Predicting Drug–Drug Interactions: An FDA Perspective
Pharmacokinetic drug interactions can lead to serious adverse events, and the evaluation of a new molecular entity’s drug–drug interaction potential is an integral part of drug development and regulatory review prior to its market approval. Alteration of enzyme and/or transporter activities involved...
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Published in | The AAPS journal Vol. 11; no. 2; pp. 300 - 306 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Boston
Springer US
01.06.2009
|
Subjects | |
Online Access | Get full text |
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Summary: | Pharmacokinetic drug interactions can lead to serious adverse events, and the evaluation of a new molecular entity’s drug–drug interaction potential is an integral part of drug development and regulatory review prior to its market approval. Alteration of enzyme and/or transporter activities involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by other concomitant drugs may lead to a change in exposure leading to altered response (safety or efficacy). Over the years, various
in vitro
methodologies have been developed to predict drug interaction potential
in vivo
.
In vitro
study has become a critical first step in the assessment of drug interactions. Well-executed
in vitro
studies can be used as a screening tool for the need for further
in vivo
assessment and can provide the basis for the design of subsequent
in vivo
drug interaction studies. Besides
in vitro
experiments,
in silico
modeling and simulation may also assist in the prediction of drug interactions. The recent FDA draft drug interaction guidance highlighted the
in vitro
models and criteria that may be used to guide further
in vivo
drug interaction studies and to construct informative labeling. This report summarizes critical elements in the
in vitro
evaluation of drug interaction potential during drug development and uses a case study to highlight the impact of
in vitro
information on drug labeling. |
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Bibliography: | Guest Editors: Lawrence X. Yu, Steven C. Sutton, and Michael B. Bolger |
ISSN: | 1550-7416 1550-7416 |
DOI: | 10.1208/s12248-009-9106-3 |