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Objective To investigate and describe the variability in cefepime exposures among 'real-world', critically ill patients by using population pharmacokinetic modelling and simulations, and with translation of these findings to visualizations. Methods A cohort of adult medical ICU patients who received cefepime with therapeutic drug monitoring was studied. Two compartment models were developed to estimate cefepime clearance (Model 1) and simulate cefepime exposures among 1000 patients, each with identical creatinine clearance of 60 mL/min and receiving a regimen of cefepime 1 gram IV over 30 minutes, every 8 hours (Model 2). Variability in the relationship between cefepime clearance and creatinine clearance (CrCL) was visualized, and a random, representative sample of 10 simulated patients was utilized to illustrate variability in cefepime exposures. Results A total of 75 adult medical ICU patients (52% female) and 98 serum cefepime samples were included in the study. Population parameter estimates for cefepime displayed a wide range of variation in Model 1 (CV: 45% to 95%), with low bias at the individual level at 0.226 mg/L but high bias in the population model 10.6 mg/L. Model 2 displayed similar fits, demonstrating that correcting for individual patient creatinine clearance slightly improves the bias of the population model (bias = 4.31 mg/L). Among 10 simulated patients that a clinician would deem similar from a dosing perspective (i.e. equivalent creatinine clearance), maximum concentrations after three simulated doses varied more than 8-fold from 41.2 to 339 mg/L at the 5th and 95th percentiles, and clearance profiles were highly different. Conclusion Creatinine clearance estimates alone are inadequate for predicting cefepime exposures. Wide variations in cefepime exposure exist among ICU patients, even for those with similar kidney function estimates. Current population adjustment schemes based solely on creatinine clearance will result in unintended high and low exposures leading to safety and efficacy concerns, respectively. [ABSTRACT FROM AUTHOR]

Objectives: Critical illness reduces β-lactam pharmacokinetic/pharmacodynamic (PK/PD) attainment. We sought to quantify PK/PD attainment in patients with hospital-acquired pneumonia.Methods: Meropenem plasma PK data (n = 70 patients) were modelled, PK/PD attainment rates were calculated for empirical and definitive targets, and between-patient variability was quantified [as a coefficient of variation (CV%)].Results: Attainment of 100% T>4×MIC was variable for both empirical (CV% = 92) and directed (CV% = 33%) treatment.Conclusions: Individualization is required to achieve suggested PK/PD targets in critically ill patients. [ABSTRACT FROM AUTHOR]

Objectives: To assess differences in vancomycin AUC estimates from two common, clinically applied first-order pharmacokinetic equation methods compared with Bayesian estimates.Methods: A cohort of patients who received vancomycin and therapeutic drug monitoring was studied. First-order population pharmacokinetic equations were used to guide initial empirical dosing. After receipt of the first dose, patients had peak and trough serum levels drawn and steady-state AUC was estimated using first-order pharmacokinetic equations as standard care. We subsequently created a Bayesian model and used individual Empirical Bayes Estimates to precisely calculate vancomycin AUC24-48, AUC48-72 and AUC72-96 in this cohort. AUC at steady state (AUCSS) differences from the first-order methods were compared numerically and categorically (i.e. below, within or above 400-600 mg·h/L) to Bayesian AUCs, which served as the gold standard.Results: A total of 65 adult inpatients with 409 plasma samples were included in this analysis. A two-compartment intravenous infusion model with first-order elimination fit the data well. The mean of Bayesian AUC24-48 was not significantly different from AUC estimates from the two first-order pharmacokinetic equation methods (P = 0.68); however, Bayesian AUC48-72 and Bayesian AUC72-96 were both significantly different when compared with both first-order pharmacokinetic equation methods (P < 0.01 for each). At the patient level, categorical classifications of AUC estimates from the two first-order pharmacokinetic equation methods differed from categorizations derived from the Bayesian calculations. Categorical agreement was ∼50% between first-order and Bayesian calculations, with declining categorical agreement observed with longer treatment courses. Differences in categorical agreement between calculation methods could potentially result in different dose recommendations for the patient.Conclusions: Bayesian-calculated AUCs between 48-72 and 72-96 h intervals were significantly different from first-order pharmacokinetic method-estimated AUCs at steady state. The various calculation methods resulted in different categorical classification, which could potentially lead to erroneous dosing adjustments in approximately half of the patients. [ABSTRACT FROM AUTHOR]

In 2020, new vancomycin guidelines were released, recommending the transition from trough-based to AUC24 monitoring for adult and paediatric patients. Given the resources required to achieve this transition, there has been debate about the costs and benefits of AUC24-based monitoring. A recent narrative review of vancomycin therapeutic drug monitoring in paediatrics claims to have uncovered the methodological weaknesses of the data that informed the guidelines and advises against premature adoption of AUC24-guided monitoring. In this article, we present supporting arguments for AUC24-guided monitoring in children, which include that: (i) troughs alone are inadequate surrogates for AUC24; (ii) vancomycin-associated nephrotoxicity has significant consequences that warrant optimization of dosing; (iii) a substantial portion of children receiving vancomycin are at high risk for poor outcomes and deserve targeted monitoring; and (iv) limited efficacy data in support of AUC24 is not a justification to revert to a less supported monitoring approach. [ABSTRACT FROM AUTHOR]

The need for precision dosing has been challenged on the basis of insufficient evidence. Herein, we argue that adequate evidence exists to conduct therapeutic drug monitoring (TDM) and precisely target antibiotic exposures. While achievement of any antibiotic concentration does not guarantee efficacy sans toxicity for any single patient, stochastic control optimizes the probability of achieving favourable responses across patients. We argue that variability in targets (such as the organism's MIC) can be considered with models. That is, complexity alone does not relegate the decision-making framework to 'clinician intuition'. We acknowledge the exposure-response relationships are modified by patient-specific factors (other drugs, baseline organ functional status etc.) and describe how precision dosing can inform clinical decision making rather than protocolize it. Finally, we call for randomized, controlled trials; however, we suggest that these trials are not necessary to make TDM standard of care for multiple classes of antibiotics. [ABSTRACT FROM AUTHOR]

Background: Vancomycin and piperacillin/tazobactam are reported in clinical studies to increase acute kidney injury (AKI). However, no clinical study has demonstrated synergistic toxicity, only that serum creatinine increases.Objectives: To clarify the potential for synergistic toxicity between vancomycin, piperacillin/tazobactam and vancomycin + piperacillin/tazobactam treatments by quantifying kidney injury in a translational rat model of AKI and using cell studies.Methods: (i) Male Sprague-Dawley rats (n = 32) received saline, vancomycin 150 mg/kg/day intravenously, piperacillin/tazobactam 1400 mg/kg/day intraperitoneally or vancomycin + piperacillin/tazobactam for 3 days. Urinary biomarkers and histopathology were analysed. (ii) Cellular injury was assessed in NRK-52E cells using alamarBlue®.Results: Urinary output increased from Day -1 to Day 1 with vancomycin but only after Day 2 for vancomycin + piperacillin/tazobactam-treated rats. Plasma creatinine was elevated from baseline with vancomycin by Day 2 and only by Day 4 for vancomycin + piperacillin/tazobactam. Urinary KIM-1 and clusterin were increased with vancomycin from Day 1 versus controls (P < 0.001) and only on Day 3 with vancomycin + piperacillin/tazobactam (P < 0.001, KIM-1; P < 0.05, clusterin). The histopathology injury score was elevated only in the vancomycin group when compared with piperacillin/tazobactam as a control (P = 0.04) and generally not so with vancomycin + piperacillin/tazobactam. In NRK-52E cells, vancomycin induced cell death with high doses (IC50 48.76 mg/mL) but piperacillin/tazobactam did not, and vancomycin + piperacillin/tazobactam was similar to vancomycin.Conclusions: All groups treated with vancomycin demonstrated AKI; however, vancomycin + piperacillin/tazobactam was not worse than vancomycin. Histopathology suggested that piperacillin/tazobactam did not worsen vancomycin-induced AKI and may even be protective. [ABSTRACT FROM AUTHOR]

Objectives: To determine the β-lactam exposure associated with positive clinical outcomes for Gram-negative blood stream infection (BSI) in critically ill patients.Patients and Methods: Pooled data of critically ill patients with mono-microbial Gram-negative BSI treated with β-lactams were collected from two databases. Free minimum concentrations (fCmin) of aztreonam, cefepime, ceftazidime, ceftriaxone, piperacillin (co-administered with tazobactam) and meropenem were interpreted in relation to the measured MIC for targeted bacteria (fCmin/MIC). A positive clinical outcome was defined as completion of the treatment course or de-escalation, without other change of antibiotic therapy, and with no additional antibiotics commenced within 48 h of cessation. Drug exposure breakpoints associated with positive clinical outcome were determined by classification and regression tree (CART) analysis.Results: Data from 98 patients were included. Meropenem (46.9%) and piperacillin/tazobactam (36.7%) were the most commonly prescribed antibiotics. The most common pathogens were Escherichia coli (28.6%), Pseudomonas aeruginosa (19.4%) and Klebsiella pneumoniae (13.3%). In all patients, 87.8% and 71.4% achieved fCmin/MIC ≥1 and fCmin/MIC >5, respectively. Seventy-eight patients (79.6%) achieved positive clinical outcome. Two drug exposure breakpoints were identified: fCmin/MIC >1.3 for all β-lactams (predicted difference in positive outcome 84.5% versus 15.5%, P < 0.05) and fCmin/MIC >4.95 for meropenem, aztreonam or ceftriaxone (predicted difference in positive outcome 97.7% versus 2.3%, P < 0.05).Conclusions: A β-lactam fCmin/MIC >1.3 was a significant predictor of a positive clinical outcome in critically ill patients with Gram-negative BSI and could be considered an antibiotic dosing target. [ABSTRACT FROM AUTHOR]

Drug-induced nephrotoxicity is responsible for 20% to 60% of cases of acute kidney injury in hospitalized patients and is associated with increased morbidity and mortality in both children and adults. Antimicrobials are one of the most common classes of medications prescribed globally and also among the most common causes of nephrotoxicity. A broad range of antimicrobial agents have been associated with nephrotoxicity, but the features of kidney injury vary based on the agent, its mechanism of injury and the site of toxicity within the kidney. Distinguishing nephrotoxicity caused by an antimicrobial agent from other potential inciting factors is important to facilitate both early recognition of drug toxicity and prompt cessation of an offending drug, as well as to avoid unnecessary discontinuation of an innocuous therapy. This review will detail the different types of antimicrobial-induced nephrotoxicity: acute tubular necrosis, acute interstitial nephritis and obstructive nephropathy. It will also describe the mechanism of injury caused by specific antimicrobial agents and classes (vancomycin, aminoglycosides, polymyxins, antivirals, amphotericin B), highlight the toxicodynamics of these drugs and provide guidance on administration or monitoring practices that can mitigate toxicity, when known. Particular attention will be paid to paediatric patients, when applicable, in whom nephrotoxin exposure is an often-underappreciated cause of kidney injury. [ABSTRACT FROM AUTHOR]

Objectives: To identify the pharmacokinetic (PK) and toxicodynamic (TD) relationship for vancomycin-induced kidney injury.Methods: Male Sprague-Dawley rats received intravenous (iv) vancomycin. Doses ranging from 150 mg/kg/day to 400 mg/kg/day were administered as a single or twice-daily injection over 24 h (total protocol duration). Controls received iv saline. Plasma was sampled with up to eight samples in 24 h per rat. Twenty-four hour urine was collected and assayed for kidney injury molecule 1 (KIM-1), osteopontin and clusterin. Vancomycin in plasma was quantified via LC-MS/MS. PK analyses were conducted using Pmetrics for R. PK exposures during the first 24 h (i.e. AUC0-24h, Cmax 0-24h and Cmin 0-24h) were calculated. PK/TD relationships were assessed with Spearman's rank coefficient (rs) and the best-fit mathematical model.Results: PK/TD data were generated from 45 vancomycin-treated and 5 control rats. A two-compartment model fit the data well (Bayesian: observed versus predicted R2 = 0.97). Exposure-response relationships were found between AUC0-24h versus KIM-1 and osteopontin (R2 = 0.61 and 0.66) and Cmax 0-24h versus KIM-1 and osteopontin (R2 = 0.50 and 0.56) using a four-parameter Hill fit. Conversely, Cmin 0-24h was less predictive of KIM-1 and osteopontin (R2 = 0.46 and 0.53). A vancomycin AUC0-24h of 482.2 corresponded to a 90% of maximal rise in KIM-1.Conclusions: Vancomycin-induced kidney injury as defined by urinary biomarkers is driven by vancomycin AUC or Cmax rather than Cmin. Further, an identified PK/TD target AUC0-24h of 482.2 mg·h/L may have direct relevance to human outcomes. [ABSTRACT FROM AUTHOR]

Objectives: To quantify the impact of varying the at-risk days definition on the overall report of at-risk days and on the calculated standardized consumption rates (SCRs) for piperacillin/tazobactam, amikacin, daptomycin and vancomycin.Methods: Data were evaluated for two system hospitals, an 894 bed academic centre and a 114 bed community hospital. Aggregate inpatient antibiotic administration and occupancy data were extracted from electronic databases at the facility-wide level. Occupancy data were reported from admission-discharge-transfer systems. At-risk days were defined as hospital days present (DP), patient days (PD), persons present (PP) and billing days (BD). Inpatient antimicrobial days of therapy (DOT) across four major antimicrobial agents were used to calculate facility-wide SCRs using each denominator and were evaluated by least-squares regression and R2 values.Results: Within the 894 bed academic hospital, the average monthly facility-wide days were 28 424, 22 198, 15 957 and 14 789 by the DP, PP, PD and BD definitions, respectively. Within the 114 bed community hospital, the average monthly facility-wide days were 5175, 3523 and 2816 by the DP, PP and PD definitions, respectively. Strong concordance was observed between facility-wide SCRs using the DP and PP definitions in both the academic (R2 = 0.99, y = 0.78x - 0.001) and community (R2 = 0.99, y = 0.68x - 0.03) centres across all four inpatient antibiotics evaluated. In an analysis of piperacillin/tazobactam SCRs, rates were over-predicted by 28%-93% at the facility-wide level across centres using alternative denominators.Conclusions: We found that data source and definitions of at-risk denominator days meaningfully impact antibiotic SCRs. Centres should carefully consider these potential sources of variation when setting consumption benchmarks and internally evaluating use. [ABSTRACT FROM AUTHOR]

Objectives: As the optimal administration time for fosfomycin peri-procedural prophylaxis is unclear, we sought to determine optimal administration times for fosfomycin peri-procedural prophylaxis. Methods: Plasma, peripheral zone and transition zone fosfomycin concentrations were obtained from 26 subjects undergoing transurethral resection of the prostate (TURP), following a single oral dose of 3 g of fosfomycin. Population pharmacokinetic modelling was completed with the Nonparametric Adaptive Grid (NPAG) algorithm (Pmetrics package for R), with a four-compartment model. Plasma and tissue concentrations were simulated during the first 24 h post-dose, comparing these with EUCAST susceptibility breakpoints for Escherichia coli, a common uropathogen. Results: Non-compartmental-determined pharmacokinetic values in our population were similar to those reported in the package insert. Predicted plasma concentrations rapidly increased after the first hour, giving more than 90% population coverage for organisms with an MIC ≤4 mg/L over the first 12 h post-dose. Organisms with higher MICs fared much worse, with organisms at the EUCAST breakpoint being covered for <10% of the population at any time. Transitional zone prostate concentrations exceeded 4 mg/L for 90% of the population between hours 1 and 9. Peripheral zone prostate concentrations were much lower and only exceeded 4 mg/L for 70% of the population between hours 1 and 4. Conclusions: Until more precise plasma and tissue data are available, we recommend that fosfomycin prophylaxis be given 1-4 h prior to prostate biopsy. We do not recommend fosfomycin prophylaxis for subjects with known organisms with MICs >4 mg/L. [ABSTRACT FROM AUTHOR]

This report reviews a contemporary methodology for determining antibiotic dosage modifications among patients with renal impairment. Historically, the approach to identifying renal dosage adjustments has focused on achieving comparable concentration–time profiles between patients with renal impairment and those with normal kidney function. While this approach is intuitive, it fails to incorporate the relationship between antibiotic exposure and effect in the renal dose selection process. A candidate renal dosing scheme that is worthy of incorporation into clinical practice should balance the probability of achieving the exposure target associated with success against the risks of toxicity and the emergence of resistance. This review describes a methodology for optimally identifying dosage adjustments in patients with impaired renal function using extended-infusion piperacillin/tazobactam as an illustrative example. [ABSTRACT FROM PUBLISHER]

Drug-induced nephrotoxicity is responsible for 20% to 60% of cases of acute kidney injury in hospitalized patients and is associated with increased morbidity and mortality in both children and adults. Antimicrobials are one of the most common classes of medications prescribed globally and also among the most common causes of nephrotoxicity. A broad range of antimicrobial agents have been associated with nephrotoxicity, but the features of kidney injury vary based on the agent, its mechanism of injury and the site of toxicity within the kidney. Distinguishing nephrotoxicity caused by an antimicrobial agent from other potential inciting factors is important to facilitate both early recognition of drug toxicity and prompt cessation of an offending drug, as well as to avoid unnecessary discontinuation of an innocuous therapy. This review will detail the different types of antimicrobial-induced nephrotoxicity: acute tubular necrosis, acute interstitial nephritis and obstructive nephropathy. It will also describe the mechanism of injury caused by specific antimicrobial agents and classes (vancomycin, aminoglycosides, polymyxins, antivirals, amphotericin B), highlight the toxicodynamics of these drugs and provide guidance on administration or monitoring practices that can mitigate toxicity, when known. Particular attention will be paid to paediatric patients, when applicable, in whom nephrotoxin exposure is an often-underappreciated cause of kidney injury. [ABSTRACT FROM AUTHOR]