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How omics will drive the next generation of surrogate endpoints
Next-gen tools offer more complete views of disease, drug response, but constraining their outputs is key
Next-generation tools offer more complete views of disease and drug response, but constraining their outputs is key.
As omics tools find their footing in the clinic, they have the potential to reveal new, more predictive surrogate endpoints, and bolster confidence in existing ones. These emerging technologies could catalyze accelerated approvals in a wider range of disease areas, but reduction to practice will require overcoming data science challenges shared by all omics methods, and technical hurdles specific to individual approaches.
The transformative potential of expedited approval pathways has been overwhelmingly limited to cancer and infectious diseases, where surrogate endpoints that predict clinical benefit have been most obvious.
Even as progress extends to other areas such as kidney disease, the scope of surrogate development is largely limited to biomarkers with long histories in clinical practice. These are the lowest hanging fruit from a data gathering point of view, but are not always the best predictors of clinical benefit.
Moreover, the most common diseases are complex, meaning they are less likely to be adequately captured by single metrics.
The solution will likely come from tools that use multiplexed, hypothesis-free measurements to fill gaps in a field’s understanding of clinical benefit.
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