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. They provide an opportunity to share examples from your work, vet ideas with peers, and be part of a group problem-solving endeavor.
These will take place IN-PERSON ONLY.
MONDAY May 15: 12:45 – 1:30 PM EST
TABLE 1: How to Discover Antibodies Against Novel/Difficult Targets
Moderator: Horacio G. Nastri, PhD, Associate Vice President, Biotherapeutics, Incyte Corporation
- What makes a target particularly difficult?
- How to evaluate the challenges
- Selection of therapeutic modality
- Selection of optimal discovery strategy
- Screening alternatives
TABLE 2: Failures and Successes in TNFRSF Agonist Antibody Drugs, and Future Outlook
Moderator: Jieyi Wang, PhD, Founder & CEO, Lyvgen Biopharma
- Mechanisms of action of TNFRSF agonistic antibodies
- Lessons learned in the clinic
- FcγR2B and tumor targeted conditional agonisms
- New clinical developments to watch
TABLE 3: Immunomodulation by Genomic “Dark Matter” and Extracellular Vesicles in Cancer
Laszlo G. Radvanyi, PhD, President & Scientific Director, Ontario Institute for Cancer Research
- What are non-coding regions or the ‘dark matter’ of the genome?
- Guiding clinical treatment
- Future of this field
TABLE 4: Production and Stabilization Membrane Proteins
Moderator: Matthew Coleman, PhD, Senior Scientist & Group Leader, Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory
Moderator: Matthew DeLisa, PhD, Director, Cornell Institute of Biotechnology, Cornell University; Co-Founder, UbiquiTx, Inc.
- What are the current major limitations of obtaining intact and stable membrane protein complexes?
- What would we like to see developed in terms scaffold/reagent supports for assessing membrane proteins?
- Are there ideal techniques/additives for long term storage of functional membrane proteins and the complexes they form?
- How do we assess the biological compatibility of nanodisc technologies for invitro and in vivo experimentation?
- What keeps cell-free expression synthesis from playing a bigger role in membrane protein production?
TABLE 5: Acceleration of Analytical Development by Digital Transformation
Moderator: Ruojia Li, PhD, Associate Director, CMC Statistics & Data Science, Bristol Myers Squibb Co.
- At what stages and areas of analytical development do you see big opportunities for digital applications?
- Success stories, major challenges and your solutions
- Types of modeling applied for analytical development and the value they bring
- Different needs for different modalities
TABLE 6: Launching Digitalization Initiatives in Pharma
Moderator: Steven J. Mehrman, PhD, Principal Scientist, Pharmaceutical Development, Johnson & Johnson Pharmaceutical R&D
- Current state assessments: what are we doing and how – and what aren’t we doing
- Data capture and standards: pain points and opportunities (ELN, systems, instruments & context)
- Setting digital goals: what has worked and at what levels of detail
- User requirements: best approaches for science and engineering
- Data flow end user experiences: good or bad and lessons learned
TABLE 7: Future Directions in Antibody Development
Moderator: Ahuva Nissim, PhD, Professor, Antibody and Therapeutic Engineering, William Harvey Research Institute, Queen Mary University of London
- How can we address the challenges of difficult targets?
- Can we still improve the next generation of repertoires?
- What are the Impacts of machine learning and bioinformatics?
- What is the niche for academic vs. industry research?
TABLE 8: Antibodies vs Small Molecules: Can Artificial Intelligence Help with Target Annotation and Commercial Tractability Analysis?
Moderator: Alex Zhavoronkov, PhD, Founder & CEO, Insilico Medicine
- When to develop biologics instead of small molecules?
- Can AI help identify the best targets for biologics or small molecules?
- What are the commercial considerations for the development of biologics vs small molecules?
WEDNESDAY May 17: 2:15 – 3:00 PM EST
TABLE 1: Implementation Challenges for Machine Learning as a Tool for Antibody Discovery
Moderator: Christopher R. Corbeil, PhD, Research Officer, Human Health Therapeutics, National Research Council Canada
- Current successes
- Experimental validation and POC
- Bottlenecks and challenges
- Needs from IT and solution providers
TABLE 2: Would Increasing In Vivo Data Generation Increase Probability of Clinical Success?
Moderator: Pierce J Ogden, PhD, Co-Founder & CSO, Manifold Biotechnologies Inc.
- What preclinical in vivo data are most often predictive of clinical success
- What are some ways we could increase in vivo throughput
- How can AI and machine learning be utilized in conjunction with in vivo data
- How to improve the in vitro to in vivo drug development process by supplementing with early in vivo discovery workflows
- Unbiased in vivo based therapeutic discovery and the required in vivo throughput
TABLE 3: CAR-Ts for Solid Tumors
Moderator: Mitchell Ho, PhD, Senior Investigator and Deputy Chief, Laboratory of Molecular Biology; Director, Antibody Engineering Program, National Cancer Institute (NCI), National Institutes of Health
- Recent advances in GPC2 and GPC1 as new targets in solid tumors
- Engineering CAR T cells for treating neuroblastoma and pancreatic cancers
- New strategies using camel nanobodies to improve efficacy of CAR T cells
TABLE 4: Commercializing Cell and Gene Therapies
Moderator: Michael D. Jacobson, PhD, Managing Partner, Cambridge Biostrategy Associates LLC
- Trends in commercializing cell and gene therapies
- De-centralized versus centralized manufacturing
- Pricing trends, reducing costs
- New trends such as in vivo CAR T delivery
TABLE 5: Therapeutic Platforms for Antibody-Mediated Protein Degradation
Moderator: Nicholas Agard, PhD, Principal Scientist, Antibody Engineering, Genentech, Inc.
- Review the multiple technologies have recently emerged to induce targeted degradation of cell surface or secreted proteins including LyTACs, PROTAbs/AbTACs, and KineTACs.
- Discuss pros and cons of targeted degradation vs. inhibition, and where targeted degradation may be most applicable.
- Compare different antibody-mediated degradation technologies and discuss where they may be optimally used.
- Discuss what protein-engineering approaches might be applicable to enhance degradation efficiency.
TABLE 6: Common Issues with Transient Protein Production
Moderator: Richard Altman, MS, Manager, Application Scientists, Delivery and Protein Expression, Biosciences Division, Life Sciences Solutions Group, Thermo Fisher Scientific
Moderator: Henry C. Chiou, PhD, Senior Director, Cell Biology, Life Science Solutions, Thermo Fisher Scientific
Moderator: Dominic Esposito, PhD, Director, Protein Expression Laboratory, Frederick National Laboratory for Cancer Research
- What are the current challenges to transient protein production?
- How do we optimize the whole protein expression workflow process?
- How can we maintain volumetric yields while scaling transient expression up or down?
- What cell line(s) should we use and when?
- What parameters can impact the quality or physical attributes of transiently produced proteins?
TABLE 7: Best Practices in Using Biophysical Methods for More Efficient, Higher Resolution Analysis of Biopharmaceutical Higher Order Structure (HOS)
Moderator: Anne Kim, PhD, Senior Principal Scientist and Group Leader, Analytical R&D, Pfizer Inc.
- Automated CD, DSC, microfluidic modulation IR, and NMR for protein characterization
- Best practice for analyzing and interpreting routine biophysical assays (DLS, DSC, SEC-MALS, and IR)
- What NMR methods are currently employed for product characterization and comparability/similarity assessments?
- What analysis software is used for analysis?
- How extensively are NMR data used in regulatory filings for biopharmaceuticals?
TABLE 8: High Throughput Mass Spectrometry in Biopharma: Challenges and Opportunities
Yoan Machado, PhD, Scientist, Molecular Analytics, Amgen
- Fast or thorough, can’t it be both? High throughput mass spectrometry-based analytics in biopharma.
- Current challenges in instrumentation and analysis software for fully automated mass spectrometry applications in biopharma.
- Role of native mass spectrometry in characterization of higher order structures and membrane proteins.
- Applying mass spectrometry for high throughput epitope mapping, are we there yet?
TABLE 9: Clinical Relevance of Anti-Drug Antibodies
Moderator: Joleen White, PhD, Head of Bioassays, Bill & Melinda Gates Medical Research Institute
- Identify assay design parameters to ensure detection of relevant antibodies
- Planning assay implementation timelines using immunogenicity risk assessment
- Designing schedule of assessments within clinical trials
- Integrate multiple findings to assess clinical relevance
- Conducting analysis using immunogenicity status as an outcome, not a baseline characteristic
TABLE 10: Predictive Assays, Studies, and Tools: How Can These be Improved?
Moderator: Rita Martello, PhD, Associate Director, EMD Serono
- In-silico and in-vitro tools: State-of-the-art
- In-vitro/in-vivo correlation of immunogenicity: What do we know?
- Immunogenicity strategy: How do we select the right assay?
- Interpretation of results and decisions: De-immunization vs immunogenicity risk and impact on project timelines
- Examining the regulatory requirements
TABLE 11: Critical Reagent Qualification for LNP encapsulated mRNA Therapeutics
Moderator: Laura Brunner, MS, Senior Scientist, Bioanalytical Sciences, Moderna
- Assay platforms for PK/PD, BioD, and immunogenicity
- Challenges in reagent identification and qualification
- Life cycle maintenance for stability
- Qualification and bridging for new labeling, processing, and manufacturing
- Planning ahead and best practices for critical reagent management
FRIDAY May 19: 7:30 – 8:25 AM ESTTABLE 1: Meaningful Representation of Biologics for Machine LearningModerator: Yu Qiu, PhD, Senior Principal Scientist, Sanofi Genzyme R&D Center
- ML doesn’t understand protein. Digital representation (numerical features) is needed as input.
- Meaningful representation (features) is a key for ML models
- Protein can be represented as 1D sequence (one hot or embedding), 3D structure (point cloud of cartesian coordinates, or graphs with nodes and edges), or surface patches
- Surface ID is deep learning derived representation, encoding geometric and chemical properties, that can be used for surface patch comparison
- Applications of Surface ID include paratope clustering, PPI classification, database mining etc.
TABLE 2: Implementation of Disruptive Digital Innovation & Deep Learning Models to Accelerate Therapeutics Discovery of Protein Therapeutics: Challenges & Opportunities
Moderator: Peter Clark, PhD, Head of Computational Science & Engineering, Janssen Pharmaceuticals, Inc.
- Explore common challenges for end-to-end integration and enterprise deployment of AI/ML models across the R&D product lifecycle
- How are organizations leveraging the growing suite of predictive models to inform and accelerate generative design and optimization of protein therapeutics?
- How can we foster collaboration between different departments, including research, development, and CMC, to establish AI as a core organizational discipline?
- What are the opportunities & best practices for incorporating AI/ML models and integrated lab automation platforms from discovery to development?
- How are advancements in computational hardware and infrastructure driving innovation in our digital platforms and business processes?
TABLE 3: ADCs in the Era of Immunotherapy - Their Current Roles and Potential Future
Moderator: Greg M. Thurber, PhD, Associate Professor, Chemical Engineering & Biomedical Engineering, University of Michigan
- Antibody-drug conjugates (ADCs) have achieved 8 new approvals in the past 5 years.
- ADCs can initiate immunogenic cell death without the broad immunosuppression of small molecule chemotherapy and interact via their Fc-domain.
- Discussion on current therapeutics that are being combined with checkpoint inhibitors, anti-VEGF therapy, and other treatments to potentially increase the immune response.
- Conversation about new avenues that are being developed to maximum the immune response with these novel therapeutics
TABLE 4: Next-Generation Antibody Drug Conjugates: What Do We Need to Do for the Next Major Step Up?
Moderator: Mahendra P. Deonarain, PhD, Chief Executive and Science Officer, Antikor Biopharma Ltd.
- Radical or incremental innovations - novel formats, conditional activation, unconventional targets or payloads
- Which innovation will make a real impact in the treatment of solid tumors?
TABLE 5: Translational Considerations When Advancing Bispecifics to the Clinic
Moderator: Michelle Morrow, PhD, Senior Vice President, Biology & Translational Science, F-Star Therapeutics, Inc.
- What approaches can be taken to align novel mechanisms of action of bispecific with the biology of disease?
- How can preclinical model systems be used to effectively generate translational hypotheses?
- What considerations are important when designing biomarker strategies for bispecifics?
TABLE 6: Combining the Benefits of Academia and Industry: Get the Best of Both Worlds
Moderator: Bjørn Voldborg, MSc, Head, National Biologics Facility, DTU Bioengineering, Technical University of Denmark
- How to raise awareness at both ends?
- How to start-up?
- What are the needs?
- Funding and pricing/who will pay?
TABLE 7: Best Practices for In Vitro/In Vivo Biotransformation/PK Analysis of Novel Modalities
Moderator: Jianzhong Wen, PhD, Principal Science & Group Leader, Merck & Co., Inc.
- Needs/techniques/workflows to characterize novel biologics PK and biotransformation (multi-specifics, fusions, ADCs, siRNA/mRNA, CAR cells)
- Critical reagent generation to facilitate the bioanalysis
- ADC PK analysis: how many components to monitor, preclinical vs. clinical? Immunoassay vs. LC-MS vs. hybrid?
- ADC DAR analysis: native vs. intact vs. subunits vs. bottom up
- Nucleotide analysis: MS vs. PCR types of analysis
TABLE 8: Characterization Challenges for mRNA Vaccines and Therapeutics
Moderator: Sharon Polleck, Senior Research Scientist, Analytical R&D, Pfizer Inc.
- CQAs impacting mRNA and nanoparticle safety and efficacy
- Emerging methods and instruments
- Best practices at different stages of development
- Process analytics and QC/release testing
- Problems and solutions