In Silico ADME Tools

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


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.


Partial Little Square Plasma Protein Binding PBPK Model Metabolic Stability Compartmental Transit 



Compartmental absorption and transit model


Physiologically based pharmacokinetic model


Cytochrome P450


Partial least squares


Structure–activity relationship


Support vector machine


  1. Cruciani G, Carosati E, De Boeck B et al (2005) MetaSite: Understanding metabolism in human cytochromes from the perspective of the chemist. J Med Chem 48:6970–6979PubMedCrossRefGoogle Scholar
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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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