Abstract

When analyzing pharmacokinetic data, one generally employs either model fitting using nonlinear regression analysis or non-compartmental analysis techniques (NCA). The methods one actually employs depends on whet 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, Tmax, Cmax 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 utilizes application of the trapezoidal rule for measurements of the area under the plasma concentration-time curve. This methods, 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.

Keywords

pharmacokinetics pharmacodynamics modeling

Published in

Methods in Molecular Biology
2012, number: 929, pages: 377-390
Book title: Computational Toxicology : Volume I
ISBN: 978-1-62703-049-6
Publisher: Springer Protocols

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