The process of Drug Discovery is a complex and high risk endeavor that requires focused attention on experimental hypotheses, the application of diverse sets of technologies and data to facilitate high quality decision-making. All is aimed at enhancing the quality of the chemical development candidate(s) through clinical evaluation and into the market. In support of the lead generation and optimization phases of this endeavor, high throughput technologies such as combinatorial/high throughput synthesis and high throughput and ultra-high throughput screening, have allowed the rapid analysis and generation of large number of compounds and data. Today, for every analog synthesized 100 or more data points can be collected and captured in various centralized databases. The analysis of thousands of compounds can very quickly become a daunting task. In this article we present the process we have developed for both analyzing and prioritizing large sets of data starting from diversity and focused uHTS in support of lead generation and secondary screens supporting lead optimization. We will describe how we use informatics and computational chemistry to focus our efforts on asking relevant questions about the desired attributes of a specific library, and subsequently in guiding the generation of more information-rich sets of analogs in support of both processes.

A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.

Molecular features of ligand binding to MHC class II HLA-DR molecules have been elucidated through a combination of peptide structure-activity studies and structure-based drug design, resulting in analogues with nanomolar affinity in binding assays. Stabilization of lead compounds against cathepsin B cleavage by N-methylation of noncritical backbone NH groups or by dipeptide mimetic substitutions has generated analogues that compete effectively against protein antigens in cellular assays, resulting in inhibition of T-cell proliferation. Crystal structures of four ternary complexes of different peptide mimetics with the rheumatoid arthritis-linked MHC DRB10401 and the bacterial superantigen SEB have been obtained. Peptide-sugar hybrids have also been identified using a structure-based design approach in which the sugar residue replaces a dipeptide. These studies illustrate the complementary roles played by phage display library methods, peptide analogue SAR, peptide mimetics substitutions, and structure-based drug design in the discovery of inhibitors of antigen presentation by MHC class II HLA-DR molecules.

The interleukin-1 proteins (IL-1 alpha and IL-1 beta) are key mediators of inflammatory and immunological responses, and several in vitro and in vivo studies with protein-based antagonists have demonstrated the potential usefulness of IL-1 receptor antagonists to treat various inflammation related diseases. Based on the X-ray crystal structures of IL-1 ligands and site-directed mutagenesis data, a noncontiguous binding epitope encompassing Arg4, Phe46, Ile56, Lys93, Lys103, and Glu105 for IL-1 beta was proposed. In this paper we describe the synthesis and binding assay results of small molecule IL-1 receptor antagonists designed on the basis of the three-dimensional structure of the binding epitope. Among these, the compound 45 was found to inhibit IL-alpha binding to the Type I receptor with an IC50 value of 3 microM. A hypothesis generated using BioCad CATALYST program is also presented to rationalize these observations.