BuzZ Sessions are informal, moderated discussions, allowing participants to exchange ideas and share experiences around a focused topic. Each discussion will be led by a facilitator who keeps the discussion on track and the group engaged. Attendees can choose to join any BuzZ topic of interest.
Thursday, January 21, 7:30 – 8:25 am
BuzZ TABLE: Troubleshooting Difficult Proteins: When to Optimize, Pivot, or Move On
Moderator: Christopher Cooper, DPhil, Senior Lecturer in Biotechnology, University of Surrey
- What signals tell you to keep optimizing versus change the construct, expression system, or purification strategy?
- Where do the most common bottlenecks arise across expression, purification, and QC, and how do you identify the real root cause?
- How should the intended downstream application influence what constitutes “good enough” yield, purity, stability, and quality?
- What strategies or lessons learned have been most successful when working with particularly difficult or recalcitrant proteins?
BuzZ TABLE: Early Discovery Cleavable Tags Translating to GMP Clinical Manufacturing
Moderator: David W. Wood, PhD, Professor, Chemical & Biomolecular Engineering, Ohio State University
- At least four cleavable tag systems have been announced for GMP production
- Validation of cleavable tag approaches has not been established
- Translation from early discovery to clinical manufacturing is untested
- Broad new product modalities enabled by these approaches
- How will early-stage discovery groups be impacted by these opportunities?
BuzZ TABLE: Cell-free Systems for Accelerating Receptor Development
Moderator: Matthew A. Coleman, PhD, Senior Scientist & Group Leader, Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory
- Accelerate design-build-test cycles
- Reduce process complexity
- Enable rapid prototyping of difficult targets
- Support faster translation to applications
BuzZ TABLE: Scaling High-Throughput Production: Challenges of Today, Opportunities for Tomorrow
Moderator: Manoj Rajaure, PhD, Associate Principal Scientist, Biologics Engineering, AstraZeneca
- Breaking the bottleneck: Overcoming capacity, speed, quality, and supply-chain challenges
- AI-powered optimization: Leveraging AI to improve process efficiency, yield, and throughput
- Quality by design: Predicting failures, reducing variability, and strengthening product quality
- The future of scale: Advancing toward agile, autonomous, and sustainable production systems
- Smarter automation: Integrating robotics, advanced analytics, and real-time decision-making to enhance manufacturing performance
BuzZ TABLE: From Prediction to Decision: Making Developability Models Actionable in Biologics Discovery
Moderator: Krishna D. Bharadwaj Anapindi, PhD, Senior Scientist, Biology, Gilead Sciences Inc.
- Integrating sequence-, structure-, and experimental biophysical data to identify developability risks early in discovery.
- Moving beyond individual metrics toward multiparameter assessments that support practical candidate-selection decisions.
- Defining where AI/ML and predictive models add value—and where experimental validation remains essential.
- Applying predictions across increasingly complex biologic formats while accounting for format-specific developability risks.
- Building useful feedback loops between computational predictions, high-throughput experiments, and molecular engineering to improve models and guide redesign.
BuzZ TABLE: Accelerated Stability Testing for Frozen and Lyophile Biologicals (proteins, LNPs, AAVs)
Moderator: Evgenyi Y. Shalaev, PhD, FAAPS, Distinguished Research Fellow, Pharmaceutical Sciences, Abbvie, Inc.
- Are there any examples of Arrhenius temperature behavior for frozen biologicals?
- Selection of acceleratory stability conditions (e.g., temperature range for frozen forms, and temperature and water content ranges for lyophiles)
- What are common kinetic patterns for degradation of frozen and lyophile biologicals? (e.g., zero order, first order, square root of time, biphasic, etc)
- Is activation energy depends on specific kinetic model selected (when same degradation kinetics dataset is analyzed using two different kinetic models)?
- Share examples of using accelerated stability data in projects and filing (non-proprietary discussion)
BuzZ TABLE: Next-Generation T Cell Engagers: Overcoming Barriers to Safety and Efficacy
Moderator: Valeria G. Nicolini, PhD, Head, Pharma Research & Early Development, F. Hoffmann-La Roche AG
- Overcoming barriers to T cell engagers in solid tumors
- Balancing potency, tumor selectivity, and systemic toxicity
- Expanding T cell activation beyond conventional CD3 engagement
- Emerging strategies to improve the therapeutic window
BuzZ TABLE: The Future of Functional Biologics: How AI Is Transforming the Discovery and Design of Multispecific Antibodies
Moderator: Alon Wellner, Vice President, Biology, Co-Founder, Aureka Biotechnologies
- Predicting functional activity beyond binding
- AI-guided target and format selection
- Integrating computational design with functional screening
- Building datasets that improve predictive models
BuzZ TABLE: Accelerating Peptide Discovery: Streamlining Workflows from Display Libraries to Lead Selection
Moderator: Sunhee Hwang, PhD, Scientist 4, Peptide Therapeutics, Genentech Inc.
This session discussion addresses key bottlenecks across the peptide discovery pipeline - focusing on methods to shorten discovery timelines, strategies to bridge translational success, and multi-parametric workflows to prioritize viable candidates faster.
Discussion points:
- How can we accelerate the discovery process?
- How can we overcome the in-vitro to in-vivo gap?
- How can we effectively prioritize viable candidates?
BuzZ TABLE: Same Sequence, Different Answer: Peptide Hit Validation and Batch Reproducibility
Moderator: Deborah Moore-Lai, PhD, Vice President, Protein Sciences, ProFound Therapeutics
Summary: Peptide hits that look solid in a primary screen often behave differently when the next batch arrives, and the cause is rarely the biology. This roundtable examines what actually counts as a confirmed hit, how much apparent irreproducibility traces back to quantitation and peptide chemistry rather than true batch variation, and where the field should set its thresholds. Participants are encouraged to bring specific cases, including the ones that didn’t resolve.
Discussion points:
- What is the minimum for calling a hit confirmed?
- How much of "batch variability" is really a concentration error?
- Is it the concentration or the chemistry?
- Thresholds: which differences matter and what is the deciding factor?