Standard BioTools focuses on protein biomarkers and has carried out thousands of studies in collaboration with international consortia.
A newly released database containing 250 million protein analyses in neurodegenerative diseases presents exciting opportunities, and we are also collaborating with PRECISE, a major consortium based in Singapore.
In this seminar, Roger Chang from Standard BioTools will highlight how to access these public databases and approaches for analyzing proteomics data.
More about the seminar:
Drug development remains a challenging process, with nearly 90% of efforts not reaching approval, often due to limited tools for accurately predicting risk and therapeutic response. Proteomics provides an emerging framework for addressing these challenges by offering detailed insights into disease biology and supporting earlier, more reliable prediction of clinical outcomes.
High-plex proteomic technologies, such as the aptamer-based proteomic assay, are capable of simultaneously quantifying more than 11,000 proteins with a level of precision and reproducibility that makes it suitable to detect and monitor phenotypes. When combined with machine learning, these datasets can be used to construct multi-protein models that improve upon conventional clinical models for predicting outcomes, including major adverse cardiovascular events.
One example is a 27-protein surrogate endpoint model for cardiovascular events, validated across more than 170,000 participant-years, which has shown stronger performance than standard risk prediction methods and consistent applicability across diverse populations.
This model has been applied in cardiovascular outcome research, including Phase 3 trials of semaglutide, where changes in proteomic risk scores aligned with results from large outcome trials but required fewer participants and shorter study durations.
Additional studies have demonstrated the use of proteomic profiling for monitoring cardiometabolic effects of weight loss interventions and for retrospectively identifying early signals of drug-related risk, as in the case of torcetrapib.
Together, these examples illustrate the potential of high-plex proteomics to support clinical research by informing trial design, improving safety monitoring, and enabling earlier decision-making.
By generating mechanistic insights and supporting the development of validated surrogate endpoints, proteomic approaches may help reduce the cost and duration of clinical trials while contributing to more precise strategies in drug development.
