Druggability Assessment for Selected Serine Proteases in a Pharmaceutical Industry Setting.

Target druggability assessment is an integral part of the early target characterization and selection process in pharmaceutical industry. Here, we investigate a set of five different serine proteases from the blood coagulation cascade. The aim of this study is twofold. Firstly, leveraging the wealth of available in-house high-throughput screening (HTS) data, we analyze HTS hit rates and discuss their predictive value for the development of small molecule (SMOL) candidates. Purely structure-activity relationship (SAR) based druggability ratings are compared with computational protein-structure based druggability assessments. Secondly, we evaluate the impact of using conformational ensembles from molecular dynamics (MD) simulations instead of single static crystal structures as basis for computational druggability assessments. Based on this study, we recommend incorporating molecular dynamics routinely into the early target characterization process, especially if only a single X-ray structure is available.

Big pharma screening collections: more of the same or unique libraries? The AstraZeneca-Bayer Pharma AG case.

In this study, the screening collections of two major pharmaceutical companies (AstraZeneca and Bayer Pharma AG) have been compared using a 2D molecular fingerprint by a nearest neighborhood approach. Results revealed a low overlap between both collections in terms of compound identity and similarity. This emphasizes the value of screening multiple compound collections to expand the chemical space that can be accessed by high-throughput screening (HTS).

A cell-based nitric oxide reporter assay useful for the identification and characterization of modulators of the nitric oxide/guanosine 3',5'-cyclic monophosphate pathway.

Nitric oxide (NO) plays an important role in protection against the onset and progression of various cardiovascular disorders. Therefore, the NO/guanosine 3',5'-cyclic monophosphate (cGMP) pathway has gained considerable attention and has become a target for new drug development. We have established a rapid, homogeneous, cell-based, and highly sensitive reporter assay for NO generated by endothelial nitric oxide synthase (eNOS). In a coculture system, NO production is indirectly monitored in living cells via soluble guanylyl cyclase (sGC) activation and calcium influx mediated by the olfactory cyclic nucleotide-gated (CNG) cation channel CNGA2, acting as the intracellular cGMP sensor. Using this NO reporter assay, we performed a fully automated high-throughput screening campaign for stimulators of NO synthesis. The coculture system reflects most aspects of the natural NO/cGMP pathway, namely, Ca(2+)-dependent and Ca(2+)-independent regulation of eNOS activity by G protein-coupled receptor agonists, oxidative stress, phosphorylation, and cofactor availability as well as NO-mediated stimulation of cGMP synthesis by sGC activation. The NO reporter assay allows the real-time detection of NO synthesis within living cells and makes it possible to identify and characterize activators and inhibitors of enzymes involved in the NO/cGMP signaling pathway.

Structural unit analysis identifies lead series and facilitates scaffold hopping in combinatorial chemistry.

Combinatorial chemistry and high-throughput screening technologies produce huge amounts of data on a regular basis. Sieving through these libraries of compounds and their associated assay data to identify appropriate series for follow-up is a daunting task, which has created a need for computational techniques that can find coherent islands of structure-activity relationships in this sea. Structural unit analysis (SUA) examines an entire data set so as to identify the molecular substructures or fragments that distinguish compounds with high activity from those with average activity. The algorithm is iterative and follows set heuristics in order to generate the structural units. It produces graphs that represent a set of units, which become SUA rules. Finding all of the input structures that match these graphs generates clusters. The Apriori algorithm for association rule mining is adapted to explore all of the combinations of structural units that define useful series. User-defined constraints are applied toward series selection and the refinement of rules. The significance of a series is determined by applying statistical methods appropriate to each data set. Application to the NCI-H23 (DTP Human Tumor Cell Line Screen) database serves to illustrate the process by which structural series are identified. An application of the method to scaffold hopping is then discussed in connection with proprietary screening data from a lead optimization project directed toward the treatment of respiratory tract infections at Bayer Healthcare. SUA was able to successfully identify promising alternative core structures in addition to identifying compounds with above-average activity and selectivity.