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現地での参加者のみ対象

5月15日（月） 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?

5月17日（水） 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

5月19日（金） 7:30 - 8:25 AM EST

TABLE 1: Meaningful Representation of Biologics for Machine Learning
Moderator: 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: Bjorn 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?
Limitations?

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.