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Contains fulltext : 299630.pdf (Publisher’s version ) (Open Access), OBJECTIVE: To describe whether contemporary dosing of antifungal drugs achieves therapeutic exposures in critically ill patients that are associated with optimal outcomes. Adequate antifungal therapy is a key determinant of survival of critically ill patients with fungal infections. Critical illness can alter an antifungal agents' pharmacokinetics, increasing the risk of inappropriate antifungal exposure that may lead to treatment failure and/or toxicity. DESIGN SETTING AND PARTICIPANTS: This international, multicentre, observational pharmacokinetic study will comprise adult critically ill patients prescribed antifungal agents including fluconazole, voriconazole, posaconazole, isavuconazole, caspofungin, micafungin, anidulafungin, and amphotericin B for the treatment or prophylaxis of invasive fungal disease. A minimum of 12 patients are targeted for enrolment for each antifungal agent, across 12 countries and 30 intensive care units to perform descriptive pharmacokinetics. Pharmacokinetic sampling will occur during two dosing intervals (occasions): firstly, between days 1 and 3, and secondly, between days 4 and 7 of the antifungal course, collecting three samples per occasion. Patients' demographic and clinical data will be collected. MAIN OUTCOME MEASURES: The primary endpoint of the study is attainment of pharmacokinetic/pharmacodynamic target exposures that are associated with optimal efficacy. Thirty-day mortality will also be measured. RESULTS AND CONCLUSIONS: This study will describe whether contemporary antifungal drug dosing achieves drug exposures associated with optimal outcomes. Data will also be used for the development of antifungal dosing algorithms for critically ill patients. Optimised drug dosing should be considered a priority for improving clinical outcomes for critically ill patients with fungal infections.

Contains fulltext : 249068.pdf (Publisher’s version ) (Open Access), OBJECTIVES: The aim of this study was to develop a mechanistic protein-binding model to predict the unbound flucloxacillin concentrations in different patient populations. METHODS: A mechanistic protein-binding model was fitted to the data using non-linear mixed-effects modelling. Data were obtained from four datasets, containing 710 paired total and unbound flucloxacillin concentrations from healthy volunteers, non-critically ill and critically ill patients. A fifth dataset with data from hospitalized patients was used for evaluation of our model. The predictive performance of the mechanistic model was evaluated and compared with the calculation of the unbound concentration with a fixed unbound fraction of 5%. Finally, we performed a fit-for-use evaluation, verifying whether the model-predicted unbound flucloxacillin concentrations would lead to clinically incorrect dose adjustments. RESULTS: The mechanistic protein-binding model predicted the unbound flucloxacillin concentrations more accurately than assuming an unbound fraction of 5%. The mean prediction error varied between -26.2% to 27.8% for the mechanistic model and between -30.8% to 83% for calculation with a fixed factor of 5%. The normalized root mean squared error varied between 36.8% and 69% respectively between 57.1% and 134%. Predicting the unbound concentration with the use of the mechanistic model resulted in 6.1% incorrect dose adjustments versus 19.4% if calculated with a fixed unbound fraction of 5%. CONCLUSIONS: Estimating the unbound concentration with a mechanistic protein-binding model outperforms the calculation with the use of a fixed protein binding factor of 5%, but neither demonstrates acceptable performance. When performing dose individualization of flucloxacillin, this should be done based on measured unbound concentrations rather than on estimated unbound concentrations from the measured total concentrations. In the absence of an assay for unbound concentrations, the mechanistic bindin

Item does not contain fulltext, BACKGROUND: Precision dosing programs are promising tools for optimising antimicrobial dosing. Selecting the ideal program for local application may be challenging due to the large variety of available programs with differing characteristics. OBJECTIVES: The objectives of this study were to systematically identify available precision dosing software programs to optimize antimicrobial dosing and describe the characteristics of each program. Details on the ability of programs to provide beta-lactam dosing support was also gathered. SOURCES: A systematic review search strategy was used to identify candidate software programs described in the literature in Embase and PubMed. A detailed survey was then developed to identify characteristics of programs, including details on the underlying methodology driving dosing software recommendations, interface characteristics, costs and regulatory affairs. Software developers from all identified programs were invited to participate in the survey. CONTENT: The systematic search results identified 18 programs. Fifteen developers responded to the survey (83%) and 11 programs provide dosing support for at least one beta-lactam. Fourteen programs can utilize measured drug concentrations to generate dosing recommendations, with 13 able to generate empiric dosing recommendations. Six programs integrate with local electronic health records and four are registered with at least one regulatory agency. Pharmacokinetic models in combination with Bayesian statistics is the most common methodology used to generate dosing recommendations, with 14 programs utilizing this method. IMPLICATIONS: There was significant variability in the available antimicrobial profiles and characteristics among dosing software programs. As healthcare providers will differ in their requirements within their local settings, clinicians should use these findings to identify potential candidate programs and, if feasible, trial these to ensure they meet their specific requi

Staphylococcus aureus bloodstream (SAB) infection is a common and severe infectious disease, with a 90-day mortality of 15%–30%. Despite this, <3000 people have been randomized into clinical trials of treatments for SAB infection. The limited evidence base partly results from clinical trials for SAB infections being difficult to complete at scale using traditional clinical trial methods. Here we provide the rationale and framework for an adaptive platform trial applied to SAB infections. We detail the design features of the Staphylococcus aureus Network Adaptive Platform (SNAP) trial that will enable multiple questions to be answered as efficiently as possible. The SNAP trial commenced enrolling patients across multiple countries in 2022 with an estimated target sample size of 7000 participants. This approach may serve as an exemplar to increase efficiency of clinical trials for other infectious disease syndromes.

Background: Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX). Method(s): In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung-Knapp-Sidik-Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. Result(s): A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3

BACKGROUND: The COVID-19 pandemic has generated significant debate about how emerging infections can be treated in the absence of evidence-based therapies to combat disease. In particular, the use of off-label therapies outside of a clinical trial setting has been controversial. AIM: We aimed to longitudinally study policies and prescribing practices pertaining to therapies for COVID-19 in Australian Health Services in 2020. METHOD(S): Prospective data was collected from participating Australian health services who may care for patients with COVID-19 via an electronic portal. A single informant from each health service was emailed a survey link at regular intervals. Information was sought regarding changes to COVID-19 policy at their service and use of therapies for COVID-19. RESULT(S): Overall, 78 hospitals were represented from 39 respondents with longitudinal data collection from May to December 2020. All Australian states/territories were represented with the majority of respondents located in a major city (34/39; 87%). Just over half (20/39) of respondents had a written policy for COVID-19 therapy use at their health service at survey enrolment and policies changed frequently throughout the pandemic. Therapy use outside of a clinical trial was reported in 54% of health services, most frequently in Victoria, correlating with higher numbers of COVID-19 cases. At study commencement hydroxychloroquine was most frequently used, with corticosteroids and remdesivir use increasingly throughout the study period. CONCLUSION(S): Our results reflect the reactive nature of prescribing of therapies for COVID-19 and highlight the importance of evidence-based guidelines to assist prescribers. This article is protected by copyright. All rights reserved.

This paper presents a nonlinear formulation of a coarse mesh transport method based on work performed in recent years at Georgia Tech[1, 2]. In the original formulation, a global problem is decomposed into local problems for which response functions are computed with the fission source treated implicitly. A global solution is found via outer iterations on the global eigenvalue and inner iterations on the lo-cal boundary conditions.By casting the problem in a nonlinear form,robust nonlinear solvers can be employed,which offer super linear convergence and may be easier to parallelize.

Contains fulltext : 229191.pdf (Publisher’s version ) (Open Access), There is an urgent need to identify optimal antiviral therapies for COVID-19 caused by SARS-CoV-2. We have conducted a rapid and comprehensive review of relevant pharmacological evidence, focusing on (1) the pharmacokinetics (PK) of potential antiviral therapies; (2) coronavirus-specific pharmacodynamics (PD); (3) PK and PD interactions between proposed combination therapies; (4) pharmacology of major supportive therapies; and (5) anticipated drug-drug interactions (DDIs). We found promising in vitro evidence for remdesivir, (hydroxy)chloroquine and favipiravir against SARS-CoV-2; potential clinical benefit in SARS-CoV-2 with remdesivir, the combination of lopinavir/ritonavir (LPV/r) plus ribavirin; and strong evidence for LPV/r plus ribavirin against Middle East Respiratory Syndrome (MERS) for post-exposure prophylaxis in healthcare workers. Despite these emerging data, robust controlled clinical trials assessing patient-centred outcomes remain imperative and clinical data have already reduced expectations with regard to some drugs. Any therapy should be used with caution in the light of potential drug interactions and the uncertainty of optimal doses for treating mild versus serious infections.

Contains fulltext : 225430.pdf (Publisher’s version ) (Open Access), PURPOSE: The DIANA study aimed to evaluate how often antimicrobial de-escalation (ADE) of empirical treatment is performed in the intensive care unit (ICU) and to estimate the effect of ADE on clinical cure on day 7 following treatment initiation. METHODS: Adult ICU patients receiving empirical antimicrobial therapy for bacterial infection were studied in a prospective observational study from October 2016 until May 2018. ADE was defined as (1) discontinuation of an antimicrobial in case of empirical combination therapy or (2) replacement of an antimicrobial with the intention to narrow the antimicrobial spectrum, within the first 3 days of therapy. Inverse probability (IP) weighting was used to account for time-varying confounding when estimating the effect of ADE on clinical cure. RESULTS: Overall, 1495 patients from 152 ICUs in 28 countries were studied. Combination therapy was prescribed in 50%, and carbapenems were prescribed in 26% of patients. Empirical therapy underwent ADE, no change and change other than ADE within the first 3 days in 16%, 63% and 22%, respectively. Unadjusted mortality at day 28 was 15.8% in the ADE cohort and 19.4% in patients with no change [p = 0.27; RR 0.83 (95% CI 0.60-1.14)]. The IP-weighted relative risk estimate for clinical cure comparing ADE with no-ADE patients (no change or change other than ADE) was 1.37 (95% CI 1.14-1.64). CONCLUSION: ADE was infrequently applied in critically ill-infected patients. The observational effect estimate on clinical cure suggested no deleterious impact of ADE compared to no-ADE. However, residual confounding is likely.

Contains fulltext : 225334.pdf (Publisher’s version ) (Open Access), BACKGROUND: Initial appropriate anti-infective therapy is associated with improved outcomes in patients with severe infections. In critically ill patients, altered pharmacokinetic (PK) behaviour is common and known to influence the achievement of PK/pharmacodynamic targets. OBJECTIVES: To describe population PK and optimized dosing regimens for flucloxacillin in critically ill patients. METHODS: First, we developed a population PK model, estimated between-patient variability (BPV) and identified covariates that could explain BPV through non-linear mixed-effects analysis, using total and unbound concentrations obtained from 35 adult critically ill patients treated with intermittent flucloxacillin. Second, we validated the model using external datasets from two different countries. Finally, frequently prescribed dosing regimens were evaluated using Monte Carlo simulations. RESULTS: A two-compartment model with non-linear protein binding was developed and validated. BPV of the maximum binding capacity decreased from 42.2% to 30.4% and BPV of unbound clearance decreased from 88.1% to 71.6% upon inclusion of serum albumin concentrations and estimated glomerular filtration rate (eGFR; by CKD-EPI equation), respectively. PTA (target of 100%fT>MIC) was 91% for patients with eGFR of 33 mL/min and 1 g q6h, 87% for patients with eGFR of 96 mL/min and 2 g q4h and 71% for patients with eGFR of 153 mL/min and 2 g q4h. CONCLUSIONS: For patients with high creatinine clearance who are infected with moderately susceptible pathogens, therapeutic drug monitoring is advised since there is a risk of underexposure to flucloxacillin. Due to the non-linear protein binding of flucloxacillin and the high prevalence of hypoalbuminaemia in critically ill patients, dose adjustments should be based on unbound concentrations.

Contains fulltext : 218616.pdf (Publisher’s version ) (Closed access), BACKGROUND: Antimicrobial de-escalation (ADE) is a strategy of antimicrobial stewardship, aiming at preventing the emergence of antimicrobial resistance (AMR) by decreasing the exposure to broad-spectrum antimicrobials. There is no high-quality research on ADE and its effects on AMR. Its definition varies and there is little evidence-based guidance for clinicians to use ADE in the intensive care unit (ICU). METHODS: A task force of 16 international experts was formed in November 2016 to provide with guidelines for clinical practice to develop questions targeted at defining ADE, its effects on the ICU population and to provide clinical guidance. Groups of 2 experts were assigned 1-2 questions each within their field of expertise to provide draft statements and rationale. A Delphi method, with 3 rounds and an agreement threshold of 70% was required to reach consensus. RESULTS: We present a comprehensive document with 13 statements, reviewing the evidence on the definition of ADE, its effects in the ICU population and providing guidance for clinicians in subsets of clinical scenarios where ADE may be considered. CONCLUSION: ADE remains a topic of controversy due to the complexity of clinical scenarios where it may be applied and the absence of evidence to the effects it may have on antimicrobial resistance.

There is an urgent need to identify optimal antiviral therapies for COVID-19 caused by SARS-CoV-2. We have conducted a rapid and comprehensive review of relevant pharmacological evidence, focusing on (1) the pharmacokinetics (PK) of potential antiviral therapies; (2) coronavirus-specific pharmacodynamics (PD); (3) PK and PD interactions between proposed combination therapies; (4) pharmacology of major supportive therapies; and (5) anticipated drug–drug interactions (DDIs). We found promising in vitro evidence for remdesivir, (hydroxy)chloroquine and favipiravir against SARS-CoV-2; potential clinical benefit in SARS-CoV-2 with remdesivir, the combination of lopinavir/ritonavir (LPV/r) plus ribavirin; and strong evidence for LPV/r plus ribavirin against Middle East Respiratory Syndrome (MERS) for post-exposure prophylaxis in healthcare workers. Despite these emerging data, robust controlled clinical trials assessing patient-centred outcomes remain imperative and clinical data have already reduced expectations with regard to some drugs. Any therapy should be used with caution in the light of potential drug interactions and the uncertainty of optimal doses for treating mild versus serious infections.

We perform a stability analysis on a discrete analogue of a known, continuous model ofmutualism. We illustrate how the introduction of delays affects the asymptotic stabilityof the system’s positive nontrivial equilibrium point. In the second part of the paper weexplore the insights that the model can provide when it is used in relation to interactingfinancial markets. We also note the limitations of such an approach