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In Silico ADME Tools

  • Siamak Cyrus Khojasteh
  • Harvey Wong
  • Cornelis E. C. A. Hop
Chapter

Abstract

The breath and predictive power of in silico ADME tools has increased rapidly during the last 10 years. The quality of many models is such that they can successfully influence decision making in drug discovery and development. In drug discovery they can influence decision related to synthesis of compounds and in development they can influence decisions to perform certain clinical trials or the design of the trial.

Keywords

Partial Little Square Plasma Protein Binding PBPK Model Metabolic Stability Compartmental Transit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

CAT

Compartmental absorption and transit model

PBPK

Physiologically based pharmacokinetic model

P450

Cytochrome P450

PLS

Partial least squares

SAR

Structure–activity relationship

SVM

Support vector machine

References

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Additional Reading

  1. Espié P, Tytgat D, Sargentini–Maier M-L et al (2009) Physiologically based pharmacokinetics (PBPK). Drug Metab Rev 41:391–407Google Scholar
  2. Hou T, Wang J (2008) Structure–ADME relationship: still a long way to go. Expert Opin Drug Metab Toxicol 4:759–770PubMedCrossRefGoogle Scholar
  3. Kharkar PS (2010) Two-dimensional (2D) in silico models for absorption, distribution, metabolism, excretion and toxicity (ADME/T) in drug discovery. Curr Top Med Chem 10:116–126CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Siamak Cyrus Khojasteh
    • 1
  • Harvey Wong
    • 1
  • Cornelis E. C. A. Hop
    • 1
  1. 1.Genentech, Inc.San FranciscoUSA

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