Nonlinearity detection: Advantages of nonlinear mixed-effects modeling
Journal Title: The AAPS Journal - Year 2000, Vol 2, Issue 3
Abstract
The purpose of this study was to address the question of whether the use of nonlinear mixed-effect models has an impact on the detection and characterization of nonlinear processes (pharmacokinetic and pharmacodynamic) in rich data obtained from a few subjects. Simulations were used to assess the difference between applying population analysis, ie, nonlinear mixed-effects models as implemented in NONMEM, and the standard 2-stage (STS) method as the data analysis method for detection and characterization of nonlinearities. Three situations were considered, 2 pharmacokinetic and 1 pharmacodynamic. Both the first-order (FO) and FO conditional estimation (FOCE) algorithms were used for the population analyses. Within each situation, rich data were simulated for 8 subjects at multiple dose levels. The true nonlinear model and a simpler linear model were fit to each data set using each of the STS, FO, and FOCE methods. Criteria were prespecified to determine when each data analysis method detected the true nonlinear model. For all 3 simulated situations, the application of population analysis with the FOCE algorithm enabled the detection and characterization of the true nonlinear models in at least a 4-fold lower dose level than the STS approach. For both of the pharmacokinetic settings, population analysis with the FO algorithm performed much more poorly than the STS approach. The superior detection and characterization of nonlinearities provided by population analysis with the FOCE algorithm should allow drug developers to better predict and define how a drug should be used in clinical practice in such situations.
Authors and Affiliations
E. Niclas Jonsson, Mats O. Karlsson, Janet R. Wade
Keywords
Related Articles
- EP ID EP682112
- DOI 10.1208/ps020332
- Views 110
- Downloads 0
Assay Formats: Recommendation for Best Practices and Harmonization from the Global Bioanalysis Consortium Harmonization Team
As part of the GBC (Global Bioanalysis Consortium), the L3 assay format team has focused on reviewing common platforms used to support ligand binding assays in the detection of biotherapeutics. The following review is an...
Large Molecule Specific Assay Operation: Recommendation for Best Practices and Harmonization from the Global Bioanalysis Consortium Harmonization Team
The L2 Global Harmonization Team on large molecule specific assay operation for protein bioanalysis in support of pharmacokinetics focused on the following topics: setting up a balanced validation design, specificity tes...
Applying Biopharmaceutical Classification System (BCS) Criteria to Predict Oral Absorption of Drugs in Dogs: Challenges and Pitfalls
The Biopharmaceutical Classification System (BCS) has been a prognostic tool for assessing the potential effects of formulation on the human drug oral bioavailability. When used in conjunction with in vitro dissolution t...
Bioanalytical Approaches to Quantify “Total” and “Free” Therapeutic Antibodies and Their Targets: Technical Challenges and PK/PD Applications Over the Course of Drug Development
The predominant driver of bioanalysis in supporting drug development is the intended use of the data. Ligand-binding assays (LBA) are widely used for the analysis of protein biotherapeutics and target ligands (L) to supp...
Pharmacokinetically-Guided Lead Optimization of Nitrofuranylamide Anti-Tuberculosis Agents
In an effort to develop novel and more potent therapies to treat tuberculosis, a new class of chemical agents, nitrofuranylamides, is being developed. The present study examines biopharmaceutic properties and preclinical...