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?
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?
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
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
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
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
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
