Is your team stress-testing biomarkers before clinical translation?

Your preclinical biomarker looks perfect. Clean data. Beautiful curves.

Statistically significant.

Then you take it to the clinic and... nothing.

Here's what may have gone wrong:

📊 Overfitting in small studies - Your n=8 mouse study found 47 "significant" biomarkers. Most are statistical noise.

⚡ Underpowered early clinical studies - You need proper sample size calculations, not hope.

🔬 Assay translation failure - Your research ELISA won't cut it for clinical validation. The clinical assay may behave completely differently.

This is why robust biomarker selection matters from day one.

You need biomarkers with clinical-grade assay performance that can handle issues such as:
👉 Temperature fluctuations during shipping
👉 Delayed processing times
👉 Site-to-site variation in sample handling
👉 Real patient heterogeneity

The solution isn't more preclinical validation. It's smarter preclinical design.

Test your biomarkers in samples processed as they would be in the clinic... in FFPE... under stressed conditions. Vary your sample handling. Build in real-world variability from the start.

Because a biomarker that only works in perfect conditions isn't a biomarker—it's a laboratory curiosity.

Is your team stress-testing biomarkers before clinical translation?