Discovery and Optimization of Selective Nav1.8 Modulator Series That Demonstrate Efficacy in Preclinical Models of Pain.

Voltage-gated sodium channels, in particular Nav1.8, can be targeted for the treatment of neuropathic and inflammatory pain. Herein, we described the optimization of Nav1.

Optimisation of a novel series of selective CNS penetrant CB(2) agonists.

A series of benzimidazole CB(2) receptor agonists were prepared and their properties investigated. Optimisation of the three benzimidazole substituents led to the identification of compound 23, a potent CB(2) full agonist (EC(50) 2.7nM) with excellent selectivity over the CB(1) receptor (>3000-fold).

Structural pairwise comparisons of HLM stability of phenyl derivatives: Introduction of the Pfizer metabolism index (PMI) and metabolism-lipophilicity efficiency (MLE).

Data mining by pairwise comparison of over 150,000 human liver microsome (HLM) intrinsic clearance values stored within the internal Pfizer database has been performed by an automated tool. Systematic probability tables of specific structural changes on the intrinsic clearance of phenyl derivatives have been generated. From these data two new parameters, the Pfizer Metabolism Index (PMI) and Metabolism-Lipophilicity Efficiency (MLE) are introduced for each fragment.

A novel series of highly selective inhibitors of MMP-3.

The design and synthesis of a series of highly selective hydroxamate inhibitors of stromelysin-1 (MMP-3) is described. Substitution of a 4-biaryl piperidine sulfonamide core, which binds at the S1' subsite of MMP-3, was optimised to give potent inhibitors of MMP-3, with greater than 300-fold selectivity over MMP-1, MMP-2, MMP-9 and MMP-14. Compounds 26 and 27 were identified as having the best balance of pharmacology and properties required for topical drug delivery.

Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation schemes.

The ability to cross the blood brain barrier (BBB), sometimes expressed as BBB+ and BBB-, is a very important property in drug design. Several computational methods have been employed for the prediction of BBB-penetrating (BBB+) and nonpenetrating (BBB-) compounds with overall accuracies from 75 to 97%. However, most of these models use a large number of descriptors (67-199), and it is not easy to implement the models in order to predict values of BBB+/-.