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Background: Meropenem is a carbapenem antibiotic widely employed for serious bacterial infections. Therapeutic drug monitoring (TDM) is a strategy to optimize dosing, especially in critically ill patients. This study aims to show how TDM influences the management of meropenem in a real-life setting, not limited to intensive care units. Methods: From December 2021 to February 2022, we retrospectively analyzed 195 meropenem serum concentrations (Css). We characterized patients according to meropenem exposure, focusing on the renal function impact. Results: A total of 36% (n = 51) of the overall observed patients (n = 144) were in the therapeutic range (8–16 mg/L), whereas 64% (n = 93) required a meropenem dose modification (37 patients (26%) underexposed; 53 (38%) overexposed). We found a strong relationship between renal function and meropenem concentrations (correlation coefficient = −0.7; p-value < 0.001). We observed different dose-normalized meropenem exposure (Css/D) among renal-impaired (severe and moderate), normal, and hyperfiltrating patients, with a median (interquartile range) of 13.1 (10.9–20.2), 7.9 (6.1–9.5), 3.8 (2.6–6.0), and 2.4 (1.6–2.7), respectively (p-value < 0.001). Conclusions: Meropenem TDM in clinical practice allows modification of dosing in patients inadequately exposed to meropenem to maximize antibiotic efficacy and minimize the risk of antibiotic resistance, especially in renal alterations despite standard dose adaptations. [ABSTRACT FROM AUTHOR]

Background: Meropenem is a carbapenem antibiotic widely employed for serious bacterial infections. Therapeutic drug monitoring (TDM) is a strategy to optimize dosing, especially in critically ill patients. This study aims to show how TDM influences the management of meropenem in a real-life setting, not limited to intensive care units. Methods: From December 2021 to February 2022, we retrospectively analyzed 195 meropenem serum concentrations (Css). We characterized patients according to meropenem exposure, focusing on the renal function impact. Results: A total of 36% (n = 51) of the overall observed patients (n = 144) were in the therapeutic range (8–16 mg/L), whereas 64% (n = 93) required a meropenem dose modification (37 patients (26%) underexposed; 53 (38%) overexposed). We found a strong relationship between renal function and meropenem concentrations (correlation coefficient = −0.7; p-value < 0.001). We observed different dose-normalized meropenem exposure (Css/D) among renal-impaired (severe and moderate), normal, and hyperfiltrating patients, with a median (interquartile range) of 13.1 (10.9–20.2), 7.9 (6.1–9.5), 3.8 (2.6–6.0), and 2.4 (1.6–2.7), respectively (p-value < 0.001). Conclusions: Meropenem TDM in clinical practice allows modification of dosing in patients inadequately exposed to meropenem to maximize antibiotic efficacy and minimize the risk of antibiotic resistance, especially in renal alterations despite standard dose adaptations.

The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice. [ABSTRACT FROM AUTHOR]

The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice.

Background: Ceftazidime and imipenem have been increasingly used to treat Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) due to their extended-spectrum covering Pseudomonas aeruginosa. This study aims to describe the population pharmacokinetic (PK) and pharmacodynamic (PD) target attainment for ceftazidime and imipenem in patients with AECOPD. Methods: We conducted a prospective PK study at Bach Mai Hospital (Viet Nam). A total of 50 (ceftazidime) and 44 (imipenem) patients with AECOPD were enrolled. Population PK analysis was performed using Monolix 2019R1 and Monte Carlo simulations were conducted to determine the optimal dose regimen with respect to the attainment of 60% and 40% fT>MIC for ceftazidime and imipenem, respectively. A dosing algorithm was developed to identify optimal treatment doses. Results: Ceftazidime and imipenem PK was best described by a one-compartment population model with a volume of distribution and clearance of 23.7 L and 8.74 L/h for ceftazidime and 15.1 L and 7.88 L/h for imipenem, respectively. Cockcroft–Gault creatinine clearance represented a significant covariate affecting the clearance of both drugs. Increased doses with prolonged infusion were found to cover pathogens with reduced susceptibility. Conclusions: This study describes a novel and versatile three-level dosing algorithm based on patients’ renal function and characteristic of the infective pathogen to explore ceftazidime and imipenem optimal regimen for AECOPD.

Exposure-response and clinical outcome (CO) model for inhaled budesonide/formoterol was developed to quantify the relationship among pharmacokinetics (PK), pharmacodynamics (PD) and CO of the drugs and evaluate the covariate effect on model parameters. Sputum eosinophils cationic proteins (ECP) and forced expiratory volume (FEV1) were selected as PD markers and asthma control score was used as a clinical outcome. One- and two-compartment models were used to describe the PK of budesonide and formoterol, respectively. The indirect response model (IDR) was used to describe the PD effect for ECP and FEV1. In addition, the symptomatic effect on the disease progression model for CO was connected with IDR on each PD response. The slope for the effect of ECP and FEV1 to disease progression were estimated as 0.00008 and 0.644, respectively. Total five covariates (ex. ADRB2 genotype etc.) were searched using a stepwise covariate modeling method, however, there was no significant covariate effect. The results from the simulation study were showed that a 1 puff b.i.d. had a comparable effect of asthma control with a 2 puff b.i.d. As a result, the 1 puff b.i.d. of combination drug could be suggested as a standardized dose to minimize the side effects and obtain desired control of disease compared to the 2 puff b.i.d.

Background: Ceftazidime and imipenem have been increasingly used to treat Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) due to their extended-spectrum covering Pseudomonas aeruginosa. This study aims to describe the population pharmacokinetic (PK) and pharmacodynamic (PD) target attainment for ceftazidime and imipenem in patients with AECOPD. Methods: We conducted a prospective PK study at Bach Mai Hospital (Viet Nam). A total of 50 (ceftazidime) and 44 (imipenem) patients with AECOPD were enrolled. Population PK analysis was performed using Monolix 2019R1 and Monte Carlo simulations were conducted to determine the optimal dose regimen with respect to the attainment of 60% and 40% fT>MIC for ceftazidime and imipenem, respectively. A dosing algorithm was developed to identify optimal treatment doses. Results: Ceftazidime and imipenem PK was best described by a one-compartment population model with a volume of distribution and clearance of 23.7 L and 8.74 L/h for ceftazidime and 15.1 L and 7.88 L/h for imipenem, respectively. Cockcroft–Gault creatinine clearance represented a significant covariate affecting the clearance of both drugs. Increased doses with prolonged infusion were found to cover pathogens with reduced susceptibility. Conclusions: This study describes a novel and versatile three-level dosing algorithm based on patients' renal function and characteristic of the infective pathogen to explore ceftazidime and imipenem optimal regimen for AECOPD. [ABSTRACT FROM AUTHOR]

Exposure-response and clinical outcome (CO) model for inhaled budesonide/formoterol was developed to quantify the relationship among pharmacokinetics (PK), pharmacodynamics (PD) and CO of the drugs and evaluate the covariate effect on model parameters. Sputum eosinophils cationic proteins (ECP) and forced expiratory volume (FEV1) were selected as PD markers and asthma control score was used as a clinical outcome. One- and two-compartment models were used to describe the PK of budesonide and formoterol, respectively. The indirect response model (IDR) was used to describe the PD effect for ECP and FEV1. In addition, the symptomatic effect on the disease progression model for CO was connected with IDR on each PD response. The slope for the effect of ECP and FEV1 to disease progression were estimated as 0.00008 and 0.644, respectively. Total five covariates (ex. ADRB2 genotype etc.) were searched using a stepwise covariate modeling method, however, there was no significant covariate effect. The results from the simulation study were showed that a 1 puff b.i.d. had a comparable effect of asthma control with a 2 puff b.i.d. As a result, the 1 puff b.i.d. of combination drug could be suggested as a standardized dose to minimize the side effects and obtain desired control of disease compared to the 2 puff b.i.d. [ABSTRACT FROM AUTHOR]