Literature DB >> 17092600

Quantitative structure-pharmacokinetic/pharmacodynamic relationships.

Donald E Mager1.   

Abstract

Quantitative structure-activity relationships have long been considered a vital component of drug discovery and development, providing insight into the role of molecular properties in the biological activity of similar and unrelated compounds. Recognition that in vitro bioassay and/or pre-clinical activity are insufficient for anticipating which compounds are suitable leads for further development has shifted the focus toward integrated pharmacokinetic (PK) and pharmacodynamic (PD) processes. Over the last decade, considerable progress has been made in constructing empirical and mechanistic quantitative structure-PK relationships (QSPKR), as well as diverse mechanism-based pharmacodynamic models of drug effects. In this review, traditional and contemporary approaches to developing QSPKR models are discussed, along with selected examples of attempts to couple QSPKR and pharmacodynamic models to anticipate the intensity and time-course of the pharmacological effects of new or related compounds, or quantitative structure-pharmacodynamic relationships modeling. Such models are in accordance with the goals of systems biology and the ideal of designing drugs and delivery systems from first principles.

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Mesh:

Year:  2006        PMID: 17092600     DOI: 10.1016/j.addr.2006.08.002

Source DB:  PubMed          Journal:  Adv Drug Deliv Rev        ISSN: 0169-409X            Impact factor:   15.470


  18 in total

1.  Physiologically based pharmacokinetic model for composite nanodevices: effect of charge and size on in vivo disposition.

Authors:  Donald E Mager; Vidhi Mody; Chao Xu; Alan Forrest; Wojciech G Lesniak; Shraddha S Nigavekar; Muhammed T Kariapper; Leah Minc; Mohamed K Khan; Lajos P Balogh
Journal:  Pharm Res       Date:  2012-06-12       Impact factor: 4.200

2.  Correlation of elimination fraction area under the curve with total body clearance.

Authors:  Tomasz Grabowski; Anna Raczyńska-Pawelec; Marcin Starościak; Jerzy Jan Jaroszewski
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2014-10-14       Impact factor: 2.441

3.  Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses.

Authors:  Natalja Fjodorova; Marjan Vračko; Marjan Tušar; Aneta Jezierska; Marjana Novič; Ralph Kühne; Gerrit Schüürmann
Journal:  Mol Divers       Date:  2009-08-15       Impact factor: 2.943

Review 4.  Development of translational pharmacokinetic-pharmacodynamic models.

Authors:  D E Mager; W J Jusko
Journal:  Clin Pharmacol Ther       Date:  2008-03-26       Impact factor: 6.875

Review 5.  Mechanisms of drug resistance in kinases.

Authors:  Rina Barouch-Bentov; Karsten Sauer
Journal:  Expert Opin Investig Drugs       Date:  2011-02       Impact factor: 6.206

6.  Composite multi-parameter ranking of real and virtual compounds for design of MC4R agonists: renaissance of the Free-Wilson methodology.

Authors:  Ingemar Nilsson; Magnus O Polla
Journal:  J Comput Aided Mol Des       Date:  2012-10-02       Impact factor: 3.686

7.  Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System.

Authors:  Ayman F El-Kattan; Manthena V Varma; Stefan J Steyn; Dennis O Scott; Tristan S Maurer; Arthur Bergman
Journal:  Pharm Res       Date:  2016-09-12       Impact factor: 4.200

8.  Effect of small-molecule modification on single-cell pharmacokinetics of PARP inhibitors.

Authors:  Greg M Thurber; Thomas Reiner; Katherine S Yang; Rainer H Kohler; Ralph Weissleder
Journal:  Mol Cancer Ther       Date:  2014-02-19       Impact factor: 6.261

Review 9.  Structure-activity relationships and quantitative structure-activity relationships for breast cancer resistance protein (ABCG2).

Authors:  Yash A Gandhi; Marilyn E Morris
Journal:  AAPS J       Date:  2009-07-24       Impact factor: 4.009

Review 10.  Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction.

Authors:  Haiyan Li; Jin Sun; Xiaowen Fan; Xiaofan Sui; Lan Zhang; Yongjun Wang; Zhonggui He
Journal:  J Comput Aided Mol Des       Date:  2008-06-24       Impact factor: 3.686

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