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9:00am - 1:00pm 240 mins
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Full-Day Pre-Conference Training Course: Introduction to Antibody Engineering
Introduction to Antibody Engineering
  • Instructor: David Bramhill, PhD - Consultant, Bramhill Biological Consulting, LLC

Add-on this pre-conference training course to your main conference registration package for an additional fee and gain a comprehensive overview of antibody engineering in an easy-to-follow classroom setting to help you prepare for the main conference program. 

Training course registration begins at 8:30am.

Break Schedule: AM Break: 10:30-11:00;

Lunch: 12:30-1:30; PM break: 3:00-3:30


TRAINING COURSE OVERVIEW

Today’s wealth of knowledge of protein structures will be reviewed along with the genetics of diversity generation of antibodies, to give insights into the best strategies for improving protein function.There is particular emphasis on the choice of a functional assay to monitor effectively the changes in a desired property, and the use of functional enrichment steps where a library approach is employed.Not only is amino acid sequence amenable to engineering, but glycan structures and other modifications may also be engineered. The course will focus on the engineering and enhancement of antibodiesand antibody-like scaffolds. Examples will include work on antibody fragment affinity improvement by 100-fold to low pM affinity. Also the engineering of bispecific antibodies by diverse approaches and the adaptation to generate Chimeric Antibody Receptor (CAR) constructs will be discussed. Expression platforms for producing antibodies for testing and for manufacture will also be covered. A backgroundin biochemistry and molecular biology is useful, as the course is designed to progress rapidly from simple to advanced concepts.

COURSE AGENDA

• Functions amenable to engineering: affinity, specificity, stability, solubility, immunogenicity

• The measure of success: functional assays

• Engineering by design

• Engineering by random mutation

• Designed libraries

• Display technologies

• Improving manufacturing by protein engineering methods

• Glycosylation engineering - function and homogeneity

• Other protein modifications

• Immunogenicity engineering

• Bispecific antibodies

• Antibody-drug conjugates (ADCs)

• CAR-T strategies

• Expression of antibodies and fragments for discovery and testing

• Manufacturing platforms for antibodies and fragments

1:00pm - 1:15pm 15 mins
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
Workshop Moderator’s Remarks
  • Aran Labrijn, PhD - Principal Scientist, Antibody Research and Technology, Genmab
1:00pm - 1:15pm 15 mins
Workshop B: Machine Learning in Antibody and Protein Engineering
Workshop Co-Moderators' Remarks
  • Sai Reddy, PhD - Associate Professor, Department of Biosystems Science and Engineering at ETH Zurich, ETH Zurich
  • Andrew Bradbury, MD, PhD - Chief Scientific Officer, Specifica
1:00pm - 5:00pm 240 mins
Info
Full-Day Pre-Conference Training Course: Introduction to Antibody Engineering
Introduction to Antibody Engineering - Cont'd
  • David Bramhill, PhD - Consultant, Bramhill Biological Consulting, LLC

Add-on this pre-conference training course to your main conference registration package for an additional fee and gain a comprehensive overview of antibody engineering in an easy-to-follow classroom setting to help you prepare for the main conference program. 

Training course registration begins at 8:30am.

Break Schedule: AM Break: 10:30-11:00;

Lunch: 12:30-1:30; PM break: 3:00-3:30


Today’s wealth of knowledge of protein structures will be reviewed along with the genetics of diversity generation of antibodies, to give insights into the best strategies for improving protein function.There is particular emphasis on the choice of a functional assay to monitor effectively the changes in a desired property, and the use of functional enrichment steps where a library approach is employed.Not only is amino acid sequence amenable to engineering, but glycan structures and other modifications may also be engineered. The course will focus on the engineering and enhancement of antibodiesand antibody-like scaffolds. Examples will include work on antibody fragment affinity improvement by 100-fold to low pM affinity. Also the engineering of bispecific antibodies by diverse approaches and the adaptation to generate Chimeric Antibody Receptor (CAR) constructs will be discussed. Expression platforms for producing antibodies for testing and for manufacture will also be covered. A backgroundin biochemistry and molecular biology is useful, as the course is designed to progress rapidly from simple to advanced concepts.

COURSE AGENDA

• Functions amenable to engineering: affinity, specificity, stability, solubility, immunogenicity

• The measure of success: functional assays

• Engineering by design

• Engineering by random mutation

• Designed libraries

• Display technologies

• Improving manufacturing by protein engineering methods

• Glycosylation engineering - function and homogeneity

• Other protein modifications

• Immunogenicity engineering

• Bispecific antibodies

• Antibody-drug conjugates (ADCs)

• CAR-T strategies

• Expression of antibodies and fragments for discovery and testing

• Manufacturing platforms for antibodies and fragments

1:15pm - 1:45pm 30 mins
Info
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
Bispecific Antibodies: History and (Future) Promises
  • Aran Labrijn, PhD - Principal Scientist, Antibody Research and Technology, Genmab

Since the concept of a man-made bispecific antibody was originally described (almost 60 years ago), many technical and conceptual advances have led to the extensive bispecific antibody landscape known today. A short historical perspective will be given, including discussion of the different bispecific antibody classes, the unique opportunities for dual-targeting and the (current) challenges facing the (pre-)clinical development of bispecific antibodies.

1:15pm - 1:45pm 30 mins
Info
Workshop B: Machine Learning in Antibody and Protein Engineering
Machine Learning for Protein Engineering
  • Sai Reddy, PhD - Associate Professor, Department of Biosystems Science and Engineering at ETH Zurich, ETH Zurich

Machine learning, as a part of a family of tools related to artificial intelligence, is an emerging field of information and computer science that uses large data sets to extract features and representations. Protein engineering is reliant on experimental platforms of high-throughput expression and screening of libraries. Here, I will describe how researchers are using machine learning to assist in protein engineering experiments and thus move beyond experimental screening.

1:45pm - 2:15pm 30 mins
Info
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
Accessing Novel Functionalities of Bispecific Antibodies - Target Pair Discovery by Screening a Large "Select, Mix and Assay" Combinatorial Library
  • Helene Finney, PhD - Director, Functional Screening, UCB

To discover unique functionalities of bispecific antibodies, we have developed technology to facilitate unbiased target pair identification by functional screening of 1000’s of bispecific antibodies to 100’s of different target combinations. Our screening format & growing library allows immediate generation of assay-ready bispecific antibodies by simple mixing of selected specificities. Screening campaigns and examples of the discovery of unique obligate bispecific functions for multiple therapeutic applications will be described.

1:45pm - 2:15pm 30 mins
Info
Workshop B: Machine Learning in Antibody and Protein Engineering
Directed Evolution Guided by Machine Learning: An Application to Combinatorial Libraries
  • Zachary Wu - PhD Candidate in Frances Arnold Group, California Institute of Technology

Directed evolution, limited by throughput, often proceeds through the iterative accumulation of single or pairwise mutations. We describe an approach that embraces the epistatic nature of proteins by directly exploring combinatorial sequence space, a space that is intractable experimentally but accessible with machine learning. This approach is validated on an empirical fitness landscape, and we also demonstrate an application in evolving enzymatic enantiodivergence.

2:15pm - 2:45pm 30 mins
Info
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
Benchmarking T Cell-Redirecting Therapies for Cancer: Comparing CD3-engaging Bispecifics and CAR T Cells
  • David DiLillo, PhD - Senior Staff Scientist, Regeneron Pharmaceuticals

The two leading platforms for redirecting a patient’s T cells to recognize tumors, CD3-binding bispecific molecules and chimeric antigen receptor (CAR) T cells, both show clinical activity. We have developed pre-clinical in vitro and in vivo models to mechanistically compare these two technologies and will discuss our findings as well as the clinical implications.

2:15pm - 2:45pm 30 mins
Info
Workshop B: Machine Learning in Antibody and Protein Engineering
Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning
  • David Gifford - Professor of Electrical Engineering and Computer S, Massachusetts Institute of Technology

We show that machine learning methods can design human Immunoglobulin G (IgG) antibodies with target affinities that are superior to candidates directly derived from phage display panning experiments. We also show that machine learning can improve specificity by identifying antibodies that bind to a specific epitope.

2:45pm - 3:15pm 30 mins
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
Networking Refreshment Break
2:45pm - 3:15pm 30 mins
Workshop B: Machine Learning in Antibody and Protein Engineering
Networking Refreshment Break
3:15pm - 3:45pm 30 mins
Info
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
A Biparatopic Agonist Antibody for OX40 That Exhibits Superior Activity Without Secondary Crosslinking
  • Jonathan Davis, Ph.D. - Head of Discovery and Innovation, Invenra, Inc.

The development of agonistic antibodies that activate T-cell co-stimulatory pathways represents a therapeutic strategy with significant clinical potential. However, challenges remain for the translation from in vitro efficacy to clinical success. OX40 and other tumor necrosis factor receptor (TNFR) superfamily members are notorious for requiring high-order receptor clustering in order to achieve full activity. For monoclonal antibodies, this high-order clustering is generally achieved through secondary cross-linking strategies. In vivo, this secondary cross-linking is often supplied through immune effector cells via Fc engagement. Bispecific and biparatopic antibodies represent an emerging class of drug molecules that enable unique mechanisms of action relative to their monoclonal counterparts. Here, we describe the use of our bispecific B-body™ platform for the generation of biparatopic OX40 agonistic antibodies. These agonist antibodies have been characterized using primary T cell assays to monitor the kinetics of growth proliferation and cytokine secretion, outperforming cross-linked antibodies currently being tested in clinical trials. In co-culture systems, these agonist antibodies were effective in inhibiting the immuno-suppressive properties of Tregs and M2 macrophages. In addition, we have shown in vivo efficacy in mouse tumor cell models. Our lead OX40 agonist antibody has been optimized for activity and developability and has entered stable cell line development to further support pre-clinical activities.

3:15pm - 3:45pm 30 mins
Info
Workshop B: Machine Learning in Antibody and Protein Engineering
Deep Learning for Antibody-specific Epitope Prediction
  • Chris Bailey-Kellogg, PhD - Professor, Computer Science, Dartmouth University

We have developed a deep learning approach that enables efficient and effective large-scale antibody epitope prediction by learning the determinants of specific recognition implicitly encoded in sequence and structure. Our method leverages several insights: (1) graph convolution aggregates the effects of coherent groups of residues in mediating interactions; (2) an attention mechanism promotes consistency of inferred interfaces across both interacting partners; (3) transfer learning allows an antibody-specific model to leverage the relatively much larger database of general protein-protein interactions. We show that our data-driven approach achieves a higher precision and recall at predicting antibody epitopes than current state-of-the-art methods, including those using explicit docking. Our method can thus support rapid characterization of potential epitope specificities of large panels of discovered antibodies, guiding further selection and experimental characterization.

3:45pm - 4:15pm 30 mins
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
Bispecifics and Blood Brain Barrier
  • Mihalis Kariolis, Ph.D. - Scientist, Antibody and Protein Engineering, Denali Therapeutics
3:45pm - 4:15pm 30 mins
Info
Workshop B: Machine Learning in Antibody and Protein Engineering
Deciphering Interaction Fingerprints from Protein Surfaces for the Optimization of Biologics

Predicting interactions between proteins and other biomolecules purely based on structure is an unsolved problem in biology. A high-level description of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a protein’s modes of interactions with other biomolecules. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF, a conceptual framework based on a new geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

4:15pm - 4:45pm 30 mins
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
Bispecifics and Trispecifics for HIV
  • Amarendra Pegu, Ph.D. - Head, Antibody Research, NIAID/NIH
4:15pm - 4:45pm 30 mins
Info
Workshop B: Machine Learning in Antibody and Protein Engineering
Identifying Antigen-specificity Patterns in Antibody Repertoires by Deep Learning

Deep sequencing of antibody repertoires has become a promising and powerful tool in basic immunology, immunodiagnostics and the drug discovery process. However, identification of relevant information in these large datasets remains challenging. I will explain how we are using deep learning to identify patterns of antigen-specificity from the antibody repertoires of immunized mice. Deep generative modeling  is then used to elucidate the antibody sequence space by generating thousands of novel and functional variants in-silico, highlighting how deep learning can be directly used for antibody discovery and engineering.

4:45pm - 5:00pm 15 mins
Workshop A: Bispecific Antibodies: New Strategies and Case Studies
Concluding Remarks and Discussion
4:45pm - 5:00pm 15 mins
Workshop B: Machine Learning in Antibody and Protein Engineering
Concluding Remarks and Discussion
5:00pm - 5:05pm 5 mins
Close of Workshops

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