Octanol-water partition: searching for predictive models.
The log n-octanol/water partition coefficient (log Po/w) still represents one of the most informative physicochemical parameters available to medicinal chemists. In the present work, principles, methodologies, and parameters are briefly reviewed for a variety of models developed to predict this parameter based on molecular structure. To include the developments of recent years, a total of more than 40 different approaches are mentioned with relevant bibliography within four major categories: group contribution methods, atomic contribution methods, molecular methods, and other physicochemical methods. To underscore once more the utility of this partition coefficient, a comprehensive and reevaluated correlation between log Po/w and in vivo permeability data of rat brain capillaries is included. Most deviants that fell below the trendline are those that have been recently found to be substrates for P-glycoprotein, a multidrug transporter that actively removes them from the brain. Accurate predictions of log Po/w may necessitate many parameters, but there is mounting evidence that molecular size and hydrogen bonding ability can account for a major part of the variance. Our recently developed, molecular size-based approach is reviewed, and it is argued that introduction of three-dimensionality allows the elimination of many empirically derived fragment constants without a significant deterioration of the predictive accuracy. A comparison of predictive power for six different methods on 145 molecules of interest for medicinal chemists is also included.
Center for Drug Discovery, University of Florida, Health Science Center, P.O. Box 100497, Gainesville, Florida 32610-0497, USA.
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Buchwald
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Bodor
D000818:Animals; D001812:Blood-Brain Barrier; D001921:Brain / Q000378:metabolism; D002196:Capillaries / Q000378:metabolism; D008954:Models, Biological; D008956:Models, Chemical; D000442:Octanols; D010599:Pharmacokinetics; D051381:Rats; D014867:Water
D000442:Octanols; D014867:Water