Predicting drug inactivation by changes in bacterial growth dynamics.

Studying how antibacterials operate at subinhibitory concentrations reveals how they impede normal growth. While previous works demonstrated drugs can impact multiple aspects of growth, such as prolonging the doubling time or reducing the maximal bacterial load, a systematic understanding of this phenomenon is lacking. It remains unknown if common principles dictate how drugs interfere with growth. We monitored growth curves across thirty-eight drugs, spanning multiple mechanisms of action in Escherichia coli to deconvolve their impact on the lag, growth rate, and carrying capacity and developed a mathematical framework to quantitatively compare their effects. We discovered that drugs induced considerably different inhibition phenotypes, which were independent from the drug's target. Functional assays of drug inactivation revealed that drug inactivation is a key shared factor underlying a lag-associated phenotype. Our work reveals that likely drug inactivation can be directly inferred from growth dynamics which is instrumental for rapidly identifying drug-inactivating bacteria.