A population model evaluating the consequences of the evolution of double-resistance and tradeoffs on the benefits of two-drug antibiotic treatments

Mathematical modelling of antibiotic interaction on evolution of antibiotic resistance: an analytical approach

Background

The emergence and spread of antibiotic-resistant pathogens have led to the exploration of antibiotic combinations to enhance clinical effectiveness and counter resistance development. Synergistic and antagonistic interactions between antibiotics can intensify or diminish the combined therapy's impact. Moreover, these interactions can evolve as bacteria transition from wildtype to mutant (resistant) strains. Experimental studies have shown that the antagonistically interacting antibiotics against wildtype bacteria slow down the evolution of resistance. Interestingly, other studies have shown that antibiotics that interact antagonistically against mutants accelerate resistance. However, it is unclear if the beneficial effect of antagonism in the wildtype bacteria is more critical than the detrimental effect of antagonism in the mutants. This study aims to illuminate the importance of antibiotic interactions against wildtype bacteria and mutants on the deacceleration of antimicrobial resistance.

Methods

To address this, we developed and analyzed a mathematical model that explores the population dynamics of wildtype and mutant bacteria under the influence of interacting antibiotics. The model investigates the relationship between synergistic and antagonistic antibiotic interactions with respect to the growth rate of mutant bacteria acquiring resistance. Stability analysis was conducted for equilibrium points representing bacteria-free conditions, all-mutant scenarios, and coexistence of both types. Numerical simulations corroborated the analytical findings, illustrating the temporal dynamics of wildtype and mutant bacteria under different combination therapies.

Results

Our analysis provides analytical clarification and numerical validation that antibiotic interactions against wildtype bacteria exert a more significant effect on reducing the rate of resistance development than interactions against mutants. Specifically, our findings highlight the crucial role of antagonistic antibiotic interactions against wildtype bacteria in slowing the growth rate of resistant mutants. In contrast, antagonistic interactions against mutants only marginally affect resistance evolution and may even accelerate it.

Conclusion

Our results emphasize the importance of considering the nature of antibiotic interactions against wildtype bacteria rather than mutants when aiming to slow down the acquisition of antibiotic resistance.

Collaterally Sensitive β-Lactam Drugs as an Effective Therapy against the Pre-Existing Methicillin Resistant Staphylococcus aureus (MRSA) Biofilms

Methicillin-resistant Staphylococcus aureus (MRSA) is among the leading causes of nosocomial infections and forms biofilms, which are difficult to eradicate because of their increasing resistance to antimicrobial agents. This is especially true for pre-existing biofilms. The current study focused on evaluating the efficacy of three β-lactam drugs, meropenem, piperacillin, and tazobactam, alone and in combination against the MRSA biofilms. When used individually, none of the drugs exhibited significant antibacterial activity against MRSA in a planktonic state. At the same time, the combination of meropenem, piperacillin, and tazobactam showed a 41.7 and 41.3% reduction in planktonic bacterial cell growth, respectively. These drugs were further assessed for biofilm inhibition and removal. The combination of meropenem, piperacillin, and tazobactam caused 44.3% biofilm inhibition, while the rest of the combinations did not show any significant effects. Results also revealed that piperacillin and tazobactam exhibited the best synergy against the pre-formed biofilm of MRSA, with 46% removal. However, adding meropenem to the piperacillin and tazobactam combination showed a slightly reduced activity towards the pre-formed biofilm of MRSA and removed 38.7% of it. Although the mechanism of synergism is not fully understood, our findings suggest that these three β-lactam drugs can be used in combination as very effective therapeutic agents for the treatment of pre-existing MRSA biofilms. The in vivo experiments on the antibiofilm activity of these drugs will pave the way for applying such synergistic combinations to clinics.

Antibiotic treatment protocols revisited: the challenges of a conclusive assessment by mathematical modelling

Hospital-acquired bacterial infections lead to prolonged hospital stays and increased mortality. The problem is exacerbated by antibiotic-resistant strains that delay or impede effective treatment. To ensure successful therapy and to manage antibiotic resistance, treatment protocols that draw on several different antibiotics might be used. This includes the administration of drug cocktails to individual patients (combination therapy) but also the random assignment of drugs to different patients (mixing) and a regular switch in the default drug used in the hospital from drug A to drug B and back (cycling). For more than 20 years, mathematical models have been used to assess the prospects of antibiotic combination therapy, mixing and cycling. But while tendencies in their ranking across studies have emerged, the picture remains surprisingly inconclusive and incomplete. In this article, we review existing modelling studies and demonstrate by means of examples how methodological factors complicate the emergence of a consistent picture. These factors include the choice of the criterion by which the effects of the protocols are compared, the model implementation and its analysis. We thereafter discuss how progress can be made and suggest future modelling directions.

Informed and uninformed empirical therapy policies

We argue that a proper distinction must be made between informed and uninformed decision making when setting empirical therapy policies, as this allows one to estimate the value of gathering more information about the pathogens and their transmission and thus to set research priorities. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in a health care facility and the emergence and spread of resistance to two drugs. We focus on information and uncertainty regarding the parameters of this model. We consider a family of adaptive empirical therapy policies. In the uninformed setting, the best adaptive policy allowsone to reduce the average cumulative infected patient days over 2 years by 39.3% (95% confidence interval (CI), 30.3-48.1%) compared to the combination therapy. Choosing empirical therapy policies while knowing the exact parameter values allows one to further decrease the cumulative infected patient days by 3.9% (95% CI, 2.1-5.8%) on average. In our setting, the benefit of perfect information might be offset by increased drug consumption.

A survey of within-host and between-hosts modelling for antibiotic resistance

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.

Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review

Selective pressure exerted by the widespread use of antibacterial drugs is accelerating the development of resistant bacterial populations. The purpose of this scoping review was to summarise the range of studies that use dynamic models to analyse the problem of bacterial resistance in relation to antibacterial use in human and animal populations. A comprehensive search of the peer-reviewed literature was performed and non-duplicate articles (n = 1486) were screened in several stages. Charting questions were used to extract information from the articles included in the final subset (n = 81). Most studies (86%) represent the system of interest with an aggregate model; individual-based models are constructed in only seven articles. There are few examples of inter-host models outside of human healthcare (41%) and community settings (38%). Resistance is modelled for a non-specific bacterial organism and/or antibiotic in 40% and 74% of the included articles, respectively. Interventions with implications for antibacterial use were investigated in 67 articles and included changes to total antibiotic consumption, strategies for drug management and shifts in category/class use. The quality of documentation related to model assumptions and uncertainty varies considerably across this subset of articles. There is substantial room to improve the transparency of reporting in the antibacterial resistance modelling literature as is recommended by best practice guidelines.

Modeling antimicrobial cycling and mixing: Differences arising from an individual-based versus a population-based perspective

In order to manage bacterial infections in hospitals in the face of antibiotic resistance, the two treatment protocols "mixing" and "cycling" have received considerable attention both from modelers and clinicians. However, the terms are not used in exactly the same way by both groups. This comes because the standard modeling approach disregards the perspective of individual patients. In this article, we investigate a model that comes closer to clinical practice and compare the predictions to the standard model. We set up two deterministic models, implemented as a set of differential equations, for the spread of bacterial infections in a hospital. Following the traditional approach, the first model takes a population-based perspective. The second model, in contrast, takes the drug use of individual patients into account. The alternative model can indeed lead to different predictions than the standard model. We provide examples for which in the new model, the opposite strategy maximizes the number of uninfected patients or minimizes the rate of spread of double resistance. While the traditional models provide valuable insight, care is hence needed in the interpretation of results.

Modeling antibiotic treatment in hospitals: A systematic approach shows benefits of combination therapy over cycling, mixing, and mono-drug therapies

Multiple treatment strategies are available for empiric antibiotic therapy in hospitals, but neither clinical studies nor theoretical investigations have yielded a clear picture when which strategy is optimal and why. Extending earlier work of others and us, we present a mathematical model capturing treatment strategies using two drugs, i.e the multi-drug therapies referred to as cycling, mixing, and combination therapy, as well as monotherapy with either drug. We randomly sample a large parameter space to determine the conditions determining success or failure of these strategies. We find that combination therapy tends to outperform the other treatment strategies. By using linear discriminant analysis and particle swarm optimization, we find that the most important parameters determining success or failure of combination therapy relative to the other treatment strategies are the de novo rate of emergence of double resistance in patients infected with sensitive bacteria and the fitness costs associated with double resistance. The rate at which double resistance is imported into the hospital via patients admitted from the outside community has little influence, as all treatment strategies are affected equally. The parameter sets for which combination therapy fails tend to fall into areas with low biological plausibility as they are characterised by very high rates of de novo emergence of resistance to both drugs compared to a single drug, and the cost of double resistance is considerably smaller than the sum of the costs of single resistance.

Amino Acid Signatures to Evaluate the Beneficial Effects of Weight Loss

Aims. We investigated the relationship between circulating amino acid levels and obesity; to what extent weight loss followed by weight maintenance can correct amino acid abnormalities; and whether amino acids are related to weight loss. Methods. Amino acids associated with waist circumference (WC) and BMI were studied in 804 participants from the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC). Changes in amino acid levels were analyzed after weight loss and weight maintenance in 12 obese subjects and evaluated in a replication cohort (n = 83). Results. Out of the eight identified BMI-associated amino acids from the MDC-CC, alanine, isoleucine, tyrosine, phenylalanine, and glutamate decreased after weight loss, while asparagine increased after weight maintenance. These changes were validated in the replication cohort. Scores that were constructed based on obesity-associated amino acids and known risk factors decreased in the ≥10% weight loss group with an associated change in BMI (R² = 0.16-0.22, p < 0.002), whereas the scores increased in the <10% weight loss group (p < 0.0004). Conclusions. Weight loss followed by weight maintenance leads to differential changes in amino acid levels associated with obesity. Treatment modifiable scores based on epidemiological and interventional data may be used to evaluate the potential metabolic benefit of weight loss.

Synergistic, collaterally sensitive β-lactam combinations suppress resistance in MRSA

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most prevalent multidrug-resistant pathogens worldwide, exhibiting increasing resistance to the latest antibiotic therapies. Here we show that the triple β-lactam combination meropenem-piperacillin-tazobactam (ME/PI/TZ) acts synergistically and is bactericidal against MRSA subspecies N315 and 72 other clinical MRSA isolates in vitro and clears MRSA N315 infection in a mouse model. ME/PI/TZ suppresses evolution of resistance in MRSA via reciprocal collateral sensitivity of its constituents. We demonstrate that these activities also extend to other carbapenem-penicillin-β-lactamase inhibitor combinations. ME/PI/TZ circumvents the tight regulation of the mec and bla operons in MRSA, the basis for inducible resistance to β-lactam antibiotics. Furthermore, ME/PI/TZ subverts the function of penicillin-binding protein-2a (PBP2a) via allostery, which we propose as the mechanism for both synergy and collateral sensitivity. Showing in vivo activity similar to that of linezolid, ME/PI/TZ demonstrates that combinations of older β-lactam antibiotics could be effective against MRSA infections in humans.