July 22, 2026

Some of the most damaging variability in a gut assay never involves the drug at all. It lives between neighboring wells on the same plate, seeded from the same batch, run on the same day. When that scatter is large enough, it swallows the effect you are trying to measure, and a real result and […]

A model can be perfectly reproducible in the lab that built it and still fall apart the moment it lands somewhere else. This is the version of the reproducibility problem that bites hardest in practice, because it shows up after you have already decided to adopt the assay. The data looked clean in the vendor’s

Here is a mistake that survives peer review more often than it should. A team runs an experiment once, measures it in triplicate wells, sees tight error bars, and reports a significant effect. The error bars are tight because the three wells came from the same run, the same batch, the same day. They describe

Most conversations about model reproducibility skip past the least glamorous part, which is also the part that decides the outcome. Before you dose a single compound, before any donor biology enters the picture, a batch of cells either performs or it does not. Lot-to-lot variability is where a lot of preclinical trouble starts. Lot release

The first question a careful scientist asks about a primary-cell gut model is almost always about donors. If the cells come from real people, and people differ, how do you keep the differences from swamping the result? It is a fair worry, and it is the right place to start. But donor variability is not

Every serious evaluation of a new preclinical model reaches the same question, usually about twenty minutes in. Someone stops asking whether the biology looks more human, because they can already see that it does, and asks whether the data will hold. Will the model give the same answer next month, in a different technician’s hands,