We conclude that exposures to ceftazidime predict microbiological as well as clinical outcome, and the %fT>MIC required to result in a likely favourable outcome is >45% of the dosing interval. This value is similar to that observed in animal models and underscores the principle that adequate dosing can be predicted and is beneficial to patient care.

Standard-of-care infliximab dosing regimens were developed prior to the routine use of therapeutic drug monitoring and identification of target concentrations. Not surprisingly, subtherapeutic infliximab concentrations in pediatric Crohn's disease (CD) are common. The primary aim was to conduct a real-world pharmacokinetic (PK) evaluation to discover blood biomarkers of rapid clearance, identify exposure targets, and a secondary aim to translate PK modeling to the clinic. In a multicenter observational study, 671 peak and trough infliximab concentrations from 78 patients with CD were analyzed with a drug-tolerant assay (Esoterix; LabCorp, Calabasas, CA). Individual area under the curve (AUC) estimates were generated as a measure of drug exposure over time. Population PK modeling (nonlinear mixed-effect modeling) identified serum albumin, antibody to infliximab, erythrocyte sedimentation rate (ESR), and neutrophil CD64 as biomarkers for drug clearance. Week 14 and week 52 biochemical remitters (fecal calprotectin < 250 µg/g) had higher infliximab exposure (AUC) throughout induction. The optimal infliximab AUC target during induction for week 14 biochemical remission was 79,348 µg*h/mL (area under the receiver operating characteristic curve (AUROC) 0.77, [0.63-0.90], 85.7% sensitive, and 64.3% specific) with those exceeding the AUC target more likely to achieve a surgery-free week 52 biochemical remission (OR 4.3,). Pretreatment predictors for subtherapeutic week 14 AUC included neutrophil CD64 > 6 (OR 4.5,), ESR > 30 mm/h (OR 3.8,), age < 10 years old (OR 4.2,), and weight < 30 kg (OR 6.6,). We created a decisionsupport PK dashboard with an iterative process and embedded the modeling program within the electronic health record. Model-informed precision dosing guided by real-world PKs is now available at the bedside in real-time.

For the 5FC-FCZ combination, the interactions determined by the IC␣ generally were in concordance with the interactions determined by the FIC index, but large discrepancies were found between both methods for the 5FC-AB combination. These could mainly be explained by shortcomings in the FIC approach. The in vitro interaction of 5FC-AB demonstrated variable results depending on the tested Candida isolate. In general, the 5FC-FCZ combination was antagonistic against Candida species, but for some Candida isolates synergism was found. For C. neoformans the interaction for both combinations was highly dependent on the tested isolate and the method used. Response surface approach is an alternative method for determining the interaction between antifungal agents. By using this approach, some of the problems encountered with the FIC were overcome.

No abstract

We developed and applied pharmacokinetic-pharmacodynamic (PK-PD) models to characterize in vitro bacterial rate of killing as a function of ceftazidime concentrations over time. For PK-PD modeling, data obtained during continuous and intermittent infusion of ceftazidime in Pseudomonas aeruginosa killing experiments with an in vitro pharmacokinetic model were used. The basic PK-PD model was a maximum-effect model which described the number of viable bacteria (N) as a function of the growth rate (lambda) and killing rate (epsilon) according to the equation dN/dt = [lambda - epsilon x [Cgamma(EC50gamma + Cgamma)]] N, where gamma is the Hill factor, C is the concentration of antibiotic, and EC50 is the concentration of antibiotic at which 50% of the maximum effect is obtained. Next, four different models with increasing complexity were analyzed by using the EDSIM program (MediWare, Groningen, The Netherlands). These models incorporated either an adaptation rate factor and a maximum number of bacteria (Nmax) factor or combinations of the two parameters. In addition, a two-population model was evaluated. Model discrimination was by Akaike's information criterion. The experimental data were best described by the model which included an Nmax term and a rate term for adaptation for a period up to 36 h. The absolute values for maximal growth rate and killing rate in this model were different from those in the original experiment, but net growth rates were comparable. It is concluded that the derived models can describe bacterial growth and killing in the presence of antibiotic concentrations mimicking human pharmacokinetics. Application of these models will eventually provide us with parameters which can be used for further dosage optimization.