In silico human and rat Vss quantitative structure-activity relationship models.
We present herein a QSAR tool enabling an entirely in silico prediction of human and rat steady-state volume of distribution (Vss), to be made prior to chemical synthesis, preceding detailed allometric or mechanistic assessment of Vss. Three different statistical methodologies, Bayesian neural networks (BNN), classification and regression trees (CART), and partial least squares (PLS) were employed to model human (N=199) and rat (N=2086) data sets. The results in prediction of an independent test set show the human model has an r2 of 0.60 and an rms error in prediction of 0.48. The corresponding rat model has an r2 of 0.53 and an rms error in prediction of 0.37, indicating both models could be very useful in the early stages of the drug discovery process. This is the first reported entirely in silico approach to the prediction of rat and human steady-state volume of distribution.
Department of Physical & Metabolic Sciences, AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leicestershire LE11 5RH, United Kingdom. paul.x.gleeson@gsk.com
M Paul
MP
Gleeson
Nigel J
NJ
Waters
Stuart W
SW
Paine
Andrew M
AM
Davis
D000818:Animals; D001499:Bayes Theorem; D016208:Databases, Factual; D015195:Drug Design; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000097:blood / Q000378:metabolism*; D010599:Pharmacokinetics; D021281:Quantitative Structure-Activity Relationship; D051381:Rats; D012044:Regression Analysis
D004364:Pharmaceutical Preparations