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We present a model for volume of distribution at steady state (VD ss ) prediction, via fraction unbound in tissues, from the Øie-equation as an extension of our and other authors' previous work. It is based on easily determined or computed physicochemical descriptors such as logD 7.4 and f i (7.4) (cationic fraction ionized at pH 7.4) in addition to fraction unbound in plasma (f up ). We had collected, as part of other work, an extensive dataset of VD ss and f up 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 f up on the same data set and compare the results between the two approaches and against the Øie-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 K p (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 VD ss in humans, at a relatively early stage of the drug discovery. SIGNIFICANCE STATEMENT: This method allows the prediction of volume of distribution at steady state for small molecules in humans without the use of animal PK data because 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 pK a and logD 7.4 , still using experimental fraction unbound in plasma, compares well with the model using experimentally derived values.

drug metabolism and disposition/dmd online

An Accurate In Vitro Prediction of Human VD<sub>ss</sub> Based on the Øie–Tozer Equation and Primary Physicochemical Descriptors. 3. Analysis and Assessment of Predictivity on a Large Dataset

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

An Accurate In Vitro Prediction of Human VD_ss Based on the Øie–Tozer Equation and Primary Physicochemical Descriptors. 3. Analysis and Assessment of Predictivity on a Large Dataset