Pharmacokinetic-pharmacodynamic models for time courses of antibiotic effects: VSI: Antimicrobial Pharmacometrics

Model-based translation of results from in vitro to in vivo experiments for afabicin activity against Staphylococcus aureus

BACKGROUND

Translation of experimental data on antibiotic activity typically relies on pharmacokinetic/pharmacodynamic (PK/PD) indices. Model-based approaches, considering the full antibiotic killing time course, could be an alternative.

OBJECTIVES

To develop a mechanism-based modelling framework to assess the in vitro and in vivo activity of the FabI inhibitor antibiotic afabicin, and explore the ability of a model built on in vitro data to predict in vivo outcome.

METHODS

A PK/PD model was built to describe bacterial counts from 162 static in vitro time-kill curves evaluating the effect of afabicin desphosphono, the active moiety of the prodrug afabicin, against 21 Staphylococcus aureus strains. Combined with a mouse PK model, outcomes of afabicin doses of 0.011-190 mg/kg q6h against nine S. aureus strains in a murine thigh infection model were predicted, and thereafter refined by estimating PD parameters.

RESULTS

A sigmoid Emax model, with EC50 scaled by the MIC described the afabicin desphosphono killing in vitro. This model predicted, without parameter re-estimation, the in vivo bacterial counts at 24 h within a ±1 log margin for most dosing groups. When parameters were allowed to be estimated, EC50 was 38%-45% lower in vivo, compared with in vitro, within the studied MIC range.

CONCLUSIONS

The developed PK/PD model described the time course of afabicin activity across experimental conditions and bacterial strains. This model showed translational capacity as parameters estimated on in vitro time-kill data could well predict the in vivo outcome for a wide variety of doses in a mouse thigh infection model.

PK/PD modelling and simulation of longitudinal meropenem in vivo effects against Escherichia coli and Klebsiella pneumoniae strains with high MICs

BACKGROUND

Carbapenem-resistant bacteria pose a threat to public health. Characterising the pharmacokinetics-pharmacodynamics (PKPD) of meropenem longitudinally in vivo against resistant bacteria could provide valuable information for development and translation of carbapenem-based therapies.

OBJECTIVES

To assess the time course of meropenem effects in vivo against strains with high MIC to predict PK/PD indices and expected efficacy in patients using a modelling approach.

METHODS

A PKPD model was built on longitudinal bacterial count data to describe meropenem effects against six Escherichia coli and Klebsiella pneumoniae strains (MIC values 32-128 mg/L) in a 24 h mouse thigh infection model. The model was used to derive PK/PD indices from simulated studies in mice and to predict the efficacy of different infusion durations with high-dose meropenem (2 g q8 h/q12 h for normal/reduced kidney function) in patients.

RESULTS

Data from 592 mice were available for model development. The estimated meropenem concentration-dependent killing rate was not associated with differences in MIC. The fraction of time that unbound concentrations exceeded EC50 (fT>EC50, EC50 = 1.01 mg/L) showed higher correlations than fT>MIC. For all investigated strains, bacteriostasis at 24 h was predicted for prolonged infusions of high-dose meropenem monotherapy in >90% of patients.

CONCLUSIONS

The developed PKPD model successfully described bacterial growth and meropenem killing over time in the thigh infection model. For the investigated strains, the MIC, determined in vitro, or MIC-based PK/PD indices, did not predict in vivo response. Simulations suggested prolonged infusions of high-dose meropenem to be efficacious in patients infected by the studied strains.

Model-informed drug development for antimicrobials: translational pharmacokinetic-pharmacodynamic modelling of apramycin to facilitate prediction of efficacious dose in complicated urinary tract infections

OBJECTIVES

The use of mouse models of complicated urinary tract infection (cUTI) has usually been limited to a single timepoint assessment of bacterial burden. Based on longitudinal in vitro and in vivo data, we developed a pharmacokinetic-pharmacodynamic (PKPD) model to assess the efficacy of apramycin, a broad-spectrum aminoglycoside antibiotic, in mouse models of cUTI.

METHODS

Two Escherichia coli strains were studied (EN591 and ATCC 700336). Apramycin exposure-effect relationships were established with in vitro time-kill data at pH 6 and pH 7.4 and in mice with cUTI. Immunocompetent mice were treated with apramycin (1.5-30 mg/kg) starting 24 h post-infection. Kidney and bladder tissue were collected 6-96 h post-infection for cfu determination. A PKPD model integrating all data was developed and simulations were performed to predict bacterial burden in humans.

RESULTS

Treatment with apramycin reduced the bacterial load in kidneys and bladder tissue up to 4.3-log compared with vehicle control. In vitro and in vivo tissue time-course efficacy data were integrated into the PKPD model, showing 76%-98% reduction of bacterial net growth and 3- to 145-fold increase in apramycin potency in vivo compared with in vitro. Simulations suggested that an 11 mg/kg daily dose would be sufficient to achieve bacterial stasis in kidneys and bladder in humans.

CONCLUSIONS

PKPD modelling with in vitro and in vivo PK and PD data enabled simultaneous evaluation of the different components that influence drug effect, an approach that had not yet been evaluated for antibiotics in the cUTI model and that has potential to enhance model-informed drug development of antibiotics.

Application of MIDD to accelerate the development of anti-infectives: Current status and future perspectives

This review examines the role of model-informed drug development (MIDD) in advancing antibacterial and antiviral drug development, with an emphasis on the inclusion of host system dynamics into modeling efforts. Amidst the growing challenges of multidrug resistance and diminishing market returns, innovative methodologies are crucial for continuous drug discovery and development. The MIDD approach, with its robust capacity to integrate diverse data types, offers a promising solution. In particular, the utilization of appropriate modeling and simulation techniques for better characterization and early assessment of drug resistance are discussed. The evolution of MIDD practices across different infectious disease fields is also summarized, and compared to advancements achieved in oncology. Moving forward, the application of MIDD should expand into host system dynamics as these considerations are critical for the development of "live drugs" (e.g. chimeric antigen receptor T cells or bacteriophages) to address issues like antibiotic resistance or latent viral infections.

Antibiotic dose optimisation in the critically ill: targets, evidence and future strategies

PURPOSE OF REVIEW

To highlight the recent evidence for antibiotic pharmacokinetics and pharmacodynamics (PK/PD) in enhancing patient outcomes in sepsis and septic shock. We also summarise the limitations of available data and describe future directions for research to support translation of antibiotic dose optimisation to the clinical setting.

RECENT FINDINGS

Sepsis and septic shock are associated with poor outcomes and require antibiotic dose optimisation, mostly due to significantly altered pharmacokinetics. Many studies, including some randomised controlled trials have been conducted to measure the clinical outcome effects of antibiotic dose optimisation interventions including use of therapeutic drug monitoring. Current data support antibiotic dose optimisation for the critically ill. Further investigation is required to evolve more timely and robust precision antibiotic dose optimisation approaches, and to clearly quantify whether any clinical and health-economic benefits support expanded use of this treatment intervention.

SUMMARY

Antibiotic dose optimisation appears to improve outcomes in critically ill patients with sepsis and septic shock, however further research is required to quantify the level of benefit and develop a stronger knowledge of the role of new technologies to facilitate optimised dosing.

The effects of single and multiple resistance mechanisms on bacterial response to meropenem

OBJECTIVES

Meropenem is commonly used against Pseudomonas aeruginosa. Traditionally, the time unbound antibiotic concentration exceeds the MIC (fT>MIC) is used to select carbapenem regimens. We aimed to characterize the effects of different baseline resistance mechanisms on bacterial killing and resistance emergence; evaluate whether fT>MIC can predict these effects; and, develop a novel Quantitative and Systems Pharmacology (QSP) model to describe the effects of baseline resistance mechanisms on the time-course of bacterial response.

METHODS

Seven isogenic P. aeruginosa strains with a range of resistance mechanisms and MICs were used in 10-day hollow-fiber infection model studies. Meropenem pharmacokinetic profiles were simulated for various regimens (t1/2,meropenem = 1.5 h). All viable counts on drug-free, 3 × MIC, and 5 × MIC meropenem-containing agar across all strains, five regimens, and control (n = 90 profiles) were simultaneously subjected to QSP modeling. Whole genome sequencing was completed for total population samples and emergent resistant colonies at 239 h.

RESULTS

Regimens achieving ≥98%fT>1×MIC suppressed resistance emergence of the mexR knockout strain. Even 100%fT>5 × MIC failed to achieve this against the strain with OprD loss and the ampD and mexR double-knockout strain. Baseline resistance mechanisms affected bacterial outcomes, even for strains with the same MIC. Genomic analysis revealed that pre-existing resistant subpopulations drove resistance emergence. During meropenem exposure, mutations in mexR were selected in strains with baseline oprD mutations, and vice versa, confirming these as major mechanisms of resistance emergence. Secondary mutations occurred in lysS or argS, coding for lysyl and arginyl tRNA synthetases, respectively.

DISCUSSION

The QSP model well-characterized all bacterial outcomes of the seven strains simultaneously, which fT>MIC could not.

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing, and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Impact of continuous-infusion meropenem degradation and infusion bag changes on bacterial killing of Pseudomonas aeruginosa based on model-informed translation

BACKGROUND

Continuous infusion of meropenem has been proposed to increase target attainment in critically ill patients, although stability might limit its practical use. This study investigated the impact of meropenem degradation and infusion bag changes on the concentration-time profiles and bacterial growth and killing of P. aeruginosa given different continuous-infusion solutions.

METHODS

A semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) model quantifying meropenem concentrations (CMEM) and bacterial counts of a resistant P. aeruginosa strain (ARU552, MIC = 16 mg/L) over 24 h was used to translate in vitro antibiotic effects to patients with severe infections. Concentration-dependent drug degradation of saline infusion solutions was considered using an additional compartment in the population PK model. CMEM, fT>MIC (time that concentrations exceed the MIC) and total bacterial load (BTOT) after 24 h were simulated for different scenarios (n = 144), considering low- and high-dose regimens (3000/6000 mg/day±loading dose), clinically relevant infusion solutions (20/40/50 mg/mL), different intervals of infusion bag changes (every 8/24 h, q8/24 h), and varied renal function (creatinine clearance 40/80/120 mL/min) and MIC values (8/16 mg/L).

RESULTS

Highest deviations between changing infusion bags q8h and q24h were observed for 50 mg/mL solutions and scenarios with CMEM_24h close to the MIC, with differences (Δ) in CMEM_24h up to 4.9 mg/L, ΔfT>MIC≤65.7%, and ΔBTOT_24h≤1.1 log10 CFU/mL, thus affecting conclusions on whether bacteriostasis was reached.

CONCLUSIONS

In summary, this study indicated that for continuous infusion of meropenem, eight-hourly infusion bag changes improved PK/PD target attainment and might be beneficial particularly for high meropenem concentrations of saline infusion solutions and for plasma concentrations in close proximity to the MIC.

Integration of individual preclinical and clinical anti-infective PKPD data to predict clinical study outcomes

The AIDA randomized clinical trial found no significant difference in clinical failure or survival between colistin monotherapy and colistin-meropenem combination therapy in carbapenem-resistant Gram-negative infections. The aim of this reverse translational study was to integrate all individual preclinical and clinical pharmacokinetic-pharmacodynamic (PKPD) data from the AIDA trial in a pharmacometric framework to explore whether individualized predictions of bacterial burden were associated with the trial outcomes. The compiled dataset included for each of the 207 patients was (i) information on the infecting Acinetobacter baumannii isolate (minimum inhibitory concentration, checkerboard assay data, and fitness in a murine model), (ii) colistin plasma concentrations and colistin and meropenem dosing history, and (iii) disease scores and demographics. The individual information was integrated into PKPD models, and the predicted change in bacterial count at 24 h for each patient, as well as patient characteristics, was correlated with clinical outcomes using logistic regression. The in vivo fitness was the most important factor for change in bacterial count. A model-predicted growth at 24 h of ≥2-log10 (164/207) correlated positively with clinical failure (adjusted odds ratio, aOR = 2.01). The aOR for one unit increase of other significant predictors were 1.24 for SOFA score, 1.19 for Charlson comorbidity index, and 1.01 for age. This study exemplifies how preclinical and clinical anti-infective PKPD data can be integrated through pharmacodynamic modeling and identify patient- and pathogen-specific factors related to clinical outcomes - an approach that may improve understanding of study outcomes.

Influence of β-lactam pharmacodynamics on the systems microbiology of gram-positive and gram-negative polymicrobial communities

Objectives

We sought to evaluate the pharmacodynamics of β-lactam antibacterials against polymicrobial communities of clinically relevant gram-positive and gram-negative pathogens.

Methods

Two Enterococcus faecalis isolates, two Staphylococcus aureus isolates, and three Escherichia coli isolates with varying β-lactamase production were evaluated in static time-killing experiments. Each gram-positive isolate was exposed to a concentration array of ampicillin (E. faecalis) or cefazolin (S. aureus) alone and during co-culture with an E. coli isolate that was β-lactamase-deficient, produced TEM-1, or produced KPC-3/TEM-1B. The results of the time-killing experiments were summarized using an integrated pharmacokinetic/pharmacodynamics analysis as well as mathematical modelling to fully characterize the antibacterial pharmacodynamics.

Results

In the integrated analysis, the maximum killing of ampicillin (Emax) against both E. faecalis isolates was ≥ 4.11 during monoculture experiments or co-culture with β-lactamase-deficient E. coli, whereas the Emax was reduced to ≤ 1.54 during co-culture with β-lactamase-producing E. coli. In comparison to monoculture experiments, culturing S. aureus with KPC-producing E. coli resulted in reductions of the cefazolin Emax from 3.25 and 3.71 down to 2.02 and 2.98, respectively. Two mathematical models were created to describe the interactions between E. coli and either E. faecalis or S. aureus. When in co-culture with E. coli, S. aureus experienced a reduction in its cefazolin Kmax by 24.8% (23.1%RSE). Similarly, β-lactamase-producing E. coli preferentially protected the ampicillin-resistant E. faecalis subpopulation, reducing Kmax,r by 90.1% (14%RSE).

Discussion

β-lactamase-producing E. coli were capable of protecting S. aureus and E. faecalis from exposure to β-lactam antibacterials.

Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model

Co-administering a low dose of colistin (CST) with ciprofloxacin (CIP) may improve the antibacterial effect against resistant Escherichia coli, offering an acceptable benefit-risk balance. This study aimed to quantify the interaction between ciprofloxacin and colistin in an in silico pharmacokinetic-pharmacodynamic model from in vitro static time-kill experiments (using strains with minimum inhibitory concentrations, MICCIP 0.023-1 mg/L and MICCST 0.5-0.75 mg/L). It was also sought to demonstrate an approach of simulating concentrations at the site of infection with population pharmacokinetic and whole-body physiologically based pharmacokinetic models to explore the clinical value of the combination when facing more resistant strains (using extrapolated strains with lower susceptibility). The combined effect in the final model was described as the sum of individual drug effects with a change in drug potency: for ciprofloxacin, concentration at half maximum killing rate (EC50) in combination was 160% of the EC50 in monodrug experiments, while for colistin, the change in EC50 was strain-dependent from 54.1% to 119%. The benefit of co-administrating a lower-than-commonly-administrated colistin dose with ciprofloxacin in terms of drug effect in comparison to either monotherapy was predicted in simulated bloodstream infections and pyelonephritis. The study illustrates the value of pharmacokinetic-pharmacodynamic modelling and simulation in streamlining rational development of antibiotic combinations.

Revisiting the Checkerboard to Inform Development of β-Lactam/β-Lactamase Inhibitor Combinations

A two-dimensional "checkerboard" array employing systematic titration (e.g., serial two-fold dilutions) is a well-established in vitro method for exploring the antibacterial effects of novel drug combinations. Minimum inhibitory concentrations (MICs) on the checkerboard are isoeffective points at which the antibiotic potency is the same. Representations of checkerboard MIC curves for a β-lactam and β-lactamase inhibitor combination are used in hypothetical "thought experiments" and reveal the ways in which current practices can be improved. Because different types of response (i.e., independence vs. additivity vs. one effective agent; interaction vs. noninteraction) produce different MIC curves, data from different strains/isolates should not be pooled indiscriminately, as the composition of a pooled dataset will influence any derived pharmacokinetic/pharmacodynamic (PK/PD) index. Because the β-lactamase inhibitor threshold concentration (CT) parameter is a function of the β-lactam partner dosing regimen, it is not possible to derive a universal PK/PD index target based on CT. Alternative susceptibility testing methods represent different planes through the checkerboard; a fixed ratio method is less prone to bias for all β-lactam and β-lactamase inhibitor combinations. Susceptibility test MICs will often not reflect the sensitivity of the strain/isolate to the β-lactamase inhibitor, so the use of these MICs to normalize PK/PD indices is inappropriate.

Model-based learn and confirm: designing effective treatment regimens against multidrug resistant Gram-negative pathogens

Over the last decade, there has been a growing appreciation for the use of in vitro and in vivo infection models to generate robust and informative nonclinical PK/PD data to accelerate the clinical translation of treatment regimens. The objective of this study was to develop a model-based "learn and confirm" approach to help with the design of combination regimens using in vitro infection models to optimise the clinical utility of existing antibiotics. Static concentration time-kill studies were used to evaluate the PD activity of polymyxin B (PMB) and meropenem against two carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates; BAA2146 (PMB-susceptible) and BRKP67 (PMB-resistant). A mechanism-based model (MBM) was developed to quantify the joint activity of PMB and meropenem. In silico simulations were used to predict the time-course of bacterial killing using clinically-relevant PK exposure profiles. The predictive accuracy of the model was further evaluated by validating the model predictions using a one-compartment PK/PD in vitro dynamic infection model (IVDIM). The MBM captured the reduction in bacterial burden and regrowth well in both the BAA2146 and BRKP67 isolate (R2 = 0.900 and 0.940, respectively). The bacterial killing and regrowth predicted by the MBM were consistent with observations in the IVDIM: sustained activity against BAA2146 and complete regrowth of the BRKP67 isolate. Differences observed in PD activity suggest that additional dose optimisation might be beneficial in PMB-resistant isolates. The model-based approach presented here demonstrates the utility of the MBM as a translational tool from static to dynamic in vitro systems to effectively perform model-informed drug optimisation.

Unraveling resistance mechanisms in combination therapy: A comprehensive review of recent advances and future directions

Antimicrobial resistance is a global health threat. Misuse and overuse of antimicrobials are the main drivers in developing drug-resistant bacteria. The emergence of the rapid global spread of multi-resistant bacteria requires urgent multisectoral action to generate novel treatment alternatives. Combination therapy offers the potential to exploit synergistic effects for enhanced antibacterial efficacy of drugs. Understanding the complex dynamics and kinetics of drug interactions in combination therapy is crucial. Therefore, this review outlines the current advances in antibiotic resistance's evolutionary and genetic dynamics in combination therapies-exposed bacteria. Moreover, we also discussed four pivotal future research areas to comprehend better the development of antibiotic resistance in bacteria treated with combination strategies.

Supernatant of platelet-Klebsiella pneumoniae coculture induces apoptosis-like death in Klebsiella pneumoniae

Multidrug-resistant Klebsiella pneumoniae strains, especially carbapenem-resistant K. pneumoniae, have become a rapidly emerging crisis worldwide, greatly limiting current therapeutic options and posing new challenges to infection management. Therefore, it is imperative to develop novel and effective biological agents for the treatment of multidrug-resistant K. pneumoniae infections. Platelets play an important role in the development of inflammation and immune responses. The main component responsible for platelet antibacterial activity lies in the supernatant stimulated by gram-positive bacteria. However, little research has been conducted on the interaction of gram-negative bacteria with platelets. Therefore, we aimed to explore the bacteriostatic effect of the supernatant derived from platelet-K. pneumoniae coculture and the mechanism underlying this effect to further assess the potential of platelet-bacterial coculture supernatant. We conducted this study on the gram-negative bacteria K. pneumoniae and CRKP and detected turbidity changes in K. pneumoniae and CRKP cultures when grown with platelet-K. pneumoniae coculture supernatant added to the culture medium. We found that platelet-K. pneumoniae coculture supernatant significantly inhibited the growth of K. pneumoniae and CRKP in vitro. Furthermore, transfusion of platelet-K. pneumoniae coculture supernatant alleviated the symptoms of K. pneumoniae and CRKP infection in a murine model. Additionally, we observed apoptosis-like changes, such as phosphatidylserine exposure, chromosome condensation, DNA fragmentation, and overproduction of reactive oxygen species in K. pneumoniae following treatment with the supernatant. Our study demonstrates that the platelet-K. pneumoniae coculture supernatant can inhibit K. pneumoniae growth by inducing an apoptosis-like death, which is important for the antibacterial strategies development in the future.IMPORTANCEWith the widespread use of antibiotics, bacterial resistance is increasing, and a variety of multi-drug resistant Gram-negative bacteria have emerged, which brings great challenges to the treatment of infections caused by Gram-negative bacteria. Therefore, finding new strategies to inhibit Gram-negative bacteria and even multi-drug- resistant Gram-negative bacteria is crucial for treating infections caused by Gram-negative bacteria, improving the abuse of antibiotics, and maintaining the balance between bacteria and antibiotics. K. pneumoniae is a common clinical pathogen, and drug-resistant CRKP is increasingly difficult to cure, which brings great clinical challenges. In this study, we found that the platelet-K. pneumoniae coculture supernatant can inhibit K. pneumoniae growth by inducing an apoptosis-like death. This finding has inspired the development of future antimicrobial strategies, which are expected to improve the clinical treatment of Gram-negative bacteria and control the development of multidrug-resistant strains.

Characterization of Pseudomonas aeruginosa resistance to ceftolozane-tazobactam due to ampC and/or ampD mutations observed during treatment using semi-mechanistic PKPD modeling

A double ampC (AmpCG183D) and ampD (AmpDH157Y) genes mutations have been identified by whole genome sequencing in a Pseudomonas aeruginosa (PaS) that became resistant (PaR) in a patient treated by ceftolozane/tazobactam (C/T). To precisely characterize the respective contributions of these mutations on the decreased susceptibility to C/T and on the parallel increased susceptibility to imipenem (IMI), mutants were generated by homologous recombination in PAO1 reference strain (PAO1- AmpCG183D, PAO1-AmpDH157Y, PAO1-AmpCG183D/AmpDH157Y) and in PaR (PaR-AmpCPaS/AmpDPaS). Sequential time-kill curve experiments were conducted on all strains and analyzed by semi-mechanistic PKPD modeling. A PKPD model with adaptation successfully described the data, allowing discrimination between initial and time-related (adaptive resistance) effects of mutations. With PAO1 and mutant-derived strains, initial EC50 values increased by 1.4, 4.1, and 29-fold after AmpCG183D , AmpDH157Y and AmpCG183D/AmpDH157Y mutations, respectively. EC50 values were increased by 320, 12.4, and 55-fold at the end of the 2 nd experiment. EC50 of PAO1-AmpCG183D/AmpDH157Y was higher than that of single mutants at any time of the experiments. Within the PaR clinical background, reversal of AmpCG183D, and AmpDH157Y mutations led to an important decrease of EC50 value, from 80.5 mg/L to 6.77 mg/L for PaR and PaR-AmpCPaS/AmpDPaS, respectively. The effect of mutations on IMI susceptibility mainly showed that the AmpCG183D mutation prevented the emergence of adaptive resistance. The model successfully described the separate and combined effect of AmpCG183D and AmpDH157Y mutations against C/T and IMI, allowing discrimination and quantification of the initial and time-related effects of mutations. This method could be reproduced in clinical strains to decipher complex resistance mechanisms.

An improved PKPD modeling approach to characterize the pharmacodynamic interaction over time between ceftazidime/avibactam and colistin from in vitro time-kill experiments against multidrug-resistant Klebsiella pneumoniae isolates

In contrast to the checkerboard method, bactericidal experiments [time-kill curves (TKCs)] allow an assessment of pharmacodynamic (PD) interactions over time. However, TKCs in combination pose interpretation problems. The objective of this study was to characterize the PD interaction over time between ceftazidime/avibactam (CZA) and colistin (CST) using TKC against four multidrug-resistant Klebsiella pneumoniae susceptible to both antibiotics and expressing a widespread carbapenemase determinant KPC-3. In vitro TKCs were performed and analyzed using pharmacokinetic/pharmacodynamic (PKPD) modeling. The general pharmacodynamic interaction model was used to characterize PD interactions between drugs. The 95% confidence intervals (95%CIs) of the expected additivity and of the observed interaction were built using parametric bootstraps and compared to evaluate the in vitro PD interaction over time. Further simulations were conducted to investigate the effect of the combination at varying concentrations typically observed in patients. Regrowth was observed in TKCs at high concentrations of drugs alone [from 4 to 32× minimum inhibitory concentrations (MIC)], while the combination systematically prevented the regrowth at concentrations close to the MIC. Significant synergy or antagonism were observed under specific conditions but overall 95%CIs overlapped widely over time indicating an additive interaction between antibiotics. Moreover, simulations of typical PK profile at standard dosages indicated that the interaction should be additive in clinical conditions. The nature of the PD interaction varied with time and concentration in TKC. Against the four K. pneumoniae isolates, the bactericidal effect of CZA + CST combination was predicted to be additive and to prevent the emergence of resistance at clinical concentrations.

Time-Kill Analysis of Canine Skin Pathogens: A Comparison of Pradofloxacin and Marbofloxacin

Time-kill curves (TKCs) are more informative compared with the use of minimum inhibitory concentration (MIC) as they allow the capture of bacterial growth and the development of drug killing rates over time, which allows to compute key pharmacodynamic (PD) parameters. Our study aimed, using a semi-mechanistic mathematical model, to estimate the best pharmacokinetic/pharmacodynamic (PK/PD) indices (ƒAUC/MIC or %ƒT > MIC) for the prediction of clinical efficacy of veterinary FQs in Staphylococcus pseudintermedius, Staphylococcus aureus, and Escherichia coli collected from canine pyoderma cases with a focus on the comparison between marbofloxacin and pradofloxacin. Eight TCKs for each bacterial species (4 susceptible and 4 resistant) were analysed in duplicate. The best PK/PD index was ƒAUC24h/MIC in both staphylococci and E. coli. For staphylococci, values of 25-40 h were necessary to achieve a bactericidal effect, whereas the calculated values (25-35 h) for E. coli were lower than those predicting a positive clinical outcome (100-120 h) in murine models. Pradofloxacin showed a higher potency (lower EC50) in comparison with marbofloxacin. However, no difference in terms of a maximal possible pharmacological killing rate (Emax) was observed. Taking into account in vivo exposure at the recommended dosage regimen (3 and 2 mg/kg for pradofloxacin and marbofloxacin, respectively), the overall killing rates (Kdrug) computed were also similar in most instances.

Optimal drug therapy for Staphylococcus aureus bacteraemia in adults

PURPOSE OF REVIEW

Staphylococcus aureus is a significant human pathogen, causing a variety of infections, from skin and soft tissue infections to endocarditis, bone and joint infections and deep tissue abscesses. Mortality from S. aureus bacteraemia remains high, without major therapeutic advances in recent decades.

RECENT FINDINGS

In recent years, optimized dosing of antibiotics is increasingly being recognized as a cornerstone of management for severe infections including S. aureus bacteraemia. This comprehensive review details the pharmacokinetics/pharmacodynamics (PK/PD) targets for commonly used antistaphylococcal antibiotics and the doses predicted to achieve them in clinical practice. Recent advances in dosing of teicoplanin and use of cefazolin in CNS infections and findings from combination therapy studies are discussed. Drug exposure relationships related to toxicity are also detailed.

SUMMARY

This review details the different PK/PD targets for drugs used to treat S. aureus bacteraemia and how to apply them in various scenarios. The drug doses that achieve them, and the risks of toxicity are also provided.

Predicting treatment response to vancomycin using bacterial DNA load as a pharmacodynamic marker in premature and very low birth weight neonates: A population PKPD study

Background: While positive blood cultures are the gold standard for late-onset sepsis (LOS) diagnosis in premature and very low birth weight (VLBW) newborns, these results can take days, and early markers of possible treatment efficacy are lacking. The objective of the present study was to investigate whether the response to vancomycin could be quantified using bacterial DNA loads (BDLs) determined by real-time quantitative polymerase chain reaction (RT-qPCR). Methods: VLBW and premature neonates with suspected LOS were included in a prospective observational study. Serial blood samples were collected to measure BDL and vancomycin concentrations. BDLs were measured with RT-qPCR, whereas vancomycin concentrations were measured by LC-MS/MS. Population pharmacokinetic-pharmacodynamic modeling was performed with NONMEM. Results: Twenty-eight patients with LOS treated with vancomycin were included. A one-compartment model with post-menstrual age (PMA) and weight as covariates was used to describe the time PK profile of vancomycin concentrations. In 16 of these patients, time profiles of BDL could be described with a pharmacodynamic turnover model. The relationship between vancomycin concentration and first-order BDL elimination was described with a linear-effect model. Slope S increased with increasing PMA. In 12 patients, no decrease in BDL over time was observed, which corresponded with clinical non-response. Discussion: BDLs determined through RT-qPCR were adequately described with the developed population PKPD model, and treatment response to vancomycin using BDL in LOS can be assessed as early as 8 h after treatment initiation.

Optimizing Clinical Outcomes Through Rational Dosing Strategies: Roles of Pharmacokinetic/Pharmacodynamic Modeling Tools

Significant progress in previous decades has led to several methodologies developed to facilitate the design of optimal antimicrobial dosing. In this review, we highlight common pharmacokinetic/pharmacodynamic (PKPD) modeling techniques and their roles in guiding rational dosing regimen design. In the early drug development phases, dose fractionation studies identify the PKPD index most closely associated with bacterial killing. Once discerned, this index is linked to clinical efficacy end points, and classification and regression tree analysis can be used to define the PKPD target goal. Monte Carlo simulations integrate PKPD and microbiological data to identify dosing strategies with a high probability of achieving the established PKPD target. Results then determine dosing regimens to investigate and/or validate the findings of randomized controlled trials. Further improvements in PKPD modeling could lead to an era of precision dosing and personalized therapeutics.

Model-Informed Translation of In Vitro Effects of Short-, Prolonged- and Continuous-Infusion Meropenem against Pseudomonas aeruginosa to Clinical Settings

Pharmacokinetic-pharmacodynamic (PKPD) models have met increasing interest as tools to identify potential efficacious antibiotic dosing regimens in vitro and in vivo. We sought to investigate the impact of diversely shaped clinical pharmacokinetic profiles of meropenem on the growth/killing patterns of Pseudomonas aeruginosa (ARU552, MIC = 16 mg/L) over time using a semi-mechanistic PKPD model and a PK/PD index-based approach. Bacterial growth/killing were driven by the PK profiles of six patient populations (infected adults, burns, critically ill, neurosurgery, obese patients) given varied pathogen features (e.g., EC50, growth rate, inoculum), patient characteristics (e.g., creatinine clearance), and ten dosing regimens (including two dose levels and 0.5-h, 3-h and continuous-infusion regimens). Conclusions regarding the most favourable dosing regimen depended on the assessment of (i) the total bacterial load or fT>MIC (time that unbound concentrations exceed the minimum inhibitory concentration); (ii) the median or P0.95 profile of the population; and (iii) 8 h or 24 h time points. Continuous infusion plus loading dose as well as 3-h infusions (3-h infusions: e.g., for scenarios associated with low meropenem concentrations, P0.95 profiles, and MIC ≥ 16 mg/L) appeared superior to standard 0.5-h infusions at 24 h. The developed platform can serve to identify promising strategies of efficacious dosing for clinical trials.