years of life science experience, Ian specializes in applying advanced AI and machine learning techniques to enhance research and discovery. His previous work included data and feature engineering, predictive model creation, and helping companies leverage AI to drive innovation in the pharmaceutical and healthcare sectors. Ian’s passion for solving complex scientific challenges fuels his current role of helping life science companies integrate AI, including LLMs and GPTs, into their life sciences workflows and processes.
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