We compare three different approaches to scale clearance (CL) from human hepatocyte and microsome CL(int) (intrinsic CL) for 52 drug compounds. By using the well-stirred model with protein binding included only 11% and 30% of the compounds were predicted within 2-fold and the average absolute fold errors (AAFE) for the predictions were 5.9 and 4.1 for hepatocytes and microsomes, respectively. When predictions were performed without protein binding, 59% of the compounds were predicted within 2-fold using either hepatocytes or microsomes and the AAFE was 2.2 and 2.3, respectively. For hepatocytes and microsomes there were significant correlations (P = 8.7 x 10(-13), R(2) = 0.72; P = 2.8 x 10(-9), R(2) = 0.61) between predicted CL(int in vivo) (obtained from in vitro CL(int)) and measured CL(int in vivo) (obtained using the well-stirred model). When CL was calculated from the regression, 76% and 70% of the compounds were predicted within 2-fold and the AAFE was 1.6 and 1.8 for hepatocytes and microsomes, respectively. We demonstrate that microsomes and hepatocytes are in many cases comparable when scaling of CL is performed from regression. By using the hepatocyte regression, CL for 82% of the compounds in an independent test set (n = 11) were predicted within 2-fold (AAFE 1.4). We suggest that a regression line that adjusts for systematic under-predictions should be the first-hand choice for scaling of CL.

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.

The structure of the O‐specific side‐chain of the Hafnia alvei strain PCM 1206 lipopolysaccharide has been investigated. Methylation analysis, partial acid hydrolysis, FAB‐MS/MS and 1H‐NMR and 13C‐NMR spectroscopy were the principal methods used. d‐Allothreonine (d‐aThr), amide‐linked to the d‐galacturonic acid, was identified as a constituent in the polysaccharide and the following structure of a pentasaccharide repeating unit was established:

We have developed two parallel series, A and B, of CX3CR1 antagonists for the treatment of multiple sclerosis. By modifying the substituents on the 7-amino-5-thio-thiazolo[4,5-d]pyrimidine core structure, we were able to achieve compounds with high selectivity for CX3CR1 over the closely related CXCR2 receptor. The structure-activity relationships showed that a leucinol moiety attached to the core-structure in the 7-position together with α-methyl branched benzyl derivatives in the 5-position displayed promising affinity, and selectivity as well as physicochemical properties, as exemplified by compounds 18a and 24h. We show the preparation of the first potent and selective orally available CX3CR1 antagonists.

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