An Accurate In Vitro Prediction of Human VDss Based on the ØIE-TOZER Equation and Primary Physicochemical Descriptors. 3. Analysis and Assessment of Predictivity on a Large Dataset.

We present a model for volume of distribution at steady-state (VDss) prediction, via fraction unbound in tissues (fut), from the Oie-Tozer equation as an extension of our and other authors previous work. It based on easily determined or computed physicochemical descriptors such as logD7.4 and fi (7.4) (cationic fraction ionized at pH 7.4) in addition to fraction unbound in plasma (fup). We had collected, as part of other work, an extensive dataset of VDss and fup values and used the descriptors above, gathered from the literature, for a preliminary assessment of the robustness of the method applied to 191 different compounds belonging to different charge classes and scaffolds. After this step we addressed the use of easily computed physicochemical descriptors and experimentally derived fup on the same data set and compare the results between the two approaches and against the Oie-Tozer equation using in vivo data. This approach positions itself between fully computational models and scaling methods based on in vivo animal models or in vitro Kp (tissue:plasma) data utilizing model tissues. We consider it a useful and orthogonal complement to the two very diverse approaches mentioned yet requiring minimal in vitro experimental work. It offers a relatively inexpensive, rapid, intuitive and simple way to predict VDss in human, at a relatively early stage of the drug discovery. SIGNIFICANCE STATEMENT This method allows the prediction of VDss for small molecules in human without the use of animal PK data since it utilizes only in vitro data. It is therefore amenable to use at early stages, simple, intuitive, animal-sparing and quite accurate and it may serve scaling efforts well. Furthermore, utilizing the same dataset, we show that the performance of a model using computed pKa and logD7.4, still using experimental fup, compares well with the model using experimentally derived values.