2025 Interactive Discussions (In-Person Only)

These in-person group discussions are open to all attendees, speakers, sponsors, and exhibitors. Participants choose a specific discussion group to join. Each group has a moderator to ensure focused discussions around key issues within the topic. This format allows participants to meet potential collaborators, share examples from their work, vet ideas with peers, and be part of a group problem-solving endeavor. The discussions provide an informal exchange of ideas and are not meant to be a corporate or specific product discussion. All group discussions will be offered IN-PERSON ONLY.

Wednesday, October 8 | 2:20 PM

TABLE 1: Strategies for Peptide Immunogenicity Assessment
Mohsen Rajabi Abhari, PhD, Director, Scientific Governance PK Sciences Drug Disposition, Novartis
Faye Vazvaei, Executive Director, Merck

What is your company's experience with submitting ISI for peptides, and when did you start preparing ISI?
What do you include in ISI?
What is the stage adequate assessment in each phase of development (from discovery to post market)
What are the impurity thresholds and limits for peptide therapeutics, including their impact on clinical immunogenicity and Agency recommendations?
Without clear guidance, what should the industry prioritize regarding peptide-related impurities and immunogenicity risk, such as impurity thresholds, impurity classes, and prediction tools?
What pre-clinical approaches are commonly used to assess immunogenicity risk in peptides, and what are their pros and cons? Is there a tool that more accurately predicts this risk?
What is your clinical ADA assessment strategy for peptides, such as banking samples but only testing if triggered, addressing cross-reactivity with endogenous counterparts, and performing Nab testing?

TABLE 2: Designing ADA Assays for Detection of Clinically Relevant Responses
Lauren Stevenson, PhD, CSO & Head, Translational Sciences, Immunologix Labs

Common challenges with overly sensitive assays that lead to high ADA positive incidence without clinical impact
Considerations for assay development to mitigate these common challenges
Utility of placebo sample S/N values to define biological variability in the population and provide context for ADA datasets
Data analysis approaches to identify clinically relevant thresholds using S/N
Reimagining ADA assays as biomarker assays focused on context-of-use to define appropriate assay performance criteria

TABLE 3: Clinical Context of 100 ng/mL ADA: Do We Always Need Highly Sensitive Immunogenicity Assays?
Kamalika Mukherjee, PhD, Principal Scientist, Bioanalytical Strategy, Regeneron Pharmaceuticals Inc
* The views expressed by the participants are those of the individuals and not necessarily those of their respective employers.

In your experience, what magnitude (concentration) of ADA has typically shown impact on clinical parameters (PK, safety, efficacy)?
Can you provide some examples about molecule type, route of administration, etc. which resulted in clinically impactful immunogenicity?
In your experience, have you seen an association between level of drug exposure and magnitude of ADA required to have clinical impact?
What range of trough concentration do you typically see for your drugs, especially for monoclonal antibodies?
What challenges have you encountered in developing ADA assays with 100 ng/mL sensitivity, and in the presence of high drug concentrations? What mitigation strategies have you employed for the challenges above?
Do you think expectations need to be reassessed for development of highly sensitive clinical ADA assays (≤100 ng/mL ADA) in the presence of high drug concentrations?

Friday, October 10 | 9:30 AM

TABLE 4: AI and Machine Learning to Predict Protein Immunogenicity
Yuri Iozzo, PhD, Head of Digital Biology, Biologics Drug Discovery, ModeX Therapeutics

Anti-Drug Antibodies (ADAs): Mechanisms, prediction challenges, and clinical implications
MHC binding, Treg epitopes, and ADA rates: Is there a predictive link?
The data quality dilemma: Noise, bias, and the need for better training sets
Cytokine-based assays: A more direct window into immunogenicity?
Beyond binding: Are we ready for data-driven immunogenicity scoring?

TABLE 5: In Vitro Immunogenicity Assays: Time for Standardization and Benchmarking
Sofie Pattijn, Founder & CTO, IQVIA Laboratories

How to select and define the most appropriate assay/format (T cell assays, MAPPs assays, peptide assays)
Which assay controls and references to include (HESI AAPS panel)
The importance of the PBMC quality
How many donors to include
Initiatives for harmonization and standardization (European Immunogenicity Platform, AAPS IRAM working group)

TABLE 6: Development and Use of Potency Assays for the Right Purpose
Jan Amstrup, PhD, Senior Specialist, Novo Nordisk AS

In vitro assays for characterization
Using automation in bioassays: When, where, and how to integrate
New modalities including cell and gene therapies, immunotherapies, oncolytic virus therapies, and antibodies
Potency assay development
New bioassay technologies to increase speed and sensitivity

TABLE 7: Bioassays for Demonstrating Drug Mechanism-of-Action (MoA): Approaches and Challenges
Natalia Kozhemyakina, PhD, Head, Bioassay Department, JSC Biocad

Designing assays to fit into a clinical workflow
Bioassays to understand a drug's Mechanism-of-Action (MoA)
Designing bioassays for biologics with multiple Mechanism-of-Action (MoA)
Robotization of cell based assays: When to use it and when not to use it
Reference Standards for innovative drugs

TABLE 8: Streamlining and Revolutionizing Bioanalytical Workflows with AI, Machine Learning, and Natural Language Processing
Weifeng Xu, PhD, Director, Bioanalytical, Merck
Chuying Ma, PhD, Senior Scientist, Pharmacokinetics, Dynamics, Metabolism & Bioanalytics, Merck