The effective permeability (Peff) in the human jejunum (in vivo) of 22 structurally diverse compounds was correlated with both experimentally determined lipophilicity values and calculated molecular descriptors. The permeability data were previously obtained by using a regional in vivo perfusion system in the proximal jejunum in humans as part of constructing a biopharmaceutical classification system for oral immediate-release products. pKa, log P, and, where relevant, log Pion values were determined using the pH-metric technique. On the basis of these experiments, log D values were calculated at pH 5.5, 6.5, and 7.4. Multivariate data analysis was used to derive models that correlate passive intestinal permeability to physicochemical descriptors. The best model obtained, based on 13 passively transcellularly absorbed compounds, used the variables HBD (number of hydrogen bond donors), PSA (polar surface area), and either log D5.5 or log D6.5 (octanol/water distribution coefficient at pH 5.5 and 6.5, respectively). Statistically good models for prediciting human in vivo Peff values were also obtained by using only HBD and PSA or HBD, PSA, and CLOGP. These models can be used to predict passive intestinal membrane diffusion in humans for compounds that fit within the defined property space. We used one of the models obtained above to predict the log Peff values for an external validation set consisting of 34 compounds. A good correlation with the absorption data of these compounds was found.

New experimental methodologies were applied to measure the unbound brain-to-plasma concentration ratio (K(p,uu,brain)) and the unbound CSF-to-plasma concentration ratio (K(p,uu,CSF)) in rats for 43 structurally diverse drugs. The relationship between chemical structure and K(p,uu,brain) was dominated by hydrogen bonding. Contrary to popular understanding based on the total brain-to-plasma concentration ratio (logBB), lipophilicity was not a determinant of unbound brain exposure. Although changing the number of hydrogen bond acceptors is a useful design strategy for optimizing K(p,uu,brain), future improvement of in silico prediction models is dependent on the accommodation of active drug transport. The structure-brain exposure relationships found in the rat also hold for humans, since the rank order of the drugs was similar for human and rat K(p,uu,CSF). This cross-species comparison was supported by K(p,uu,CSF) being within 3-fold of K(p,uu,brain) in the rat for 33 of 39 drugs. It was, however, also observed that K(p,uu,CSF) overpredicts K(p,uu,brain) for highly effluxed drugs, indicating lower efflux capacity of the blood-cerebrospinal fluid barrier compared to the blood-brain barrier.

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In silico tools to investigate absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK) properties of new chemical entities are an integral part of the current industrial drug discovery paradigm. While many companies are active in the field, scientists engaged in this area do not necessarily share the same background and have limited resources when seeking guidance on how to initiate and maintain an in silico ADME-PK infrastructure in an industrial setting. This work summarizes the views of a group of industrial in silico and experimental ADME scientists, participating in the In Silico ADME Working Group, a subgroup of the International Consortium for Innovation through Quality in Pharmaceutical Development (IQ) Drug Metabolism Leadership Group. This overview on the benefits, caveats, and impact of in silico ADME-PK should serve as a resource for medicinal chemists, computational chemists, and DMPK scientists working in drug design to increase their knowledge in the area.

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