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Vancomycin induces exposure-related acute kidney injury. However, the pharmacokinetic-toxicodynamic (PK-TD) relationship remains unclear. Sprague-Dawley rats received intravenous (i.v.) vancomycin doses of 300 mg/kg/day and 400 mg/kg/day, divided into once-, twice-, three-times-, or four-times-daily doses (i.e., QD, BID, TID, or QID) over 24 h. Up to 8 samples plus a terminal sample were drawn during the 24-h dosing period. Twenty-four-hour urine was collected and assayed for kidney injury molecule-1 (KIM-1). Vancomycin was quantified via liquid chromatography-tandem mass spectrometry (LC-MS/MS). Following terminal sampling, nephrectomy and histopathologic analyses were conducted. PK analyses were conducted using Pmetrics. PK exposures (i.e., area under the concentration-time curve from 0 to 24 h [AUC(0–24)] and maximum concentration from 0 to 24 h [C(max0–24)]) were calculated for each rat, and PK-TD relationships were discerned. A total of 53-rats generated PK-TD data. A 2-compartment model fit the data well (Bayesian observed versus predicted concentrations; R(2) = 0.96). KIM-1 values were greater in QD and BID groups (P for QD versus TID

The objective of this study was to develop a population pharmacokinetic model and to determine a dosing regimen for caspofungin in critically ill patients. Nine blood samples were drawn per dosing occasion. Fifteen patients with (suspected) invasive candidiasis had one dosing occasion and five had two dosing occasions, measured on day 3 (±1) of treatment. Pmetrics was used for population pharmacokinetic modeling and probability of target attainment (PTA). A target 24-h area under the concentration-time curve (AUC) value of 98 mg·h/liter was used as an efficacy parameter., The objective of this study was to develop a population pharmacokinetic model and to determine a dosing regimen for caspofungin in critically ill patients. Nine blood samples were drawn per dosing occasion. Fifteen patients with (suspected) invasive candidiasis had one dosing occasion and five had two dosing occasions, measured on day 3 (±1) of treatment. Pmetrics was used for population pharmacokinetic modeling and probability of target attainment (PTA). A target 24-h area under the concentration-time curve (AUC) value of 98 mg·h/liter was used as an efficacy parameter. Secondarily, the AUC/MIC targets of 450, 865, and 1,185 were used to calculate PTAs for Candida glabrata, C. albicans, and C. parapsilosis, respectively. The final 2-compartment model included weight as a covariate on volume of distribution (V). The mean V of the central compartment was 7.71 (standard deviation [SD], 2.70) liters/kg of body weight, the mean elimination constant (Ke) was 0.09 (SD, 0.04) h−1, the rate constant for the caspofungin distribution from the central to the peripheral compartment was 0.44 (SD, 0.39) h−1, and the rate constant for the caspofungin distribution from the peripheral to the central compartment was 0.46 (SD, 0.35) h−1. A loading dose of 2 mg/kg on the first day, followed by 1.25 mg/kg as a maintenance dose, was chosen. With this dose, 98% of the patients were expected to reach the AUC target on the first day and 100% of the patients on the third day. The registered caspofungin dose might not be suitable for critically ill patients who were all overweight (≥120 kg), over 80% of median weight (78 kg), and around 25% of lower weight (≤50 kg). A weight-based dose regimen might be appropriate for achieving adequate exposure of caspofungin in intensive care unit patients.

BackgroundVancomycin induces exposure-related acute kidney injury. However, the pharmacokinetic-toxicodynamic (PK-TD) relationship remains unclear.MethodsSprague-Dawley rats received IV vancomycin doses of 300mg/kg/day and 400mg/kg/day, divided once, twice, thrice or 4xdaily (i.e., QD, BID, TID or QID) over 24-hours. Up to 8-samples were drawn during the 24-hour dosing period. Twenty-four-hour urine was collected and assayed for kidney injury molecule-1 (KIM-1). Vancomycin was quantified via LC-MS/MS. Following terminal sampling, nephrectomy and histopathologic analyses were conducted. PK analyses were conducted using Pmetrics. PK exposures (i.e. AUC0-24h, CMAX0-24h,) were calculated for each rat, and PK-TD relationships were discerned.ResultsA total of 53-rats generated PK-TD data. A 2-compartment model fit the data well (Bayesian observed vs. predicted concentrations, R2=0.96). KIM-1 values were greater in QD and BID groups (P-values: QD vs TID:0–24h and AUC0-24h (R2□=□ 0.7 and 0.68). Corrected Akaike’s information criterion showed CMAX0-24h as most predictive PK-TD driver for vancomycin-induced kidney injury (VIKI) (−5.28 versus −1.95).ConclusionsWhile PK-TD indices are often inter-correlated, maximal concentrations and fewer doses (for the same total daily amount) resulted in increased VIKI in our rat model.

Given that aminoglycosides, such as amikacin, may be used for multi-drug resistant infections, optimization of therapy is paramount for improved treatment outcomes. This study aims to investigate the pharmacodynamics of different simulated intravenous amikacin doses on susceptible to inform ventilator-associated pneumonia and sepsis treatment choices.A hollow-fibre infection model with two isolates (MIC 2 and 8 mg/L) with an initial inoculum ∼10 colony-forming unit/mL was used to test different amikacin dosing regimens. Three regimens (15, 25 and 50 mg/kg) simulating a blood exposure and a 30 mg/kg regimen simulating the epithelial lining fluid (ELF) for potential respiratory tract infection were tested. Data were described using a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model. Whole genome sequencing was used to identify mutations associated with resistance emergence.While bacterial density was reduced by >6-logs within the first 12 h in simulated blood exposures, following this initial bacterial kill, there was amplification of a resistant sub-population with ribosomal mutations that were likely mediating amikacin resistance. No appreciable bacterial killing occurred with subsequent doses. There was less (

Cefepime is commonly used in the intensive care unit (ICU) to treat bacterial infections. The time during which the free cefepime concentration is above the MIC (fT(>MIC)) should be optimized to increase the efficacy of the regimen. We aim to optimize the exposure of cefepime in ICU patients by using population pharmacokinetic (PK) modeling and simulations. Two data sets were included in this study. The first was a prospective study of pediatric patients who received cefepime at 50 mg/kg of body weight and had extensive PK sampling. The second study comprised retrospective data for adult ICU patients admitted to UF Health Shands Hospital who received cefepime and had their cefepime concentrations measured. The population PK model was developed, and simulations were performed, using Pmetrics. The target exposures were 100% fT(>MIC) and 100% fT(>4×MIC). The studies included a total of 266 patients, and the mean ages were 3.9 years in the pediatric group and 55 years in adult group. More than half of the patients were males. The mean (standard deviation [SD]) creatinine clearance (CrCl) was 125 (93) ml/min. The mean (SD) daily dose for adults was 4.9 (1.6) g. Cefepime was well described by a two-compartment model with weight as a covariate on the volume of distribution and elimination rate constant (k(el)), and CrCl and age group as covariates on k(el). At a MIC of 8 mg/liter, a cefepime loading dose of 4 g as an extended infusion followed by a 6-g continuous infusion was needed for good target attainment. In conclusion, prolonged or continuous infusions will be needed to achieve optimal cefepime exposure for ICU patients. Given the observed variability, therapeutic drug monitoring can help individualize therapy.

Objectives Area under the time–concentration curve (AUC) -guided dosing provides better estimates of exposure than vancomycin trough concentrations. Though clinical benefits have been reported, the costs of AUC-guided dosing are uncertain. The objective of this study was to quantify the costs of single-sample Bayesian or two-sample AUC strategies versus trough-guided dosing. Methods A cost–benefit analysis from the institutional perspective was conducted using a decision tree to model the probabilities and costs of acute kidney injury (AKI) associated with vancomycin administered over 48 hours up to 21+ days. Costs included vancomycin concentrations, Bayesian software and AKI hospitalization costs, and probabilities were obtained from primary literature. Robustness was assessed via both one-way and probabilistic sensitivity analyses. Results In the base-case model, two-sample AUC versus trough dosing saved an average of US$ 846 per patient encounter, and single-sample Bayesian AUC versus trough dosing saved an average of US$ 2065 per patient encounter. This translates into annual cost-savings of US$ 846 810 and US$ 2 065 720 for two-sample and single-sample Bayesian methods versus trough dosing, respectively, assuming 1000 vancomycin-treated patients per year. Assuming a budget of US$ 100 000 per year for Bayesian software, an institution would need to treat ≥41 patients with vancomycin for at least 48 hours to break even. Conclusions There are significant institutional cost benefits using two-sample AUC or single-sample Bayesian methods over trough dosing, even after accounting for the annual costs of Bayesian programs. The potential to decrease rates of AKI, improve clinical outcomes and reduce costs to the institution strongly warrants consideration of improved dosing methods for vancomycin.

Multidrug therapy is often required. Examples include antiviral therapy, nosocomial infections, and, most commonly, anti-Mycobacterium tuberculosis therapy. Our laboratory previously identified a mathematical approach to identify 2-drug regimens with a synergistic or additive interaction using a full factorial study design. Our objective here was to generate a method to identify an optimal 3-drug therapy. We studied M. tuberculosis isolate H37Rv in log-phase growth in flasks. Pretomanid and moxifloxacin were chosen as the base 2-drug regimen. Bedaquiline (plus M2 metabolite) was chosen as the third drug for evaluation. Total bacterial burden and bacterial burden less-susceptible to study drugs were enumerated. A large mathematical model was fit to all the data. This allowed extension to evaluation of the 3-drug regimen by employing a Monte Carlo simulation. Pretomanid plus moxifloxacin demonstrated excellent bacterial kill and suppressed amplification of less-susceptible pathogens. Total bacterial burden was driven to extinction in 3 weeks in 6 of 9 combination therapy evaluations. Only the lowest pretomanid/moxifloxacin exposures in combination did not extinguish the bacterial burden. No combination regimen allowed resistance amplification. Generation of 95% credible intervals about estimates of the interaction parameters α (α(s), α(r-p), and α(r-m)) by bootstrapping showed the interaction was near synergistic. The addition of bedaquiline/M2 metabolite was evaluated by forming a 95% confidence interval regarding the decline in bacterial burden. The addition of bedaquiline/M2 metabolite shortened the time to eradication by 1 week and was significantly different. A model-based system approach to evaluating combinations of 3 agents shows promise to rapidly identify the most promising combinations that can then be trialed.

Given that aminoglycosides, such as amikacin, may be used for multidrug-resistant Pseudomonas aeruginosa infections, optimization of therapy is paramount for improved treatment outcomes. This study aims to investigate the pharmacodynamics of different simulated intravenous amikacin doses on susceptible P. aeruginosa to inform ventilator-associated pneumonia (VAP) and sepsis treatment choices. A hollow-fiber infection model with two P. aeruginosa isolates (MICs of 2 and 8 mg/liter) with an initial inoculum of ∼10(8) CFU/ml was used to test different amikacin dosing regimens. Three regimens (15, 25, and 50 mg/kg) were tested to simulate a blood exposure, while a 30 mg/kg regimen simulated the epithelial lining fluid (ELF) for potential respiratory tract infection. Data were described using a semimechanistic pharmacokinetic/pharmacodynamic (PK/PD) model. Whole-genome sequencing was used to identify mutations associated with resistance emergence. While bacterial density was reduced by >6 logs within the first 12 h in simulated blood exposures following this initial bacterial kill, there was amplification of a resistant subpopulation with ribosomal mutations that were likely mediating amikacin resistance. No appreciable bacterial killing occurred with subsequent doses. There was less (

Busulfan therapeutic drug monitoring (TDM) is necessary to better achieve the target exposure in children before hematopoietic stem cell transplantation (HSCT). However, TDM-based dosing may be challenging if intra-individual pharmacokinetic variability (also denoted inter-occasion variability [IOV]) occurs during therapy. The objectives of this study were to describe and quantify busulfan IOV in children, and to investigate its potential determinants. We performed a new analysis of published data from children who received intravenous busulfan over 4 days before HSCT. We calculated individual pharmacokinetic parameters on each day of therapy using a published population pharmacokinetic model of busulfan and analyzed their changes. Population estimation of IOV was also performed with non-linear mixed effects (NLME) modeling. Potential predictors of significant decrease in busulfan clearance (CL) were assessed by using machine learning approaches. IOV could be assessed in 136 children. Between day (D) 1 and D2, most patients (80%) experienced a decrease in busulfan CL, with a median change of − 7.9%. However, both large decreases (minimum, − 48.5%) and increases in CL (maximum, + 44%) were observed. Over D1–D3 of therapy, mean CL significantly decreased (− 15%), with a decrease of ≥ 20% in 22% of patients. Some patients also showed unstable CL from day to day. NLME modeling of IOV provided a coefficient of variation of 10.6% and 13.1% for volume of distribution (Vd) and CL, respectively. Some determinants of significant decreases in busulfan CL were identified, but predictive performance of the models was limited. Significant busulfan intra-individual variability may occur in children who receive a HSCT and is hardly predictable. The main risk is busulfan overexposure. Performing TDM repeatedly over therapy appears to be the best way to accurately estimate busulfan exposure and perform precision dosing.

Population pharmacokinetic (PK) modeling is a widely used approach to analyze PK data obtained from groups of individuals, in both industry and academic research. The approach can also be used to analyze pharmacodynamic (PD) data and pooled PK/PD data. There are 2 main families of population PK methods: parametric and nonparametric. The objectives of this article are to present an overview of nonparametric methods used in population pharmacokinetic modeling and to explain their specific characteristics to inform scientists and clinicians about their potential value for data analysis, simulation, dosage design, and therapeutic drug monitoring (TDM). Nonparametric methods have several interesting characteristics for population PK analysis, including computation of exact likelihoods, the ability to accommodate parameter probability distributions of any shape (eg, non-Gaussian), and to detect subpopulations and outliers. Nonparametric population methods are also highly relevant for model-based TDM and design of individualized drug dosage regimens. Several algorithms have been developed to estimate model parameter values within an individual and compute that individual's dosage to achieve target drug exposure with maximum precision and accuracy. Nonparametric modeling methods for both population and individual PK analysis are available under user-friendly packages.

Background The clinical value of therapeutic drug monitoring can be increased most significantly by integrating assay results into clinical pharmacokinetic models for optimal dosing. The correct weighting in the modeling process is 1/variance, therefore, knowledge of the standard deviations (SD) of each measured concentration is important. Because bioanalytical methods are heteroscedastic, the concentration-SD relationship must be modeled using assay error equations (AEE). We describe a methodology of establishing AEE’s for liquid chromatography-tandem mass spectrometry (LC-MS/MS) drug assays using carbamazepine, fluconazole, lamotrigine and levetiracetam as model analytes. Methods Following method validation, three independent experiments were conducted to develop AEE’s using various least squares linear or nonlinear, and median-based linear regression techniques. SD’s were determined from zero concentration to the high end of the assayed range. In each experiment, precision profiles of 6 (“small” sample sets) or 20 (“large” sample sets) out of 24 independent, spiked specimens were evaluated. Combinatorial calculations were performed to attain the most suitable regression approach. The final AEE’s were developed by combining the SD’s of the assay results, established in 24 specimens/spiking level and using all spiking levels, into a single precision profile. The effects of gross hyperbilirubinemia, hemolysis and lipemia as laboratory interferences were investigated. Results Precision profiles were best characterized by linear regression when 20 spiking levels, each having 24 specimens and obtained by performing 3 independent experiments, were combined. Theil’s regression with the Siegel estimator was the most consistent and robust in providing acceptable agreement between measured and predicted SD’s, including SD’s below the lower limit of quantification. Conclusions In the framework of precision pharmacotherapy, establishing the AEE of assayed drugs is the responsibility of the therapeutic drug monitoring service. This permits optimal dosages by providing the correct weighting factor of assay results in the development of population and individual pharmacokinetic models.

When a drug has a narrow margin of safety and requires therapeutic drug monitoring or management, we must keep its dosage not too low, but not too high. We will see more clearly what this means as we go on. If it is too low, the result may be a poor effect. If too high, the patient may become toxic. We must carefully individualize the dosage for each patient, to achieve most precisely a specific target goal (not a range!) which we judge is clinically most appropriate for his or her unique need, such as a serum concentration or effect (a target prothrombin time or INR, for example, and not just one range for everyone, as is the case now), its profile over time, a desired area under the serum concentration curve, or the response of a biomarker.

Purpose This Position Paper aims to review and discuss the available data on therapeutic drug monitoring (TDM) of antibacterials, antifungals and antivirals in critically ill adult patients in the intensive care unit (ICU). This Position Paper also provides a practical guide on how TDM can be applied in routine clinical practice to improve therapeutic outcomes in critically ill adult patients. Methods Literature review and analysis were performed by Panel Members nominated by the endorsing organisations, European Society of Intensive Care Medicine (ESICM), Pharmacokinetic/Pharmacodynamic and Critically Ill Patient Study Groups of European Society of Clinical Microbiology and Infectious Diseases (ESCMID), International Association for Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) and International Society of Antimicrobial Chemotherapy (ISAC). Panel members made recommendations for whether TDM should be applied clinically for different antimicrobials/classes. Results TDM-guided dosing has been shown to be clinically beneficial for aminoglycosides, voriconazole and ribavirin. For most common antibiotics and antifungals in the ICU, a clear therapeutic range has been established, and for these agents, routine TDM in critically ill patients appears meritorious. For the antivirals, research is needed to identify therapeutic targets and determine whether antiviral TDM is indeed meritorious in this patient population. The Panel Members recommend routine TDM to be performed for aminoglycosides, beta-lactam antibiotics, linezolid, teicoplanin, vancomycin and voriconazole in critically ill patients. Conclusion Although TDM should be the standard of care for most antimicrobials in every ICU, important barriers need to be addressed before routine TDM can be widely employed worldwide. Electronic supplementary material The online version of this article (10.1007/s00134-020-06050-1) contains supplementary material, which is available to authorized users.

Background Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results. Methods Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m2 (interquartile range 104–124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software. Results For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (Ke). The parametric population parameter estimates were Ke 0.637 h−1 (between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (Vc) 29.6 L (without BSV). The nonparametric values were Ke 0.681 h−1 (34.0% CV) and Vc 31.1 L (42.6% CV). Conclusions Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies. Electronic supplementary material The online version of this article (10.1007/s40262-020-00859-1) contains supplementary material, which is available to authorized users.

OBJECTIVES The objectives of this study were to 1) define extent and potential clinical impact of increased or decreased renal elimination of meropenem in children with sepsis, based on analysis of renal function during the first 2 days of PICU stay; and 2) estimate the risk of subtherapeutic meropenem exposure attributable to increased renal clearance. METHODS This retrospective study evaluated patients with a diagnosis of sepsis, receiving meropenem from the PICU at Rady Children's Hospital San Diego from 2015–2017. Meropenem exposure was estimated by using FDA-approved doses (20 and 40 mg/kg/dose) on day 1 and day 2 of PICU stay, based on a population pharmacokinetic (PK) model. For this population with sepsis, we assessed time-above-minimum inhibitory concentration (T>MIC) for pathogen MICs. RESULTS Meropenem treatment was documented in 105 episodes of sepsis with a 48% rate of pathogen detection. By day 2, increased eGFR (>120 mL/min/1.73 m2) was documented in 49% of patients, with 17% meeting criteria for augmented renal clearance ([ARC] >160 mL/min/1.73 m2) and 10%, for decreased function. Simulations documented that 80% of PICU patients with ARC did not achieve therapeutic meropenem exposure for Pseudomonas aeruginosa with a MIC of 2, using standard doses to achieve a pharmacodynamic goal of 80% T>MIC. CONCLUSIONS Approximately 3 of every 20 children with sepsis exhibited ARC during the first 48 hours of PICU stay. Simulations documented an increased risk for subtherapeutic meropenem exposure, suggesting that higher meropenem doses may be required to achieve adequate antibiotic exposure early in the PICU course.

Veno-occlusive disease (VOD) is a severe adverse reaction to busulfan-containing regimens used in the preparation of children for hematopoietic stem cell transplantation (HSCT). We conducted a retrospective analysis of data to examine determinants of VOD in children who received IV busulfan for HSCT conditioning. Busulfan PK parameters as well as various indices (maximal concentration—Cmax, area under the concentration-time curve—AUC) were estimated using a validated Bayesian approach. The influence of available PK, demographic, and clinical variables on the incidence of VOD was evaluated by using logistic regression and classification and regression tree (CART) analyses. Among the 293 patients included, the mean age was 6.5 years and the mean actual body weight was 26.3 kg. The incidence of VOD was 25.6%. Busulfan Cmax as well as weight

OBJECTIVES:To identify factors associated with longer length of stay (LOS) and higher 30-day hospital revisit rates for children hospitalized with bacterial tracheostomy–associated respiratory tract infections (bTARTIs).METHODS:This was a multicenter, retrospective cohort study using administrative data from the Pediatric Health Information System database between 2007 and 2014 of patients 30 days to 17 years old with a principal discharge diagnosis of bTARTI or a principal discharge diagnosis of bTARTI symptoms with a secondary diagnosis of bTARTI. Primary outcomes of LOS (in days) and 30-day all-cause revisit rates (inpatient, observation, or emergency department visit) were analyzed by using a 3-level hierarchical regression model (discharges within patients within hospital).RESULTS:We included 3715 unique patients and 7355 discharges. The median LOS was 4 days (interquartile range: 3–8 days), and the 30-day revisit rate was 30.5%. Compared with children 1 to 4 years old, children aged 30 days to 12 months had both longer LOS (adjusted length of stay [aLOS] = +0.9 days; 95% confidence interval [CI]: 0.6 to 1.3) and increased hospital revisit risk (adjusted odds ratio [aOR] = 1.5; 95% CI: 1.3 to 1.7). Other factors associated with longer LOS included public insurance (aLOS = +0.5 days; 95% CI: 0.2 to 0.8), 3 or more complex chronic conditions (CCCs), mechanical ventilation (acute or chronic), and empirical anti-Pseudomonas aeruginosa antibiotics (aLOS = +0.6 days; 95% CI: 0.3 to 0.9). Other factors associated with 30-day revisit included 4 or more CCCs (aOR = 1.3; 95% CI: 1.1 to 1.6) and chronic ventilator dependency (aOR = 1.1; 95% CI: 1.0 to 1.3).CONCLUSIONS:Ventilator-dependent patients

OBJECTIVE: Identify characteristics associated with hospital readmission due to bacterial respiratory tract infections (bRTI) after tracheotomy. STUDY DESIGN: Retrospective study of 8009 children 0–17 years undergoing tracheotomy from 2007–2013 at 48 children’s hospitals in the Pediatric Health Information System database. The primary outcome was first hospital admission after tracheotomy for bRTI (i.e. primary diagnosis of bRTI or a primary diagnosis of bRTI symptom and secondary diagnosis of bRTI). We used Cox-proportional hazard modeling to assess associations between patient demographic and clinical characteristics and bRTI hospital readmission. RESULTS: Median age at tracheotomy admission was 5 months [interquartile range (IQR): 1–50 months]. Thirty-six percent (n=2,899) had at least one bRTI admission. Median time-to-readmission for bRTI was 275 days (IQR: 141–530). Factors independently associated with increased risk for bRTI readmission were younger age [e.g., age 2 complex chronic conditions (vs. zero; aHR 1.96; 95% CI: 1.34–2.86) and discharge to home (vs. post-acute care setting; aHR 1.19; 95% CI: 1.08–1.32). Trauma diagnosis at tracheotomy (aHR 0.83; 95% CI: 0.69–1) and ventilator dependency (aHR 0.88; 95% CI: 0.81–0.97) were associated with decreased risk. CONCLUSIONS: Young, Hispanic children with multiple complex chronic conditions who use Medicaid insurance and are not discharged to post-acute care are at the highest risk for hospital readmission for bRTI post-tracheotomy. Future research should investigate strategies to mitigate this risk for these children.

Jason Shafrin,1 Jeffrey Sullivan,1 Jacquelyn W Chou,1 Michael N Neely,2 Justin F Doan,3 J Ross Maclean1 1Precision Health Economics, LosAngeles, CA, USA; 2Department of Pediatrics, Keck School of Medicine, University of Southern California, LosAngeles, CA, USA; 3Worldwide Health Economics and Outcomes Research, Bristol-Myers Squibb, Princeton, NJ, USA Objective: To examine how observed medication nonadherence to 2 second-line, oral anticancer medications (axitinib and everolimus) affects progression-free survival (PFS) among patients with renal cell carcinoma. Methods: We used an adherence–exposure–outcome model to simulate the impact of adherence on PFS. Using a pharmacokinetic/pharmacodynamic (PK/PD) population model, we simulated drug exposure measured by area under the plasma concentration–time curve (AUC) and minimum blood or trough concentration (Cmin) under 2 scenarios: 1) optimal adherence and 2) real-world adherence. Real-world adherence was measured using the medication possession ratios as calculated from health insurance claims data. A population PK/PD model was simulated on individuals drawn from the Medical Expenditure Panel Survey (MEPS), a large survey broadly representative of the US population. Finally, we used previously published PK/PD models to estimate the effect of drug exposure (i.e., Cmin and AUC) on PFS outcomes under optimal and real-world adherence scenarios. Results: Average adherence measured using medication possession ratios was 76%. After applying our simulation model to 2164 individuals in MEPS, drug exposure was significantly higher among adherent patients compared with nonadherent patients for axitinib (AUC: 249.5 vs. 159.8 ng×h/mL, P

Objective Identify risk factors for readmission due to a bacterial tracheostomy-associated respiratory tract infection (bTARTI) within 12 months of discharge after tracheotomy. Design/Methods We performed a retrospective cohort study of 240 children who underwent tracheotomy and were discharged with tracheotsomy in place between January 1, 2005 and June 30, 2013. Children with prolonged total or post-tracheotomy length of stay (LOS), less than 12 months of follow-up, or who died during the index hospitalization were excluded. Readmission for a bTARTI (eg, pneumonia, tracheitis) treated with antibiotics, as ascertained by manual chart review, was the outcome variable. We used multivariate logistic regression to identify the independent association between risk factors and hospital readmission for bTARTI within 12 months. Results At index hospitalizations for tracheotomy, the median admission age was 5 months (interquartile range [IQR] 2-43 months) and median LOS was 73 days (IQR 43-121 days). Most patients were of Hispanic ethnicity (n = 162, 68%) and were publicly insured (n = 213, 89%). Nearly half (n = 112, 47%) were discharged on positive pressure mechanical ventilation. Many (n = 103, 43%) were admitted for bTARTI within 12 months of discharge. Only Hispanic ethnicity (adjusted odds ratio [AOR] 2.0; 95% confidence interval [CI]: 1.1-3.9; P = 0.03) and acquisition of Pseudomonas aeruginosa between tracheotomy and discharge from index hospitalization (AOR 3.2; 95%CI: 1.2-8.3; P = 0.02) were independently associated with increased odds of bTARTI readmission, while discharge on gastrointestinal pro-motility agents was associated with decreased risk (AOR = 0.4; 95%CI: 0.2-0.8; P = 0.01). Conclusions Hispanic ethnicity and post-tracheotomy acquisition of P. aeruginosa during initial hospitalization are associated with bTARTI readmission.

Monte Carlo simulations allow prediction and comparison of concentration-time profiles arising from different dosing regimens in a defined population, provided a population pharmacokinetic model has been established. The aims of this study were to evaluate the population pharmacokinetics of imipenem in critically ill patients with hospital-acquired pneumonia (HAP) and to assess the probability of target attainment (PTA) and cumulative fraction of response (CFR) using EUCAST data. A two-compartment model based on a data set of 19 subjects was employed. Various dosage regimens at 0.5-h and 3-h infusion rates and as continuous infusion were evaluated against the pharmacodynamic targets of 20%fT>MIC, 40%fT>MIC and 100%fT>MIC. For the target of 40%fT>MIC, all 0.5-h infusion regimens achieved optimal exposures (CFR ≥ 90%) against Escherichia coli and Staphylococcus aureus, with nearly optimal exposure against Klebsiella pneumoniae (CFR ≥ 89.4%). The 3-h infusions and continuous infusion exceeded 97% CFR against all pathogens with the exception of Pseudomonas aeruginosa and Acinetobacter spp., where the maximum CFRs were 85.5% and 88.4%, respectively. For the 100%fT>MIC target, only continuous infusion was associated with nearly optimal exposures. Higher PTAs for the targets of 40%fT>MIC and 100%fT>MIC were achieved with 3-h infusions and continuous infusion in comparison with 0.5-h infusions; however, continuous infusion carries a risk of not reaching the MIC of less susceptible pathogens in a higher proportion of patients. In critically ill patients with HAP with risk factors for Gram-negative non-fermenting bacteria, maximum doses administered as extended infusions may be necessary.

Background Isoniazid (INH) is a first-line antituberculosis (TB) agent with a pharmacokinetic profile characterized by high interindividual variation; however, population pharmacokinetic studies in patients with TB are scarce. The aim was to develop a population model for INH in Colombian patients with TB suitable for predicting drug exposure and assessing the probability of target attainment of pharmacodynamic goals. Methods Ten hospitalized adult patients with TB undergoing INH treatment were recruited. After an 8-hour fasting, subjects took 300 mg of INH, and 10 samples were taken from 0 to 12 hours. INH was quantified by high-performance liquid chromatography-UV, and data were analyzed with the Pmetrics R package software. A Monte Carlo simulation with the model parameters was run to determine the probability of target attainment for optimal efficacy. Results The best model included 2 compartments, first-order absorption (Ka), delayed absorption (Tlag), and linear clearance (CL). Median Tlag was 0.25 hours, 5.54 hour for Ka, (Equation is included in full-text article.)for CL, (Equation is included in full-text article.)for the volume of the central compartment (Vc), 1.04 L/h for intercompartmental clearance (Q), and 788 L for the volume of the peripheral compartment (Vp). CL and Vc were allometrically scaled on basis of the normalized body weight. Conclusions The Monte Carlo simulation indicated that 300 mg of INH per day is appropriate for Mycobacterium tuberculosis strains with minimal inhibitory concentration (MIC) up to 0.03 mg/L (target: area under the concentration-time curve/MIC >597); however, to cover strains with MIC up to 0.125 mg/L (80% of clinical isolates), a dose of 900 mg per day would be required.

Background Resistance in Neisseria gonorrhoeae to all therapeutic antimicrobials for gonorrhoea has emerged. Novel antimicrobials for treatment are imperative and the first-in-class spiropyrimidinetrione zoliflodacin appears promising. Zoliflodacin could be introduced in dual antimicrobial therapies to prevent the emergence and/or spread of resistance. We investigated the in vitro activity and induction/selection of resistance to zoliflodacin alone and in combination with six novel, currently or previously used therapeutic antimicrobials against N. gonorrhoeae. Methods The international gonococcal reference strains examined were WHO F (wild-type), and WHO O, WHO V, and WHO X (strains with different AMR profiles). Zoliflodacin was evaluated alone or in combination with ceftriaxone, spectinomycin, gentamicin, tetracycline, cethromycin, and sitafloxacin in checkerboard assays, time-kill curve analysis, and induction/selection of resistance studies. Results Zoliflodacin alone or in combination with all six antimicrobials showed rapid rates of in vitro bacterial killing against all examined strains in time-kill studies. Tetracycline or cethromycin combined with zoliflodacin decreased the rate of zoliflodacin growth inhibition, while ceftriaxone or gentamicin increased the rate of cell killing. The frequency of induced/selected zoliflodacin resistance mutations was low for zoliflodacin and further reduced for all antimicrobial combinations. All resistant mutants contained the GyrB mutations D429N, K450T or K450N, resulting in zoliflodacin MICs of 0.5–4 mg/L consistent with previous results. Conclusion Zoliflodacin, alone or in combination with STI therapeutic antimicrobials has a rapid and high in vitro efficacy against gonococci with low resistance emergence. Zoliflodacin remains a promising novel oral therapeutic for gonorrhoea monotherapy and as part of dual antimicrobial therapy with low resistance emergence potential. A phase III clinical trial evaluating efficacy and safety of zoliflodacin for uncomplicated gonorrhoea treatment is planned in 2019. Disclosure No significant relationships.

ObjectivesResistance in Neisseria gonorrhoeae to all gonorrhoea therapeutic antimicrobials has emerged. Novel therapeutic antimicrobials are imperative and the first-in-class spiropyrimidinetrione zoliflodacin appears promising. Zoliflodacin could be introduced in dual antimicrobial therapies to prevent the emergence and/or spread of resistance. We investigated the in vitro activity of and selection of resistance to zoliflodacin alone and in combination with six gonorrhoea therapeutic antimicrobials against N. gonorrhoeae.MethodsThe international gonococcal reference strains WHO F (WT) and WHO O, WHO V and WHO X (strains with different AMR profiles) were examined. Zoliflodacin was evaluated alone or combined with ceftriaxone, cefixime, spectinomycin, gentamicin, tetracycline, cethromycin or sitafloxacin in chequerboard assays, time–kill curve analysis and selection-of-resistance studies.ResultsZoliflodacin alone or in combination with all six antimicrobials showed rapid growth inhibition against all examined strains. The time–kill curve analysis indicated that tetracycline or cethromycin combined with zoliflodacin can significantly decrease the zoliflodacin kill rate in vitro. The frequency of selected zoliflodacin-resistance mutations was low when evaluated as a single agent and further reduced for all antimicrobial combinations. All resistant mutants contained the GyrB mutations D429N, K450T or K450N, resulting in zoliflodacin MICs of 0.5–4 mg/L.ConclusionsZoliflodacin, alone or in combination with sexually transmitted infection therapeutic antimicrobials, rapidly kills gonococci with infrequent resistance emergence. Zoliflodacin remains promising for gonorrhoea oral monotherapy and as part of dual antimicrobial therapy with low resistance emergence potential. A Phase III trial evaluating efficacy and safety of zoliflodacin for uncomplicated gonorrhoea treatment is planned in 2019.

Voriconazole is a first-line antifungal agent. Therapeutic drug monitoring is a standard of care., Voriconazole is a first-line antifungal agent. Therapeutic drug monitoring is a standard of care. The best way to adjust dosages to achieve desired drug exposure endpoints is unclear due to nonlinear and variable pharmacokinetics. Previously described software was used to prospectively adjust voriconazole dosages. The CYP2C19, CYP3A4, and CYP3A5 genotypes were determined. The primary endpoint was the proportion of patients with a Cmin at 120 h in the range 1 to 3 mg/liter using software to adjust voriconazole dosages. A total of 19 patients were enrolled, and 14 were evaluable. Of these, 12/14 (85.7%; 95% confidence interval = 57.2 to 98.2%) had a Cmin at 120 h posttreatment initiation of 1 to 3 mg/liter, which was higher than the a priori expected proportion of 33%. There was no association of CYP genotype-derived metabolizer phenotype with voriconazole AUC. Software can be used to adjust the dosages of voriconazole to achieve drug exposures that are safe and effective. (The clinical trial discussed in this paper has been registered in the European Clinical Trials Database under EudraCT no. 2013-0025878-34 and in the ISRCTN registry under no. ISRCTN83902726.)

BACKGROUND: Early administration of anti-influenza medications is recommended for all children hospitalized with influenza. We investigated whether early use of anti-influenza medications is associated with improved outcomes in children with tracheostomy hospitalized with influenza. METHODS: We performed a multicenter retrospective cohort study through the Pediatric Health Information System database for patients aged 30 days to 19 years who were discharged between October 1, 2007, and September 30, 2015 with diagnostic codes for both influenza and tracheostomy. Our primary predictor was receipt of anti-influenza medications on hospital day 0 or 1. We used propensity score matching to adjust for confounding by indication. Primary outcomes were length of stay (LOS) and 30-day all-cause revisit rate (emergency department visit or hospital admission). RESULTS: Of 1436 discharges screened, 899 met inclusion criteria. The median admission age was 5 years (interquartile range: 2–10). The majority had multiple complex chronic conditions (median 3; interquartile range: 3–4) and technology dependence, such as gastrostomy tube (73.6%). After matching 772 unique admissions by propensity score, LOS was shorter for the cohort receiving early anti-influenza medications (6.4 vs 7.5 days; P = .01) without increase in revisit rate (27.5% vs 24.1%; P = .28). More than 80% in both cohorts received empirical antibiotics, and the duration of antibiotic therapy was similar (5.0 vs 5.6 days; P = .11). CONCLUSIONS: Early use of anti-influenza medications in children with tracheostomy hospitalized with influenza is associated with shorter LOS, but these children continue to receive antibiotics despite identification and treatment of their viral infections.

International audience; The 2019 Nîmes International Symposium in Antibiotic Therapy Optimisation aimed at determining the best approaches of a number of the antibiotic management strategies for critically ill patients. Experts reviewed the latest literature relating to requirements for an optimal antibiotic stewardship program, risks of sub-therapeutic dosing of antibiotics in critically ill patients, persisting issues about efficiency of combination therapy and the value of de-escalation, new perspectives of pharmacokinetics, drug toxicities including collateral damages-associated with antibiotics, the place of nebulisation of antibiotics, management of patients receiving extracorporeal therapies and the place of new antibiotics. In this paper, each of these issues is discussed with key messages presented after a brief review of evidence.

A major goal for improving tuberculosis therapy is to identify drug regimens with improved efficacy and shorter treatment durations. Shorter therapies improve patient adherence to the antibiotic regimens that, in turn, decreases resistance emergence. M. tuberculosis exists in multiple metabolic states. At the initiation of therapy, the bulk of the population is in Log-Phase growth. Consequently, it is logical to focus initial therapy on these organisms. Moxifloxacin has good early bactericidal activity against Log-Phase growth bacteria and is a logical component of initial therapy. It would be optimal if this agent also possessed activity against Acid-Phase and Non-Replicative Persister (NRP) Phenotype organisms. We studied multiple exposures to moxifloxacin (equivalent to 200 mg-800 mg daily) in our Hollow Fiber Infection Model against strain H37Rv in Acid-Phase and against strain 18b in streptomycin starvation, which is a model for NRP-Phase organisms. Moxifloxacin possesses good activity against Acid-Phase organisms, generating from 3.75 Log 10 (CFU/ml) cell kill (200 mg daily) to 5.16 Log 10 (CFU/ml) cell kill (800 mg daily) over the 28 days of the experiment. Moxifloxacin also has activity against the streptomycin-starved strain 18b. The 400 to 800 mg daily regimens achieved extinction at day 28, while the no-treatment control still had 1.96 Log 10 (CFU/ml) culturable. The lowest dose (200 mg daily) still had 0.7 Log 10 (CFU/ml) measurable at day 28, a net kill of 1.26 Log 10 (CFU/ml). Moxifloxacin is an attractive agent for early therapy, as it possesses activity against three metabolic states of M. tuberculosis .

Vancomycin has been associated with acute kidney injury (AKI). However, the pharmacokinetic/toxicodynamic relationship for AKI is not well defined. Allometrically scaled vancomycin exposures were used to assess the relationship between vancomycin exposure and AKI. Male Sprague-Dawley rats received clinical-grade vancomycin in normal saline (NS) as intraperitoneal (i.p.) injections for 24- to 72-h durations with doses ranging 0 to 200 mg/kg of body weight divided once or twice daily. Urine was collected over the protocol's final 24 h. Renal histopathology was qualitatively scored. Urinary biomarkers (e.g., cystatin C, clusterin, kidney injury molecule 1 [KIM-1], osteopontin, lipocalin 2/neutrophil gelatinase-associated lipocalin 2) were assayed using a Luminex xMAP system. Plasma vancomycin concentrations were assayed by high-performance liquid chromatography with UV detection. A three-compartment vancomycin pharmacokinetic model was fit to the data with the Pmetrics package for R. The exposure-response in the first 24 h was evaluated using Spearman's nonparametric correlation coefficient ( r s ) values for the area under the concentration-time curve during the first 24 h (AUC 0–24 ), the maximum concentration in plasma during the first 24 h (C max 0–24 ), and the lowest (minimum) concentration in plasma after the dose closest to 24 h (C min 0–24 ). A total of 52 rats received vancomycin ( n = 42) or NS ( n = 10). The strongest exposure-response correlations were observed between AUC 0–24 and C max 0–24 and urinary AKI biomarkers. Exposure-response correlations ( r s values) for AUC 0–24 , C max 0–24 , and C min 0–24 were 0.37, 0.39, and 0.22, respectively, for clusterin; 0.42, 0.45, and 0.26, respectively, for KIM-1; and 0.52, 0.55, and 0.42, respectively, for osteopontin. However, no differences in histopathological scores were observed. Optimal sampling times after administration of the i.p. dose were 0.25, 0.75, 2.75, and 8 h for the once-daily dosing schemes and 0.25, 1.25, 14.5, and 17.25 h for the twice-daily dosing schemes. Our observations suggest that AUC 0–24 or C max 0–24 correlates with increases in urinary AKI biomarkers.

Pharmacokinetics of meropenem differ widely in the critically ill population. It is imperative to maintain meropenem concentrations above the inhibitory concentrations for most of the interdose interval. A population pharmacokinetic/pharmacodynamic model was developed to determine the probability of target attainment for 3-hour and 30-minute infusion regimens in this population.This study was performed in an intensive care setting among adult patients who were initiated on meropenem at a dose of 1000 mg. Multiple blood specimens were collected at predetermined time points during the interdose period, and meropenem concentrations were measured using high performance liquid chromatography. Using Pmetrics, a pharmacokinetic/pharmacodynamic model was developed and validated. Monte Carlo simulation was performed, and probability of target attainment (100% Tminimum inhibitory concentration (MIC), with a probability0.9) for doubling MICs was determined for different regimens of meropenem.A 2-compartment multiplicative gamma error model best described the population parameters from 34 patients. The pharmacokinetic parameters used in the final model were Ke (elimination rate constant from the central compartment), Vc (volume of distribution of central compartment), KCP and KPC (intercompartmental rate constants), and IC2 (the fitted amount of meropenem in the peripheral compartment). Inclusion of creatinine clearance (CLcreat) and body weight as covariates improved the model prediction (Ke = Ke0 × (Equation is included in full-text article.), Vc = Vc0 × Weight). The Ke and Vc [geometric mean (range)] of the individuals were 0.54 (0.01-2.61)/h and 9.36 (4.35-21.62) L, respectively. The probability of attaining the target, TMIC of 100%, was higher for 3-hour infusion regimens compared with 30-minute infusion regimens for all ranges of CLcreat.This study emphasizes that extended regimens of meropenem are preferable for treating infections caused by bacteria with higher MICs. The nonparametric analysis using body weight and CLcreat as covariate adequately predicted the pharmacokinetics of meropenem in critically ill patients with a wide range of renal function.

Differences in physiology related to young or old age and/or organ system impairment alter the absorption, distribution, metabolism, and excretion of many medications and consequently their effectiveness and toxicity. This module discusses common alterations in medication use and dosage that are required in the pediatric age group, in the elderly, and in patients with renal or hepatic disease.

Voriconazole is the agent of choice for the treatment of invasive aspergillosis in children at least 2 years of age. The galactomannan index is a routinely used diagnostic marker for invasive aspergillosis and can be useful for following the clinical response to antifungal treatment. The aim of this study was to develop a pharmacokinetic-pharmacodynamic (PK-PD) mathematical model that links the pharmacokinetics of voriconazole with the galactomannan readout in children. Twelve children receiving voriconazole for treatment of proven, probable, and possible invasive fungal infections were studied. A previously published population PK model was used as the Bayesian prior. The PK-PD model was used to estimate the average area under the concentration-time curve (AUC) in each patient and the resultant galactomannan-time profile. The relationship between the ratio of the AUC to the concentration of voriconazole that induced half maximal killing (AUC/EC 50 ) and the terminal galactomannan level was determined. The voriconazole concentration-time and galactomannan-time profiles were both highly variable. Despite this variability, the fit of the PK-PD model was good, enabling both the pharmacokinetics and pharmacodynamics to be described in individual children. (AUC/EC 50 )/15.4 predicted terminal galactomannan ( P = 0.003), and a ratio of >6 suggested a lower terminal galactomannan level ( P = 0.07). The construction of linked PK-PD models is the first step in developing control software that enables not only individualized voriconazole dosages but also individualized concentration targets to achieve suppression of galactomannan levels in a timely and optimally precise manner. Controlling galactomannan levels is a first critical step to maximizing clinical response and survival.

Introduction: We have reported that mouse and human adipocytes take up and metabolize the anthracycline, daunorubicin (DNR), reducing its concentration in the adipocyte microenvironments. This may contribute to anthracycline resistance for cancers, which reside in adipocyte rich environments such as omentum and bone marrow. Adipocytes express several carbonyl- and aldo-keto reductases (CBRs and AKRs) which metabolize and inactivate anthracyclines, and it is unclear which of these might be important targets to improve treatment outcome. Experimental Procedures: We knocked out AKR1C3 in the human preadipocyte cell line Chub S7 using CRISPR/Cas9 gene editing technology. We chose AKR1C3 first as it is one of the overexpressed enzymes in adipocytes with the highest anthracycline-metabolizing activity. We delivered ribonucleoprotein complexes of CRISPR-Cas9 enzyme plus guide RNAs by nucleofection. Then we established single-cell derived clones and tested for successful KO by Western blot. Finally, we quantified adipocyte lysate AKR activity using a colorimetric assay based on NADPH-dependent reduction of phenanthrenequinone. Data Summary: We chose three Chub S7 preadipocyte clones that demonstrated successful AKR1C3 knockout based on almost undetectable protein expression by western blot. AKR activity was significantly reduced in all three clones; control preadipocytes had 44±4.7, while clones had activity of 28±7.3, 33±3.4, and 35±5.7 pmol/min/µg (p Conclusions: These findings demonstrate that AKR1C3 knockout can be successfully done in human preadipocytes using a CRISPR-Cas9 system. Knockout of AKR1C3 significantly reduces overall aldoketoreductase activity in preadipocyte cells. This implies that a substantial portion of preadipocyte AKR activity is dependent on the isoenzyme, AKR1C3. Future testing is needed to determine whether AKR1C3 KO will reduce the clearance of anthracyclines from the cancer microenvironment, and may represent a treatment target to enhance anthracycline cytotoxicity. Citation Format: Vladislava Paharkova, Etan Orgel, Michael Neely, Stan. Louie, Steven Mittelman. Targeting the anthracycline metabolizing enzyme AKR1C3 in adipocytes to improve cytotoxicity [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6059.

Treating high-density bacterial infections is a challenging clinical problem. We have a paucity of new agents that can address this problem. Pseudomonas aeruginosa is a particularly difficult pathogen to treat effectively because of the plethora of resistance mechanisms it carries. Fosfomycin is an agent discovered circa 40 years ago. Recently, it has been resurrected in the United States and studied for intravenous therapy. We hypothesized that, to maximize its utility, it would require combination chemotherapy when used in a clinical circumstance in high-bacterial-burden infections. We chose to examine the combination of meropenem plus fosfomycin. These agents were studied in the hollow-fiber infection model. We utilized a fully factorial study design, looking at 2 doses of meropenem alone (1 and 2 g 8-hourly) and two doses of fosfomycin alone (6 and 8 g 8-hourly), as well as all possible combinations plus a no-treatment control. We used a high-dimensional model of 5 inhomogeneous differential equations with 5 system outputs to analyze all data simultaneously. Combination therapy outperformed all monotherapy regimens, with all combinations driving >6 log(10) CFU/ml of bacterial killing. Combination therapy was able to counterselect resistance emergence (meropenem mutants being killed by the combination, as well as fosfomycin mutants being killed by the combination) in all regimens studied. The analysis demonstrated that the combination was significantly synergistic for bacterial cell killing and resistance suppression. Meropenem plus fosfomycin is a promising combination for therapy of high-burden Pseudomonas aeruginosa infections and requires further study.

Pediatric clinical pharmacology now encompasses a wide range of activities, including drug pharmacokinetic and pharmacodynamic modeling and simulation, also known as pharmacometrics. Pediatric clinical pharmacologists may be physicians but are more likely to be pharmacists or PhD scientists, and pediatric clinical pharmacology today is largely a research specialty rather than a subspecialty for direct patient care. Pharmacometrics, including "top-down" population modeling and "bottom-up" physiologically based pharmacokinetic modeling, has become an indispensable tool for pharmaceutical industry scientists, government regulators, academic researchers, and even a handful of patient-oriented practitioners. This review summarizes the application of pharmacometrics within each of these domains and predicts future trends of further applications across the spectrum of pediatric clinical pharmacology from drug development to patient care.

The therapy for treatment of Mycobacterium tuberculosis infections is long and arduous. It has been hypothesized that the therapy duration is driven primarily by populations of organisms in different metabolic states that replicate slowly or not at all (acid-phase and nonreplicative-persister [NRP]-phase organisms). Linezolid is an oxazolidinone antimicrobial with substantial activity against Log-phase M. tuberculosis . Here, we examined organisms in acid-phase growth and nonreplicative-persister-phenotype growth and determined the effect of differing clinically relevant exposures to linezolid in a hollow-fiber infection model (HFIM). The endpoints measured were bacterial kill over 29 days and whether organisms that were less susceptible to linezolid could be recovered during that period. In addition, we evaluated the effect of administration schedule on linezolid activity, contrasting daily administration with administration of twice the daily dose every other day. Linezolid demonstrated robust activity when administered daily against both acid-phase and NRP-phase organisms. We demonstrated a clear dose response, with 900 mg of linezolid daily generating ≥3 Log(CFU/ml) killing of acid-phase and NRP-phase M. tuberculosis over 29 days. Amplification of a population less susceptible to linezolid was not seen. Activity was reduced with every 48-h dosing, indicating that the minimum concentration ( C min )/MIC ratio drove the microbiological effect. We conclude that once-daily linezolid dosing has substantial activity against M. tuberculosis in acid-phase and NRP-phase metabolic states. Other studies have shown activity against Log-phase M. tuberculosis . Linezolid is a valuable addition to the therapeutic armamentarium for M. tuberculosis and has the potential for substantially shortening therapy duration.

We hypothesized that dosing vancomycin to achieve trough concentrations of >15 mg/liter overdoses many adults compared to area under the concentration-time curve (AUC)-guided dosing. We conducted a 3-year, prospective study of vancomycin dosing, plasma concentrations, and outcomes. In year 1, nonstudy clinicians targeted trough concentrations of 10 to 20 mg/liter (infection dependent) and controlled dosing. In years 2 and 3, the study team controlled vancomycin dosing with BestDose Bayesian software to achieve a daily, steady-state AUC/MIC ratio of ≥400, with a maximum AUC value of 800 mg · h/liter, regardless of trough concentration. For Bayesian estimation of AUCs, we used trough samples in years 1 and 2 and optimally timed samples in year 3. We enrolled 252 adults who were ≥18 years old with ≥1 available vancomycin concentration. Only 19% of all trough concentrations were therapeutic versus 70% of AUCs ( P < 0.0001). After enrollment, median trough concentrations by year were 14.4, 9.7, and 10.9 mg/liter ( P = 0.005), with 36%, 7%, and 6% over 15 mg/liter ( P < 0.0001). Bayesian AUC-guided dosing in years 2 and 3 was associated with fewer additional blood samples per subject (3.6, 2.0, and 2.4; P = 0.003), shorter therapy durations (8.2, 5.4, and 4.7 days; P = 0.03), and reduced nephrotoxicity (8%, 0%, and 2%; P = 0.01). The median inpatient stay was 20 days among nephrotoxic patients versus 6 days ( P = 0.002). There was no difference in efficacy by year, with 42% of patients having microbiologically proven infections. Compared to trough concentration targets, AUC-guided, Bayesian estimation-assisted vancomycin dosing was associated with decreased nephrotoxicity, reduced per-patient blood sampling, and shorter length of therapy, without compromising efficacy. These benefits have the potential for substantial cost savings. (This study has been registered at ClinicalTrials.gov under registration no. NCT01932034.)

Substitution by generic drugs is allowed when bioequivalence to the originator drug has been established. However, it is known that similarity in exposure may not be achieved at every occasion for all individual patients when switching between formulations. The ultimate aim of our research is to investigate if pharmacokinetic subpopulations exist when subjects are exposed to bioequivalent formulations. For that purpose, we developed a pharmacokinetic model for gabapentin, based on data from a previously conducted bioavailability study comparing gabapentin exposure following administration of the gabapentin originator and three generic gabapentin formulations in healthy subjects. Both internal and external validation confirmed that the optimal model for description of the gabapentin pharmacokinetics in this comparative bioavailability study was a two-compartment model with absorption constant, an absorption lag time, and clearance adjusted for renal function, in which each model parameter was separately estimated per administered formulation.

Background Cefepime (CEF) is commonly used for adult and pediatric infections. Several studies have examined CEF’s pharmacokinetics (PK) in various populations; however, a unifying PK model for adult and pediatric subjects does not yet exist. We developed a combined population model for adult and pediatric patients and validated the model. Methods The initial model includes adult and pediatric patients with a rich cefepime sampling design. All adults received 2 g CEF while pediatric subjects received a mean of 49 (SD 5) mg/kg. One- and two-compartment models were considered as base models and were fit using a non-parametric adaptive grid algorithm within the Pmetrics package 1.5.2 (Los Angeles, CA) for R 3.5.1. Compartmental model selection was based on Akaike information criteria (AIC). Covariate relationships with PK parameters were visually inspected and mathematically assessed. Predictive performance was evaluated using bias and imprecision of the population and individual prediction models. External validation was conducted using a separate adult cohort. Results A total of 45 subjects (n = 9 adults; n = 36 pediatrics) were included in the initial PK model build and 12 subjects in the external validation cohort. Overall, the data were best described using a two-compartment model with volume of distribution (V) normalized to total body weight (TBW/70 kg) and an allometric scaled elimination rate constant (Ke) for pediatric subjects (AIC = 4,138.36). Final model observed vs. predicted plots demonstrated good fit (population R2 = 0.87, individual R2 = 0.97, Figure 1a and b). For the final model, the population median parameter values (95% credibility interval) were V0 (total volume of distribution), 11.7 L (10.2–14.6); Ke for adult, 0.66 hour−1 (0.38–0.78), Ke for pediatrics, 0.82 hour−1 (0.64–0.85), KCP (rate constant from central to peripheral compartment), 1.4 hour−1 (1.3–1.8), KPC (rate constant from peripheral to central compartment), 1.6 hour−1 (1.2–1.8). The validation cohort has 12 subjects, and the final model fit the data well (individual R2 = 0.75). Conclusion In this diverse group of adult and pediatrics, a two-compartment model described CEF PK well and was externally validated with a unique cohort. This model can serve as a population prior for real-time PK software algorithms. Disclosures All authors: No reported disclosures.

OBJECTIVES Ganciclovir is the most widely used treatment for cytomegalovirus infections. However, neutropenia is a frequent associated adverse effect leading to a decrease in the ganciclovir dose or discontinuation of the therapy, thereby favouring viral resistance. In the present study, the objectives were: (i) to describe the pharmacokinetics of blood and intracellular ganciclovir and its metabolites; and (ii) to explore the relationship between exposure to ganciclovir and/or its metabolites and evolution of the neutrophil count under treatment. METHODS Pharmacokinetic profiles (pre-dose and 1, 2, 3 and 5 h after dosing) of ganciclovir and its metabolites were measured in 22 adult renal transplant patients and further modelled by a non-parametric approach (PMetrics(®)). The relationship between exposure indices to ganciclovir and the slope of the neutrophil count was investigated using multiple linear regression. RESULTS A four-compartment open model was able to accurately describe ganciclovir and its intracellular forms. A significant association was found between intracellular ganciclovir triphosphate concentrations (AUC0-5) and the decrease in neutrophil count over the first 3 months of treatment (β= -0.0019 ± 5 × 10(-4); P < 0.01). CONCLUSIONS In this population of renal transplant patients, the decrease in neutrophil count, used as a surrogate marker of haematological toxicity, was associated with ganciclovir triphosphate accumulation in blood cells. Further studies are needed to test this biomarker as a predictive factor for toxicity.

Early after renal transplantation, it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization using population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians.A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first 8 weeks after transplantation. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target, 3-7 μg/L) and high-risk (8-12 μg/L) recipients were analyzed separately.Eighty renal transplant recipients were randomized, and 78 were included in the analysis (computerized dosing (n = 39): 32 standard risk/7 high-risk, conventional dosing (n = 39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians, 90% [95% confidence interval {95% CI}, 84-95%] vs 78% [95% CI, 76-82%], respectively, P0.001) and in high-risk patients (medians, 77% [95% CI, 71-80%] vs 59% [95% CI, 40-74%], respectively, P = 0.04).Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.

Few studies have characterized longitudinal female plasma and genital antiretroviral pharmacokinetics and pharmacodynamics. Among 20 regimen-naive HIV-infected adult women initiating atazanavir-based therapy (n = 9) or efavirenz-based therapy (n = 11), we measured blood CD4+ T lymphocytes, and paired plasma and genital HIV RNA and atazanavir or efavirenz 2 days before starting therapy and 2, 4, 7, 10, 21, 28, 60, 120, and 180 days after. The mean (range) log10 baseline plasma viral load was 4.89 copies/mL (2.64-6.09 copies/mL), and genital was3.30 (1.60-5.00). In the atazanavir and efavirenz groups, mean (SD) days to a 50% decrease in plasma viral load was 8.2 (4.9) versus 9.3 (7.4), P = .7, and in the genital tract it was 7.3 (3.5) versus 9.3 (7.7), P = .5. The median (interquartile range) plasma:genital concentration ratio for atazanavir was 0.11 (0.001-0.46) versus 0.34 (0.05-1.30) for efavirenz, P = .5. Average plasma efavirenz or atazanavir concentrations over time did not affect virologic response. Blood CD4+ percentages increased by +2.3 (P = .06) and +3.0 (P = .003) for every 1 mg/L increase in average plasma and genital drug concentration, respectively. Plasma and genital viral pharmacodynamics were similar between the groups and independent of average concentrations, but blood CD4+ response was related in particular to genital extravascular drug concentrations.

While pharmacokinetic-pharmacodynamic targets for vancomycin therapy are recognized for invasive methicillin-resistant Staphylococcus aureus infections, scant data are available to guide therapy for other Gram-positive infections. A retrospective single-center cohort of patients with Enterococcus bacteremia hospitalized between 1 January 2009 and 31 May 2015 were studied. The average vancomycin AUC 0–24 was computed using a Bayesian approach. The MIC was determined by gradient diffusion (Etest; bioMérieux), and the average AUC 0–24 /MIC value over the initial 72 h of therapy was calculated. We assessed 30-day all-cause mortality as the primary outcome. Classification and regression tree analysis (CART) was used to identify the vancomycin AUC 0–24 /MIC value associated with 30-day mortality. Fifty-seven patients with enterococcal bacteremia (32 E. faecium , 21 E. faecalis , and 4 other Enterococcus spp.) were studied. The median vancomycin MIC was 0.75 mg/liter (range, 0.38 to 3 mg/liter). All-cause 30-day mortality occurred in 10 of 57 patients (17.5%). A CART-derived vancomycin AUC/MIC Etest value of ≥389 was associated with reduced mortality ( P = 0.017); failure to achieve this independently predicted 30-day mortality (odds ratio, 6.83 [95% confidence interval = 1.51 to 30.84]; P = 0.01). We found that a vancomycin AUC/MIC Etest value of ≥389 achieved within 72 h was associated with reduced mortality. Larger, prospective studies are warranted to verify the vancomycin pharmacodynamic targets associated with maximal clinical outcomes and acceptable safety.

Our objective was to identify drug interactions between ledipasvir (LDV) and sofosbuvir (SOF) against a genotype 1b replicon to determine optimal exposures for each agent that will maximize antiviral activity against susceptible and drug-resistant subpopulations. LDV and SOF were evaluated using a fully factorial experimental design in the BelloCell system. Replicon levels and drug-resistant variants were quantified at various times post-therapy for 14 days and a high-dimensional mathematical model was fit to the data. Mutations associated with SOF resistance were not detected; but LDV-resistant mutants were selected and mutant subpopulations increased as exposure intensity increased. Combination therapy was additive for the total replicon population and the LDV-resistant population, but a threshold concentration of 100 ng/ml of SOF must be attained to suppress LDV-resistant subpopulations. These novel findings hold important implications for not only improving therapeutic outcomes, but also maximizing the clinical utility of LDV and SOF combination regimens.

Obesity is associated with poorer outcome for many cancers. Previously, we observed that adipocytes protect acute lymphoblastic leukemia (ALL) cells from the anthracycline, daunorubicin. In this study, it is determined whether adipocytes clear daunorubicin from the tumor microenvironment (TME). Intracellular daunorubicin concentrations were evaluated using fluorescence. Daunorubicin and its largely inactive metabolite, daunorubicinol, were analytically measured in media, cells, and tissues using liquid chromatography/mass spectrometry (LC/MS). Expression of daunorubicin-metabolizing enzymes, aldo-keto reductases (AKR1A1, AKR1B1, AKR1C1, AKR1C2, AKR1C3, and AKR7A2) and carbonyl reductases (CBR1, CBR3), in human adipose tissue, were queried using public databases and directly measured by quantitative PCR (qPCR) and immunoblot. Adipose tissue AKR activity was measured by colorimetric assay. Adipocytes absorbed and efficiently metabolized daunorubicin to daunorubicinol, reducing its antileukemia effect in the local microenvironment. Murine studies confirmed adipose tissue conversion of daunorubicin to daunorubicinol in vivo. Adipocytes expressed high levels of AKR and CBR isoenzymes that deactivate anthracyclines. Indeed, adipocyte protein levels of AKR1C1, AKR1C2, and AKR1C3 are higher than all other human noncancerous cell types. To our knowledge, this is the first demonstration that adipocytes metabolize and inactivate a therapeutic drug. Adipocyte-mediated daunorubicin metabolism reduces active drug concentration in the TME. These results could be clinically important for adipocyte-rich cancer microenvironments such as omentum, breast, and marrow. As AKR and CBR enzymes metabolize several drugs, and can be expressed at higher levels in obese individuals, this proof-of-principle finding has important implications across many diseases. Implications: Adipocyte absorption and metabolism of chemotherapies can reduce cytotoxicity in cancer microenvironments, potentially contributing to poorer survival outcomes. Mol Cancer Res; 15(12); 1704–13. ©2017 AACR.