December 5-7, 2017
Boston, MA

Day One
Wednesday, December 6 2017

Day Two
Thursday, December 7 2017

Breaking Down Barriers to Reduce Organizational Inertia for CDD for Biopharmaceuticals

08.00
Atom-Rich, Data-Poor: How Can We Utilize Structural Modeling & Machine Learning Tools Together to Enhance Biologics Development?

  • Vanita Sood Global Head of Drug Structure, Prediction & Design, EMD Serono

Synopsis

• Overview of modeling tools used at EMD Serono
• Exploring practical applications in biologics discovery
• Outlook for the future: what do we need to do now to maximize in silico optimization of our future drugs?

08.30
Panel Discussion and Open Q&A: Why CDD & Why Now?

  • Vanita Sood Global Head of Drug Structure, Prediction & Design, EMD Serono
  • Sarah Sirin Structural Bioinformatician, AbbVie
  • Stanley Krystek Senior Principal Scientist, Bristol-Myers Squibb

Synopsis

• Developing cross-industry collaborations that encourage data and open library development
• Defining a strategic framework for the changing landscape of biotherapeutic development
• Business process engineering: exploring ways to enhance cross departmental collaboration and communication

09.15
Speed Networking & Morning Refreshments

10.15
A Computational Tool Set for the Detection of Liabilities in Bio-Therapeutics

Synopsis

· A method for the identification of aggregation hotspots that is based on the calculation of hydrophobic and electrostatic surface patches on the interaction surface of 3D structures
· Scoring function for aggregation propensity factoring in the intensity, size and relative orientation of the respective surface patches
· Using this approach we can demonstrate how aggregation prone regions can be identified and rationally explained using the Protein Surface Analyzer tool
· The concept of the surface patches has also been employed to develop a series of descriptors that are used in combination with other protein-specific descriptors to derive QSAR-based prediction models for aggregation, solubility and viscosity

Antigen Engineering for Feature Selection of Candidate Therapeutics

10.45
“Garbage In, Garbage Out”: Liability Models for Antigen Design

Synopsis

• Highlighting approaches used for antigen design
• Epitope steering leads to diverse antibody repertoire
• Modeling epitope/paratope contact residues

11.15
Computational Prediction of Antigen Driven Secondary Immune Repertoire for Novel Antibody Discovery

Synopsis

• Exploring the use of error and biases free NGS library preparation to capture original B cell diversity
• Demonstrating feature selection/engineering relevant to antibody discovery
• Highlighting the uses of machine learning in antibody discovery from total B cell repertoire
• Showcasing a comparison of in silico predicted hits with conventional methods (yeast display library) for affinity

11.45
Analysis of Antibody – Antigen Interactions by Directed In-Silico Modeling & Generation of Interface Fingerprints

Synopsis

• Demonstrating modeling of an antibody-antigen interface and its validation
• Describing the different inputs that improve modeling accuracy
• Showcasing cutting-edge tools to generate fingerprints of protein surfaces and their impact on the classification of binding sites
• Revealing the utility of antibody-antigen interface fingerprint to predict the binding energy through a vector regression model

12.15
Lunch & Networking

In Silico Approaches for Macromolecular Discovery

13.15
Computational Antibody Design: Computational Tools for Accelerating Antibody Design & Discovery

Synopsis

• The need for high-affinity and high-specificity antibodies in research and medicine
• Modeling fundamental physical-chemical principles that regulate activity
• Highlighting the utility and performance of computational tools in antibody engineering

13.45
Prediction of Protein-Protein Binding Sites & Epitope Mapping

  • John Gunn Senior Research Scientist, Chemical Computing Group

Synopsis

Protein surface patch analysis and characterization with patch-type interaction fingerprints
· Conformational ensemble methodology for computational prediction of protein binding motifs
· Direct incorporation of experimental HDX data in identifying and scoring interactions

14.15
High-Throughput In Silico Assessment of Antibody Developability

  • Sandeep Somani Senior Scientist - High Performance Computing, Janssen

Synopsis

• Exploring case studies of experimental validation of developability prediction models
• Questioning whether useful predictions can be made based on antibody homology models
• Highlighting approaches for incorporating structural flexibility in prediction models
• Considerations for incorporating predictive models in antibody discovery programs

14.45
Afternoon Refreshments & Networking

Accelerating Biologics Discovery & Development Using Mechanistic PK/PD Modeling

15.45
Optimal Affinity of Monoclonal Antibodies: Guiding Principles Derived from the Mechanistic Modeling Analyses

Synopsis

• Highlighting general principles or rules of thumb to determine the optimal affinity range for monoclonal antibodies against three of the most common types of targets:
• Soluble membrane-bound targets that internalize, targets that exist in both membrane-bound and shed soluble forms
• Demonstrating that, similar to the Lipinski’s rule of 5 for small molecule drugs, these general principles are developed to provide actionable guidance for project teams considering biotherapeutic drug development

16.15
Design Criteria for Bispecific Antibodies: Learnings from Mathematical Modeling

  • John Rhoden Sr Research Scientist, Eli Lilly and Company

Synopsis

• Showcasing in silico and experimental data for the enhanced potency of bispecific antibodies relative to the combination of parental mab
• Highlighting the utility of mathematical models for bispecific antibody interactions with their cellular receptor targets in designing antibodies with optimal affinity, valency, and structure
• Detailing the preclinical effort to support predictions and engineer a novel bispecific to minimize clearance while maintaining target cell binding
• Providing insight into non-intuitive mechanisms and features of bispecifics, such as the role of the relative antigen density of the target receptors

16.45
Quantitative Predictions of Target Mediated Drug Disposition of Monoclonal Antibodies from Preclinical to Clinic

  • Donald Mager Professor of Pharmaceutical Sciences , University at Buffalo SUNY

Synopsis

• The basic tenets and expectations of target-mediated drug disposition (TMDD)
• Contrast simple compartmental and physiologically-based models of TMDD properties
• Highlight examples in which mechanistic TMDD models are used to translate preclinical pharmacokinetics to humans and guide antibody drug development

17.15
Close of Day One

17.20
Drinks Reception

Synopsis

Relax with after a hard day’s learning with your peers and take this great opportunity to network over drinks and canapes. Kindly sponsored by Schrödinger

Schrodinger