8:50 am Chair’s Opening Remarks
Utilising Current and Novel Predictive Models for Enhanced Forecasting of Developability and Manufacturability
9:00 am Computational Tools to Aid in Developability and Early Risk Assessment of Biologics
Synopsis
- Overview of the in silico tools developed to aid in assessing physical and chemical liabilities of biologics such as aggregation, oxidation, deamidation etc
- Demonstrate the use of these tools both in Discovery and Development for developability assessment and in the identification of key critical quality attributes (CQAs) during manufacturing
- Case studies will be discussed to showcase the use of computational tools to aid in mechanistic understanding of degradation as well as higher-order structural characterization
9:30 am Modeling Protein Properties using pH-dependent Conformational Sampling
Synopsis
- Sampling protein conformational and charge states using grand-canonical dynamics
- Calculating ensemble-averaged properties and descriptors in a variable-pH ensemble
- Developing models for biologics developability assessment and liability prediction
10:00 am Building a Computational Pipeline for Early Developability Assessment and Optimization
Synopsis
- Overview of the developability funnel at Denali and impact of structural analysis on predicting aggregation propensity
- Introducing an ML tool for experimentalists to quickly glean insight from their sequence-“activity” data and rationally design next round of variants with improved developability characteristics
- Examples from integrating a design-predict-make-test cycle for antibody engineering and hit triage
10:30 am Morning Refreshments and Networking
Implementing the Capabilities of AI and Machine Learning into the Process of Engineering Complex Biologics
11:30 am Modifying Discovery Workflow to Utilize Machine Learning Algorithms for Better and Faster Optimization of Biologics
Synopsis
- Merck’s biologics developability discovery platform
- High throughput analytical characterization data management
- Example of analytical characterization data overview; looking for patterns and correlations in analytical characterization data
- Case study 1: Hydrophobicity and developability profiles
- Case study 2: Thermal stability and developability profiles
12:00 pm AI & Machine Learning for High Quality Targets and Disease Indications for Both Biologics and Small Molecules
Synopsis
- Practical and strategic bioinformatics to enable drug discovery
- Published examples of how to use AI and ML to predict clinical successes
- How to find the first and additional disease indications for your assets leveraging the computational solutions
12:30 pm Mastermind Session
Synopsis
This is an interactive session designed to help broaden your understanding of the challenges with the field and potential opportunities. Engage with different industry stakeholders and develop a deeper appreciation for how the field can move forward.
1:00 pm Lunch and Networking
Computational Tools for Accelerating Antibody Discovery & Lead Selection
2:00 pm Computational Design of Single Domain Antibodies
Synopsis
- Challenges in the computational design of single domain antibodies
- Selection of scaffolds for single domain antibody design
- Utilization of machine learning and molecular dynamic simulations for single domain antibody design
2:30 pm Antibody Dynamics from the Nanosecond to Millisecond Timescale – Implications for Affinity Maturation and Humanization
Synopsis
- Linking loop flexibility and affinity maturation
- Relative VH-VL interdomain dynamics
- Kinetics vs. thermodynamics
3:00 pm CDR-H3 Loop Dynamics – Implications for Antibody Design and Structure predictions
Synopsis
- Conformational ensembles of CDR-H3 in solution vs. crystal structures
- Induced fit and Conformational selection as paradigms for binding
- Timescales of conformational transitions of loops