Mechanistic population pharmacokinetics of total and unbound paclitaxel for a new nanodroplet formulation versus Taxol in cancer patients

Bulitta, Jürgen B.; Zhao, Ping; Arnold, Robert D.; Kessler, Dean R; Daifuku, Richard; Pratt, James; Luciano, Gabriel; Hanauske, Axel‐R.; Gelderblom, Hans; Awada, Ahmad; Jusko, William J.

doi:10.1007/s00280-008-0827-2

Cited by 46 publications

(39 citation statements)

References 29 publications

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“…The availability of more powerful estimation algorithms, software packages, and computers enabled scientists to incorporate more sophisticated mechanisms in PK/PD models for clinical and experimental studies (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). Whenever true biological variability between individuals is present and significant, population PK/PD modeling is a powerful concept to describe and predict the behavior of such systems.…”

Section: Discussionmentioning

confidence: 99%

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

Bulitta

Landersdorfer

2011

AAPS J

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Abstract. 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. 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.

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Section: Discussionmentioning

confidence: 99%

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

Bulitta

Landersdorfer

2011

AAPS J

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“…The analysis was facilitated by the SADAPT-TRAN tool (17,18). Competing models were assessed by the objective function (Ϫ1 ϫ log-likelihood), plausibility of the parameter estimates, standard diagnostic plots, visual predictive checks, and normalized prediction distribution error plots, as described previously (19)(20)(21). Noncompartmental analysis was performed using the linear-up/log-down trapezoidal rule as implemented in WinNonlin Professional (version 5.3; Pharsight, Cary, NC).…”

Section: Methodsmentioning

confidence: 99%

Population Pharmacokinetics and Target Attainment of Meropenem in Plasma and Tissue of Morbidly Obese Patients after Laparoscopic Intraperitoneal Surgery

Wittau

Scheele

Kurlbaum

et al. 2015

Antimicrob Agents Chemother

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c Meropenem serves as a clinically important, broad-spectrum antibiotic. While meropenem is commonly used in obese patients, its pharmacokinetics in this patient group is not well known. Our aim was to characterize the population pharmacokinetics and target attainment in plasma, subcutaneous tissue, and peritoneal fluid for meropenem in morbidly obese patients. Four doses of 1g meropenem were given as 15-min infusions every 8 h to five morbidly obese patients (body mass index [BMI], 47.6 to 62.3 kg/m 2 ). After the fourth dose, serial meropenem concentrations were determined in plasma and, via microdialysis, in subcutaneous tissue and peritoneal fluid. All concentrations were analyzed simultaneously via population modeling, and target attainment probabilities predicted via Monte Carlo simulations using the target of unbound meropenem concentrations above the MIC for at least 40% of the dosing interval. For patients with 53 kg fat-free mass, total clearance was 18.7 liters/h and volume of distribution at steady state was 27. W hile antimicrobial resistance is one of the greatest threats to human health, the number of new antibiotics against multidrug-resistant bacteria declined drastically over the last 3 decades (1-3). Meropenem continues to serve as an important component of our antibiotic armamentarium and covers a large range of clinically relevant pathogens for antibiotic therapy, including those causing intra-abdominal infections or infections of the subcutaneous tissue. Meropenem is a potent, broad-spectrum ␤-lactam antibiotic that yields relatively rapid bacterial killing and is among the first antibiotic options for treatment of severe infections; it covers most of the pathogens relevant for intra-abdominal infections (4). Meropenem is a hydrophilic molecule, and it is unknown whether meropenem penetrates well into the subcutaneous tissue and peritoneal fluid of obese patients.Obese patients are at a high risk of postoperative and hospitalrelated infections (5), and optimal management of these infections is crucial to improve the outcome of obese patients with severe infections. The selection of the antibiotic and dose are critical to manage those infections (5, 6). Recommended daily doses are based on pharmacokinetic/pharmacodynamic (PK/PD) studies usually conducted in nonobese healthy volunteers (5). However, PK variables may differ in obese and nonobese patients, potentially resulting in inadequate antibiotic plasma and tissue concentrations. Thus, PK studies in obese, noninfected individuals are essential to avoid the risk of over-or underdosing.Only a few studies have assessed the PK of meropenem in obese patients (4-8), and some of these studies found considerably different clearances and volumes of distribution in obese and nonobese patients. These studies did not perform population pharmacokinetic modeling and did not assess the peritoneal fluid and subcutaneous tissue penetration of meropenem in obese patients.As meropenem is a hydrophilic molecule, it is important to determine whether its PK is...

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“…This benefit is greatest for mechanistic models that may contain 20 and more random model parameters (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27), as the likelihood for model specification errors increases with model complexity. The bi-directional and hierarchical error checks of the SADAPT-TRAN pre-processor (Fig.…”

Section: Discussionmentioning

confidence: 99%

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

Bulitta

Bingölbali

Shin

et al. 2011

AAPS J

111

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Abstract. 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 covariatestratified 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, lognormally, 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.

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Mechanistic population pharmacokinetics of total and unbound paclitaxel for a new nanodroplet formulation versus Taxol in cancer patients

Cited by 46 publications

References 29 publications

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

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

Population Pharmacokinetics and Target Attainment of Meropenem in Plasma and Tissue of Morbidly Obese Patients after Laparoscopic Intraperitoneal Surgery

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

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