Literature DB >> 22965626

Effective absorption modeling in relative bioavailability study risk assessment.

John P Rose1.   

Abstract

Absorption modeling is an excellent strategic fit to perform a risk assessment for relative bioavailability (RBA) studies as it provides direct input into the question that is at the core of the RBA decision, namely, how does the absorption of the test drug product compare to the reference and is it likely to be different enough to justify an RBA study. The main limitation to absorption modeling in risk assessment is the inherent uncertainty associated with modeling. The extent to which the absorption modeling is integrated into the risk assessment should depend on the level of confidence in the modeling. It is difficult, however, to quantify the level of confidence on a case by case basis. The effective application of absorption modeling for RBA risk assessment therefore requires a general understanding of when modeling is expected to be reliable and also how to build reliability directly into the modeling. This paper describes a framework for effective modeling in RBA risk assessment that is based on four fundamental building blocks: (1) relate severity of drug product change and API properties to reliability of modeling, (2) use critical model variables to express the critical differences in the drug products, (3) generate a fraction-absorbed response surface expressed in terms of the critical model variables to evaluate the relative performance of the drug products, and (4) tie the first three building blocks together by following good model building practices that assure the highest quality model is built. The building blocks are demonstrated by a simple but common example of a change in solid state from free base to HCl salt.

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Year:  2012        PMID: 22965626      PMCID: PMC3475858          DOI: 10.1208/s12248-012-9402-1

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  4 in total

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Authors:  Xuan Ding; John P Rose; Jan Van Gelder
Journal:  Int J Pharm       Date:  2012-02-13       Impact factor: 5.875

2.  A pH-dilution method for estimation of biorelevant drug solubility along the gastrointestinal tract: application to physiologically based pharmacokinetic modeling.

Authors:  Yi Gao; Robert A Carr; Julie K Spence; Weili W Wang; Teresa M Turner; John M Lipari; Jonathan M Miller
Journal:  Mol Pharm       Date:  2010-08-17       Impact factor: 4.939

3.  Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR.

Authors:  Robert P Sheridan; Bradley P Feuston; Vladimir N Maiorov; Simon K Kearsley
Journal:  J Chem Inf Comput Sci       Date:  2004 Nov-Dec

4.  A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability.

Authors:  G L Amidon; H Lennernäs; V P Shah; J R Crison
Journal:  Pharm Res       Date:  1995-03       Impact factor: 4.200

  4 in total
  1 in total

1.  Use of preclinical dog studies and absorption modeling to facilitate late stage formulation bridging for a BCS II drug candidate.

Authors:  Filippos Kesisoglou
Journal:  AAPS PharmSciTech       Date:  2014-02       Impact factor: 3.246

  1 in total

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