Discovery of potent selective bioavailable phosphodiesterase 2 (PDE2) inhibitors active in an osteoarthritis pain model. Part II: optimization studies and demonstration of in vivo efficacy.

Selective phosphodiesterase 2 (PDE2) inhibitors are shown to have efficacy in a rat model of osteoarthritis (OA) pain. We identified potent, selective PDE2 inhibitors by optimizing residual PDE2 activity in a series of phosphodiesterase 4 (PDE4) inhibitors, while minimizing PDE4 inhibitory activity. These newly designed PDE2 inhibitors bind to the PDE2 enzyme in a cGMP-like binding mode orthogonal to the cAMP-like binding mode found in PDE4. Extensive structure activity relationship studies ultimately led to identification of pyrazolodiazepinone, 22, which was >1000-fold selective for PDE2 over recombinant, full length PDEs 1B, 3A, 3B, 4A, 4B, 4C, 7A, 7B, 8A, 8B, 9, 10 and 11. Compound 22 also retained excellent PDE2 selectivity (241-fold to 419-fold) over the remaining recombinant, full length PDEs, 1A, 4D, 5, and 6. Compound 22 exhibited good pharmacokinetic properties and excellent oral bioavailability (F=78%, rat). In an in vivo rat model of OA pain, compound 22 had significant analgesic activity 1 and 3h after a single, 10 mg/kg, subcutaneous dose.

Discovery of potent, selective, bioavailable phosphodiesterase 2 (PDE2) inhibitors active in an osteoarthritis pain model, part I: transformation of selective pyrazolodiazepinone phosphodiesterase 4 (PDE4) inhibitors into selective PDE2 inhibitors.

We identified potent, selective PDE2 inhibitors by optimizing residual PDE2 activity in a series of PDE4 inhibitors, while simultaneously minimizing PDE4 activity. These newly designed PDE2 inhibitors bind to the PDE2 enzyme in a cGMP-like mode in contrast to the cAMP-like binding mode found in PDE4. Structure activity relationship studies coupled with an inhibitor bound crystal structure in the active site of the catalytic domain of PDE2 identified structural features required to minimize PDE4 inhibition while simultaneously maximizing PDE2 inhibition.

A novel method for assessing inhibition of ibuprofen chiral inversion and its application in drug discovery.

An inhibition assay to assess the potential for chiral inversion of compounds was developed using R(-)-ibuprofen as the probe substrate. Inhibition of the chiral inversion of R(-)-ibuprofen by structurally similar compounds in cyropreserved rat hepatocytes was studied using chiral HPLC and LC/MS methods for the chromatographic separation and detection of enantiomers. Concept validation of this assay was performed with three commercially available compounds and four Pfizer compounds. The results of these studies demonstrated that compounds that are structurally similar to ibuprofen inhibited the formation of S(+)-ibuprofen, suggesting that they may undergo similar enzymatic chiral inversion pathways or compete for the same enzyme active sites. Additionally, an application of this assay in early drug discovery for a specific class of compounds was demonstrated. Thirty-three in-house compounds were screened for their chiral inversion potential utilizing this assay to investigate the structure activity relationship (SAR) for this class of compounds.

The development and validation of a computational model to predict rat liver microsomal clearance.

As the cost of discovering and developing new pharmaceutically relevant compounds continues to rise, it is increasingly important to select the right molecules to prosecute very early in drug discovery. The development of high throughput in vitro assays of hepatic metabolic clearance has allowed for vast quantities of data generation; however, these large screens are still costly and remain dependant on animal usage. To further expand the value of these screens and ultimately aid in animal usage reduction, we have developed an in silico model of rat liver microsomal (RLM) clearance. This model combines a large amount of rat clearance data (n = 27,697) generated at multiple Pfizer laboratories to represent the broadest possible chemistry space. The model predicts RLM stability (with 82% accuracy and a kappa value of 0.65 for test data set) based solely on chemical structural inputs, and provides a clear assessment of confidence in the prediction. The current in silico model should help accelerate the drug discovery process by using confidence-based stability-driven prioritization, and reduce cost by filtering out the most unstable/undesirable molecules. The model can also increase efficiency in the evaluation of chemical series by optimizing iterative testing and promoting rational drug design.