Classic reaction kinetics can explain complex patterns of antibiotic action

Abstract

Finding optimal dosing strategies for treating bacterial infections is extremely difficult, and improving therapy requires costly and time-intensive experiments. To date, an incomplete mechanistic understanding of drug effects has limited our ability to make accurate quantitative predictions of drug-mediated bacterial killing and impeded the rational design of antibiotic treatment strategies. Three poorly understood phenomena complicate predictions of antibiotic activity: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. Here, we show that chemical binding kinetics alone are sufficient to explain these three phenomena, using single cell data and time-kill curves of Escherichia coli and Vibrio cholerae exposed to a variety of antibiotics in combination with a theoretical model that links chemical reaction kinetics to bacterial population biology. Our model reproduces existing observations, has a high predictive power across different experimental setups (R2= 0.86), and makes several testable predictions, which we verified in new experiments and by analysing published data from a clinical trial on tuberculosis therapy. While a variety of biological mechanisms have previously been invoked to explain post-antibiotic growth suppression, density-dependent antibiotic effects, and especially persister cell formation, our findings reveal that a simple model which considers only binding kinetics provides a parsimonious and unifying explanation for these three complex, phenotypically distinct behaviours. Current antibiotic and other chemotherapeutic regimens are often based on trial-and-error or expert opinion. Our ‘chemical reaction kinetics’-based approach may inform new strategies, that are based on rational design.

Antibiotic efficacy — context matters

“…Mass spectrometry allows interrogation into spatial determinants of antibiotic efficacy [110,111] and synthetic biology has contributed novel tools for perturbing essential genes [112]. Current quantitative models are providing mechanistic insights into several nonlinear components of antibiotic lethality [56,113] and are useful for predicting multidrug treatment outcomes [114,115] and antibiotic drug synergies [116,117]. Integration of these tools will enable identification of additional mechanistic details between primary target inhibition and subsequent lethality.…”

Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies

“…Typically, all measures of antibiotic efficacy are defined relative to the MIC of the specific bacteria/drug pair ( C max /MIC, AUC/MIC and T C>MIC ) to circumvent this problem. To be able to use a modeling framework based on physicochemical characteristics of drug action, it is therefore useful to define the MIC based on physicochemical properties [23]. In the simplest case, when we assume a constant antibiotic concentration in this framework, the MIC depends on two parameters: the drug target affinity ( K D ) and the threshold of bound target ( f c ) at which the net growth of a bacterial population is zero (Eq (3)).…”

“…For example, cells exposed to 80% MIC ampicillin show no measurable defect in either growth or elongation rates, and all cells remain intact (S1 Fig). In contrast, translation inhibitors such as chloramphenicol and tetracycline do affect bacterial growth below MIC, and it has previously been shown a nearly complete suppression of growth at 80% MIC [23]. When fitting Eq (10) to data from single cells exposed to constant sub-MIC concentrations of antibiotics [23], it has previously been estimated that a high threshold of bound ribosomes must be met to interrupt all bacterial growth ( f c = 98%) and that there is a low diffusion barrier ( p = 1.2x 10 −2 /s).…”

“…Here, we extend a modeling framework [23] that integrates bacterial population biology with the intracellular reaction kinetics of antibiotic-target binding to investigate how the kinetics of drug-target binding affect bacterial response to fluctuating antibiotic concentrations. We find that the physicochemical characteristics of drug action predict differences in antibiotic pharmacodynamics at fluctuating concentrations and correlate well with observed data.…”

Antimicrobial combinations: Bliss independence and Loewe additivity derived from mechanistic multi-hit models

“…While this may be a valid assumption for AMPs [9], many antibiotics inhibit the replication of pathogens in addition to killing them. To use the approach to describe the action of antibiotics will thus require to extend the multi-hit models by an effect on the replication rates [42]. This applies even more to antibodies that primarily inhibit pathogen replication and induce death only through very specific mechanisms, such as antibody-dependent cell-mediated cytotoxicity.…”