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Refereed/Peer-reviewed Individualization of vancomycin dosing based on therapeutic drug monitoring (TDM) data is known to improve patient outcomes compared with fixed or empirical dosing strategies. There is increasing evidence to support area-under-the-curve (AUC24)-guided TDM to inform vancomycin dosing decisions for patients receiving therapy for more than 48 hours. It is acknowledged that there may be institutional barriers to the implementation of AUC24-guided dosing, and additional effort is required to enable the transition from trough-based to AUC24-based strategies. Adequate documentation of sampling, correct storage and transport, accurate laboratory analysis, and pertinent data reporting are required to ensure appropriate interpretation of TDM data to guide vancomycin dosing recommendations. Ultimately, TDM data in the clinical context of the patient and their response to treatment should guide vancomycin therapy. Endorsed by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology, the IATDMCT Anti-Infectives Committee, provides recommendations with respect to best clinical practice for vancomycin TDM.

Antimicrobial resistance (particularly through extended-spectrum β-lactamase and aminoglycoside-modifying enzyme production) in neonatal sepsis is a global problem, particularly in low- and middle-income countries, with significant mortality rates. High rates of resistance are reported for the current WHO-recommended first-line antibiotic regimen for neonatal sepsis, i.e., ampicillin and gentamicin. We assessed the utility of fosfomycin and amikacin as a potential alternative regimen to be used in settings of increasingly prevalent antimicrobial resistance. The combination was studied in a 16-arm dose-ranged hollow-fiber infection model (HFIM) experiment. The combination of amikacin and fosfomycin enhanced bactericidal activity and prevented the emergence of resistance, compared to monotherapy with either antibiotic. Modeling of the experimental quantitative outputs and data from checkerboard assays indicated synergy. We further assessed the combination regimen at clinically relevant doses in the HFIM with nine Enterobacterales strains with high fosfomycin and amikacin MICs and demonstrated successful kill to sterilization for 6/9 strains. From these data, we propose a novel combination breakpoint threshold for microbiological success for this antimicrobial combination against Enterobacterales strains, i.e., MIC(F) × MIC(A) < 256 (where MIC(F) and MIC(A) are the fosfomycin and amikacin MICs, respectively). Monte Carlo simulations predict that a standard fosfomycin-amikacin neonatal regimen would achieve >99% probability of pharmacodynamic success for strains with MICs below this threshold. We conclude that the combination of fosfomycin with amikacin is a viable regimen for the empirical treatment of neonatal sepsis and is suitable for further clinical assessment in a randomized controlled trial.

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.

The repurposed agent moxifloxacin has become an important addition to the physician’s armamentarium for the therapy of Mycobacterium tuberculosis. When a drug is administered, we need to have metrics for success. As for most antimicrobial chemotherapy, we contend that for Mycobacterium tuberculosis therapy, these metrics should be a decline in the susceptible bacterial burden and the suppression of amplification of less-susceptible populations. To achieve optimal outcomes relative to these metrics, a dose and schedule of administration need to be chosen. For large populations of patients, there are true between-patient differences in important pharmacokinetic parameters. These distributions of parameter values may have an impact on these metrics, depending on what measure of drug exposure drives the metrics. To optimize dose and schedule choice of moxifloxacin, we performed a dose fractionation experiment in the hollow fiber infection model. We examined 12-, 24-, and 48-h dosing intervals with doses of 200, 400, and 800 mg for each interval, respectively. Within each interval, we had an arm where half-lives of 12, 8, and 4 h were simulated. We attempted to keep the average concentration (C(avg)) or area under the concentration-time curve (AUC) constant across arms. We found that susceptible bacterial load decline was linked to C(avg), as we had indicated previously. Resistance suppression, a nonmonotonic function, had minimum concentration (C(min)) as the linked index. The 48-h interval with the 4-h half-life had the largest less-susceptible population. Balancing bacterial kill, resistance suppression, toxicity (linked to peak concentration [C(peak)]), and adherence, we conclude that the dose of 400 mg daily is optimal for moxifloxacin.

The Mycobacterium tuberculosis drug discovery effort has generated a substantial number of new/repurposed drugs for therapy for this pathogen. The arrival of these drugs is welcome, but another layer of difficulty has emerged. Single agent therapy is insufficient for patients with late-stage tuberculosis because of resistance emergence. To achieve our therapeutic ends, it is requisite to identify optimal combination regimens. These regimens go through a lengthy and expensive evaluative process. If we have a modest group of 6 to 8 new or repurposed agents, this translates into 15 to 28 possible 2-drug combinations. There is neither time nor resources to give an extensive evaluation for all combinations. We sought a screening procedure that would identify combinations that had a high likelihood of achieving good bacterial burden decline. We examined pretomanid, moxifloxacin, linezolid, and bedaquiline in log-phase growth, acid-phase growth, and nonreplicative persister (NRP) phase in the Greco interaction model. We employed the interaction term α and the calculated bacterial burden decline as metrics to rank different regimens in different metabolic states. No relationship was found between α and bacterial kill. We chose bacterial kill as the prime metric. The combination of pretomanid plus moxifloxacin emerged as the clear frontrunner, as the largest bacterial declines were seen in log phase and acid phase with this regimen and it was second best in NRP phase. Bedaquiline also produced good kill. This screening process may identify optimal combinations that can be further evaluated in both the hollow-fiber infection model and in animal models of Mycobacterium tuberculosis infection.

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.

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.

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.

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.

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.

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.

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 (rs) values for the area under the concentration-time curve during the first 24 h (AUC0-24), the maximum concentration in plasma during the first 24 h (Cmax0-24 ), and the lowest (minimum) concentration in plasma after the dose closest to 24 h (Cmin0-24 ). A total of 52 rats received vancomycin (n = 42) or NS (n = 10). The strongest exposure-response correlations were observed between AUC0-24 and Cmax0-24 and urinary AKI biomarkers. Exposure-response correlations (rs values) for AUC0-24, Cmax0-24 , and Cmin0-24 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 AUC0-24 or Cmax0-24 correlates with increases in urinary AKI biomarkers.

Summary An analysis of serial galactomannan index (GMI) in patients with invasive aspergillosis from a phase 3 clinical trial demonstrated that increases in GMI in the first week of therapy significantly increase the likelihood of death and unsuccessful response., Background. The ability to make early therapeutic decisions when treating invasive aspergillosis using changes in biomarkers as a surrogate for therapeutic response could significantly improve patient outcome. Methods. Cox proportional hazards model and logistic regression were used to correlate early changes in galactomannan index (GMI) to mortality and overall response, respectively, from patients with positive baseline GMI (≥0.5) and serial GMI during treatment from a phase 3 clinical trial for the treatment of invasive mold disease. Pharmacokinetic/pharmacodynamic (PK/PD) analysis in patients with isavuconazole plasma concentrations was conducted to establish the exposure necessary for GMI negativity at the end of therapy. Results. The study included 158 patients overall and 78 isavuconazole patients in the PK/PD modeling. By day 7, GMI increases of >0.25 units from baseline (3/130 survivors; 9/28 who died) significantly increased the risk of death compared to those with no increase or increases

Objective: Pharmacokinetic interactions exist between combined oral contraceptives and protease inhibitors (PI). However, such information is lacking for progestin-only oral contraception. We sought to define the steady-state pharmacokinetic interaction between norethindrone (NET) and PI in HIV-infected women. Methods and Design: We conducted an open-label, prospective, nonrandomized trial to characterize the steady-state pharmacokinetics of serum NET in HIV-infected women receiving PI compared with a control group of HIV-infected women receiving other noninteracting drugs. After 21 days of 0.35 mg of NET ingestion once daily, serial serum samples were obtained at 0, 1, 2, 3, 4, 6, 8, 12, 24, 48, and 72 hours. The area under the curve between 0 and 72 hours after ingestion was calculated by trapezoidal approximation. Results: Thirty-five women were enrolled, 2 withdrew. Sixteen women in the PI group and 17 controls completed the study. NET half-life and maximum concentration were not significantly different between the 2 groups. Minimum concentration of NET was significantly higher in the PI group (P = 0.01). The ratio of the geometric mean NET area under the curve in the PI group compared with controls was 1.5 (90% confidence interval: 1.21 to 1.86). NET serum concentrations were significantly higher in HIV-infected women taking a PI compared with controls (P = 0.004). Conclusions: Coadministration of PI inhibits NET metabolism as shown by higher serum NET area under the curve levels, a surrogate marker for therapeutic contraceptive efficacy. This study supports the increased utilization of progestin-only pills in HIV-infected women receiving certain PI regimens.

An experimental design approach is presented for individualized therapy in the special case where the prior information is specified by a nonparametric (NP) population model. Here, a nonparametric model refers to a discrete probability model characterized by a finite set of support points and their associated weights. An important question arises as to how to best design experiments for this type of model. Many experimental design methods are based on Fisher Information or other approaches originally developed for parametric models. While such approaches have been used with some success across various applications, it is interesting to note that they largely fail to address the fundamentally discrete nature of the nonparametric model. Specifically, the problem of identifying an individual from a nonparametric prior is more naturally treated as a problem of classification, i.e., to find a support point that best matches the patient’s behavior. This paper studies the discrete nature of the NP experiment design problem from a classification point of view. Several new insights are provided including the use of Bayes Risk as an information measure, and new alternative methods for experiment design. One particular method, denoted as MMopt (Multiple-Model Optimal), will be examined in detail and shown to require minimal computation while having distinct advantages compared to existing approaches. Several simulated examples, including a case study involving oral voriconazole in children, are given to demonstrate the usefulness of MMopt in pharmacokinetics applications.

There is uncertainty about the optimal teicoplanin regimens for neonates. The study aim was to determine the population pharmacokinetics (PK) of teicoplanin in neonates, evaluate currently recommended regimens and explore the exposure-effect relationships.An open-label PK study was conducted. Neonates from 26 to 44 weeks post-menstrual age were recruited (n = 18). The teicoplanin regimen was a 16 mg/kg loading dose, followed by 8 mg/kg once daily. Therapeutic drug monitoring and dose adjustment were not conducted. A standard two-compartment PK model was developed, followed by models that incorporated weight. A PK/pharmacodynamic (PD) model with C-reactive protein serial measurements as the PD input was fitted to the data. Monte Carlo simulations (n = 5000) were performed using Pmetrics. The AUCs at steady state and the proportion of patients achieving the recommended drug exposures (i.e. Cmin >15 mg/L) were determined. The study was registered in the European Clinical Trials Database Registry (EudraCT: 2012-005738-12).The PK allometric model best accounted for the observed data. The PK parameters medians were: clearance = 0.435 × (weight/70)(0.75) (L/h); volume = 0.765 (L); Kcp = 1.3 (h(-1)); and Kpc = 0.629 (h(-1)). The individual time-course of C-reactive protein was well described using the Bayesian posterior estimates for each patient. The simulated median AUC96-120 was 302.3 mg·h/L and the median Cmin at 120 h was 12.9 mg/L; 38.8% of patients attained a Cmin >15 mg/L by 120 h.Teicoplanin population PK is highly variable in neonates, weight being the best descriptor of PK variability. A low percentage of neonates were able to achieve Cmin >15 mg/L. The routine use of therapeutic drug monitoring and improved knowledge on the PD of teicoplanin is required.

Background: Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4–9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden. Methods: The authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time–concentration curve at a national reference laboratory in a cohort of patients not included in model building. Results: Mean (range) age of the 53 model-building subjects was 7.8 years (0.2–19.0 years) and weight was 26.5 kg (5.6–78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6–26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25–18 years) and 46.4 kg (5.2–110.9 kg). Mean bias and imprecision of the 1-compartment model-predicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600–900 ng/mL were 1.1 mg/kg (≤12 kg, 67% in the target range) and 1.0 mg/kg (>12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (−2.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was −9.2% (−16.7% to −1.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (−0.3% to 4.2%, P = 0.08). Conclusions: BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6–9 samples for standard noncompartmental dose calculations.

Background. Cryptococcal meningoencephalitis is a lethal infection with relatively few therapeutic options. The optimal dosage of liposomal amphotericin B (LAmB) alone or in combination with flucytosine is not known. Methods. A murine model of cryptococcal meningoencephalitis was used. The fungal density in the brain was determined using quantitative cultures. Pharmacokinetic–pharmacodynamic relationships were determined for LAmB and flucytosine administered alone. The effect of the combination was described using the Greco model and a mathematical model. The results were bridged to humans. Results. Inoculation resulted in hematogenous dissemination and logarithmic growth within the central nervous system. There was histological evidence of multifocal infection throughout the brain. Both LAmB and flucytosine produced a dose-dependent reduction in fungal burden. The effect of the combination of agents in the brain was additive. Bridging studies suggested that a human dosage of LAmB 3 mg/kg/d resulted in a submaximal antifungal effect. Regimens of LAmB 6 mg/kg/d alone, LAmB 3 mg/kg/d plus fl ucytosine 50 mg/kg/d, and LAmB 3m g/kg/d plus flucytosine 100 mg/kg/d all resulted in near-maximal antifungal activity. Conclusions. Potential regimens for further study in clinical trials include LAmB 6 mg/kg/d alone, LAmB 3 mg/kg/d plus flucytosine 50 mg/kg/d, and LAmB 3 mg/kg/d plus flucytosine 100 mg/kg/d.

Nonparametric population modeling algorithms have a theoretical superiority over parametric methods to detect pharmacokinetic and pharmacodynamic subgroups and outliers within a study population.The authors created "Pmetrics," a new Windows and Unix R software package that updates the older MM-USCPACK software for nonparametric and parametric population modeling and simulation of pharmacokinetic and pharmacodynamic systems. The parametric iterative 2-stage Bayesian and the nonparametric adaptive grid (NPAG) approaches in Pmetrics were used to fit a simulated population with bimodal elimination (Kel) and unimodal volume of distribution (Vd), plus an extreme outlier, for a 1-compartment model of an intravenous drug.The true means (SD) for Kel and Vd in the population sample were 0.19 (0.17) and 102 (22.3), respectively. Those found by NPAG were 0.19 (0.16) and 104 (22.6). The iterative 2-stage Bayesian estimated them to be 0.18 (0.16) and 104 (24.4). However, given the bimodality of Kel, no subject had a value near the mean for the population. Only NPAG was able to accurately detect the bimodal distribution for Kel and to find the outlier in both the population model and in the Bayesian posterior parameter estimates.Built on over 3 decades of work, Pmetrics adopts a robust, reliable, and mature nonparametric approach to population modeling, which was better than the parametric method at discovering true pharmacokinetic subgroups and an outlier.

New approaches to expedite the development of safe and effective pediatric dosing regimens and first-in-child doses are urgently needed. Model-based approaches require quantitative functions on the maturation of different metabolic pathways. In this study, we directly incorporated a pediatric covariate model for the glucuronidation of morphine into a pediatric population model for zidovudine glucuronidation. This model was compared with a reference model that gave the statistically best description of the data. Both models had adequate goodness-of-fit plots and normalized prediction distribution errors (NPDE), similar population clearance values for each individual, and a Δobjective function value of 13 points (Δ2df). This supports our hypothesis that pediatric pharmacokinetic covariate models contain system-specific information that can be used as semiphysiological functions in pediatric population models. Further research should explore the validity of the semiphysiological function for other UDP-glucuronosyltransferase 2B7 substrates and patient populations and reveal how this function can be used for pediatric physiologically based pharmacokinetic models.CPT: PharmacometricsSystems Pharmacology (2012) 1, e9; doi:10.1038/psp.2012.11; advance online publication 3 October 2012.

Most aminoglycoside pharmacokinetic models include an index of renal function, such as creatinine clearance, to describe drug clearance. However, the best clinical descriptor of renal function for the pharmacokinetic modeling of aminoglycosides has not been established. This analysis was based on 412 gentamicin concentrations from 92 geriatric patients who received intravenous gentamicin for various infectious diseases. Four two-compartment population models were fitted to gentamicin concentrations in a learning set of 64 patients using the nonparametric adaptive grid (NPAG) algorithm. Each model included an index of renal function, namely, the Cockcroft-Gault (CG), Jelliffe (JEL), modification of diet in renal disease (MDRD), or modified MDRD (MDRDm; adjusted to individual body surface area) equation as a covariate influencing gentamicin serum clearance. Goodness of fit and predictive performance of the four models were compared using standard criteria in both the learning set and in a validation set of 28 patients. A final analysis was performed to estimate the population pharmacokinetic parameter values of the entire 92-patient group. In the learning set, the CG-based model best fit the data, followed by JEL-, MDRD-, and MDRDm-based models, with relative reductions of the Akaike information criterion of 29.4, 20.2, 14.2, and 4.2, respectively. Bias and precision of population predictions were significantly different among the four models. In the validation set, individual predictions from the four models showed marginally different biases. The final estimation confirmed the previous results. Specifically, the CG-based model showed predictive performance that was comparable to or better than that of the MDRD-based model at each stage of the analysis. This study shows that methods used to estimate renal function should not be considered interchangeable for the model-based estimation of gentamicin concentrations.

In adult protease inhibitor (PI)-experienced patients, a lopinavir (LPV) phenotypic inhibitory quotient (PIQ) of >15 has been associated with a higher likelihood of viral suppression. The aims of this study were to develop a population pharmacokinetic (PK) model of LPV in children and to estimate the probability of achieving a PIQ of >15. HIV-infected, PI-experienced children receiving LPV were intensively sampled for 12 h to measure plasma LPV. The data were fitted to candidate PK models (using MM-USCPACK software), and the final model was used to simulate 1,000 children to determine the probability of achieving an LPV PIQ of >15. In 50 patients (4 to 18 years old), the median LPV plasma 12-hour-postdose concentration was 5.9 mg/liter (range, 0.03 to 16.2 mg/liter) lower than that reported in adults. After a delay, LPV was absorbed linearly into a central compartment whose size was dependent on the weight and age of the patient. Elimination was dependent on weight. The regression line of observed versus predicted LPV had an R 2 of 0.99 and a slope of 1.0. Visual predictive checks against all available measured concentrations showed good predictive ability of the model. The probability of achieving an LPV PIQ of >15 was >90% for wild-type virus but

Caspofungin is a parenteral antifungal that inhibits beta-1,3- d -glucan synthesis. Although licensed for adult use, the appropriate caspofungin dosing regimen in pediatric patients is not yet known. We therefore investigated the pharmacokinetics and safety of caspofungin in pediatric patients. Thirty-nine children (ages 2 to 11 years) and adolescents (ages 12 to 17 years) with neutropenia were administered caspofungin using either a weight-based regimen (1 mg/kg of body weight/day) or a body surface area regimen (50 mg/m 2 /day or 70 mg/m 2 /day). Plasma samples for caspofungin profiles were collected on days 1 and 4. These results were compared to those from adults treated with either 50 or 70 mg/day for mucosal candidiasis. In children receiving 1 mg/kg/day (maximum, 50 mg/day), the area under the concentration-time curve over 24 h (AUC 0-24 ) was significantly smaller (46% after multiple doses) than that observed in adults receiving 50 mg/day ( P < 0.001). In children and adolescents receiving 50 mg/m 2 /day (maximum, 70 mg/day), the AUC 0-24 following multiple doses was similar to that for the exposure in adults receiving 50 mg/day. The AUC 0-24 and concentration trough (at 24 h) in pediatric patients receiving the 50-mg/m 2 daily regimen were consistent across the range of ages. Caspofungin was generally well tolerated in this study. None of the patients developed a serious drug-related adverse event or were discontinued for toxicity. These results demonstrate that caspofungin at 1 mg/kg/day in pediatric patients is suboptimal. Caspofungin administration at 50 mg/m 2 /day provides a comparable exposure to that of adult patients treated with 50 mg/day.

Background: The pharmacokinetic characteristics of the antifungal drug caspofungin have not been reported in children. Objective: The aim of this study was to report limited caspofungin pharmacokinetic data for pediatric liver transplant patients. Methods: Two pediatric liver transplant patients, aged 5 years (not dialyzed) and 9 months (dialyzed), were assessed. Using a novel, validated, liquid-phase extraction with high-performance liquid chromatography, we measured plasma caspofungin concentrations from blood samples obtained within a 24-hour period after the patients were given 1 mg/kg IV of caspofungin. Results: Noncompartmental analysis for the nondialyzed patient showed an elimination half-life of 10.7 hours, a volume of distribution of 0.11 L/kg, and a systemic clearance of 0.12 mL/min/kg. Liver enzyme activities increased briefly; the increase may have been due to concomitant graft rejection. For the dialyzed patient, the half-life was 11.7 hours, with an adjusted volume of distribution of 0.18 L/kg and a systemic clearance of 0.24 mL/min/kg. No clinically relevant treatment-related adverse events were noted. Conclusions: Pharmacokinetic data found in the 2 patients in this study are similar to those reported in adults. Until more thorough data are published, caspofungin 1 mg/kg may be considered a reasonable, tolerable dose for children.

The delivery of personalized antimicrobial therapy is a critical component in the treatment of patients with invasive infections. Vancomycin, the drug of choice for infections due to methicillin-resistant Staphylococcus aureus, requires the use of therapeutic drug monitoring (TDM) for delivery of optimal therapy. Current guidance on vancomycin TDM includes the measurement of a trough concentration as a surrogate for achieving an AUC to minimum inhibitory concentration (MIC) by broth microdilution (AUC/MICBMD) ratio ≥400. Although trough-only monitoring has been widely integrated into clinical practice, there is a high degree of inter-individual variability between a measured trough concentration and the actual AUC value. The therapeutic discordance between AUC and trough may lead to suboptimal outcomes among patients with infections due to less susceptible pathogens or unnecessarily increase the probability of acute kidney injury (AKI) in others. Given the potentially narrow vancomycin AUC range for optimal effect and minimal AKI, clinicians need a “real-time” system to predict accurately the AUC with limited pharmacokinetic (PK) sampling. This article reviews two innovative approaches for calculating the vancomycin AUC in clinical practice based on one or two drug concentrations. One such approach involves the use of Bayesian computer software programs to estimate the “true” vancomycin AUC value with minimal PK sampling and provide AUC-guided dosing recommendations at the bedside. An alternative involves use of two concentrations (peak and trough) and simple analytic equations to estimate AUC values. Both approaches provide considerable improvements over the current trough-only concentration monitoring method.