Novel rate-area-shape modeling approach to quantify bacterial killing and regrowth for in vitro static time-kill studies

Abstract

In vitro static concentration time-kill (SCTK) studies are a cornerstone for antibiotic development and designing dosage regimens. However, mathematical approaches to efficiently model SCTK curves are scarce. The currently used model-free, descriptive metrics include the log10 change in CFU from 0 h to a defined time and the area under the viable count versus time curve. These metrics have significant limitations, as they do not characterize the rates of bacterial killing and regrowth and lack sensitivity. Our aims were to develop a novel rate-area-shape modeling approach and to compare, against model-free metrics, its relative ability to characterize the rate, extent, and timing of bacterial killing and regrowth from SCTK studies. The rate-area-shape model and the model-free metrics were applied to data for colistin and doripenem against six Acinetobacter baumannii strains. Both approaches identified exposure-response relationships from 0.5- to 64-fold the MIC. The model-based approach estimated an at least 10-fold faster killing by colistin than by doripenem at all multiples of the MIC. However, bacterial regrowth was more extensive (by 2 log10) and occurred approximately 3 h earlier for colistin than for doripenem. The model-free metrics could not consistently differentiate the rate and extent of killing between colistin and doripenem. The time to 2 log10 killing was substantially faster for colistin. The rate-area-shape model was successfully implemented in Excel. This new model provides an improved framework to distinguish between antibiotics with different rates of bacterial killing and regrowth and will enable researchers to better characterize SCTK experiments and design subsequent dynamic studies.