Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

In silico human and rat Vss quantitative structure-activity relationship models.

M Paul Gleeson1, Nigel J Waters, Stuart W Paine

  • 1Department of Physical & Metabolic Sciences, AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leicestershire LE11 5RH, United Kingdom. paul.x.gleeson@gsk.com

Journal of Medicinal Chemistry
|March 17, 2006
PubMed
Summary

We developed a new computational tool for predicting drug distribution volume (Vss) in humans and rats before synthesis. This quantitative structure-activity relationship (QSAR) model aids early drug discovery by providing in silico Vss estimations.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Antiviral Therapies for Adults With Mild to Moderate COVID-19 Infection.

JAMA·2026
Same author

More than an attachment module: covalent inhibitor warheads influence BTK dynamics and function.

bioRxiv : the preprint server for biology·2026
Same author

Management of Crohn Disease in Adults.

JAMA·2026
Same author

Fertility Preservation in People With Cancer.

JAMA·2026
Same author

Management of Opioid Use Disorder.

JAMA·2026
Same author

Computationally guided design of N<sup>4</sup>-(2-methyl-2H-indazol-6-yl)-N<sup>2</sup>-phenylpyrimidine-2,4-diamine inhibitors of EGFR kinase targeting Cys797.

Bioorganic & medicinal chemistry·2026

Area of Science:

  • Pharmacokinetics
  • Computational Chemistry
  • Drug Discovery

Background:

  • Accurate prediction of steady-state volume of distribution (Vss) is crucial for drug development.
  • Current methods for Vss assessment often require chemical synthesis or detailed analysis, delaying early-stage decisions.
  • A need exists for rapid, in silico methods to estimate Vss early in the drug discovery pipeline.

Purpose of the Study:

  • To develop and validate an entirely in silico Quantitative Structure-Activity Relationship (QSAR) tool for predicting human and rat steady-state volume of distribution (Vss).
  • To enable Vss prediction prior to chemical synthesis and detailed mechanistic assessment.

Main Methods:

  • Employed three statistical methodologies: Bayesian neural networks (BNN), Classification and Regression Trees (CART), and Partial Least Squares (PLS).

Related Experiment Videos

  • Modeled human (N=199) and rat (N=2086) data sets using these methodologies.
  • Validated model performance on an independent test set.

    • Main Results:

    • The developed human Vss prediction model achieved an r2 of 0.60 and an RMS error in prediction of 0.48.
    • The corresponding rat Vss prediction model achieved an r2 of 0.53 and an RMS error in prediction of 0.37.
    • These results indicate the models' utility for early-stage drug discovery.

    Conclusions:

    • This study presents the first entirely in silico approach for predicting both human and rat steady-state volume of distribution.
    • The developed QSAR models offer a valuable tool for accelerating the early stages of drug discovery by providing rapid Vss estimations.
    • The models demonstrate potential for guiding compound selection and development decisions.