In-Person Interactive Discussions

Engage in in-depth discussions with industry experts and your peers about the progress, trends and challenges you face in your research! Interactive discussion groups play an integral role in networking with potential collaborators, provide an opportunity to share examples from your work, and be part of a group problem-solving endeavor.

These will take place IN-PERSON ONLY.

Wednesday, May 14, 1:10 – 1:55 PM

TABLE 1: Integrating AI/ML to Transform Biologics Design and Optimization
Moderator: Kallol Ray, PhD, Vice President & Head, Global Biologics, Takeda Pharmaceuticals Inc.

Impacts of AI/ML in protein therapeutics design: speed and innovation,
Key advancements in AI/ML applications in biologics
Emerging trends and potential future developments
Major challenges (technical and computational hurdles, integration with existing workflows and systems)

TABLE 2: Optimal Therapy for Obesity and Other Digestive Disorders
Moderator: Michael Wolfe, MD, Professor, Physiology and Biophysics, Case Western Reserve University

Science-oriented approach to managing obesity
Ligand vs. receptor antibodies: which provides greater benefit?
Biological agents to treat inflammatory bowel diseases and other GI disorders

TABLE 3: What Format for What Disease? How do different antibody formats stack up versus CAR-T, T cell engagers, multispecifics, protein degraders, RNA etc?
Moderator: Catherine Hutchings, PhD, Independent Consultant

How are different antibody formats performing vs. other Ab-related therapeutic modalities to address efficacy and success in the clinic?
Hematological vs. solid tumors (and target tissue)
Oncology vs. non-cancer indications (what have we learnt from each)
Format and target biology challenges
The role of Fc domain, epitope, target combinations, IgG versus fragments (Fab, VHH, etc.)?

TABLE 4: T Cell Engaging Bispecific Antibodies for Treating Solid Tumors
Moderator: Shelley Force Aldred, PhD, Co-Founder and CEO, Rondo Therapeutics

Increasing therapeutic windows (tuning potencies, better targeting, masking, etc.)
Combining CD3 and co-stim signaling (bispecific combos vs. trispecifics)
Challenges with preclinical models for efficacy and tox
Indication selection + monotherapies vs. combo therapies

TABLE 5: Masking Approaches for TCEs
Moderator: Marcela Guzman Ayala, PhD, Head of In Vitro Pharmacology, Molecular Partners
T cell engagers (TCEs) are effective cancer therapies but systemic toxicity and limited specificity, restrict their full potential. Several masking approaches for TCEs have been designed to specifically be “unmasked” in the tumor microenvironment, with the aim of increasing their therapeutic index. Molecular Partners’ logic-gated CD3 Switch-DARPin enables reversible, tumor-specific T cell activation in the presence of a defined combination of tumor-associated antigens (TAAs).

What are the patient's remaining needs?
What are the advantages and disadvantages of current masking methods?
What criteria should everyone aim for, and what needs to be established to declare success?
Should TCEs’ masking strategies be customized to address TME heterogeneity?

TABLE 6: Navigating the Future of Cell & Gene Therapy: Overcoming Development, Manufacturing, and Commercialization Challenges
Moderator: Michael D. Jacobson, PhD, Managing Partner, Cambridge Biostrategy Associates LLC

Scaling Up: From Lab to Commercial Manufacturing – Addressing bottlenecks, automation, and cost efficiency
Regulatory & Quality Challenges – Adapting to evolving global requirements and ensuring product consistency
Supply Chain & Logistics – Maintaining speed, safety, and traceability in an increasingly complex ecosystem
Cost & Reimbursement – Innovative pricing models and strategies for improving patient access
Scientific & Clinical Hurdles – Managing safety risks, durability concerns, and trial complexities
Ethical & Market Considerations – Balancing innovation, equitable access, and the future direction of CGTs

TABLE 7: The Promise of AI in Therapeutic Discovery - Has it Lived up to the Hype?
Moderator: Timothy Riley, PhD, SVP, Discovery, 310.ai

Review of AI successes and failures
Evaluate how AI has impacted the speed, cost, and accuracy of therapeutic outcomes
Expectations vs. reality
Challenges and limitations

TABLE 8: Prokaryotic Expression Systems: Applications, Advancements, and Strategies for Optimization
Moderator: Alex Kirkpatrick, Ph.D, Field Application Scientist, Thermo Fisher Scientific

Prokaryotic expression systems, particularly E. coli, have long been the workhorse for protein expression, significantly contributing to the generation of recombinant proteins for various applications. Join us to explore the applications, strategies & practical solutions for improvement, and advancements in prokaryotic cell-based expression systems.

Advantages of Different Prokaryotic Systems
Optimization Strategies for Protein Production
Current Challenges of Prokaryotic Hosts
Innovative Approaches and Emerging Trends

TABLE 9: Getting the Math Right: Adding and Subtracting Post-Translational Modifications (PTMs) to Recombinant Proteins
Moderator: Christopher Cooper, PhD, Director and Head of Protein Sciences, CHARM Therapeutics

Choosing an appropriate QC method for your budget
Moving on from biotinupcoming alternative protein labelling technologies
Challenges in addition of site-specific PTMs (e.g. phosphorylation) and protein PTMs (e.g. ubiquitin, SUMO)
Advances in (de)glycosylation and its analysis

TABLE 10: Developability Screening for Multispecific Antibodies
Moderator: Michael Dyson, PhD, Vice President, Antibody Discovery & Engineering, Ichnos Glenmark Innovation

Pre-screen monoclonal arms or final format or both?
Which are the best HTP developability screens?
Can in silico methods reliably predict developability and help fix poorly behaved Multispecifics without an antibody structure?
What are the best late stage developability assays to perform to help Clinical Candidate Selection (CCS)

TABLE 11: Characterization of Protein Therapeutics Using Mass Spectrometry
Moderator: Chris M. Chumsae, PhD, Associate Director, Analytical Development, Bristol-Myers Squibb

Confirmation of protein primary structure
Detection of common PTMs and influence by process
Determination of unexpected covalent variants and root cause

TABLE 12: HLA Class II Peptide Presentation and Immunogenicity Screening of Therapeutic Antibodies with HLAIIPred
Moderator: Mojtaba Haghighatlari, PhD, Senior Machine Learning Scientist, Pfizer Inc.

Best practices in data preparation for machine learning of peptidomics datasets
Novel deep learning approaches for predicting MHC antigen presentation and the modeling challenges
Interpretability and explainability of the available deep learning models
New screening strategies for predicting immunogenic hotspots in therapeutic antibodies

TABLE 13: Predicting Immunogenicity with AI/ML Tools
Moderator: Sivan Cohen, PhD, Senior Principal Scientist, Genentech

Enhance AI/ML model performance for immunogenicity prediction by optimizing HLA-II selection and tolerance determination.
What confidence level is needed for immunogenicity prediction algorithms to impact regulatory decisions
How can in silico and in vitro approaches for immunogenicity evaluation be strategically integrated, especially when their data conflict?
Developing AI/ML algorithms for B cell prediction - what does the future hold?
Application of the in silico prediction tools for the immunogenicity of AAV

Friday, May 16, 7:30 – 8:25 AM

Over Continental Breakfast

TABLE 1: Delivering on the AI Antibody Promise: The AIntibody Benchmarking Competition
Moderators:
Andrew R.M. Bradbury, MD, PhD, CSO, Specifica, an IQVIA business
M. Frank Erasmus, PhD, Head, Bioinformatics, Specifica, an IQVIA business

AI promises in antibody discovery and optimization: will they really revolutionize the field? or just another way of addressing solved problems?
What can AI do now? And where are we seeing the greatest value relative to existing technologies?
The AIntibody benchmarking competition: Did AI deliver on the AIntibody challenges?
Ideas for future benchmarking competitions

TABLE 2: Opportunities for Protein Engineering in ADC Development
Moderator: Greg M. Thurber, PhD, Associate Professor, Chemical Engineering & Biomedical Engineering, University of Michigan

TABLE 4: Next-Generation T Cell Engagers (TCEs)
Moderator: Vincent Muczynski, PhD, Director, NovalGen

The principle of a bispecific antibody targeting CD3 to redirect cytotoxic T cells toward a tumor target has undeniably contributed to the success of T cell engagers and led to the recent approval of several molecules showing undisputed efficacy in both haematological malignancies and solid tumours. A broad range of research programs are now looking at ways to enhance the potential of these molecules and develop next-generation TCEs.

New technologies to optimize the balance between efficacy and adverse events
Harnessing multispecific antibody formats for enhanced efficacy/specificity
Maximizing the efficiency of TCEs in the solid tumor microenvironment?

TABLE 5: Switchable Bispecific T Cell Nanoengagers for Controllable Cancer Immunotherapy
Moderator: Noor Momin, PhD, Assistant Professor, University of Pennsylvania

Design parameters and objectives for T cell engagers
Engineering strategies to improve their on-target (correct receptor) on-tissue (correct localization)
Engineering strategies to reduce their on-target (correct receptors) off-tissue (wrong tissue localization)
Considerations for the timing, reversibility, and translation of these approaches

TABLE 6: ML to Optimize Immune Cell Engagers
Moderator: Caleb A. Lareau, PhD, Assistant Professor, Memorial Sloan Kettering Cancer Center

Dive into the challenges with engineering immune cell engagers where machine learning might have an impact (i.e. development speed, therapeutic efficacy, clinical safety)
Discuss the data and models needed to develop superior therapies.
Highlight different machine learning methods being applied to the design and optimisation of immune cell engagers.
Explore future directions for machine learning in immune cell engagers.

TABLE 7: R&D with the End-in-Mind
Moderator: Bjørn Voldborg, MSc, Head, National Biologics Facility, DTU Bioengineering, Technical University of Denmark

Determine what your end-product could look like
Consider the full pipeline of your candidate as early as possible
Select suitable expression systems early on
Use regulatory acceptable hosts as soon as possible

TABLE 8: Particulate Impurity Analysis in Gene Therapy Products: Challenges & Opportunities
Moderator: Andrea Hawe, PhD, CSO, Coriolis Pharma Research GmbH

Key challenges and strategies for analyzing particulate impurities in gene therapy products, such viral vectors, mRNA-LNPs, etc.
What types and sizes of particles should we expect?
How do we assess impurities, when the active itself is a particle?
What are key techniques for sizing, counting, characterization, and identification or particles?
Are particulate impurities cQAs for gene therapy products, as in biologics?