The Inoculum Effect in the Era of Multidrug Resistance: Minor Differences in Inoculum Have Dramatic Effect on MIC Determination

Smith, Kenneth P.; Kirby, James E.

doi:10.1128/aac.00433-18

Cited by 86 publications

(86 citation statements)

References 34 publications

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“…However, in vitro testing may not always correlate to in vivo efficacy. An inoculum effect was discovered to be an in vivo concern, and this represents only one of the tightly regulated parameters (51). Generally speaking, for all antibiotics tested here (except for trimethoprim, which was ineffective), as the concentration of bacteria increases, the effectiveness of the antibiotic decreases, consistent with the antibiotic being slowly dosed out (Fig.…”

Section: The Host Physiological Environment Changes the Efficacy Of Pasmentioning

confidence: 59%

Phage-Antibiotic Synergy is Driven by a Unique Combination of Antibacterial Mechanism of Action and Stoichiometry

Liu

Green

Min

et al. 2020

Preprint

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The continued rise in antibiotic resistance is precipitating a medical crisis. Bacteriophage (phage) has been hailed as one possible therapeutic option to augment the efficacy of antibiotics.However, only a handful of studies have addressed the synergistic relationship between phage and antibiotics. Here, we report a comprehensive analysis of phage-antibiotic interaction that evaluates synergism, additivism, and antagonism for all classes of antibiotics across clinically achievable stoichiometries. We combined an optically-based real-time microtiter plate readout with a matrix-like heatmap of treatment potencies to measure phage and antibiotic synergy (PAS), a process we term synography. Phage-antibiotic synography was performed against a pandemic drug-resistant clonal group of E. coli (ExPEC) with antibiotic levels blanketing the minimum inhibitor concentration (MIC) across seven orders of viral titers. Our results suggest that, under certain conditions, phages provide an adjuvating effect by lowering the MIC for drugresistant strains. Furthermore, synergistic and antagonistic interactions are highly dependent on the mechanism of bacterial inhibition by the class of antibiotic paired to the phage, and when synergism is observed, it suppresses the emergence of resistant cells. Host conditions that simulate the infection environment, including serum and urine, suppress PAS in a bacterial growth-dependent manner. Lastly, phage burst size seems to be a significant driver of synergism.Collectively, this data suggests lytic phages can resuscitate an ineffective antibiotic for previously resistant bacteria, while also synergize with antibiotics in a class-dependent manner, processes that may be dampened by lower bacterial growth rates found in host environments. Significance StatementBacteriophage (phage) therapy is a promising approach to combat the rise of multi-drug resistant bacteria. Currently, the preferred clinical modality is to pair phage with an antibiotic, a practice thought to improve efficacy. However, antagonism between phage and antibiotics has been reported, the choice of phage and antibiotic is not often empirically determined, and the effect of the host factors on the effectiveness is unknown. Here, we interrogate phage-antibiotic interactions across antibiotics with different mechanisms of action. Our results suggest that phage can lower the working MIC for bacterial strains already resistant to the antibiotic, is dependent on the antibiotic class and stoichiometry of the pairing, and is dramatically influenced by the host microenvironment.

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Section: The Host Physiological Environment Changes the Efficacy Of Pasmentioning

confidence: 59%

Phage-Antibiotic Synergy is Driven by a Unique Combination of Antibacterial Mechanism of Action and Stoichiometry

Liu

Green

Min

et al. 2020

Preprint

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“…Therefore, the single cell in this article is equivalent to single colony forming unit (CFU). One bacteria per 2nL droplet is equivalent to 5x10 5 CFU/mL which is the standard for MIC tests 11,13 . In principle, the antibiotic screening in droplets is 'digital MIC assay' 14,15 where outcome of each discrete experiment in droplet is binary: '1-positive' with detectable bacteria growth or '0-negative' without growth.…”

Section: Main Textmentioning

confidence: 99%

“…[We looked how susceptibility to the drug changes with increasing bacteria inoculum density] β-lactam antibiotics are often subject to the inoculum effect (IE) where the efficiency of the drug depends on the starting inoculum density of the bacteria 20 . IE is often overlooked in traditional MICassays due to the inaccuracy in setting inoculum density to recommended level using conventional ODmeasurement 13 .…”

Section: Main Textmentioning

confidence: 99%

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“Antibiotic inhibition of bacteria growth in droplets reveals heteroresistance pattern at the single cell level”

Scheler

Makuch

Debski

et al. 2018

Preprint

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Heteroresistance is a phenomenon where isogenic bacteria population exhibits a diverse antibiotic resistance pattern at sub-population or single cell level. The sub-populations with higher resistance can remain undetected with conventional diagnostics which makes them subsequently harder to treat. Such surviving phenotypically heterogeneous sub-populations are also a potential hotbed for novel mutations, thus increasing the resistance permanently in bacteria. Droplet microfluidics gives tools for high-throughput analysis of bacteria and their response to antibiotics at single cell level, which is difficult to obtain with traditional agar plate technologies. In here we show for the first time the precise digital quantification of drug resistance profile in isogenic population at single cell level. We also see that the inhibiting amount of drug per bacteria remains quite stable regardless of bacteria density. Interestingly, the bacteria clump together preferably near these sub-inhibitory conditions. The technology and findings we describe here provide novel quantitative insight into the heteroresistance which is a key step in understanding the pathways leading to drug resistance. This knowledge is crucial in the context of global drug resistance threat as it can help us to find tools to prevent further escalation of drug resistance.

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“…Assuming mass action kinetics, the number of free antimicrobial molecules is reduced proportional to bacterial cell density, which results in changes in the efficacy of the antimicrobial. In addition to this binding kinetic effect, bacteria can produce extracellular enzymes that further decrease the concentration of antimicrobials ( 15–20 ). Some bacteria, for example, produce β -lactamase, which is able to degrade β -lactam antibiotics.…”

Section: Introductionmentioning

confidence: 99%

The pharmacodynamic inoculum effect from the perspective of bacterial population modeling

Baeder

2019

Preprint

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1Efficacy assessment of antimicrobials is essential, both to determine the best 2 clinical use of the antimicrobial and as input for predictive mathematical mod-3 eling. The pharmacodynamic (PD) function is a tool to describe antimicrobial 4 efficacy and can be seen as an extension of the commonly used MIC. While the 5 PD function describes the efficacy of a given dose of antimicrobials, it is based 6 on one bacterial inoculum size only. Therefore, the PD function does not inform 7 us about the change in efficacy due to changes in the inoculum size, also known 8 as the inoculum effect. Here, we used mathematical modeling to quantify the 9 inoculum effect in terms of PD parameters. In particular, we used the multi-hit 10 model that describes the mechanism of action of antimicrobials: When a cer-11 tain number of antimicrobial molecules have hit a bacterial cell, it dies. This 12 framework allowed us to examine the effect of reversible binding and enzymatic 13 degradation of antimicrobials on the pharmacodynamics. A change in bacte-14 rial inoculum size resulted in a change of the PD parameter A 50 , M IC P D , and 15 therefore κ due to reversible binding. We determined an extended PD function 16 that captured the inoculum effect as change in the PD parameters. When we 17 allowed for degradation of antimicrobials by bacterial enzymes, the inoculum 18 effect was intensified. The extended PD function could mimic long term pop-19 ulation dynamics based on the multi-hit model with reversible binding only, 20 1 but deviated from long-term predictions based on a multi-hit model including 21 degradation. We then used the multi-hit framework to estimate outcomes of 22 competition experiments with a sensitive and resistant strain and compared it 23 to predictions of the PD function. When we do not take the inoculum effect into 24 account, our simulations underestimated the ability of the sensitive population 25 to survive for a given PK regime and the competitiveness of the sensitive strain 26 against the resistant strain. Our work emphasizes that the PD function -and 27 in general any efficacy measure -should, at least, include information about 28 the inoculum size and, ideally, account for the inoculum effect. 29 Introduction 30 Efficacy of antimicrobials against bacteria is quantified using time-kill curves 31 and is subsequently captured in so-called pharmacodynamic (PD) functions, 32 also known as E max and Zhi models (1 -3 ). A time-kill curve captures how 33 a bacterial population changes over time in presence of a given concentration 34 of an antimicrobial (fig. 1a). During exponential growth, time-kill curves are 35 approximately linear on a logarithmic scale. Thus, the slopes of these log-linear 36 time-kill curves describe the change of the bacterial population over time and 37 are a direct measure of the bacterial net growth rate. The PD function returns 38 the net growth rate for any given antimicrobial concentration. The parameters 39 of the PD function are estimated based on several time-kill curves with the...

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The Inoculum Effect in the Era of Multidrug Resistance: Minor Differences in Inoculum Have Dramatic Effect on MIC Determination

Cited by 86 publications

References 34 publications

Phage-Antibiotic Synergy is Driven by a Unique Combination of Antibacterial Mechanism of Action and Stoichiometry

Phage-Antibiotic Synergy is Driven by a Unique Combination of Antibacterial Mechanism of Action and Stoichiometry

“Antibiotic inhibition of bacteria growth in droplets reveals heteroresistance pattern at the single cell level”

The pharmacodynamic inoculum effect from the perspective of bacterial population modeling

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