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AAPS J. 2009 Jun;11(2):217-24. doi: 10.1208/s12248-009-9098-z. Epub 2009 Apr 21.

Mechanistic approaches to predicting oral drug absorption.

Author information

1
Office of Generic Drugs, Food and Drug Administration, 7519 Standish Place, Rockville, Maryland 20855, USA.

Abstract

Modeling and simulation of oral drug absorption have been widely used in drug discovery, development, and regulation. Predictive absorption models are used to determine the rate and extent of oral drug absorption, facilitate lead drug candidate selection, establish formulation development strategy, and support the development of regulatory policies. This review highlights the development of recent drug absorption models including dispersion and compartmental models. The compartmental models include the compartmental absorption and transit model; Grass model; gastrointestinal transit absorption model; advanced compartmental absorption and transit model; and advanced dissolution, absorption, and metabolism model. Compared to the early absorption models, the above models developed or extended since the mid-1990s have demonstrated greatly improved predictive performance by accounting for multiple factors such as drug degradation, gastric emptying, intestinal transit, first-pass metabolism, and intestinal transport. For future model development, more heterogeneous features of the gastrointestinal tract (villous blood flow, metabolizing enzymes, and transporters), food effects, and drug-drug interactions should be fully characterized and taken into consideration. Moreover, predicting population inter- and intravariability in oral drug absorption can be useful and important for the evaluation of clinical safety and efficacy of drugs. Establishing databases and libraries that contain accurate pharmaceutical and pharmacokinetic information for commercialized and uncommercialized drugs may also be helpful for model development and validation.

PMID:
19381841
PMCID:
PMC2691458
DOI:
10.1208/s12248-009-9098-z
[Indexed for MEDLINE]
Free PMC Article

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