Conference Day Two | Thursday, October 24, 2024
8:00 am Coffee & Networking
8:55 am Chairs Opening Remarks
Data Management & Integration in Biologics: Ensuring Quality, Security & Standardization
9:00 am Advancing Data Management & Integration Strategies for Enhanced Compliance & Efficiency in Biologics Development
Synopsis
- Overview of challenges and strategies for storing, capturing, and structuring data within CMC and Development laboratory settings
- Challenges associated with integrating diverse CMC datasets to operationalize AI/ML
- Lessons learned from building and deploying a “Digital Twin” for CMC workflows
- Discussion on the role of automation and advanced analytics in improving data and product quality
9:30 am Data Management & Integration Strategies to Enable Continuous Feedback in Biologics Development
Synopsis
- Streamlining data into the right format for biologics drug development
- Strategies on how to enable continuous learning from data.
- Exploring methods and best practices for automated data collection
10:00 am Maximizing Impact with Minimal Data: Ensuring Quality & Relevance for Effective Machine Learning in Biologics
Synopsis
- Explaining the Importance of data quality over quantity in leveraging smaller data sets effectively
- Discussing the Importance of selecting relevant and representative data points for model training
- Examples of successful machine learning applications in biologics research with limited datasets
10:30 am Morning Break
11:30 am Panel Discussion: Enhancing Data Consistency in Biologics Research: Overcoming Variability & Promoting Standardization
Synopsis
- Discussing the impact of varying experimental conditions (e.g., protein concentration, pH, assay methods) on data quality and comparability
- Exploring potential benefits of establishing benchmark datasets for improving the reproducibility and validity of research outcomes
- Collaborative efforts and data-sharing initiatives to promote standardized practices across the biologics research community
- Best practices for data validation and moralization to mitigate bias and variability
Computational Approaches for Enhancing Antibody Developability & Safety
12:00 pm Computational Tools for Reducing T Cell immunogenicity
Synopsis
- The existing experimental data and computational tools focus on identification of T cell epitopes, but the outcome is not sensitive and quantitative enough for small changes in sequences
- Developing new datasets and in silico tools better suited for protein engineering
- Exploring the best practices for combining AI driven tools and different experimental assessments to optimize biologics and reduce immunogenicity risks
12:30 pm Application of Computational Tools to Address Developability-related Properties in Antibodies
Synopsis
- Successful and unsuccessful examples of using computational tools for antibody structure prediction and hydrophobicity prediction
- Highlighting the merits and demerits of antibody hydrophobicity prediction tools through real-world project examples
- Case studies demonstrating the use of computational tools to reduce self-association likelihood by targeting negative charge patches in antibodies
12:50 pm Session Reserved For OmniAb
1:00 pm Lunch Break & Networking
2:00 pm Computational Tools to Assess Developability & Critical Quality Attributes of Biologics
Synopsis
- Outlining the computational tools developed to model the structure and critical quality attributes (CQAs) of biologics that pose significant challenges during drug development
- Demonstrating the use of these tools both in Discovery and Development for developability assessment, as well as for mechanistic understanding of degradation and higher order structural characterization
- Discussing the cross-industry efforts to compile CQA data from various pharmaceutical companies, aiming to create a comprehensive dataset for validating and enhancing the accuracy of modeling tools
2:30 pm Machine Learning for Late Stage Developability in Biologics
Synopsis
- Leading machine learning initiatives to enhance the development and delivery of biologic drugs.
- Delivering AI solutions for the comprehensive characterization of antibodies and biologics liability, developability, and manufacturability
- In-silico approaches to facilitate the screening of antibody developability properties
Computational Innovations in the Design & Optimization of Complex Biologics
3:00 pm Advancing AAV Capsid Design: Harnessing Machine Learning for Enhanced Therapeutic Efficacy
Synopsis
- Discuss the challenges in AAV capsid design and engineering for enhanced tissue targeting and transduction efficiency
- Explore how machine learning algorithms can analyse large datasets of capsid sequences and predict novel variants with improved properties
- Showcase case studies demonstrating the application of deep learning models to optimize AAV capsid features