Voriconazole exposure is influenced by inflammation: A population pharmacokinetic model.

BACKGROUND: Voriconazole is an antifungal drug used for the treatment of invasive fungal infections. Due to highly variable drug exposure, therapeutic drug monitoring (TDM) has been recommended. TDM may be helpful to predict exposure accurately, but covariates, such as severe inflammation, that influence the metabolism of voriconazole have not been included in the population pharmacokinetic (popPK) models suitable for routine TDM. OBJECTIVES: To investigate whether the effect of inflammation, reflected by C-reactive protein (CRP), could improve a popPK model that can be applied in clinical care. PATIENTS AND METHODS: Data from two previous studies were included in the popPK modelling. PopPK modelling was performed using Edsim++. Different popPK models were compared using Akaike Information Criterion and goodness-of-fit plots. RESULTS: In total, 1060 voriconazole serum concentrations from 54 patients were included in this study. The final model was a one-compartment model with non-linear elimination. Only CRP was a significant covariate, and was included in the final model and found to affect the maximum rate of enzyme activity (Vmax). For the final popPK model, the mean volume of distribution was 145 L [coefficient of variation percentage (CV%)=61%], mean Michaelis-Menten constant was 5.7 mg/L (CV%=119%), mean Vmax was 86.4 mg/h (CV%=99%) and mean bioavailability was 0.83 (CV%=143%). Internal validation using bootstrapping resulted in median values close to the population parameter estimates. CONCLUSIONS: This one-compartment model with non-linear elimination and CRP as a covariate described the pharmacokinetics of voriconazole adequately.

Population Pharmacokinetic Modelling and Limited Sampling Strategies for Therapeutic Drug Monitoring of Pyrazinamide in Patients with Tuberculosis.

Pyrazinamide is one of the first-line antituberculosis drugs. The efficacy of pyrazinamide is associated with the ratio of 24-h area under the concentration-time curve (AUC24) to MIC. The objective of this study was to develop and validate a limited sampling strategy (LSS) based on a population pharmacokinetic (popPK) model to predict AUC24. A popPK model was developed using an iterative two-stage Bayesian procedure and was externally validated. Using data from 20 treatment-naive adult tuberculosis (TB) patients, a one compartment model with transit absorption and first-order elimination best described pyrazinamide pharmacokinetics and fed state was the only significant covariate for absorption rate constant (ka). External validation, using data from 26 TB patients, showed that the popPK model predicted AUC24 with a slight underestimation of 2.1%. LSS were calculated using Monte Carlo simulation (n = 10,000). External validation showed LSS with time points 0 h, 2 h, and 6 h performed best with RMSE of 9.90% and bias of 0.06%. Food slowed absorption of pyrazinamide, but did not affect bioavailability, which may be advantageous in case of nausea or vomiting in which food can be used to diminish these effects. In this study, we successfully developed and validated a popPK model and LSS, using 0 h, 2 h, and 6 h postdose samples, that could be used to perform therapeutic drug monitoring (TDM) of pyrazinamide in TB patients.

MTXPK.org: A Clinical Decision Support Tool Evaluating High-Dose Methotrexate Pharmacokinetics to Inform Post-Infusion Care and Use of Glucarpidase.

Methotrexate (MTX), an antifolate, is administered at high doses to treat malignancies in children and adults. However, there is considerable interpatient variability in clearance of high-dose (HD) MTX. Patients with delayed clearance are at an increased risk for severe nephrotoxicity and life-threatening systemic MTX exposure. Glucarpidase is a rescue agent for severe MTX toxicity that reduces plasma MTX levels via hydrolysis of MTX into inactive metabolites, but is only indicated when MTX concentrations are > 2 SDs above the mean excretion curve specific for the given dose together with a significant creatinine increase (> 50%). Appropriate administration of glucarpidase is challenging due to the ambiguity in the labeled indication. A recent consensus guideline was published with an algorithm to provide clarity in when to administer glucarpidase, yet clinical interpretation of laboratory results that do not directly correspond to the algorithm prove to be a limitation of its use. The goal of our study was to develop a clinical decision support tool to optimize the administration of glucarpidase for patients receiving HD MTX. Here, we describe the development of a novel 3-compartment MTX population pharmacokinetic (PK) model using 31,672 MTX plasma concentrations from 772 pediatric patients receiving HD MTX for the treatment of acute lymphoblastic leukemia and its integration into the online clinical decision support tool, MTXPK.org. This web-based tool has the functionality to utilize individualized demographics, serum creatinine, and real-time drug concentrations to predict the elimination profile and facilitate model-informed administration of glucarpidase.

Electronic Health Record-Embedded Decision Support Platform for Morphine Precision Dosing in Neonates.

Morphine is the opioid most commonly used for neonatal pain management. In intravenous form, it is administered as continuous infusions and intermittent injections, mostly based on empirically established protocols. Inadequate pain control in neonates can cause long-term adverse consequences; however, providing appropriate individualized morphine dosing is particularly challenging due to the interplay of rapid natural physiological changes and multiple life-sustaining procedures in patients who cannot describe their symptoms. At most institutions, morphine dosing in neonates is largely carried out as an iterative process using a wide range of starting doses and then titrating to effect based on clinical response and side effects using pain scores and levels of sedation. Our background data show that neonates exhibit large variability in morphine clearance resulting in a wide range of exposures, which are poorly predicted by dose alone. Here, we describe the development and implementation of an electronic health record-integrated, model-informed decision support platform for the precision dosing of morphine in the management of neonatal pain. The platform supports pharmacokinetic model-informed dosing guidance and has functionality to incorporate real-time drug concentration information. The feedback is inserted directly into prescribers' workflows so that they can make data-informed decisions. The expected outcomes are better clinical efficacy and safety with fewer side effects in the neonatal population.

Use of Monte Carlo simulations to select therapeutic doses and provisional breakpoints of BAL9141.

BAL9141, a new antimicrobial agent belonging to the class of parenteral pyrrolidinone-3-ylidenemethyl cephalosporins, is active against most gram-positive microorganisms, including methicillin-resistant variants (methicillin-resistant Staphylococcus aureus [MRSA] and methicillin-resistant Staphylococcus epidermidis [MRSE]), as well as against penicillin-resistant pneumococci (PRP) and many gram-negative microorganisms. BAL9141 is administered as the prodrug BAL5788, which is rapidly converted to BAL9141 by plasma esterases. Pharmacokinetic (PK) data obtained in a previous multiple ascending dose study were used to fit a population PK model to using the NPEM2 program, yielding PK parameter estimates and its covariance matrix for BAL9141. These estimates and matrix were used to perform Monte Carlo simulations (MCSs) and obtain unbiased target attainment rates (TARs) for various time periods during which the concentration remains above the MIC (T(>MIC)). Assuming a T(>MIC) of 40%, TARs of 100% were reached with a dose of 500 mg/liter every 12 h for pathogens with MICs of 2 mg/liter and with a dose of 750 mg/liter every 12 h for pathogens with MICs of 4 mg/liter. Because MICs are