PKreport: report generation for checking population pharmacokinetic model assumptions

Effect of Short-Term Fasting on Systemic Cytochrome P450-Mediated Drug Metabolism in Healthy Subjects: A Randomized, Controlled, Crossover Study Using a Cocktail Approach

Background and Objective

Short-term fasting can alter drug exposure but it is unknown whether this is an effect of altered oral bioavailability and/or systemic clearance. Therefore, the aim of our study was to assess the effect of short-term fasting on oral bioavailability and systemic clearance of different drugs.

Methods

In a randomized, controlled, crossover trial, 12 healthy subjects received a single administration of a cytochrome P450 (CYP) probe cocktail, consisting of caffeine (CYP1A2), metoprolol (CYP2D6), midazolam (CYP3A4), omeprazole (CYP2C19) and warfarin (CYP2C9), on four occasions: an oral (1) and intravenous (2) administration after an overnight fast (control) and an oral (3) and intravenous (4) administration after 36 h of fasting. Pharmacokinetic parameters of the probe drugs were analyzed using the nonlinear mixed-effects modeling software NONMEM.

Results

Short-term fasting increased systemic caffeine clearance by 17% (p = 0.04) and metoprolol clearance by 13% (p < 0.01), whereas S-warfarin clearance decreased by 19% (p < 0.01). Fasting did not affect bioavailability.

Conclusion

The study demonstrates that short-term fasting alters CYP-mediated drug metabolism in a non-uniform pattern without affecting oral bioavailability.

Phxnlme: An R package that facilitates pharmacometric workflow of Phoenix NLME analyses

BACKGROUND AND OBJECTIVE

Pharmacometric analyses are integral components of the drug development process, and Phoenix NLME is one of the popular software used to conduct such analyses. To address current limitations with model diagnostic graphics and efficiency of the workflow for this software, we developed an R package, Phxnlme, to facilitate its workflow and provide improved graphical diagnostics.

METHODS

Phxnlme was designed to provide functionality for the major tasks that are usually performed in pharmacometric analyses (i.e. nonlinear mixed effects modeling, basic model diagnostics, visual predictive checks and bootstrap). Various estimation methods for modeling using the R package are made available through the Phoenix NLME engine. The Phxnlme R package utilizes other packages such as ggplot2 and lattice to produce the graphical output, and various features were included to allow customizability of the output. Interactive features for some plots were also added using the manipulate R package.

RESULTS

Phxnlme provides enhanced capabilities for nonlinear mixed effects modeling that can be accessed using the phxnlme() command. Output from the model can be graphed to assess the adequacy of model fits and further explore relationships in the data using various functions included in this R package, such as phxplot() and phxvpc.plot(). Bootstraps, stratified up to three variables, can also be performed to obtain confidence intervals around the model estimates. With the use of an R interface, different R projects can be created to allow multi-tasking, which addresses the current limitation of the Phoenix NLME desktop software. In addition, there is a wide selection of diagnostic and exploratory plots in the Phxnlme package, with improvements in the customizability of plots, compared to Phoenix NLME.

CONCLUSIONS

The Phxnlme package is a flexible tool that allows implementation of the analytical workflow of Phoenix NLME with R, with features for greater overall efficiency and improved customizable graphics. Phxnlme is freely available for download on the CRAN repository (https://cran.r-project.org/web/packages/Phxnlme/).

Exploring population pharmacokinetic modeling with resampling visualization

Background. In the last decade, population pharmacokinetic (PopPK) modeling has spread its influence in the whole process of drug research and development. While targeting the construction of the dose-concentration of a drug based on a population of patients, it shows great flexibility in dealing with sparse samplings and unbalanced designs. The resampling approach has been considered an important statistical tool to assist in PopPK model validation by measuring the uncertainty of parameter estimates and evaluating the influence of individuals. Methods. The current work describes a graphical diagnostic approach for PopPK models by visualizing resampling statistics, such as case deletion and bootstrap. To examine resampling statistics, we adapted visual methods from multivariate analysis, parallel coordinate plots, and multidimensional scaling. Results. Multiple models were fitted, the information of parameter estimates and diagnostics were extracted, and the results were visualized. With careful scaling, the dependencies between different statistics are revealed. Using typical examples, the approach proved to have great capacity to identify influential outliers from the statistical perspective, which deserves special attention in a dosing regimen. Discussion. By combining static graphics with interactive graphics, we are able to explore the multidimensional data from an integrated and systematic perspective. Complementary to current approaches, our proposed method provides a new way for PopPK modeling analysis.

Integration of biostatistics and pharmacometrics computing platforms for efficient and reproducible PK/PD analysis: a case study

Results of pharmacometric analyses influence high-level decisions such as clinical trial design, drug approval, and labeling. Key challenges for timely delivery of pharmacometric analyses are the data assembly process and tracking and documenting the modeling process and results. Since clinical efficacy and safety data typically reside in the biostatistics computing area, an integrated computing platform for pharmacometric and biostatistical analyses would be ideal. A case study is presented integrating a pharmacometric modeling platform into an existing statistical computing environment (SCE). The feasibility and specific configurations of running common PK/PD programs such as NONMEM and R inside of the SCE are provided. The case study provides an example of an integrated repository that facilitates efficient data assembly for pharmacometrics analyses. The proposed platform encourages a good pharmacometrics working practice to maintain transparency, traceability, and reproducibility of PK/PD models and associated data in supporting drug development and regulatory decisions.