title: Non-compartmental analysis.
abstract: When analyzing pharmacokinetic data, one generally employs either model fitting using nonlinear regression analysis or non-compartmental analysis techniques (NCA). The method one actually employs depends on what is required from the analysis. If the primary requirement is to determine the degree of exposure following administration of a drug (such as AUC), and perhaps the drug's associated pharmacokinetic parameters, such as clearance, elimination half-life, T                 (max), C                 (max), etc., then NCA is generally the preferred methodology to use in that it requires fewer assumptions than model-based approaches. In this chapter we cover NCA methodologies, which utilize application of the trapezoidal rule for measurements of the area under the plasma concentration-time curve. This method, which generally applies to first-order (linear) models (although it is often used to assess if a drug's pharmacokinetics are nonlinear when several dose levels are administered), has few underlying assumptions and can readily be automated.In addition, because sparse data sampling methods are often utilized in toxicokinetic (TK) studies, NCA methodology appropriate for sparse data is also discussed.
authors.affiliation: Division of Pharmacology and Toxicology, Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, Sweden. Johan.Gabrielsson@SLU.SE
authors.forename: Johan
authors.initials: J
authors.lastname: Gabrielsson
authors.forename: Daniel
authors.initials: D
authors.lastname: Weiner
mesh_terms: D019540:Area Under Curve; D019295:Computational Biology; D004347:Drug Interactions; D017711:Nonlinear Dynamics; D010599:Pharmacokinetics