DRUG DISCOVERY INTERFACEIonization-Specific Prediction of Blood–Brain Permeability
Section snippets
INTRODUCTION
Blood–brain barrier (BBB) is the most important biological formation separating brain tissue from the peripheral circulation. Many central nervous system (CNS) drug candidates have been rejected due to the low brain penetration,1 so accurate prediction of this factor for any new molecules is highly desirable. This goal is difficult to achieve due to an immense complexity of BBB permeation mechanisms and scarcity of experimental data that is required for building robust QSAR models.
Flow-Limited Permeability (PF)
In absorptive systems if only passive diffusion is considered, drug's permeability may be viewed as a combination of two steps (1) permeation across the unstirred water layer (UWL), and (2) permeation across phospholipid membrane (m). Each step can be characterized by a particular “resistance” that equals to inverse permeability in a given compartment (1/Pj). The cumulative resistance (1/Pe) is a sum of all particular resistances (Σ1/Pj).20 Since the thickness of UWL of brain capillary
Data Compilation
Quantitative log PS values were compiled from various experimental works that used one of the following methods—intravenous administration6 (IV), brain uptake index6, 7 (BUI), and in situ perfusion.6, 9 Although most studies were conducted with rats, Dagenais et al.8 adapted in situ perfusion method for use with mice. Since then a significant amount of high quality data on mouse brain uptake were published. To bring this data on a single scale, the following correlation was obtained using 25
Statistical Results
Figure 2 shows the obtained scatter plots for training and validation sets. Since log PS < −5 are beyond sensitivity of in situ perfusion measurements, all such values were assigned with log PS = −5. In both plots we observe very good correlations with R2 close to 0.9 and RMSE < 0.5 log units. As one can see in Table 1, the observed statistics is much better than in any previous methods, and the considered diversity of compounds is much wider. Since both training and validation sets produced
CONCLUSION
The obtained predictive model provides both good statistical significance and simple physicochemical explanations. Thus it can be used for practical “baseline” predictions of BBB absorption, aiming at identification of more complex effects. If our predictions largely deviate from experimental log PS or log BB estimations, then carrier-mediated transport and/or other effects different from passive diffusion are likely occurring. Furthermore, a model with clear physicochemical explanations can
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