Evaluation of Pharmacokinetic/Pharmacodynamic Model-Based Optimized Combination Regimens against Multidrug-Resistant Pseudomonas aeruginosa in a Murine Thigh Infection Model by Using Humanized Dosing Schemes

Age of Antibiotic Resistance in MDR/XDR Clinical Pathogen of Pseudomonas aeruginosa

Antibiotic resistance in Pseudomonas aeruginosa remains one of the most challenging phenomena of everyday medical science. The universal spread of high-risk clones of multidrug-resistant/extensively drug-resistant (MDR/XDR) clinical P. aeruginosa has become a public health threat. The P. aeruginosa bacteria exhibits remarkable genome plasticity that utilizes highly acquired and intrinsic resistance mechanisms to counter most antibiotic challenges. In addition, the adaptive antibiotic resistance of P. aeruginosa, including biofilm-mediated resistance and the formation of multidrug-tolerant persisted cells, are accountable for recalcitrance and relapse of infections. We highlighted the AMR mechanism considering the most common pathogen P. aeruginosa, its clinical impact, epidemiology, and save our souls (SOS)-mediated resistance. We further discussed the current therapeutic options against MDR/XDR P. aeruginosa infections, and described those treatment options in clinical practice. Finally, other therapeutic strategies, such as bacteriophage-based therapy and antimicrobial peptides, were described with clinical relevance.

Penicillin-Binding Protein 5/6 Acting as a Decoy Target in Pseudomonas aeruginosa Identified by Whole-Cell Receptor Binding and Quantitative Systems Pharmacology

The β-lactam antibiotics have been successfully used for decades to combat susceptible Pseudomonas aeruginosa, which has a notoriously difficult to penetrate outer membrane (OM). However, there is a dearth of data on target site penetration and covalent binding of penicillin-binding proteins (PBP) for β-lactams and β-lactamase inhibitors in intact bacteria. We aimed to determine the time course of PBP binding in intact and lysed cells and estimate the target site penetration and PBP access for 15 compounds in P. aeruginosa PAO1. All β-lactams (at 2 × MIC) considerably bound PBPs 1 to 4 in lysed bacteria. However, PBP binding in intact bacteria was substantially attenuated for slow but not for rapid penetrating β-lactams. Imipenem yielded 1.5 ± 0.11 log10 killing at 1h compared to <0.5 log10 killing for all other drugs. Relative to imipenem, the rate of net influx and PBP access was ~ 2-fold slower for doripenem and meropenem, 7.6-fold for avibactam, 14-fold for ceftazidime, 45-fold for cefepime, 50-fold for sulbactam, 72-fold for ertapenem, ~ 249-fold for piperacillin and aztreonam, 358-fold for tazobactam, ~547-fold for carbenicillin and ticarcillin, and 1,019-fold for cefoxitin. At 2 × MIC, the extent of PBP5/6 binding was highly correlated (r2 = 0.96) with the rate of net influx and PBP access, suggesting that PBP5/6 acted as a decoy target that should be avoided by slowly penetrating, future β-lactams. This first comprehensive assessment of the time course of PBP binding in intact and lysed P. aeruginosa explained why only imipenem killed rapidly. The developed novel covalent binding assay in intact bacteria accounts for all expressed resistance mechanisms.

Proof-of-concept for incorporating mechanistic insights from multi-omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae

Carbapenemase-resistant Klebsiella pneumoniae (KP) resistant to multiple antibiotic classes necessitates optimized combination therapy. Our objective is to build a workflow leveraging omics and bacterial count data to identify antibiotic mechanisms that can be used to design and optimize combination regimens. For pharmacodynamic (PD) analysis, previously published static time-kill studies (J Antimicrob Chemother 70, 2015, 2589) were used with polymyxin B (PMB) and chloramphenicol (CHL) mono and combination therapy against three KP clinical isolates over 24 h. A mechanism-based model (MBM) was developed using time-kill data in S-ADAPT describing PMB-CHL PD activity against each isolate. Previously published results of PMB (1 mg/L continuous infusion) and CHL (C_max : 8 mg/L; bolus q6h) mono and combination regimens were evaluated using an in vitro one-compartment dynamic infection model against a KP clinical isolate (10⁸ CFU/ml inoculum) over 24 h to obtain bacterial samples for multi-omics analyses. The differentially expressed genes and metabolites in these bacterial samples served as input to develop a partial least squares regression (PLSR) in R that links PD responses with the multi-omics responses via a multi-omics pathway analysis. PMB efficacy was increased when combined with CHL, and the MBM described the observed PD well for all strains. The PLSR consisted of 29 omics inputs and predicted MBM PD response (R²  = 0.946). Our analysis found that CHL downregulated metabolites and genes pertinent to lipid A, hence limiting the emergence of PMB resistance. Our workflow linked insights from analysis of multi-omics data with MBM to identify biological mechanisms explaining observed PD activity in combination therapy.

Clinical Distribution and Drug Resistance of Pseudomonas aeruginosa in Guangzhou, China from 2017 to 2021

The aim of the current study was to analyse the distribution of antimicrobial drug resistance (AMR) among Pseudomonas aeruginosa (P. aeruginosa, PA) isolates from Guangdong Provincial People's Hospital (GDPH) from 2017 to 2021, and the impact of the COVID-19 outbreak on changes in the clinical distribution and drug resistance rate of P. aeruginosa to establish guidelines for empiric therapy. Electronic clinical data registry records from 2017 to 2021 were retrospectively analysed to study the AMR among P. aeruginosa strains from GDPH. The strains were identified by VITEK 2 Compact and MALDI-TOF MS, MIC method or Kirby-Bauer method for antibiotic susceptibility testing. The results were interpreted according to the CLSI 2020 standard, and the data were analysed using WHONET 5.6 and SPSS 23.0 software. A total of 3036 P. aeruginosa strains were detected in the hospital from 2017 to 2021, and they were primarily distributed in the ICU (n = 1207, 39.8%). The most frequent specimens were respiratory tract samples (59.6%). The detection rate for P. aeruginosa in 5 years was highest in September, and the population distribution was primarily male(68.2%). For the trend in the drug resistance rate, the 5-year drug resistance rate of imipenem (22.4%), aztreonam (21.5%) and meropenem (19.3%) remained at high levels. The resistance rate of cefepime decreased from 9.4% to 4.8%, showing a decreasing trend year by year (p < 0.001). The antibiotics with low resistance rates were aminoglycoside antibiotics, which were gentamicin (4.4%), tobramycin (4.3%), and amikacin (1.4%), but amikacin showed an increasing trend year by year (p = 0.008). Our analysis indicated that the detection rate of clinically resistant P. aeruginosa strains showed an upwards trend, and the number of multidrug-resistant (MDR) strains increased year by year, which will lead to stronger pathogenicity and mortality. However, after the outbreak of COVID-19 in 2020, the growth trend in the number of MDR bacteria slowed, presumably due to the strict epidemic prevention and control measures in China. This observation suggests that we should reasonably use antibiotics and treatment programs in the prevention and control of P. aeruginosa infection. Additionally, health prevention and control after the outbreak of the COVID-19 epidemic (such as wearing masks, washing hands with disinfectant, etc., which reduced the prevalence of drug resistance) led to a slowdown in the growth of the drug resistance rate of P. aeruginosa in hospitals, effectively reducing the occurrence and development of drug resistance, and saving patient's treatment costs and time.

Analysis of the Distribution and Antibiotic Resistance of Pathogens Causing Infections in Hospitals from 2017 to 2019

Background. Antibiotic resistance is a global public health problem, leading to high mortality and treatment costs. To achieve more efficient treatment protocols and better patient recovery, the distribution and drug resistance of pathogens in our hospital were investigated, allowing significant clinical guidance for the use of antimicrobials. Methods. In this retrospective study (2017–2019), 3482 positive samples were isolated from 43,981 specimens in 2017; 3750 positive specimens were isolated from 42,923 specimens in 2018; and 3839 positive pathogens were isolated from 46,341 specimens in 2019. These samples were from various parts of the patients, including the respiratory tract, urine, blood, wound secretions, bile, and puncture fluids. The distribution and antibiotic resistance of these isolated pathogens from the whole hospital were analyzed. Results. The results from pathogen isolation showed that Escherichia coli (12.8%), Staphylococcus aureus (11%), Klebsiella pneumoniae (10.8%), Pseudomonas aeruginosa (10.7%), and Acinetobacter baumannii (6.4%) represented the five main pathogenic bacteria in our hospital. Pseudomonas aeruginosa (16.2% and 17.5%) occupied the largest proportion in the central intensive care unit (central ICU) and respiratory intensive care unit (RICU), while Acinetobacter baumannii (15.4%) was the most common pathogen in the emergency intensive care unit (EICU). The resistance rate of Escherichia coli to trimethoprim and minocycline was 100%, and the sensitivity rate to ertapenem, furantoin, and amikacin was above 90%. The resistance rate of Pseudomonas aeruginosa to all antibiotics, such as piperacillin and ciprofloxacin, was under 40%. The sensitivity rate of Acinetobacter baumannii to tigecycline and minocycline was less than 30%, and the resistance rate to many drugs such as piperacillin, ceftazidime, and imipenem was above 60%. Extended-spectrum β-lactamases (ESBLs)-producing Klebsiella pneumoniae (ESBLs-KPN) and carbapenem-resistant Klebsiella pneumoniae (CRE-KPN), ESBLs-producing Escherichia coli (ESBLs-ECO) and carbapenem-resistant Escherichia coli (CRE-ECO), multidrug-resistant Acinetobacter baumannii (MDR-AB), multidrug-resistant Pseudomonas aeruginosa (MDR-PAE), and methicillin-resistant Staphylococcus aureus (MRSA) are all important multidrug-resistant bacteria found in our hospital. The resistance rate of ESBLs-producing Enterobacteriaceae to ceftriaxone and amcarcillin-sulbactam was above 95%. CRE Enterobacteriaceae bacteria showed the highest resistance to amcarcillin-sulbactam (97.1%), and the resistance rates of MDR-AB to cefotaxime, cefepime, and aztreonam were 100%. The resistance rates of MDR-PAE to ceftazidime, imipenem, and levofloxacin were 100%, and the sensitivity rate to polymyxin B was above 98%. The resistance rate of MRSA to oxacillin was 100%, and the sensitivity rate to linezolid and vancomycin was 100%. Conclusion. The distribution of pathogenic bacteria in different hospital departments and sample sources was markedly different. Therefore, targeted prevention and control of key pathogenic bacteria in different hospital departments is necessary, and understanding both drug resistance and multiple drug resistance of the main pathogenic bacteria may provide guidance for the rational use of antibiotics in the clinic.

Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing

In the era of “Bad Bugs, No Drugs,” optimizing antibiotic therapy against multi-drug resistant (MDR) pathogens is crucial. Mathematical modelling has been employed to further optimize dosing regimens. These models include mechanism-based PK/PD models, systems-based models, quantitative systems pharmacology (QSP) and population PK models. Quantitative systems pharmacology has significant potential in precision antimicrobial chemotherapy in the clinic. Population PK models have been employed in model-informed precision dosing (MIPD). Several antibiotics require close monitoring and dose adjustments in order to ensure optimal outcomes in patients with infectious diseases. Success or failure of antibiotic therapy is dependent on the patient, antibiotic and bacterium. For some drugs, treatment responses vary greatly between individuals due to genotype and disease characteristics. Thus, for these drugs, tailored dosing is required for successful therapy. With antibiotics, inappropriate dosing such as insufficient dosing may put patients at risk of therapeutic failure which could lead to mortality. Conversely, doses that are too high could lead to toxicities. Hence, precision dosing which customizes doses to individual patients is crucial for antibiotics especially those with a narrow therapeutic index. In this review, we discuss the various strategies in optimizing antimicrobial therapy to address the challenges in the management of infectious diseases and delivering personalized therapy.

Predicting Antimicrobial Activity at the Target Site: Pharmacokinetic/Pharmacodynamic Indices versus Time-Kill Approaches

Antibiotic dosing strategies are generally based on systemic drug concentrations. However, drug concentrations at the infection site drive antimicrobial effect, and efficacy predictions and dosing strategies should be based on these concentrations. We set out to review different translational pharmacokinetic-pharmacodynamic (PK/PD) approaches from a target site perspective. The most common approach involves calculating the probability of attaining animal-derived PK/PD index targets, which link PK parameters to antimicrobial susceptibility measures. This approach is time efficient but ignores some aspects of the shape of the PK profile and inter-species differences in drug clearance and distribution, and provides no information on the PD time-course. Time–kill curves, in contrast, depict bacterial response over time. In vitro dynamic time–kill setups allow for the evaluation of bacterial response to clinical PK profiles, but are not representative of the infection site environment. The translational value of in vivo time–kill experiments, conversely, is limited from a PK perspective. Computational PK/PD models, especially when developed using both in vitro and in vivo data and coupled to target site PK models, can bridge translational gaps in both PK and PD. Ultimately, clinical PK and experimental and computational tools should be combined to tailor antibiotic treatment strategies to the site of infection.

Limitations of Antibiotic MIC-Based PK-PD Metrics: Looking Back to Move Forward

Within a few years after the first successful clinical use of penicillin, investigations were conducted in animal infection models to explore a range of factors that were considered likely to influence the antibacterial response to the drug. Those studies identified that the response was influenced by not only the total daily dose but also the interval between individual doses across the day, and whether penicillin was administered in an intermittent or continuous manner. Later, as more antibiotics were discovered and developed, antimicrobial pharmacologists began to measure antibiotic concentrations in biological fluids. This enabled the linking of antibacterial response at a single time point in an animal or in vitro infection model with one of three summary pharmacokinetic (PK) measures of in vivo exposure to the antibiotic. The summary PK exposure measures were normalised to the minimum inhibitory concentration (MIC), an in vitro measure of the pharmacodynamic (PD) potency of the drug. The three PK-PD indices (ratio of maximum concentration to MIC, ratio of area under the concentration-time curve to MIC, time concentration is above MIC) have been used extensively since the 1980s. While these MIC-based summary PK-PD metrics have undoubtedly facilitated the development of new antibiotics and the clinical application of both new and old antibiotics, it is increasingly recognised that they have a number of substantial limitations. In this article we use a historical perspective to review the origins of the three traditional PK-PD indices before exploring in detail their limitations and the implications arising from those limitations. Finally, in the interests of improving antibiotic development and dosing in patients, we consider a model-based approach of linking the full time-course of antibiotic concentrations with that of the antibacterial response. Such an approach enables incorporation of other factors that can influence treatment outcome in patients and has the potential to drive model-informed precision dosing of antibiotics into the future.

Losing the Battle but Winning the War: Can Defeated Antibacterials Form Alliances to Combat Drug-Resistant Pathogens?

Despite the recent development of antibacterials that are active against multidrug-resistant pathogens, drug combinations are often necessary to optimize the killing of difficult-to-treat organisms. Antimicrobial combinations typically are composed of multiple agents that are active against the target organism; however, many studies have investigated the potential utility of combinations that consist of one or more antibacterials that individually are incapable of killing the relevant pathogen. The current review summarizes in vitro, in vivo, and clinical studies that evaluate combinations that include at least one drug that is not active individually against Pseudomonas aeruginosa, Klebsiella pneumoniae, Acinetobacter baumannii, or Staphylococcus aureus. Polymyxins were often included in combinations against all three of the Gram-negative pathogens, and carbapenems were commonly incorporated into combinations against K. pneumoniae and A. baumannii. Minocycline, sulbactam, and rifampin were also frequently investigated in combinations against A. baumannii, whereas the addition of ceftaroline or another β-lactam to vancomycin or daptomycin showed promise against S. aureus with reduced susceptibility to vancomycin or daptomycin. Although additional clinical studies are needed to define the optimal combination against specific drug-resistant pathogens, the large amount of in vitro and in vivo studies available in the literature may provide some guidance on the rational design of antibacterial combinations.

Key Challenges in Providing Effective Antibiotic Therapy for Critically Ill Patients with Bacterial Sepsis and Septic Shock

Early initiation of effective antibiotic therapy is vitally important for saving the lives of critically ill patients with sepsis or septic shock. The susceptibility of the infecting pathogen and the ability of the selected dosage regimen to safely achieve the required antibiotic exposure need to be carefully considered to achieve a high probability of a successful outcome. Critically ill patients commonly experience substantial pathophysiological changes that impact the functions of various organs, including the kidneys. Many antibiotics are predominantly renally eliminated and thus renal function is a major determinant of the regimen needed to achieve the required antibiotic exposure. However, currently, there is a paucity of guidelines to inform antibiotic dosing in critically ill patients, including those with sepsis or septic shock. This paper briefly reviews methods that are commonly used in critically ill patients to provide a measure of renal function, and approaches that describe the relationship between the exposure to an antibiotic and its antibacterial effects. Two common conditions that very substantially complicate the use of antibiotics in critically ill patients with sepsis, unstable renal function, and augmented renal clearance, are considered in detail and their potential therapeutic implications are explored. Suggestions are provided on how treatment of bacterial infections in critically ill patients with sepsis might be improved. Of high potential are model-informed approaches that aim to individualize initial treatment regimens based on patient and bacterial characteristics, with refinement of regimens during treatment in response to monitoring antibiotic concentrations, responsive measures of renal function, and other important clinical data.

Meropenem-Tobramycin Combination Regimens Combat Carbapenem-Resistant Pseudomonas aeruginosa in the Hollow-Fiber Infection Model Simulating Augmented Renal Clearance in Critically Ill Patients

Augmented renal clearance (ARC) is common in critically ill patients and is associated with subtherapeutic concentrations of renally eliminated antibiotics. We investigated the impact of ARC on bacterial killing and resistance amplification for meropenem and tobramycin regimens in monotherapy and combination. Two carbapenem-resistant Pseudomonas aeruginosa isolates were studied in static-concentration time-kill studies. One isolate was examined comprehensively in a 7-day hollow-fiber infection model (HFIM). Pharmacokinetic profiles representing substantial ARC (creatinine clearance of 250 ml/min) were generated in the HFIM for meropenem (1 g or 2 g administered every 8 h as 30-min infusion and 3 g/day or 6 g/day as continuous infusion [CI]) and tobramycin (7 mg/kg of body weight every 24 h as 30-min infusion) regimens. The time courses of total and less-susceptible bacterial populations and MICs were determined for the monotherapies and all four combination regimens. Mechanism-based mathematical modeling (MBM) was performed. In the HFIM, maximum bacterial killing with any meropenem monotherapy was ∼3 log₁₀ CFU/ml at 7 h, followed by rapid regrowth with increases in resistant populations by 24 h (meropenem MIC of up to 128 mg/liter). Tobramycin monotherapy produced extensive initial killing (∼7 log₁₀ at 4 h) with rapid regrowth by 24 h, including substantial increases in resistant populations (tobramycin MIC of 32 mg/liter). Combination regimens containing meropenem administered intermittently or as a 3-g/day CI suppressed regrowth for ∼1 to 3 days, with rapid regrowth of resistant bacteria. Only a 6-g/day CI of meropenem combined with tobramycin suppressed regrowth and resistance over 7 days. MBM described bacterial killing and regrowth for all regimens well. The mode of meropenem administration was critical for the combination to be maximally effective against carbapenem-resistant P. aeruginosa.

Epidemiology and Treatment of Multidrug-Resistant and Extensively Drug-Resistant Pseudomonas aeruginosa Infections

In recent years, the worldwide spread of the so-called high-risk clones of multidrug-resistant or extensively drug-resistant (MDR/XDR) Pseudomonas aeruginosa has become a public health threat. This article reviews their mechanisms of resistance, epidemiology, and clinical impact and current and upcoming therapeutic options. In vitro and in vivo treatment studies and pharmacokinetic and pharmacodynamic (PK/PD) models are discussed. Polymyxins are reviewed as an important therapeutic option, outlining dosage, pharmacokinetics and pharmacodynamics, and their clinical efficacy against MDR/XDR P. aeruginosa infections. Their narrow therapeutic window and potential for combination therapy are also discussed. Other "old" antimicrobials, such as certain β-lactams, aminoglycosides, and fosfomycin, are reviewed here. New antipseudomonals, as well as those in the pipeline, are also reviewed. Ceftolozane-tazobactam has clinical activity against a significant percentage of MDR/XDR P. aeruginosa strains, and its microbiological and clinical data, as well as recommendations for improving its use against these bacteria, are described, as are those for ceftazidime-avibactam, which has better activity against MDR/XDR P. aeruginosa, especially strains with certain specific mechanisms of resistance. A section is devoted to reviewing upcoming active drugs such as imipenem-relebactam, cefepime-zidebactam, cefiderocol, and murepavadin. Finally, other therapeutic strategies, such as use of vaccines, antibodies, bacteriocins, anti-quorum sensing, and bacteriophages, are described as future options.

Pharmacodynamic Drug-Drug Interactions

Pharmacodynamic drug-drug interactions (DDIs) occur when the pharmacological effect of one drug is altered by that of another drug in a combination regimen. DDIs often are classified as synergistic, additive, or antagonistic in nature, albeit these terms are frequently misused. Within a complex pathophysiological system, the mechanism of interaction may occur at the same target or through alternate pathways. Quantitative evaluation of pharmacodynamic DDIs by employing modeling and simulation approaches is needed to identify and optimize safe and effective combination therapy regimens. This review investigates the opportunities and challenges in pharmacodynamic DDI studies and highlights examples of quantitative methods for evaluating pharmacodynamic DDIs, with a particular emphasis on the use of mechanism-based modeling and simulation in DDI studies. Advancements in both experimental and computational techniques will enable the application of better, model-informed assessments of pharmacodynamic DDIs in drug discovery, development, and therapeutics.

Emerging Strategies to Combat ESKAPE Pathogens in the Era of Antimicrobial Resistance: A Review

The acronym ESKAPE includes six nosocomial pathogens that exhibit multidrug resistance and virulence: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. Persistent use of antibiotics has provoked the emergence of multidrug resistant (MDR) and extensively drug resistant (XDR) bacteria, which render even the most effective drugs ineffective. Extended spectrum β-lactamase (ESBL) and carbapenemase producing Gram negative bacteria have emerged as an important therapeutic challenge. Development of novel therapeutics to treat drug resistant infections, especially those caused by ESKAPE pathogens is the need of the hour. Alternative therapies such as use of antibiotics in combination or with adjuvants, bacteriophages, antimicrobial peptides, nanoparticles, and photodynamic light therapy are widely reported. Many reviews published till date describe these therapies with respect to the various agents used, their dosage details and mechanism of action against MDR pathogens but very few have focused specifically on ESKAPE. The objective of this review is to describe the alternative therapies reported to treat ESKAPE infections, their advantages and limitations, potential application in vivo, and status in clinical trials. The review further highlights the importance of a combinatorial approach, wherein two or more therapies are used in combination in order to overcome their individual limitations, additional studies on which are warranted, before translating them into clinical practice. These advances could possibly give an alternate solution or extend the lifetime of current antimicrobials.

Novel Polymyxin Combination with the Antiretroviral Zidovudine Exerts Synergistic Killing against NDM-Producing Multidrug-Resistant Klebsiella pneumoniae

Polymyxins are used as a last-line therapy against multidrug-resistant (MDR) New Delhi metallo-β-lactamase (NDM)-producing Klebsiella pneumoniae However, polymyxin resistance can emerge with monotherapy; therefore, novel strategies are urgently needed to minimize the resistance and maintain their clinical utility. This study aimed to investigate the pharmacodynamics of polymyxin B in combination with the antiretroviral drug zidovudine against K. pneumoniae Three isolates were evaluated in static time-kill studies (0 to 64 mg/liter) over 48 h. An in vitro one-compartment pharmacokinetic/pharmacodynamic (PK/PD) model (IVM) was used to simulate humanized dosage regimens of polymyxin B (4 mg/liter as continuous infusion) and zidovudine (as bolus dose thrice daily to achieve maximum concentration of drug in broth [C _max] of 6 mg/liter) against K. pneumoniae BM1 over 72 h. The antimicrobial synergy of the combination was further evaluated in a murine thigh infection model against K. pneumoniae 02. In the static time-kill studies, polymyxin B monotherapy produced rapid and extensive killing against all three isolates followed by extensive regrowth, whereas zidovudine produced modest killing followed by significant regrowth at 24 h. Polymyxin B in combination with zidovudine significantly enhanced the antimicrobial activity (≥4 log₁₀ CFU/ml) and minimized bacterial regrowth. In the IVM, the combination was synergistic and the total bacterial loads were below the limit of detection for up to 72 h. In the murine thigh infection model, the bacterial burden at 24 h in the combination group was ≥3 log₁₀ CFU/thigh lower than each monotherapy against K. pneumoniae 02. Overall, the polymyxin B-zidovudine combination demonstrates superior antimicrobial efficacy and minimized emergence of resistance to polymyxins.

Polymyxin B in Combination with Enrofloxacin Exerts Synergistic Killing against Extensively Drug-Resistant Pseudomonas aeruginosa

Polymyxins are increasingly used as a last-resort class of antibiotics against extensively drug-resistant (XDR) Gram-negative bacteria. However, resistance to polymyxins can emerge with monotherapy. As nephrotoxicity is the major dose-limiting factor for polymyxin monotherapy, dose escalation to suppress the emergence of polymyxin resistance is not a viable option. Therefore, novel approaches are needed to preserve this last-line class of antibiotics. This study aimed to investigate the antimicrobial synergy of polymyxin B combined with enrofloxacin against Pseudomonas aeruginosa Static time-kill studies were conducted over 24 h with polymyxin B (1 to 4 mg/liter) and enrofloxacin (1 to 4 mg/liter) alone or in combination. Additionally, in vitro one-compartment model (IVM) and hollow-fiber infection model (HFIM) experiments were performed against P. aeruginosa 12196. Polymyxin B and enrofloxacin in monotherapy were ineffective against all of the P. aeruginosa isolates examined, whereas polymyxin B-enrofloxacin in combination was synergistic against P. aeruginosa, with ≥2 to 4 log₁₀ kill at 24 h in the static time-kill studies. In both IVM and HFIM, the combination was synergistic, and the bacterial counting values were below the limit of quantification on day 5 in the HFIM. A population analysis profile indicated that the combination inhibited the emergence of polymyxin resistance in P. aeruginosa 12196. The mechanism-based modeling suggests that the synergistic killing is a result of the combination of mechanistic and subpopulation synergy. Overall, this is the first preclinical study to demonstrate that the polymyxin-enrofloxacin combination is of considerable utility for the treatment of XDR P. aeruginosa infections and warrants future clinical evaluations.

Combating Carbapenem-Resistant Acinetobacter baumannii by an Optimized Imipenem-plus-Tobramycin Dosage Regimen: Prospective Validation via Hollow-Fiber Infection and Mathematical Modeling

We aimed to prospectively validate an optimized combination dosage regimen against a clinical carbapenem-resistant Acinetobacter baumannii (CRAB) isolate (imipenem MIC, 32 mg/liter; tobramycin MIC, 2 mg/liter). Imipenem at constant concentrations (7.6, 13.4, and 23.3 mg/liter, reflecting a range of clearances) was simulated in a 7-day hollow-fiber infection model (inoculum, ∼10^7.2 CFU/ml) with and without tobramycin (7 mg/kg q24h, 0.5-h infusions). While monotherapies achieved no killing or failed by 24 h, this rationally optimized combination achieved >5 log₁₀ bacterial killing and suppressed resistance.