The field of protein engineering is at an exciting point in its development, with new generations of therapeutic antibodies now progressing through development and into the market, great advances in protein science and discovery technology and a body
of clinical evidence that can be used to inform the development of safe, highly effective therapies for unmet medical needs. The PEGS Engineering Antibodies conference explores case examples of the most significant emerging technologies
used by protein engineers working at the discovery and design stages to quickly and efficiently craft biotherapeutics directed at the most elusive targets and biological functions.
Final Agenda
WEDNESDAY, APRIL 10
7:15 am Registration (Commonwealth Hall) and Morning Coffee (Harbor Level)
7:25 - 8:25 PANEL DISCUSSION: Women in Science – Inspired Professional and Personal Stories (Continental breakfast provided) (Waterfront 1&2)
Moderator:
Jennifer S. Chadwick, PhD, Director of Biologic Development, BioAnalytix, Inc.; Co-Chair, Mentors Advisors and Peers Program, Women In Bio, Boston Chapter
Panelists:
Joanna Brewer, PhD, Vice President, Platform Technologies, AdaptImmune
Charlotte A. Russell, MD, DMSc, CMO, Alligator Bioscience
Susan Richards, PhD, Presidential Scientific Fellow, Translational Medicine Early Development, Sanofi R&D
Kristi Sarno, Senior Director, Business Development, Pfenex
8:30 Chairperson’s Opening Remarks
Jane Seagal, PhD, Principal Research Scientist, Global Biologics, AbbVie
8:40 Antibody Affinity and Repertoires from Single Antibody-Producing Cells
Pierre Bruhns, PhD, Professor,
Antibodies in Therapy & Pathology, Pasteur Institute, France
The antibody response represents the circulating fraction of antibodies produced by plasma blasts and plasma cells. Weak to lack of expression of membrane-bound antibody prevents conventional technologies to assess their antibody specificity/polyreactivity,
affinity and repertoires on a single cell level. We circumvented this issue by using single-cell compartmentalization in droplet microfluidics and characterized plasmablasts and plasma cells following immunization or autoimmune antibody responses
in mice and humans.
9:10 Microfluidic Technology to Identify and Isolate Antigen-Specific T Cells
Julie Brouchon, PhD, Postdoctoral
Associate, Weitz Lab, Harvard University
Isolation of antigen-specific T cells is fundamental to study autoimmune diseases and to develop immunotherapies. Unfortunately, these cells are rare and cannot be easily identified by surface markers. We overcome these challenges by compartmentalizing
cells into microfluidic droplets and performing a functional assay relying on T cell-target cell interaction and subsequent fluorescent detection of cytokine secretion. In one day, we can screen several million cells to isolate live, antigen-specific
T cells.
9:40 High Throughput Antibody Discovery in the Digital Age
Jane Seagal, PhD, Principal Research
Scientist, Global Biologics, AbbVie
The constantly growing demand for novel therapeutic biologics drives technology innovation enabling efficient antibody discovery. Optimization and ‘digitalization’ of antibody discovery workflows is essential for successful identification
of antibodies against challenging targets and the sampling diverse antibody repertoires. In this talk, automated platform technologies enabling both hybridoma and single B cell workflows are presented highlighting the integration of sequence information,
screening data, and informatics for large panels of antibodies.
10:10 Coffee Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)
10:15 Women in Science Speed Networking in the Exhibit Hall (Commonwealth Hall)
10:55 Antibody Discovery and Engineering Using Deep Learning
Sai Reddy, PhD, Assistant Professor, Biosystems
Science and Engineering, ETH Zurich, Switzerland
Deep 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. Antibody discovery and engineering is reliant
on experimental platforms of high-throughput expression and screening of libraries. Here, I will describe how we are applying deep learning to augment the discovery and engineering of antibodies by moving beyond experimental screening.
11:25 KEYNOTE PRESENTATION: Implementing Artificial Intelligence in Biotherapeutic Discovery and Development
Philip Tagari, PhD, Vice President,
Research, Amgen
The very rapid “democratization” of machine learning provides a broad range of opportunities in the complete lifecycle of therapeutics discovery and development. Strategies and examples will be discussed in molecule design and selection, manufacturability
assessment, novel prognostic biomarkers, clinical trial design and “beyond the medicine”.
11:55 Machine Learning in Computational Biology to Accelerate Heterologous Protein Production
Elizabeth Brunk, PhD,
Postdoctoral Research Fellow, Systems Biology Research Group, University of California, San Diego
Standardized, multi-omics datasets are becoming increasingly available, but major impediments prevent the realization of impact of big data resources. Modern machine learning methods bring the promise of leveraging large-scale omics data to make accurate
predictions. Here, I present recent efforts on the development of appropriate in silico tools and cross-disciplinary training resources, which are paramount for further progress in big data science.
12:25 pm Biologics by Design: Incorporating Physics-based Methods into Prediction Models for Protein Stability and Binding Affinity
Eliud Oloo, PhD, Senior Principal Scientist, Schrödinger
Combining experiment with physics-based computational approaches and Machine Learning is emerging as a promising strategy for advancing the discovery of biologic treatments, including monoclonal antibodies, vaccines, and enzyme replacement therapies.
To develop effective statistical correlations and learned predictive models applicable to the design and optimization of biologics, it is important to take into account structure-based features. Structural properties are however often difficult and
expensive to obtain by experiment. We describe physics-based computational methods that narrow this gap by calculating properties derived from structure and simulation, thus complementing experimental measurements.
12:55 Luncheon Presentation I: Build Better Biologics with Machine Learning and Synbio
Claes Gustafsson, PhD,
CCO, Co-Founder, ATUM
This presentation will showcase how ATUM combines recent developments in genome engineering, automation, big data and product analytics to increase efficiency of engineering and developability of biologics and cell lines. Cell lines generated using the
LeapIn® transposase combined with optimized vector constructs, proprietary codon optimization and QSAR-based protein engineering allow for an information rich and efficient optimization of mAbs, bispecifics, CAR-T molecules, and the increasingly
complex biologics approaching the market place.
1:25 Luncheon Presentation II (Sponsorship Opportunity Available)
1:55 Session Break
2:10 Chairperson’s Remarks
Gregory C. Ippolito, PhD, Research Assistant Professor, Molecular Biosciences, LIVESTRONG Cancer Institute, The University of Texas at Austin
2:15 Lessons Learned on Rapid Discovery from the DARPA Pandemic Prevention Platform (P3)
Gregory C. Ippolito, PhD, Research
Assistant Professor, Molecular Biosciences, LIVESTRONG Cancer Institute, The University of Texas at Austin
The DARPA P3 program is focused on the rapid discovery, production, and delivery of antibody countermeasures to halt infectious disease outbreaks in 60 days or less. Four P3 performer teams were chosen to deploy distinct technologies for antibody discovery
related to this effort. Lessons learned shall be discussed.
2:45 Case Study of Antibody Discovery and Development from a Small and Nimble Biotech
Abhishek Datta, PhD, Director,
Antibody Discovery & Engineering, Scholar Rock
Since its inception 6 years ago, Scholar Rock has successfully executed multiple antibody drug discovery campaigns, including one for its lead antibody, SRK-015 that is currently in the clinic. In this presentation, Scholar Rock discusses a case study
wherein the combination of an accelerated naïve discovery and optimization campaign, coupled with development/availability of an in vivo model that enabled rapid pharmacodynamic read-out, led to the successful
identification of an antibody candidate.
3:15 Fast and Deep: Rapid
High-Throughput Antibody Discovery from Deep Mining of Natural Immune
Repertoires
Kevin Heyries, PhD, Co-Founder, Business Development and Strategy Lead, AbCellera
AbCellera’s antibody discovery platform can screen millions of single antibody secreting cells in less than a day using multistep and multiplexed secretion assays and high-throughput imaging. We will demonstrate how this technology is being applied
to rapid identification of antibodies from humans exposed to viral pathogens under the DARPA Pandemic Prevention Platform (P3) project and how these capabilities can be applied to difficult membrane protein targets.
3:45 Refreshment Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)
4:45 Problem-Solving Breakout Discussions - Click here for details(Commonwealth Hall)
5:45 Networking Reception in the Exhibit Hall with Poster Viewing (Commonwealth Hall)
7:00 End of Day
THURSDAY, APRIL 11
8:00 am Registration (Commonwealth Hall) and Morning Coffee (Harbor Level)
8:30 Chairperson’s Remarks
Christoph Spiess, PhD, Senior Scientist, Antibody Engineering, Genentech
8:35 A Paradigm Shift: Moving from Traditional Screening towards Data Driven Antibody Design
Kristin Brown,
Director, Protein Design and Informatics, GlaxoSmithKline
Machine learning, artificial intelligence, and in silico design are phrases that have had high visibility in the news and scientific papers over the last few years. Effectively incorporating these techniques within antibody
discovery has the potential to transform how we design antibodies. This transformation requires a shift in how we design experiments to generate relevant data. I will be discussing different strategies that enable this change in paradigm.
9:05 New High-Throughput Technologies to Design and Optimize Non-Antibody Scaffolds
Gabriel Rocklin,
PhD, Assistant Professor, Department of Pharmacology & Center for Synthetic Biology, Northwestern University
Optimizing the therapeutic suitability of protein scaffolds remains an unsolved challenge. We have developed a multiplexed mass spectrometry approach to assay thousands of de novo designed scaffolds for properties that
are inaccessible to other high-throughput techniques, including stability against protein degradation and aggregation, the extent of conformational fluctuations, and the efficiency of intracellular delivery. Data from these large-scale assays enables
us to optimize these properties in de novo scaffold design.
9:35 NEW: From Screening to Quality: Integrated Systems and Databases for Biologics Discovery
Erin Williams, Associate Lab,
Head, Quality Control, Protein Sciences - Quality Control, EMD Serono
We have established a global data and workflow platform at Merck for increasing the efficiency of our antibody discovery, protein production, and quality control processes. We share examples of how the use of this platform has transformed our daily
research work, e.g., in organizing and performing QC of produced proteins, including standard and bispecific antibodies, and how it supports discovery and engineering groups using B-cell cloning workflows and engineering of (SEED) bispecific antibodies.
10:05 Coffee Break in the Exhibit Hall with Poster Viewing (Commonwealth Hall)
11:05 Using Deep Sequencing Datasets to Tailor Specificity
Tim Whitehead, Associate
Professor, Department Chemical and Biological Engineering, University of Colorado, Boulder
Next-generation sequencing has presented protein scientists with the ability to observe entire populations of molecules before, during, and after a high-throughput screen or selection for function. My talk will present an overview of how we can leverage
this large amount of sequence-function information to program antibody specificity in typical antibody discovery workflows. I will also present on how we use NGS to mimic evolutionary landscapes of neutralizing, specific anti-Influenza antibodies.
11:35 Engineering of a T-Cell Dependent Bispecific Antibody to Broaden the Therapeutic Index for Solid Tumors
Christoph Spiess, PhD, Senior Scientist, Antibody Engineering, Genentech
The lack of tumor-specific targets for solid tumors is a challenge for the successful and safe targeting of T-cell dependent bispecific (TDB) antibodies. By fine-tuning the avidity driven binding to HER2, we engineered an anti-HER2/CD3 TDB that selectively
binds and kills HER2 overexpressing tumor cells with high potency, while sparing cells with normal expression levels. The underlying concept may expand to other targets and applications that require an improved therapeutic index.
12:05 pm Intratumoral Delivery and Retention of Cytokines and Agonist Antibodies
K. Dane Wittrup, PhD, C.P.
Dubbs Professor, Chemical Engineering and Biological Engineering; Associate Director, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
Despite broad efforts, delivery of immune agonistic payloads via systemic administration is plagued by poor therapeutic indices, due to extensive off-target exposure. We are examining the fundamental micropharmacokinetic issues involved in intratumoral
administration and find that bispecific constructs that are retained in particular subdomains of the tumor, as defined by extracellular matrix composition, can exert profound therapeutic effects while largely sparing from systemic exposure or
toxicity.
12:35 End of Engineering Antibodies