Thursday, April 23rd at 15:00 GMT | 16:00 CET | 10:00 EST | 07:00 PST
Drug discovery still struggles with translation. Promising candidates fail because models do not reflect human biology. In this webinar, we will explore how a more translational and predictive human iPSC-derived system can support reproducible and scalable screening, supported by a real-world cardiac case study developed in collaboration with LUMC.
Key learning objectives
- Why current models fail in predictivity, scalability, and reproducibility
- How iPSC-derived models improve human relevance in drug discovery
- What robust assay design looks like in practice, from workflow to QC metrics
- How a cardiac case study demonstrates scalable, reliable screening with actionable outcomes
Who should attend?
For preclinical, translational, and screening scientists evaluating scalable, predictive human-relevant models for high-throughput drug discovery workflows.
Register to learn how to implement scalable, human-relevant screening models and assess whether this approach fits your pipeline and targets.
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.
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