Cornelia B. Landersdorfer

Population Pharmacokinetics of Intravenous Polymyxin B in Critically Ill Patients: Implications for Selection of Dosage Regimens

BACKGROUND Polymyxin B is a last-line therapy for multidrug-resistant gram-negative bacteria. There is a dearth of pharmacokinetic data to guide dosing in critically ill patients. METHODS Twenty-four critically ill patients were enrolled and blood/urine samples were collected over a dosing interval at steady state. Polymyxin B concentrations were measured by liquid chromatography-tandem mass spectrometry. Population pharmacokinetic analysis and Monte Carlo simulations were conducted. RESULTS Twenty-four patients aged 21-87 years received intravenous polymyxin B (0.45-3.38 mg/kg/day). Two patients were on continuous hemodialysis, and creatinine clearance in the other patients was 10-143 mL/min. Even with very diverse demographics, the total body clearance of polymyxin B when scaled by total body weight (population mean, 0.0276 L/hour/kg) showed remarkably low interindividual variability (32.4% coefficient of variation). Polymyxin B was predominantly nonrenally cleared with median urinary recovery of 4.04%. Polymyxin B total body clearance did not show any relationship with creatinine clearance (r(2) = 0.008), APACHE II score, or age. Median unbound fraction in plasma was 0.42. Monte Carlo simulations revealed the importance of initiating therapeutic regimens with a loading dose. CONCLUSIONS Our study showed that doses of intravenous polymyxin B are best scaled by total body weight. Importantly, dosage selection of this drug should not be based on renal function.

Penetration of antibacterials into bone: pharmacokinetic, pharmacodynamic and bioanalytical considerations.

Antibacterials play a key role in the treatment of bone infections and appropriate surgical prophylaxis. The rate and extent of penetration of antimicrobials into bone has been assessed and shown to be important for successful treatment in numerous studies. However, no recent review or critical evaluation of the analytical techniques is available. This review compares established and new sample preparation and analytical methods to measure bone concentrations.We performed a systematic literature search in MEDLINE, EMBASE, conference abstracts and references from published articles on bone penetration of antibacterials. This article focuses on the standardization of drug analysis in bone, the extent and rate of bone penetration of antibacterials, and the design, evaluation and reporting techniques of pharmacokinetic studies of bone penetration. The focus is on studies conducted between 1998 and 2007, since a previous review was published in 1999. WinNonlin® Professional version 5.0.1 software was used for statistics.Very different methods for sample preparation, drug analysis, data handling and reporting have been employed in bone penetration studies. There is substantial variability in the reported mean bone penetration between drugs and between different studies of the same drug. Quinolones, macrolides and linezolid have mean bone:serum concentration ratios that are commonly between 0.3 and 1.2, and higher ratios have been found for azithromycin (bone concentration in mg/kg of total bone). The ratios are usually between 0.15 and 0.3 for cephalosporins and glycopeptides, and between 0.1 and 0.3 for penicillins. Cephalosporins and penicillins have shown significantly lower (p<0.05) concentration ratios than linezolid. For 20 of 25 different drugs, the ratios were higher for cancellous bone than for cortical bone.The available data show a larger extent of bone penetration for quinolones, macrolides and linezolid than for β-lactams. The bone penetration of penicillins and cephalosporins was significantly lower than that of linezolid. Guidelines on sample preparation, drug analysis, study design and pharmacokinetic evaluation of bone penetration studies are vitally needed.

Acrylamide: increased concentrations in homemade food and first evidence of its variable absorption from food, variable metabolism and placental and breast milk transfer in humans.

We have developed a liquid chromatography/mass spectrometry (LC-MS/MS) assay to determine acrylamide in various body fluids. The assay also allows the reliable quantitation of acrylamide in food. In a total of 11 healthy male and female subjects, we were able to show that acrylamide from food given to humans is in fact absorbed from the gut. The half-lives determined in two male subjects were 2.2 and 7 h. Acrylamide was found in human breast milk and penetrated the human placenta (n = 3). The variability of acrylamide concentrations found in this investigation is most likely caused by variable intersubject bioavailability and metabolism. This may be an important indication that the assessment of the risk from acrylamide for the individual may be very difficult without knowing the concentrations of acrylamide in the body. This should be considered in the design of any risk assessment study or post hoc analysis of earlier studies. At this time, we suggest that pregnant women and breast-feeding mothers avoid acrylamide-containing food.

Pharmacodynamic Modeling of Anti-Cancer Activity of Tetraiodothyroacetic Acid in a Perfused Cell Culture System

Unmodified or as a poly[lactide-co-glycolide] nanoparticle, tetraiodothyroacetic acid (tetrac) acts at the integrin αvβ3 receptor on human cancer cells to inhibit tumor cell proliferation and xenograft growth. To study in vitro the pharmacodynamics of tetrac formulations in the absence of and in conjunction with other chemotherapeutic agents, we developed a perfusion bellows cell culture system. Cells were grown on polymer flakes and exposed to various concentrations of tetrac, nano-tetrac, resveratrol, cetuximab, or a combination for up to 18 days. Cells were harvested and counted every one or two days. Both NONMEM VI and the exact Monte Carlo parametric expectation maximization algorithm in S-ADAPT were utilized for mathematical modeling. Unmodified tetrac inhibited the proliferation of cancer cells and did so with differing potency in different cell lines. The developed mechanism-based model included two effects of tetrac on different parts of the cell cycle which could be distinguished. For human breast cancer cells, modeling suggested a higher sensitivity (lower IC50) to the effect on success rate of replication than the effect on rate of growth, whereas the capacity (Imax) was larger for the effect on growth rate. Nanoparticulate tetrac (nano-tetrac), which does not enter into cells, had a higher potency and a larger anti-proliferative effect than unmodified tetrac. Fluorescence-activated cell sorting analysis of harvested cells revealed tetrac and nano-tetrac induced concentration-dependent apoptosis that was correlated with expression of pro-apoptotic proteins, such as p53, p21, PIG3 and BAD for nano-tetrac, while unmodified tetrac showed a different profile. Approximately additive anti-proliferative effects were found for the combinations of tetrac and resveratrol, tetrac and cetuximab (Erbitux), and nano-tetrac and cetuximab. Our in vitro perfusion cancer cell system together with mathematical modeling successfully described the anti-proliferative effects over time of tetrac and nano-tetrac and may be useful for dose-finding and studying the pharmacodynamics of other chemotherapeutic agents or their combinations.

Development of a New Pre- and Post-Processing Tool (SADAPT-TRAN) for Nonlinear Mixed-Effects Modeling in S-ADAPT

Mechanistic modeling greatly benefits from automated pre- and post-processing of model code and modeling results. While S-ADAPT provides many state-of-the-art parametric population estimation methods, its pre- and post-processing capabilities are limited. Our objective was to develop a fully automated, open-source pre- and post-processor for nonlinear mixed-effects modeling in S-ADAPT. We developed a new translator tool (SADAPT-TRAN) based on Perl scripts. These scripts (a) automatically translate the core model components into robust Fortran code, (b) perform extensive mutual error checks across all input files and the raw dataset, (c) extend the options of the Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm, and (d) improve the numerical robustness of the model code. The post-processing scripts automatically summarize the results of one or multiple models as tables and, by generating problem specific R scripts, provide an extended series of standard and covariate-stratified diagnostic plots. The SADAPT-TRAN package substantially improved the efficiency to specify, debug, and evaluate models and enhanced the flexibility of using the MC-PEM algorithm for parallelized estimation in S-ADAPT. The parameter variability model can take any combination of normally, log-normally, or logistically distributed parameters and the SADAPT-TRAN package can automatically generate the Fortran code required to specify between occasion variability. Extended estimation features are available to avoid local minima, estimate means with negligible variances, and estimate variances for fixed means. The SADAPT-TRAN package significantly facilitated model development in S-ADAPT, reduced model specification errors, and provided useful error messages for beginner and advanced users. This benefit was greatest for complex mechanistic models.

Combination therapy for carbapenem-resistant Gram-negative bacteria

The emergence of resistant to carbapenems Gram-negative bacteria (CR GNB) has severely challenged antimicrobial therapy. Many CR GNB isolates are only susceptible to polymyxins; however, therapy with polymyxins and other potentially active antibiotics presents some drawbacks, which have discouraged their use in monotherapy. In this context, along with strong pre-clinical evidence of benefit in combining antimicrobials against CR GNB, the clinical use of combination therapy has been raised as an interesting strategy to overcome these potential limitations of a single agent. Polymyxins, tigecycline and even carbapenems are usually the cornerstone agents in combination schemes. Optimization of the probability to attain the pharmacokinetic/pharmacodynamic targets by both cornerstone drug and adjuvant drug is of paramount importance to achieve better clinical and microbiological outcomes. Clinical evidence of the major drugs utilized in combination schemes and how they should be prescribed considering pharmacokinetic/pharmacodynamic characteristics against CR GNB will be reviewed in this article.

Evaluation by Monte Carlo Simulation of the Pharmacokinetics of Two Doses of Meropenem Administered Intermittently or as a Continuous Infusion in Healthy Volunteers

ABSTRACT Meropenem is a broad-spectrum carbapenem antibacterial agent. In order to optimize levels in plasma relative to the MICs, the ideal dose level and dosage regimen need to be determined. The pharmacokinetics of meropenem were studied in two groups, each comprising eight healthy volunteers who received the following doses: 500 mg as an intravenous infusion over 30 min three times a day (t.i.d.) versus a 250-mg loading dose followed by a 1,500 mg continuous infusion over 24 h for group A and 1,000 mg as an intravenous infusion over 30 min t.i.d. versus a 500-mg loading dose followed by a 3,000-mg continuous infusion over 24 h for group B. Meropenem concentrations in plasma and urine were determined by liquid chromatography-mass spectrometry/mass spectrometry and high-performance liquid chromatography with UV detection, respectively. Pharmacokinetic calculations were done by use of a two-compartment open model, and the data were extrapolated by Monte Carlo simulations for 10,000 simulated subjects for pharmacodynamic evaluation. There were no significant differences in total clearance and renal clearance between group A and group B or between the intermittent treatment and the continuous infusion. The analyses of the probability of target attainment by MIC for the high- and low-dose continuous infusions were robust up to MICs of 4 mg/liter and 2 mg/liter, respectively. The corresponding values for intermittent infusions were only 0.5 mg/liter and 0.25 mg/liter. When these observations were correlated with MICs obtained from the MYSTIC database, intermittent infusion results in adequate activity against two of the most common nosocomially acquired pathogens, Klebsiella pneumoniae and Enterobacter cloacae. However, against Pseudomonas aeruginosa, the evaluation shows a clear advantage of high-dose therapy administered as a continuous infusion. We believe that in the empirical therapy situation, the continuous-infusion mode of administration is most worth the extra efforts. We conclude that clinical trials for evaluation of the continuous infusions of meropenem in critically ill patients are warranted.

Pharmacokinetic/Pharmacodynamic Modelling in Diabetes Mellitus

Diabetes mellitus is a major health risk in many countries, and the incidence rates are increasing. Diverse therapeutic agents are applied to treat this condition. Since 1960, numerous mathematical models have been developed to describe the glucose-insulin system, analyse data from diagnostic tests and quantify drug effects. This review summarizes the present state-of-the-art in diabetes modelling, with a focus on models describing drug effects, and identifies major strengths and limitations of the published models.For diagnostic purposes, the minimal model has remained the most popular choice for several decades, and numerous extensions have been developed. Use of the minimal model is limited for applications other than diagnostic tests. More mechanistic models that include glucose-insulin feedback in both directions have been applied. The use of biophase distribution models for the description of drug effects is not always appropriate. More recently, the effects of various antidiabetic agents on glucose and insulin have been modelled with indirect response models. Such models provide good curve fits and mechanistic descriptions of the effects of antidiabetic drugs on glucose-insulin homeostasis. These and other types of models were used to describe secondary drug effects on glucose and insulin, and effects on ancillary biomarkers. Modelling of disease progression in diabetes can utilize indirect response models as a disturbance of homeostasis.Future needs are to include glucose-insulin feedback more often, develop mechanistic models for new drug groups, consider dual drug effects on complementary subsystems, and incorporate elements of disease progression.

‘Old’ antibiotics for emerging multidrug-resistant bacteria

Purpose of review Increased emergence of bacterial resistance and the decline in newly developed antibiotics have necessitated the reintroduction of previously abandoned antimicrobial agents active against multidrug-resistant bacteria. Having never been subjected to contemporary drug development procedures, these ‘old’ antibiotics require redevelopment in order to optimize therapy. This review focuses on colistin as an exemplar of a successful redevelopment process and briefly discusses two other old antibiotics, fusidic acid and fosfomycin. Recent findings Redevelopment of colistin led to an improved understanding of its chemistry, pharmacokinetics and pharmacodynamics, enabling important steps towards optimizing its clinical use in different patient populations. A scientifically based dosing algorithm was developed for critically ill patients, including those with renal impairment. As nephrotoxicity is a dose-limiting adverse event of colistin, rational combination therapy with other antibiotics needs to be investigated. Summary The example of colistin demonstrated that state-of-the-art analytical, microbiological and pharmacokinetic/pharmacodynamic methods can facilitate optimized use of ‘old’ antibiotics in the clinic. Similar methods are now being applied to fosfomycin and fusidic acid in order to optimize therapy. To improve and preserve the usefulness of these antibiotics rational approaches for redevelopment need to be followed.

Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models

The Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm can approximate the true log-likelihood as precisely as needed and is efficiently parallelizable. Our objectives were to evaluate an importance sampling version of the MC-PEM algorithm for mechanistic models and to qualify the default estimation settings in SADAPT-TRAN. We assessed bias, imprecision and robustness of this algorithm in S-ADAPT for mechanistic models with up to 45 simultaneously estimated structural parameters, 14 differential equations, and 10 dependent variables (one drug concentration and nine pharmacodynamic effects). Simpler models comprising 15 parameters were estimated using three of the ten dependent variables. We set initial estimates to 0.1 or 10 times the true value and evaluated 30 bootstrap replicates with frequent or sparse sampling. Datasets comprised three dose levels with 16 subjects each. For simultaneous estimation of the full model, the ratio of estimated to true values for structural model parameters (median [5–95% percentile] over 45 parameters) was 1.01 [0.94–1.13] for means and 0.99 [0.68–1.39] for between-subject variances for frequent sampling and 1.02 [0.81–1.47] for means and 1.02 [0.47–2.56] for variances for sparse sampling. Imprecision was ≤25% for 43 of 45 means for frequent sampling. Bias and imprecision was well comparable for the full and simpler models. Parallelized estimation was 23-fold (6.9-fold) faster using 48 threads (eight threads) relative to one thread. The MC-PEM algorithm was robust and provided unbiased and adequately precise means and variances during simultaneous estimation of complex, mechanistic models in a 45 dimensional parameter space with rich or sparse data using poor initial estimates.