Explore the Agenda
8:00 am Check In & Light Breakfast
Workshop A
9:00 am Optimizing & Automating Sample Processing & Working Effectively With Challenging Samples to Enable Scalable, High Throughput Proteomics Across Drug Discovery Pipelines
Proteomics analysis is driven by sensitive and fast instrumentation, but sample preparation remains a critical bottleneck, especially when working with limited or low-quality data. To fully realize the potential of scalable proteomics for drug discovery, it's crucial that sample preparation workflows are accelerated and optimized.
This workshop will gather experts to discuss how to:
- Transition to scalable, automated sample processing with plate-based formats, minimizing hands-on steps, accounting for variation in sample collection, and adopting automation tools
- Overcome poor sample quality with optimized protocols for low-abundance samples, effective enrichment strategies, and methods to reduce protein loss during preparation
- Uphold quality control standards during assay development, sample collection, processing, and storage to maintain sample integrity and facilitate reproducible proteomics analysis
12:00 pm Lunch Break & Networking
Workshop B
1:00 pm Implementing Multi-Omics Into Drug Discovery Workflows to Paint a Holistic Picture of Health, Contextualize Proteomics Insights & Make Well-Informed Drug Discovery Decisions
Whilst proteomics has the potential to reveal the workings of the proteome andÂ
demystify disease signalling pathways, part of the story is still missing. CombiningÂ
genomics, transcriptomics, and metabolomics insights to contextualize proteomics isÂ
vital to better understand pathology and guide therapeutic discovery.
This workshop will gather experts to discuss how to:
- Effectively harmonize and integrate genomics, transcriptomics, and metabolomics data to bolster understanding of proteomics and generate a more holistic view of pathology and therapeutic responseÂ
- Employ innovative computational and AI tools to streamline multi-omics workflows and maximize time and cost-efficiency of large-scale data analysis
- Apply multi-omics across drug discovery, from deconvoluting disease pathways and identifying novel targets to validating biomarkers and optimizing hit identificationÂ