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日期: 2025 年 9 月 24 日, 星期三

时间: 11am EST - 12pm EST

Services: 定量药理学

概述

Antibody-drug conjugates (ADCs) are complex biologics that present unique pharmacokinetic (PK) challenges due to their multi-component structure and mechanisms of action. In this webinar, experts will guide you through the essential concepts in ADC PK—from applying non-compartmental analysis (NCA) to leveraging population pharmacokinetic (popPK) modeling strategies.

You’ll gain insight into how to evaluate ADCs at different stages of development and learn when to transition from simple to advanced PK approaches to support informed, data-driven decisions.

Key Learning Objectives:

What You’ll Learn

  • What defines an ADC and how it works
  • Differences between internalizing and non-internalizing ADCs
  • How to apply NCA techniques to extract actionable data
  • When and why to go beyond NCA to more sophisticated modeling workflows
  • Key considerations in popPK modeling for ADCs
  • Real-world examples including:
    • A 3-analyte popPK model
    • A pediatric dose recommendation derived from modeling & simulation

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Key Takeaways

  • Multi-analyte Systems: Understand which analytes (e.g., total antibody, conjugated drug, unconjugated drug) were quantified and how they influence model selection and interpretation.
  • Analyte Selection: Learn to identify the most informative measurements to support modeling and clinical decisions.
  • Mechanism of Action (MoA): Explore how the MoA (e.g., internalization, cleavable vs. non-cleavable linkers) affects ADC PK and model complexity.
  • Modeling Approaches:
    • Considerations for sequential vs. simultaneous modeling of analytes
    • When target-mediated drug disposition (TMDD) modeling is appropriate
    • Use of transit compartments to model delayed payload release
    • When to Use an Average DAR vs. Modeling DAR Changes
  • Fit-for-Purpose Strategy: Learn how to match model complexity with your specific development questions and data availability.

演讲嘉宾:

Martin Beliveau

Vice President Consulting, Certara

Dr. Martin Beliveau has over 10 years of modeling experience in clinical pharmacology. He joined Certara Strategic Consulting in 2007. Before that, he was a pharmacokinetic scientist and Study Director at Charles River Laboratories. His entire career has been in the area of pharmacokinetic analysis, modeling and simulation for regulatory submissions and decision making. His work covers areas a wide range of indications with a particular focus on translational medicine, first-in-human predictions and biodefence.

Dr. Martin Beliveau received a PhD in Public Health from the Université de Montreal under Dr. Kannan Krishnan in physiologically-based pharmacokinetic modeling in 2004. Aside from his modeling/simulation activities, Martin enjoys reading a good Spider-Man story.

Eline van Maanen

Director Consulting, Certara

Dr. Eline van Maanen brings over 16 years of experience in pharmacometric consulting. She joined Certara in 2018. Before that, she was a PKPD consultant at LAP&P Consultants. Eline has dedicated her entire career to the field of pharmacometrics. Her expertise includes mechanistic PKPD modeling across a range of therapeutic areas, such as neurology, oncology and infectious diseases.

Eline received a PhD in Pharmacology from Leiden University in 2017, where she studied systems pharmacology of the amyloid cascade under the supervision of Prof. Meindert Danhof. Eline has a background in engineering, holding a MSc degree in Life Science and Technology from Delft University of Technology. Outside of work, Eline enjoys pilot-gig rowing.

Kang Lin, PharmD, MS, BCGP

Senior Scientist, Avidity Biosciences, Inc.

Dr. Kang Lin is a Senior Scientist in Development Sciences at Avidity Biosciences, specializing in PK/PD modeling. He works across translational and clinical PK/PD modeling, including non-compartmental analysis (NCA), to support the development of antibody–oligonucleotide conjugates (AOCs). He holds both a PharmD and an MS in Pharmacometrics from the University of Maryland, Baltimore.

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