Estimating treatment prolongation for persistent infections

High rifampicin peak plasma concentrations accelerate the slow phase of bacterial decline in tuberculosis patients: Evidence for heteroresistance

BACKGROUND

Antibiotic treatments are often associated with a late slowdown in bacterial killing. This separates the killing of bacteria into at least two distinct phases: a quick phase followed by a slower phase, the latter of which is linked to treatment success. Current mechanistic explanations for the in vitro slowdown are either antibiotic persistence or heteroresistance. Persistence is defined as the switching back and forth between susceptible and non-susceptible states, while heteroresistance is defined as the coexistence of bacteria with heterogeneous susceptibilities. Both are also thought to cause a slowdown in the decline of bacterial populations in patients and therefore complicate and prolong antibiotic treatments. Reduced bacterial death rates over time are also observed within tuberculosis patients, yet the mechanistic reasons for this are unknown and therefore the strategies to mitigate them are also unknown.

METHODS AND FINDINGS

We analyse a dose ranging trial for rifampicin in tuberculosis patients and show that there is a slowdown in the decline of bacteria. We show that the late phase of bacterial killing depends more on the peak drug concentrations than the total drug exposure. We compare these to pharmacokinetic-pharmacodynamic models of rifampicin heteroresistance and persistence. We find that the observation on the slow phase's dependence on pharmacokinetic measures, specifically peak concentrations are only compatible with models of heteroresistance and incompatible with models of persistence. The quantitative agreement between heteroresistance models and observations is very good ([Formula: see text]). To corroborate the importance of the slowdown, we validate our results by estimating the time to sputum culture conversion and compare the results to a different dose ranging trial.

CONCLUSIONS

Our findings indicate that higher doses, specifically higher peak concentrations may be used to optimize rifampicin treatments by accelerating bacterial killing in the slow phase. It adds to the growing body of literature supporting higher rifampicin doses for shortening tuberculosis treatments.

Microfluidic dose-response platform to track the dynamics of drug response in single mycobacterial cells

Preclinical analysis of drug efficacy is critical for drug development. However, conventional bulk-cell assays statically assess the mean population behavior, lacking resolution on drug-escaping cells. Inaccurate estimation of efficacy can lead to overestimation of compounds, whose efficacy will not be confirmed in the clinic, or lead to rejection of valuable candidates. Time-lapse microfluidic microscopy is a powerful approach to characterize drugs at high spatiotemporal resolution, but hard to apply on a large scale. Here we report the development of a microfluidic platform based on a pneumatic operating principle, which is scalable and compatible with long-term live-cell imaging and with simultaneous analysis of different drug concentrations. We tested the platform with mycobacterial cells, including the tubercular pathogen, providing the first proof of concept of a single-cell dose-response assay. This dynamic in-vitro model will prove useful to probe the fate of drug-stressed cells, providing improved predictions of drug efficacy in the clinic.

The top 100 cited studies on bacterial persisters: A bibliometric analysis

Background: Bacterial persisters are thought to be responsible for the recalcitrance and relapse of persistent infections, and they also lead to antibiotic treatment failure in clinics. In recent years, researches on bacterial persisters have attracted worldwide attention and the number of related publications is increasing. The purpose of this study was to better understand research trends on bacterial persisters by identifying and bibliometrics analyzing the top 100 cited publications in this field.

Methods: The Web of Science Core Collection was utilized to retrieve the highly cited publications on bacterial persisters, and these publications were cross-matched with Google Scholar and Scopus. The top 100 cited publications were identified after reviewing the full texts. The main information of each publication was extracted and analyzed using Excel, SPSS, and VOSviewer.

Results: The top 100 cited papers on bacterial persisters were published between 1997 and 2019. The citation frequency of each publication ranged from 147 to 1815 for the Web of Science Core Collection, 153 to 1883 for Scopus, and 207 to 2,986 for Google Scholar. Among the top 100 cited list, there were 64 original articles, 35 review articles, and 1 editorial material. These papers were published in 51 journals, and the Journal of Bacteriology was the most productive journal with 8 papers. A total of 14 countries made contributions to the top 100 cited publications, and 64 publications were from the United States. 15 institutions have published two or more papers and nearly 87% of them were from the United States. Kim Lewis from Northeastern University was the most influential author with 18 publications. Furthermore, keywords co-occurrence suggested that the main topics on bacterial persisters were mechanisms of persister formation or re-growth. Finally, “Microbiology” was the most frequent category in this field.

Conclusion: This study identified and analyzed the top 100 cited publications related to bacterial persisters. The results provided a general overview of bacterial persisters and might help researchers to better understand the classic studies, historical developments, and new findings in this field, thus providing ideas for further research.

Understanding tolerance to cell wall-active antibiotics

Antibiotic tolerance-the ability of bacteria to survive for an extended time in the presence of bactericidal antibiotics-is an understudied contributor to antibiotic treatment failure. Herein, I review the manifestations, mechanisms, and clinical relevance of tolerance to cell wall-active (CWA) antibiotics, one of the most important groups of antibiotics at the forefront of clinical use. I discuss definitions of tolerance and assays for tolerance detection, comprehensively discuss the mechanism of action of β-lactams and other CWA antibiotics, and then provide an overview of how cells mitigate the potentially lethal effects of CWA antibiotic-induced cell damage to become tolerant. Lastly, I discuss evidence for a role of CWA antibiotic tolerance in clinical antibiotic treatment failure.

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones

Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.

When antibiotics fail: a clinical and microbiological perspective on antibiotic tolerance and persistence of Staphylococcus aureus

Staphylococcus aureus is a major human pathogen causing a vast array of infections with significant mortality. Its versatile physiology enables it to adapt to various environments. Specific physiological changes are thought to underlie the frequent failure of antimicrobial therapy despite susceptibility in standard microbiological assays. Bacteria capable of surviving high antibiotic concentrations despite having a genetically susceptible background are described as 'antibiotic tolerant'. In this review, we put current knowledge on environmental triggers and molecular mechanisms of increased antibiotic survival of S. aureus into its clinical context. We discuss animal and clinical evidence of its significance and outline strategies to overcome infections with antibiotic-tolerant S. aureus.

Reaction Kinetic Models of Antibiotic Heteroresistance

Bacterial heteroresistance (i.e., the co-existence of several subpopulations with different antibiotic susceptibilities) can delay the clearance of bacteria even with long antibiotic exposure. Some proposed mechanisms have been successfully described with mathematical models of drug-target binding where the mechanism's downstream of drug-target binding are not explicitly modeled and subsumed in an empirical function, connecting target occupancy to antibiotic action. However, with current approaches it is difficult to model mechanisms that involve multi-step reactions that lead to bacterial killing. Here, we have a dual aim: first, to establish pharmacodynamic models that include multi-step reaction pathways, and second, to model heteroresistance and investigate which molecular heterogeneities can lead to delayed bacterial killing. We show that simulations based on Gillespie algorithms, which have been employed to model reaction kinetics for decades, can be useful tools to model antibiotic action via multi-step reactions. We highlight the strengths and weaknesses of current models and Gillespie simulations. Finally, we show that in our models, slight normally distributed variances in the rates of any event leading to bacterial death can (depending on parameter choices) lead to delayed bacterial killing (i.e., heteroresistance). This means that a slowly declining residual bacterial population due to heteroresistance is most likely the default scenario and should be taken into account when planning treatment length.