Our recent paper in npj Antimicrobials and Resistance is a great example of scientific serendipity: after staring at thousands of bacterial growth curves over many studies, we started wondering whether the curve shapes themselves carry mechanistic information 1/9 🦠🧪 www.nature.com/articles/s44...

So we assembled a new carefully curated dataset with growth curves across almost forty drugs, measured across multiple sub-inhibitory concentrations. For each curve, we quantified intuitive its key features: lag, growth rate, and yield 2/9

Clustering drug by their impact on lag/rate/yield clearly revealed that they vary hugely in how they inhibited growth. In extreme cases, a drug solely affected only a single parameter 4/9

Overlaying the known mechanisms of action over the barycentric landscape ruled out this effect stem exclusively from how drug target bacteria (since drugs with the same mechanism can land in very different regions of the landscape) 5/9

That pushed us to ask whether cellular defenses might impact curve profiles. We cloned different resistance cassettes and measured how they altered the potency-matched growth curves. This strongly hinted that active drug inactivation underlies a long-lag inhibition profile 6/9

We then tested if this assosiation holds through our entire dataset. We used a functional assay for drug inactivation on all drugs and found the association holds up. A long-lag inhibition phenotype is a strong indicator of drug inactivation 7/9

Btw, note that “inactivation” can mean more than enzymatic degradation and includes any process that reduces effective drug activity over time (chemical modification, sequestration, etc) 8/9