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Objectives Combination therapy is often used for carbapenem-resistant Gram-negative bacteria. We previously demonstrated synergy of polymyxin B and minocycline against carbapenem-resistant Klebsiella pneumoniae in static time–kill experiments and developed an in silico pharmacokinetic/pharmacodynamic (PK/PD) model. The present study assessed the synergistic potential of this antibiotic combination in dynamic experiments. Methods Two clinical K. pneumoniae isolates producing KPC-3 and OXA-48 (polymyxin B MICs 0.5 and 8 mg/L, and minocycline MICs 1 and 8 mg/L, respectively) were included. Activities of the single drugs and the combination were assessed in 72 h dynamic time–kill experiments mimicking patient pharmacokinetics. Population analysis was performed every 12 h using plates containing antibiotics at 4× and 8× MIC. WGS was applied to reveal resistance genes and mutations. Results The combination showed synergistic and bactericidal effects against the KPC-3-producing strain from 12 h onwards. Subpopulations with decreased susceptibility to polymyxin B were frequently detected after single-drug exposures but not with the combination. Against the OXA-48-producing strain, synergy was observed between 4 and 8 h and was followed by regrowth. Subpopulations with decreased susceptibility to polymyxin B and minocycline were detected throughout experiments. For both strains, the observed antibacterial activities showed overall agreement with the in silico predictions. Conclusions Polymyxin B and minocycline in combination showed synergistic effects, mainly against the KPC-3-producing K. pneumoniae. The agreement between the experimental results and in silico predictions supports the use of PK/PD models based on static time–kill data to predict the activity of antibiotic combinations at dynamic drug concentrations. [ABSTRACT FROM AUTHOR]

Background: Apramycin is under development for human use as EBL-1003, a crystalline free base of apramycin, in face of increasing incidence of multidrug-resistant bacteria. Both toxicity and cross-resistance, commonly seen for other aminoglycosides, appear relatively low owing to its distinct chemical structure.Objectives: To perform a population pharmacokinetic (PPK) analysis and predict an efficacious dose based on data from a first-in-human Phase I trial.Methods: The drug was administered intravenously over 30 min in five ascending-dose groups ranging from 0.3 to 30 mg/kg. Plasma and urine samples were collected from 30 healthy volunteers. PPK model development was performed stepwise and the final model was used for PTA analysis.Results: A mammillary four-compartment PPK model, with linear elimination and a renal fractional excretion of 90%, described the data. Apramycin clearance was proportional to the absolute estimated glomerular filtration rate (eGFR). All fixed effect parameters were allometrically scaled to total body weight (TBW). Clearance and steady-state volume of distribution were estimated to 5.5 L/h and 16 L, respectively, for a typical individual with absolute eGFR of 124 mL/min and TBW of 70 kg. PTA analyses demonstrated that the anticipated efficacious dose (30 mg/kg daily, 30 min intravenous infusion) reaches a probability of 96.4% for a free AUC/MIC target of 40, given an MIC of 8 mg/L, in a virtual Phase II patient population with an absolute eGFR extrapolated to 80 mL/min.Conclusions: The results support further Phase II clinical trials with apramycin at an anticipated efficacious dose of 30 mg/kg once daily. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: To describe the population genetics and antibiotic resistance gene distribution of carbapenem-resistant Acinetobacter baumannii (CRAB) isolates causing infections in three Mediterranean countries.Methods: Isolates were collected during the 2013-17 AIDA clinical trial in six hospitals in Israel, Greece and Italy. WGS, bioinformatic characterization and antibiotic resistance profiling were performed.Results: In the 247 CRAB isolates characterized in this study, ST distribution varied by country: 29/31 (93.5%) Greek isolates, 34/41 (82.9%) Italian isolates and 70/175 (40.0%) Israeli isolates belonged to ST2. The identified ST2 isolates included eight distinct clades: 2C, 2D and 2H were significantly more common in Italy, while 2F was unique to Greece. The uncommon ST3 was not present among Greek isolates and constituted only 5/41 (12%) Italian isolates. On the other hand, it was much more common among Israeli isolates: 78/175 (44.6%) belonged to ST3. The vast majority of isolates, 240/247 (97.2%), were found to harbour acquired carbapenemases, primarily blaOXA-23. The chromosomal oxaAb (blaOXA-51-like) and ampC genes characteristic of this organism were also ubiquitous. Most (96.4%) ST3 isolates carried a broad-host-range plasmid IncP1α.Conclusions: The geographical differences in CRAB populations support the theory that clonal spread of CRAB leads to endemicity in hospitals and regions. The close association between antibiotic resistance genes and clades, and between plasmids and STs, suggest that de novo creation of MDR A. baumannii is rare. The clustering of antibiotic resistance genes and plasmids that is unique to each clade/ST, and nearly uniform within clades/STs, suggests that horizontal transmission is rare but crucial to the clade's/ST's success. [ABSTRACT FROM AUTHOR]

Therapeutic drug monitoring (TDM) and model‐informed precision dosing (MIPD) have evolved as important tools to inform rational dosing of antibiotics in individual patients with infections. In particular, critically ill patients display altered, highly variable pharmacokinetics and often suffer from infections caused by less susceptible bacteria. Consequently, TDM has been used to individualize dosing in this patient group for many years. More recently, there has been increasing research on the use of MIPD software to streamline the TDM process, which can increase the flexibility and precision of dose individualization but also requires adequate model validation and re‐evaluation of existing workflows. In parallel, new minimally invasive and noninvasive technologies such as microneedle‐based sensors are being developed, which—together with MIPD software—have the potential to revolutionize how patients are dosed with antibiotics. Nonetheless, carefully designed clinical trials to evaluate the benefit of TDM and MIPD approaches are still sparse, but are critically needed to justify the implementation of TDM and MIPD in clinical practice. The present review summarizes the clinical pharmacology of antibiotics, conventional TDM and MIPD approaches, and evidence of the value of TDM/MIPD for aminoglycosides, beta‐lactams, glycopeptides, and linezolid, for which precision dosing approaches have been recommended. [ABSTRACT FROM AUTHOR]

Background We evaluated whether carbapenem-colistin combination therapy reduces the emergence of colistin resistance, compared to colistin monotherapy, when given to patients with infections due to carbapenem-resistant Gram-negative organisms. Methods This is a pre-planned analysis of a secondary outcome from a randomized, controlled trial comparing colistin monotherapy with colistin-meropenem combination for the treatment of severe infections caused by carbapenem-resistant, colistin-susceptible Gram-negative bacteria. We evaluated rectal swabs taken on Day 7 or later for the presence of new colistin-resistant (ColR) isolates. We evaluated the emergence of any ColR isolate and the emergence of ColR Enterobacteriaceae (ColR-E). Results Data were available for 214 patients for the primary analysis; emergent ColR organisms were detected in 22 (10.3%). No difference was observed between patients randomized to treatment with colistin monotherapy (10/106, 9.4%) versus patients randomized to colistin-meropenem combination therapy (12/108, 11.1%; P =.669). ColR-E organisms were detected in 18/249 (7.2%) patients available for analysis. No difference was observed between the 2 treatment arms (colistin monotherapy 6/128 [4.7%] vs combination therapy 12/121 [9.9%]; P =.111). Enterobacteriaceae, as the index isolate, was found to be associated with development of ColR-E (hazard ratio, 3.875; 95% confidence interval, 1.475–10.184; P =.006). Conclusions Carbapenem-colistin combination therapy did not reduce the incidence of colistin resistance emergence in patients with infections due to carbapenem-resistant organisms. Further studies are necessary to elucidate the development of colistin resistance and methods for its prevention. [ABSTRACT FROM AUTHOR]

Background: The β-lactam antibiotic piperacillin (in combination with tazobactam) is commonly chosen for empirical treatment of suspected bacterial infections. However, pharmacokinetic variability among patient populations and across ages leads to uncertainty when selecting a dosing regimen to achieve an appropriate pharmacodynamic target.Objectives: To guide dosing by establishing a population pharmacokinetic model for unbound piperacillin in febrile children receiving cancer chemotherapy, and to assess pharmacokinetic/pharmacodynamic target attainment (100% fT > 1×MIC and 50% fT > 4×MIC) and resultant exposure, across body weights.Methods: Forty-three children admitted for 89 febrile episodes contributed 482 samples to the pharmacokinetic analysis. The typical doses required for target attainment were compared for various dosing regimens, in particular prolonged infusions, across MICs and body weights.Results: A two-compartment model with inter-fever-episode variability in CL, and body weight included through allometry, described the data. A high CL of 15.4 L/h (70 kg) combined with high glomerular filtration rate (GFR) values indicated rapid elimination and hyperfiltration. The target of 50% fT > 4×MIC was achieved for an MIC of 4.0 mg/L in a typical patient with extended infusions of 2-3 (q6h) or 3-4 (q8h) h, at or below the standard adult dose (75 and 100 mg/kg/dose for q6h and q8h, respectively). Higher doses or continuous infusion were needed to achieve 100% fT > 1×MIC due to the rapid piperacillin elimination.Conclusions: The licensed dose for children with febrile neutropenia (80 mg/kg q6h as a 30 min infusion) performs poorly for attainment of fT>MIC pharmacokinetic/pharmacodynamic targets. Given the population pharmacokinetic profile, feasible dosing regimens with reasonable exposure are continuous infusion (100% fT > 1×MIC) or prolonged infusions (50% fT > 4×MIC). [ABSTRACT FROM AUTHOR]

Background: Pharmacokinetic/pharmacodynamic (PKPD) models developed based on data from in vitro time-kill experiments have been suggested to contribute to more efficient drug development programmes and better dosing strategies for antibiotics. However, for satisfactory predictions such models would have to show good extrapolation properties.Objectives: To evaluate if a previously described mechanism-based PKPD model was able also to predict drug efficacy for higher bacterial densities and across bacterial strains.Methods: A PKPD model describing the efficacy of ciprofloxacin on Escherichia coli was evaluated. The predictive performance of the model was evaluated across several experimental conditions with respect to: (i) bacterial start inoculum ranging from the standard of ∼106 cfu/mL up to late stationary-phase cultures; and (ii) efficacy for seven additional strains (three laboratory and four clinical strains), not included during the model development process, based only on information regarding their MIC. Model predictions were performed according to the intended experimental protocol and later compared with observed bacterial counts.Results: The mechanism-based PKPD model structure developed based on data from standard start inoculum experiments was able to accurately describe the inoculum effect. The model successfully predicted the time course of drug efficacy for additional laboratory and clinical strains based on only the MIC values. The model structure was further developed to better describe the stationary phase data.Conclusions: This study supports the use of mechanism-based PKPD models based on preclinical data for predictions of untested scenarios. [ABSTRACT FROM AUTHOR]

Background:In view of the paucity of clinical evidence,in vitrostudies are needed to find antibiotic combinations effective against multidrug-resistant Gram-negative bacteria. Interpretation ofin vitroeffects is usually based on bacterial growth after 24 h in time-kill and checkerboard experiments. However, the clinical relevance of the effects observedin vitrois not established. In this study we explored alternative output parameters to assess the activities of colistin and meropenem againstPseudomonas aeruginosaandAcinetobacter baumannii. Methods:Four strains each ofP. aeruginosaandA. baumanniiwere exposed to colistin and meropenem, alone and in combination, in 8 h dynamic time-kill experiments. Initial (1 h), maximum and 8 h bacterial reductions and the area under the bacterial time-kill curve were evaluated. Checkerboards, interpreted based on fractional inhibitory concentration indices after 24 h, were performed for comparison. Results:In the time-kill experiments, the combination resulted in enhanced 1 h, maximum and 8 h bacterial reductions against 2, 3 and 5 of 8 strains, respectively, as compared to the single drugs. A statistically significant reduction in the area under the time-kill curve was observed for three strains. In contrast, the checkerboards did not identify synergy for any of the strains. Conclusions:Combination effects were frequently found with colistin and meropenem againstP. aeruginosaand A.baumanniiin time-kill experiments but were not detected with the checkerboard method. We propose that the early dynamics of bacterial killing and growth, which may be of great clinical importance, should be considered in futurein vitrocombination studies. [ABSTRACT FROM PUBLISHER]

Objectives: For antibiotics, extensive animal PKPD studies are often performed to evaluate the PK/PD driver for subsequent use when recommending dosing regimens. The aim of this work was to evaluate a PKPD model, developed based on in vitro time-kill data for colistin, in predicting the relationships between PK/PD indices and the bacterial killing previously observed in mice.Methods: An in silico PKPD model for Pseudomonas aeruginosa exposed to colistin was previously developed based on static in vitro time-kill data. The model was here applied to perform an in silico replication of an in vivo study where the effect of colistin on P. aeruginosa was studied in the thigh infection model. Concentration-time profiles of unbound colistin were predicted and used as input to drive the bacterial killing in the PKPD model. The predicted bacterial count at 24 h was related to each of the PK/PD indices and the results were compared with reported observations in vivo.Results: The model was found to adequately predict in vivo results from mice; both in terms of which PK/PD index best correlates to effect (fAUC/MIC) as well as the magnitude needed for a 2 log kill. The fAUC/MIC needed to achieve a 2 log reduction in bacterial counts after 24 h was here predicted to be 9 compared with 31 previously reported in vivo.Conclusions: This study provides further support that PKPD models based on longitudinal data can be a useful tool to make drug development more efficient within the infectious diseases area. [ABSTRACT FROM AUTHOR]

Objectives: Combination therapy can be a strategy to ensure effective bacterial killing when treating Pseudomonas aeruginosa, a Gram-negative bacterium with high potential for developing resistance. The aim of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model that describes the in vitro bacterial time-kill curves of colistin and meropenem alone and in combination for one WT and one meropenem-resistant strain of P. aeruginosa.Methods: In vitro time-kill curve experiments were conducted with a P. aeruginosa WT (ATCC 27853) (MICs: meropenem 1 mg/L; colistin 1 mg/L) and a meropenem-resistant type (ARU552) (MICs: meropenem 16 mg/L; colistin 1.5 mg/L). PK/PD models characterizing resistance were fitted to the observed bacterial counts in NONMEM. The final model was applied to predict the bacterial killing of ARU552 for different combination dosages of colistin and meropenem.Results: A model with compartments for growing and resting bacteria, where the bacterial killing by colistin reduced with continued exposure and a small fraction (0.15%) of the start inoculum was resistant to meropenem, characterized the bactericidal effect and resistance development of the two antibiotics. For a typical patient, a loading dose of colistin combined with a high dose of meropenem (2000 mg q8h) was predicted to result in a pronounced kill of the meropenem-resistant strain over 24 h.Conclusions: The developed PK/PD model successfully described the time course of bacterial counts following exposures to colistin and meropenem, alone and in combination, for both strains, and identified a dynamic drug interaction. The study illustrates the application of a PK/PD model and supports high-dose combination therapy of colistin and meropenem to overcome meropenem resistance. [ABSTRACT FROM AUTHOR]

Objectives: In silico pharmacokinetic/pharmacodynamic (PK/PD) models can be developed based on data from in vitro time-kill experiments and can provide valuable information to guide dosing of antibiotics. The aim was to develop a mechanism-based in silico model that can describe in vitro time-kill experiments of Escherichia coli MG1655 WT and six isogenic mutants exposed to ciprofloxacin and to identify relationships that may be used to simplify future characterizations in a similar setting.Methods: In this study, we developed a mechanism-based PK/PD model describing killing kinetics for E. coli following exposure to ciprofloxacin. WT and six well-characterized mutants, with one to four clinically relevant resistance mutations each, were exposed to a wide range of static ciprofloxacin concentrations.Results: The developed model includes susceptible growing bacteria, less susceptible (pre-existing resistant) growing bacteria, non-susceptible non-growing bacteria and non-colony-forming non-growing bacteria. The non-colony-forming state was likely due to formation of filaments and was needed to describe data close to the MIC. A common model structure with different potency for bacterial killing (EC50) for each strain successfully characterized the time-kill curves for both WT and the six E. coli mutants.Conclusions: The model-derived mutant-specific EC50 estimates were highly correlated (r(2) = 0.99) with the experimentally determined MICs, implying that the in vitro time-kill profile of a mutant strain is reasonably well predictable by the MIC alone based on the model. [ABSTRACT FROM AUTHOR]

• One hundred and thirty-two publications on pharmacokinetic/pharmacodynamic (PKPD) models for time courses of antibiotic effects were reviewed. • Points to consider when developing such PKPD models are discussed. • Models describing bacterial growth, regrowth, drug effects and interactions are shown. • An interactive online table enables the review of PKPD models from 1963 to 2021. Pharmacokinetic/pharmacodynamic (PKPD) models have emerged as valuable tools for the characterization and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics such as minimum inhibitory concentration and PKPD indices, PKPD models enable description of the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. A literature review was conducted to identify PKPD models based on: (i) antibiotic compounds; (ii) Gram-positive or Gram-negative pathogens; and (iii) in-vitro or in-vivo longitudinal colony-forming unit data. In total, 132 publications were identified that were released between 1963 and 2021, including models based on exposure to single antibiotics (n =92) and drug combinations (n =40), as well as different experimental settings (e.g. static/traditional dynamic/hollow-fibre/animal time-kill models, n =90/27/32/11). An interactive, fully searchable table summarizes the details of each model, namely variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects and interactions. Furthermore, the review highlights the main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings, and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models will assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen–antibiotic combinations over time, and ultimately facilitate model-informed antibiotic translation, dosing and drug development. [ABSTRACT FROM AUTHOR]

• In vitro studies are essential for the evaluation of new antibiotic treatments. • Animal models are an important link between in vitro data and clinical testing. • Market authorization and dosing guidelines rely on preclinical pharmacokinetic/pharmacodynamic (PK/PD) studies. • Mathematical modeling can optimize study design and translation of preclinical data. • More clinical trials for optimal dosing are warranted but are difficult to perform. Appropriate dosing of antibiotics is key in the treatment of bacterial infections to ensure clinical efficacy while avoiding toxic drug concentrations and minimizing emergence of resistance. As collection of sufficient clinical evidence is difficult for specific patient populations, infection types and pathogens, market authorization, dosing strategies and recommendations often rely on data obtained from in vitro and animal experiments. The aim of this review is to provide an overview of commonly used preclinical infection models, including their strengths and limitations. In vitro, static and dynamic time-kill experiments are the most frequently used methods for assessing pharmacokinetic/pharmacodynamic (PK/PD) associations. Limitations of in vitro models include the inability to account for the effects of the immune system, and uncertainties in clinically relevant bacterial concentrations, growth conditions and the implications of emerging resistant bacterial populations during experiments. Animal experiments, most commonly murine lung and thigh infections models, are considered a necessary link between in vitro data and the clinical situation. However, there are differences in pathophysiology, immunology, and PK between species. Mathematical modeling in which preclinical data are integrated with human population PK can facilitate translation of preclinical data to the patient's clinical situation. Moreover, PK/PD modeling and simulations can help in the design of clinical trials aiming to establish optimal dosing regimens to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Abstract: An analytical method for quantitation of colistin A and colistin B in plasma and culture medium is described. After protein precipitation with acetonitrile (ACN) containing 0.1% trifluoroacetic acid (TFA), the supernatants were diluted with 0.03% TFA. The compounds were separated on an Ultrasphere C18 column, 4.6mm×250mm, 5μm particle size with a mobile phase consisting of 25% ACN in 0.03% TFA and detected with tandem mass spectrometry. The instrument was operating in ESI negative ion mode and the precursor–product ion pairs were m/z 1167.7→1079.6 for colistin A and m/z 1153.7→1065.6 for colistin B. The lower limit of quantification (LLOQ) for 100μL plasma was 19.4 and 10.5ng/mL for colistin A and B, respectively, with CV <6.2% and accuracy <±12.6%. For culture medium (50μL+50μL plasma), LLOQ was 24.2 and 13.2ng/mL for colistin A and B, respectively, with CV <11.4% and accuracy <±8.1%. The quick sample work-up method allows for determination of colistin A and B in clinical samples without causing hydrolysis of the prodrug colistin methanesulfonate (CMS). [Copyright &y& Elsevier]