Population Pharmacokinetics of Flucloxacillin In Bone and Soft Tissue- Standard Dosing is Not Sufficient to Achieve Therapeutic Concentrations.

PURPOSE: Flucloxacillin is a ß-lactam penicillin commonly used in the treatment of bone and soft tissue infections. In a recent porcine study, we found surprisingly low time for which the free concentration was maintained above the minimal inhibitory concentration (fT>MIC) in bone and soft tissue, following flucloxacillin oral (PO) and intravenous (IV) administration at 1g every 6h (q6h). In addition to plasma, sampling was obtained from subcutaneous tissue, knee joint, cancellous bone and cortical bone, using microdialysis. To identify flucloxacillin dosing regimens that result in theoretically therapeutic concentrations, we developed a population pharmacokinetic (PK) model for the porcine data, and combined it with a human flucloxacillin population PK model for simulations. METHODS: A four-compartment model was developed, and various dosing regimens and modes of administration were simulated. Predicted concentrations were compared to %fT>MIC (0.5 mg/L and 2 mg/L). RESULTS: Continuous infusion (CI) resulted in higher %fT>MIC compared to intermittent administration. For intermittent IV dosing (4, 8 and 12g/24h), fT>MIC (0.5 mg/L) was ≥70% in plasma, and ranged between 42-96% in the sampled tissue in a typical individual. By applying CI, 4g/day was sufficient to achieve ≥98% fT>MIC (0.5 mg/L) in all sampled tissues. For MIC 2 mg/L, ≥50% fT>MIC was only achieved in plasma at CI 8 and 12g/24h and IV 3g q6h. CONCLUSIONS: To reach efficacious flucloxacillin bone and tissue concentrations, dose increment or continuous infusion needs to be considered.

In vivo evolution of antimicrobial resistance in a biofilm model of Pseudomonas aeruginosa lung infection.

The evolution of antimicrobial resistance (AMR) in biofilms has been repeatedly studied by experimental evolution in vitro, but rarely in vivo. The complex microenvironment at the infection site imposes selective pressures on the bacterial biofilms, potentially influencing the development of AMR. We report here the development of AMR in an in vivo mouse model of Pseudomonas aeruginosa biofilm lung infection. The P. aeruginosa embedded in seaweed alginate beads underwent four successive lung infection passages with or without ciprofloxacin (CIP) exposure. The development of CIP resistance was assessed at each passage by population analysis of the bacterial populations recovered from the lungs of CIP-treated and control mice, with subsequent whole-genome sequencing of selected isolates. As inflammation plays a crucial role in shaping the microenvironment at the infection site, its impact was explored through the measurement of cytokine levels in the lung homogenate. A rapid development of AMR was observed starting from the second passage in the CIP-treated mice. Genetic analysis revealed mutations in nfxB, efflux pumps (mexZ), and two-component systems (parS) contribution to CIP resistance. The control group isolates exhibited mutations in the dipA gene, likely associated with biofilm dispersion. In the initial two passages, the CIP-treated group exhibited an elevated inflammatory response compared to the control group. This increase may potentially contribute to the release of mutagenic reactive oxygen species and the development of AMR. In conclusion, this study illustrates the complex relationship between infection, antibiotic treatment, and immune response.

Integrative model-based comparison of target site-specific antimicrobial effects: A case study with ceftaroline and lefamulin.

OBJECTIVE: Predictions of antimicrobial effects typically rely on plasma-based pharmacokinetic-pharmacodynamic (PK-PD) targets, ignoring target-site concentrations and potential differences in tissue penetration between antibiotics. In this study, we applied PK-PD modelling to compare target site-specific effects of antibiotics by integrating clinical microdialysis data, in vitro time-kill curves, and antimicrobial susceptibility distributions. As a case study, we compared the effect of lefamulin and ceftaroline against methicillin-resistant Staphylococcus aureus (MRSA) at soft-tissue concentrations. METHODS: A population PK model describing lefamulin concentrations in plasma, subcutaneous adipose and muscle tissue was developed. For ceftaroline, a similar previously reported PK model was adopted. In vitro time-kill experiments were performed with six MRSA isolates and a PD model was developed to describe bacterial growth and antimicrobial effects. The clinical PK and in vitro PD models were linked to compare antimicrobial effects of ceftaroline and lefamulin at the different target sites. RESULTS: Considering minimum inhibitory concentration (MIC) distributions and standard dosages, ceftaroline showed superior anti-MRSA effects compared to lefamulin both at plasma and soft-tissue concentrations. Looking at the individual antibiotics, lefamulin effects were highest at soft-tissue concentrations, while ceftaroline effects were highest at plasma concentrations, emphasising the importance of considering target-site PK-PD in antibiotic treatment optimisation. CONCLUSION: Given standard dosing regimens, ceftaroline appeared more effective than lefamulin against MRSA at soft-tissue concentrations. The PK-PD model-based approach applied in this study could be used to compare or explore the potential of antibiotics for specific indications or in populations with unique target-site PK.

Model-based assessment of neutrophil-mediated phagocytosis and digestion of bacteria across in vitro and in vivo studies.

Neutrophil granulocytes are key components of the host response against pathogens, and severe neutropenia, with neutrophil counts below 0.5 × 106 cells/mL, renders patients increasingly vulnerable to infections. Published in vitro (n = 7) and in vivo (n = 5) studies with time-course information on bacterial and neutrophil counts were digitized to characterize the kinetics of neutrophil-mediated bacterial killing and inform on the immune systems' contribution to the clearance of bacterial infections. A mathematical model for the in vitro dynamics of bacteria and the kinetics of neutrophil-mediated phagocytosis and digestion was developed, which was extended to in vivo studies in immune-competent and immune-compromised mice. Neutrophil-mediated bacterial killing was described by two first-order processes-phagocytosis and digestion-scaled by neutrophil concentration, where 50% of the maximum was achieved at neutrophil counts of 1.19 × 106 cells/mL (phagocytosis) and 6.55 × 106 cells/mL (digestion). The process efficiencies diminished as the phagocytosed bacteria to total neutrophils ratio increased (with 50% reduction at a ratio of 3.41). Neutrophil in vivo dynamics were captured through the characterization of myelosuppressive drug effects and postinoculation neutrophil influx into lungs and by system differences (27% bacterial growth and 9.3% maximum capacity, compared with in vitro estimates). Predictions showed how the therapeutically induced reduction of neutrophil counts enabled bacterial growth, especially when falling below 0.5 × 106 cells/mL, whereas control individuals could deal with all tested bacterial burdens (up to 109 colony forming units/g lung). The model-based characterization of neutrophil-mediated bacterial killing simultaneously predicted data across in vitro and in vivo studies and may be used to inform the capacity of host-response at the individual level.

Plasma and intraperitoneal pharmacokinetics of ceftazidime/avibactam in peritoneal dialysis patients.

OBJECTIVES: Peritonitis is a serious complication in patients undergoing automated peritoneal dialysis (APD) that increases morbidity and frequently disqualifies patients from the peritoneal dialysis programme. Ceftazidime/avibactam (CAZ/AVI) is a potential treatment option for APD patients with peritonitis caused by resistant Gram-negative bacteria, but limited data exist on systemic and target-site pharmacokinetics (PK) in patients undergoing APD. This study set out to investigate the PK of CAZ/AVI in plasma and peritoneal dialysate (PDS) of patients undergoing APD. METHODS: A prospective, open-label PK study was conducted on eight patients undergoing APD. CAZ/AVI was administered as a single intravenous dose of 2 g/0.5 g over 120 minutes. APD cycles were initiated 15 hours after the study drug administration. Dense PDS and plasma sampling was performed for 24 hours after the start of administration. PK parameters were analysed with population PK modelling. Probability of target attainment (PTA) was simulated for different CAZ/AVI doses. RESULTS: PK profiles of both drugs in plasma and PDS were similar, indicating that the two drugs are well suited for a fixed-dose combination. A two-compartment model best described the PK of both drugs. A single dose of 2 g/0.5 g CAZ/AVI led to concentrations that far exceeded the PK/PD targets of both drugs. In the Monte Carlo simulations, even the lowest dose (750/190 mg CAZ/AVI) achieved a PTA of >90% for MICs up to 8 mg/L (The European Committee on Antimicrobial Susceptibility Testing epidemiological cut-off value for Pseudomonas aeruginosa) in plasma and PDS. DISCUSSION: On the basis of PTA simulations, a dose of 750/190 mg CAZ/AVI would be sufficient to treat plasma and peritoneal fluid infections in patients undergoing APD.

Profile occurrences and in vitro effects of toxic organic pollutants in metal shredding facilities in Wallonia (Belgium).

End-of-life vehicles and e-waste contain several hazardous substances that can contaminate the environment during treatment processes. Occurrences and adverse effects of toxic organic pollutants emitted from 3 shredder plants located in Wallonia, Belgium, were investigated by chemical and biological analyses of fluff, dust, and scrubbing sludge sampled in 2019. Site 1 showed the highest concentrations of chlorinated compounds in sludge with 7.5 ng/g polychlorinated dibenzo-dioxins/furans and 84.5 µg/g estimated total polychlorinated biphenyls, while site 3 led the brominated flame retardant levels in dust (53.4 µg/g). The level of polycyclic aromatic hydrocarbons was highest in the sludge samples, 78 and 71 µg/g for sites 2 and 3, respectively. The samples induced significant dioxin-like activities in murine and human cells at concentrations of around 0.01-0.1 and 0.5-1 ng (sample) per ml (medium), respectively, with the efficacy similar to 2,3,7,8-tetrachlorodibenzodioxin and EC50 values of around 1 and 10 ng/ml. The samples also displayed high estrogenic activities, already at 1 ng/ml, and several induced a response as efficient as 17ß-estradiol, albeit a low androgenic activity. Shredder workers were estimated to be highly exposed to dioxin-like compounds through dust ingestion and dermal absorption, which is of concern.

Developing a hybrid time-series artificial intelligence model to forecast energy use in buildings.

The development of a reliable energy use prediction model is still difficult due to the inherent complex pattern of energy use data. There are few studies developing a prediction model for the one-day-ahead energy use prediction in buildings and optimizing the hyperparameters of a prediction model is necessary. This study aimed to propose a hybrid artificial intelligence model for forecasting one-day ahead time-series energy consumption in buildings. The proposed model was developed based on the integration of the Seasonal Autoregressive integrated Moving average, the Firefly-inspired Optimization algorithm, and the support vector Regression (SAMFOR). A large dataset of energy consumption in 30-min intervals, temporal data, and weather data from six real-world buildings in Vietnam was used to train and test the model. Sensitivity analyses were performed to identify appropriate model inputs. Comparison results show that the SAMFOR model was more effective than the others such as the seasonal autoregressive integrated moving average (SARIMA) and support vector regression (SVR), SARIMA-SVR, and random forests (RF) models. Evaluation results on real-world building depicted that the proposed SAMFOR model achieved the highest accuracy with the root-mean-square error (RMSE) of 1.77 kWh in, mean absolute percentage error (MAPE) of 9.56%, and correlation coefficient (R) of 0.914. The comparison results confirmed that the SAMFOR model was effective for forecasting one-day-ahead energy consumption. The study contributes to (1) the knowledge domain by proposing the hybrid SAMFOR model for forecasting energy consumption in buildings; and (2) the state of practice by providing building managers or users with a powerful tool for analyzing and improving building energy performance.

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings.

The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0-1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.

Forecasting Time-Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency.

Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected from a residential building with a solar photovoltaic system. The proposed AANNs model achieved good predictive accuracy with 14.04% in mean absolute percentage error (MAPE) and 111.98 Watt-hour in the mean absolute error (MAE). Compared to the support vector regression (SVR), the AANNs model can significantly improve the accuracy which was 103.75% in MAPE. Compared to the ANNs model, accuracy improvement percentage by the AANNs model was 4.6% in MAPE. The AANNs model was the most effective forecasting model among the investigated models in predicting energy consumption, which provides building managers with a useful tool to improve energy efficiency in buildings.

Sensing by wireless reading Ag/AgCl redox conversion on RFID tag: universal, battery-less biosensor design.

Massive integration of biosensors into design of Internet-of-Things (IoT) is vital for progress of healthcare. However, the integration of biosensors is challenging due to limited availability of battery-less biosensor designs. In this work, a combination of nanomaterials for wireless sensing of biological redox reactions is described. The design exploits silver nanoparticles (AgNPs) as part of the RFID tag antenna. We demonstrate that a redox enzyme, particularly, horseradish peroxidase (HRP), can convert AgNPs into AgCl in the presence of its substrate, hydrogen peroxide. This strongly changes the impedance of the tag. The presented example exploits gold nanoparticle (AuNP)-assisted electron transfer (ET) between AgNPs and HRP. We show that AuNP is a vital intermediate for establishing rapid ET between the enzyme and AgNPs. As an example, battery-less biosensor-RFID tag designs for H2O2 and glucose are demonstrated. Similar battery-less sensors can be constructed to sense redox reactions catalysed by other oxidoreductase enzymes, their combinations, bacteria or other biological and even non-biological catalysts. In this work, a fast and general route for converting a high number of redox reaction based sensors into battery-less sensor-RFID tags is described.