Pharmacokinetic/pharmacodynamic evaluation of tigecycline dosing in a hollow fiber infection model against clinical bla-KPC producing Klebsiella Pneumoniae isolates.

The FDA announced a boxed warning for tigecycline due to progression of infections caused by Gram-negative bacteria and increased risk of mortality during treatment. Plasma exposure of tigecycline might not prevent bacteraemia in these cases from the focuses. Hence, we evaluated intensified dosing regimens and breakpoints that might suppress bloodstream infections, caused by progression of infection by e.g., Gram-negatives. A pharmacometric model was built from tigecycline concentrations (100-600 mg daily doses) against clinical Klebsiella pneumoniae isolates (MIC 0.125-0.5 mg/L). Regrowth occurred at clinically used doses and stasis was only achieved with 100 mg q8h for the strain with the lowest studied MIC of 0.125 mg/L. Stasis at 24 h was related to fAUC/MIC of 38.5. Our study indicates that even intensified dosing regimens might prevent bloodstream infections only for MIC values ≤0.125 mg/L for tigecycline. This indicates an overly optimistic breakpoint of 1 mg/L for Enterobacterales, which are deemed to respond to the tigecycline high dose regimen (EUCAST Guidance Document on Tigecycline Dosing 2022).

Tigecycline Dosing Strategies in Critically Ill Liver-Impaired Patients.

This study investigated tigecycline exposure in critically ill patients from a population pharmacokinetic perspective to support rational dosing in intensive care unit (ICU) patients with acute and chronic liver impairment. A clinical dataset of 39 patients served as the basis for the development of a population pharmacokinetic model. The typical tigecycline clearance was strongly reduced (8.6 L/h) as compared to other populations. Different models were developed based on liver and kidney function-related covariates. Monte Carlo simulations were used to guide dose adjustments with the most predictive covariates: Child-Pugh score, total bilirubin, and MELD score. The best performing covariate, guiding a dose reduction to 25 mg q12h, was Child-Pugh score C, whereas patients with Child-Pugh score A/B received the standard dose of 50 mg q12h. Of note, the obtained 24 h steady-state area under the concentration vs. time curve (AUCss) range using this dosing strategy was predicted to be equivalent to high-dose tigecycline exposure (100 mg q12h) in non-ICU patients. In addition, 26/39 study participants died, and therapy failure was most correlated with chronic liver disease and renal failure, but no correlation between drug exposure and survival was observed. However, tigecycline in special patient populations needs further investigations to enhance clinical outcome.

Evaluation of the Robustness of Therapeutic Drug Monitoring Coupled with Bayesian Forecasting of Busulfan with Regard to Inaccurate Documentation.

BACKGROUND: Inaccurate documentation of sampling and infusion times is a potential source of error in personalizing busulfan doses using therapeutic drug monitoring (TDM). Planned times rather than the actual times for sampling and infusion time are often documented. Therefore, this study aimed to evaluate the robustness of a limited sampling TDM of busulfan with regard to inaccurate documentation. METHODS: A pharmacometric analysis was conducted in NONMEM® 7.4.3 and "R" by performing stochastic simulation and estimation with four, two and one sample(s) per patient on the basis of a one-compartment- (1CMT) and two-compartment (2CMT) population pharmacokinetic model. The dosing regimens consisted of i.v. busulfan (0.8 mg/kg) every 6 h (Q6H) or 3.2 mg/kg every 24 h (Q24H) with a 2 h- and 3 h infusion time, respectively. The relative prediction error (rPE) and relative root-mean-square error (rRmse) were calculated in order to determine the accuracy and precision of the individual AUC estimation. RESULTS: A noticeable impact on the estimated AUC based on a 1CMT-model was only observed if uncertain documentation reached ± 30 min (1.60% for Q24H and 2.19% for Q6H). Calculated rPEs and rRmse for Q6H indicate a slightly lower level of accuracy and precision when compared to Q24H. Spread of rPE's and rRmse for the 2CMT-model were wider and higher compared to estimations based on a 1CMT-model. CONCLUSIONS: The estimated AUC was not affected substantially by inaccurate documentation of sampling and infusion time. The calculated rPEs and rRmses of estimated AUC indicate robustness and reliability for TDM of busulfan, even in presence of erroneous records.

Stability studies with tigecycline in bacterial growth medium and impact of stabilizing agents.

PURPOSE: This study aimed to examine the degradation of tigecycline in Mueller Hinton broth (ca-MHB), as knowledge about bacterial susceptibility is key for therapeutic decisions. METHODS: Antioxidative stabilizers were evaluated on tigecycline stability in a quantitative chromatography assay and tigecycline induced kill against Staphylococcus aureus (ATCC29213) was determined in time kill studies. RESULTS: Ascorbic acid caused rapid degradation of tigecycline and resulted in loss of antibacterial activity. Tigecycline was stabilized in aged broth by 2% pyruvate and bacterial growth, and tigecycline killing was similar to fresh broth without supplementation, but independent of age. CONCLUSION: Our results underline the importance of using freshly prepared ca-MHB or the need for stabilizers for tigecycline susceptibility testing while using aged ca-MHB.

An Integrated Dialysis Pharmacometric (IDP) Model to Evaluate the Pharmacokinetics in Patients Undergoing Renal Replacement Therapy.

PURPOSE: Clearance via renal replacement therapy (RRT) can significantly alter the pharmacokinetic profile of drugs. The aim of this study was (i) to improve the use of clinical trial data and (ii) to provide a model that allows quantification of all aspects of drug elimination via RRT including adsorption to dialysis membranes and/or degradation of the drug in the dialysate. METHODS: An integrated dialysis pharmacometric (IDP) model was developed to simultaneously incorporate all available RRT information. The sensitivity, accuracy and precision of the IDP model was compared to conventional approaches in clinical trial simulations and applied to clinical datasets of teicoplanin and doripenem. RESULTS: The IDP model was more accurate, precise and sensitive than conventional plasma-concentration-based approaches when estimating the clearanceRRT (relative bias <1%). In contrast to conventional approaches, adsorption and degradation were quantifiable using the IDP model (relative bias: -1.1% and - 1.9%, respectively). Applied to clinical data, clearanceRRT, drug degradation (effluent-half-lifedoripenem: 13.5 h-1) and adsorption (polysulphone adsorption capacityteicoplanin: 31.2 mg) were assessed. CONCLUSION: The IDP model allows accurate, precise and sensitive characterization of clearanceRRT, adsorption and degradation. Successful quantification of all aspects of clearanceRRT in clinical data demonstrated the benefit of the IDP model as compared to conventional approaches.

Assessing parameter uncertainty in small-n pharmacometric analyses: value of the log-likelihood profiling-based sampling importance resampling (LLP-SIR) technique.

Assessing parameter uncertainty is a crucial step in pharmacometric workflows. Small datasets with ten or fewer subjects appear regularly in drug development and therapeutic use, but it is unclear which method to assess parameter uncertainty is preferable in such situations. The aim of this study was to (i) systematically evaluate the performance of standard error (SE), bootstrap (BS), log-likelihood profiling (LLP), Bayesian approaches (BAY) and sampling importance resampling (SIR) to assess parameter uncertainty in small datasets and (ii) to evaluate methods to provide proposal distributions for the SIR. A simulation study was conducted and the 0-95% confidence interval (CI) and coverage for each parameter was evaluated and compared to reference CIs derived by stochastic simulation and estimation (SSE). A newly proposed LLP-SIR, combining the proposal distribution provided by LLP with SIR, was included in addition to conventional SE-SIR and BS-SIR. Additionally, the methods were applied to a clinical dataset. The determined CIs differed substantially across the methods. The CIs of SE, BS, LLP and BAY were not in line with the reference in datasets with ≤ 10 subjects. The best alignment was found for the LLP-SIR, which also provided the best coverage results among the SIR methods. The best overall results regarding the coverage were provided by LLP and BAY across all parameters and dataset sizes. To conclude, the popular SE and BS methods are not suitable to derive parameter uncertainty in small datasets containing ≤ 10 subjects, while best performances were observed with LLP, BAY and LLP-SIR.

Impact of Inaccurate Documentation of Sampling and Infusion Time in Model-Informed Precision Dosing.

BACKGROUND: Routine clinical TDM data is often used to develop population pharmacokinetic (PK) models, which are applied in turn for model-informed precision dosing. The impact of uncertainty in documented sampling and infusion times in population PK modeling and model-informed precision dosing have not yet been systematically evaluated. The aim of this study was to investigate uncertain documentation of (i) sampling times and (ii) infusion rate exemplified with two anti-infectives. METHODS: A stochastic simulation and estimation study was performed in NONMEM® using previously published population PK models of meropenem and caspofungin. Uncertainties, i.e. deviation between accurate and planned sampling and infusion times (standard deviation (SD) ± 5 min to ± 30 min) were added randomly in R before carrying out the simulation step. The estimation step was then performed with the accurate or planned times (replacing real time points by scheduled study values). Relative bias (rBias) and root mean squared error (rRMSE) were calculated to determine accuracy and precision of the primary and secondary PK parameters on the population and individual level. The accurate and the misspecified (using planned sampling times) model were used for Bayesian forecasting of meropenem to assess the impact on PK/PD target calculations relevant to dosing decisions. RESULTS: On the population level, the estimates of the proportional residual error (prop.-err.) and the interindividual variability (IIV) on the central volume of distribution (V1) were most affected by erroneous records in the sampling and infusion time (e.g. rBias of prop.-err.: 75.5% vs. 183% (meropenem) and 10.1% vs. 109% (caspofungin) for ± 5 vs. ± 30 min, respectively). On the individual level, the rBias of the planned scenario for the typical values V1, Q and V2 increased with increasing uncertainty in time, while CL, AUC and elimination half-life were least affected. Meropenem as a short half-life drug (~1 h) was more affected than caspofungin (~ 9-11 h). The misspecified model provided biased PK/PD target information (e.g. falsely overestimated time above MIC (T > MIC) when true T > MIC was <0.4 and thus patients at risk of undertreatment), while the accurate model gave precise estimates of the indices across all simulated patients. CONCLUSIONS: Even 5-minute-uncertainties caused bias and significant imprecision of primary population and individual PK parameters. Thus, our results underline the importance of accurate documentation of time.

Pharmacokinetics of trimethoprim/sulfametrole in critically ill patients on continuous renal replacement therapy.

OBJECTIVES: We investigated the effect of continuous renal replacement therapy (CRRT) on the pharmacokinetics of trimethoprim and sulfametrole. PATIENTS AND METHODS: We enrolled critically ill adults undergoing CRRT and critically ill adults with normal or slightly impaired renal function (plasma creatinine concentration <1.5 mg/dL, control group). All patients received trimethoprim/sulfametrole at standard doses. Pharmacokinetics were determined after the first dose and at steady-state. In addition, a population pharmacokinetic model using plasma data was built. We also assessed the renal clearance (CLR) and the extracorporeal clearance in patients undergoing CRRT. RESULTS: Twelve patients were enrolled in the CRRT group and 12 patients in the control group. There was no statistically significant difference in trimethoprim pharmacokinetics between the two groups. In patients on CRRT, total plasma clearance (CLtot) and V of sulfametrole were significantly higher than in the control group. However, sulfametrole exposure was not significantly altered during CRRT. The population pharmacokinetic analysis indicated that neither CRRT intensity nor residual diuresis were significant covariates on trimethoprim or sulfametrole CL. Median CL by continuous venovenous haemofiltration accounted for about one-third of CLtot of trimethoprim and for about one-half of CLtot of sulfametrole. In patients on CRRT, CLR of trimethoprim and sulfametrole were <5% of CLtot. CONCLUSIONS: During CRRT, standard doses of trimethoprim/sulfametrole appear to be adequate.