Thursday, June 11th at 15:00 GMT | 16:00 CET | 10:00 EST | 07:00 PST
You are expected to move AAV programs forward based on data that often changes once you get closer to real application. What looks promising in early screening can become inconsistent during optimization, forcing teams to revisit decisions, adjust assays, and lose time.
For R&D leaders in gene therapy, this creates ongoing pressure to ensure that early work supports what comes next. The focus is not just on generating results, but on building a screening approach that remains reliable as your program progresses. In this webinar, we focus on how to design AAV screening and optimization workflows using scalable, disease-relevant models and assays that can move toward GMP without requiring rework.
Key learning objectives
- Early AAV screening data needs to remain reliable during optimization and beyond
- Scalable cell production enables broader and more consistent AAV variant testing
- GMP-transferable assays reduce delays and rework later in development
- Disease-relevant models provide more meaningful insights into AAV performance
Register to learn how to implement an integrated early safety strategy and assess how this approach could increase your biotherapeutics’ chances of clinical success.
Who should attend?
AAV development requires decisions that hold up across the full development path. This session will walk through how to structure your screening and optimization approach so it supports scale, consistency, and transferability, with examples from Duchenne, Friedreich’s ataxia, and ALS.
Register to see how Ncardia supports AAV programs with systems designed to align early discovery with downstream development.
Speakers
Shushnat Jain, PhD, directs Ncardia's drug discovery team, helping develop biological phenotypic assays in representative model systems to improve the translation of therapeutic strategies. He brings a rich diversity of expertise to his Ncardia role, having led large, multi-year research programs to aid several drug discovery initiatives spanning numerous therapeutic areas through the use of automation and high-content methodologies. He has managed, mentored, and trained scientists of all levels, both as a scientist and manager. Shushant's research has been published in Neuron, Genome Biology, the Journal of Biomolecular Screening, and the Journal of Biological Chemistry, among others.
