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.

Succinic acid amides as P2-P3 replacements for inhibitors of interleukin-1beta converting enzyme (ICE or caspase 1).

Succinic acid amides have been found to be effective P2-P3 scaffold replacements for peptidic ICE inhibitors. Heteroarylalkyl fragments occupying the P4 position provided access to compounds with nM affinities. Utilization of an acylal prodrug moiety was required to overcome biopharmaceutical issues which led to the identification of 17f, a potential clinical candidate.

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.

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.

Evaluation of an integrated in vitro-in silico PBPK (physiologically based pharmacokinetic) model to provide estimates of human bioavailability.

PK express module is a physiologically based model of first pass metabolism, which integrates in vitro data with an in silico physiologically based pharmacokinetic (PBPK) model to predict human bioavailability (F(H)). There are three required inputs: FDp (Fraction dose absorbed, final parameter from iDEA absorption module), protein binding (fu) and disappearance kinetics in human hepatocytes. Caco-2 permeability, aqueous solubility (at multiple pH's), estimated dose and chemical structure are inputs required for the estimation of FDp (Norris et al., 2000; Stoner et al., 2004) and were determined for all compounds in our laboratory or obtained from literature. Protein binding data was collected from literature references and/or Pfizer database. Human hepatocyte data was generated in-house using an automated human hepatocyte method (using Tecan Genesis Workstation) as described previously (). Sixteen compounds (commercial and Pfizer compounds) were chosen to evaluate the PK express model and the bioavailability predicted from the module was compared with known clinical endpoints. For majority of the 16 compounds (approximately 80%), the PK express model F(H) values were comparable to the known human bioavailability (F(H)) (within 23.7 units of the known human (true) F, except for PF 3, PF 4, PF 6). In conclusion, the PK express model integrates a number of key readily available discovery parameters and provides estimates of human performance by integrating in silico and experimental variables built on a physiological based pharmacokinetic model. Information from this model in conjunction with other ADME data (e.g., P450 inhibition) will enable progression of most promising compounds for further in vivo PK and/or efficacy studies.

Implementation of an ADME enabling selection and visualization tool for drug discovery.

The pharmaceutical industry has large investments in compound library enrichment, high throughput biological screening, and biopharmaceutical (ADME) screening. As the number of compounds submitted for in vitro ADME screens increases, data analysis, interpretation, and reporting will become rate limiting in providing ADME-structure-activity relationship information to guide the synthetic strategy for chemical series. To meet these challenges, a software tool was developed and implemented that enables scientists to explore in vitro and in silico ADME and chemistry data in a multidimensional framework. The present work integrates physicochemical and ADME data, encompassing results for Caco-2 permeability, human liver microsomal half-life, rat liver microsomal half-life, kinetic solubility, measured log P, rule of 5 descriptors (molecular weight, hydrogen bond acceptors, hydrogen bond donors, calculated log P), polar surface area, chemical stability, and CYP450 3A4 inhibition. To facilitate interpretation of this data, a semicustomized software solution using Spotfire was designed that allows for multidimensional data analysis and visualization. The solution also enables simultaneous viewing and export of chemical structures with the corresponding ADME properties, enabling a more facile analysis of ADME-structure-activity relationship. In vitro and in silico ADME data were generated for 358 compounds from a series of human immunodeficiency virus protease inhibitors, resulting in a data set of 5370 experimental values which were subsequently analyzed and visualized using the customized Spotfire application. Implementation of this analysis and visualization tool has accelerated the selection of molecules for further development based on optimum ADME characteristics, and provided medicinal chemistry with specific, data driven structural recommendations for improvements in the ADME profile.