Radleigh G. Santos

Structure–activity relationships of benzimidazole derivatives as antiparasitic agents: Dual activity-difference (DAD) maps

Parasitic infections still remain a major health threat in developing countries. Herein we report a systematic characterization of the structure–activity relationships (SAR) of a comprehensive set of benzimidazole derivatives tested against Trichomonas vaginalis and Giardia intestinalis. The analysis was based on pairwise comparisons of the activity similarity and molecular similarity using different molecular representations. Overall, results encourage simultaneous lead optimization efforts for benzimidazole derivatives active against both protozoan. In order to explore the activity profile of the benzimidazoles against the two parasites, we developed the dual activity-difference (DAD) map. DAD map is a complementary approach to systematically characterize the SAR of compound data sets.

Combinatorial Libraries As a Tool for the Discovery of Novel, Broad-Spectrum Antibacterial Agents Targeting the ESKAPE Pathogens.

Mixture based synthetic combinatorial libraries offer a tremendous enhancement for the rate of drug discovery, allowing the activity of millions of compounds to be assessed through the testing of exponentially fewer samples. In this study, we used a scaffold-ranking library to screen 37 different libraries for antibacterial activity against the ESKAPE pathogens. Each library contained between 10000 and 750000 structural analogues for a total of >6 million compounds. From this, we identified a bis-cyclic guanidine library that displayed strong antibacterial activity. A positional scanning library for these compounds was developed and used to identify the most effective functional groups at each variant position. Individual compounds were synthesized that were broadly active against all ESKAPE organisms at concentrations <2 μM. In addition, these compounds were bactericidal, had antibiofilm effects, showed limited potential for the development of resistance, and displayed almost no toxicity when tested against human lung cells and erythrocytes. Using a murine model of peritonitis, we also demonstrate that these agents are highly efficacious in vivo.

Discovery of an enzyme and substrate selective inhibitor of ADAM10 using an exosite-binding glycosylated substrate

ADAM10 and ADAM17 have been shown to contribute to the acquired drug resistance of HER2-positive breast cancer in response to trastuzumab. The majority of ADAM10 and ADAM17 inhibitor development has been focused on the discovery of compounds that bind the active site zinc, however, in recent years, there has been a shift from active site to secondary substrate binding site (exosite) inhibitor discovery in order to identify non-zinc-binding molecules. In the present work a glycosylated, exosite-binding substrate of ADAM10 and ADAM17 was utilized to screen 370,276 compounds from the MLPCN collection. As a result of this uHTS effort, a selective, time-dependent, non-zinc-binding inhibitor of ADAM10 with Ki = 883 nM was discovered. This compound exhibited low cell toxicity and was able to selectively inhibit shedding of known ADAM10 substrates in several cell-based models. We hypothesize that differential glycosylation of these cognate substrates is the source of selectivity of our novel inhibitor. The data indicate that this novel inhibitor can be used as an in vitro and, potentially, in vivo, probe of ADAM10 activity. Additionally, results of the present and prior studies strongly suggest that glycosylated substrate are applicable as screening agents for discovery of selective ADAM probes and therapeutics.

Identification of beryllium-dependent peptides recognized by CD4+ T cells in chronic beryllium disease.

Identification of peptides that form complexes with beryllium and class II HLA molecules and are recognized by CD4+ T cells from patients with chronic beryllium disease.

Characterization of a comprehensive flavor database

Flavor perception involves, among a number of physiological and psychological processes, the recognition of chemicals by olfactory and taste receptors. The highly complex and multidimensional nature of flavor perception challenges our ability to both predict and design new flavor entities. Toward this endeavor, classifications of flavor descriptors have been proposed. Here, we developed a fingerprint‐based representation of a large data set comprising 4181 molecules taken from the commercially available Leffingwell & Associates Canton, Georgia, USA database marketed as Flavor‐Base Pro© 2010. Flavor descriptions of the materials in this database were composite descriptions, collected from numerous sources over the course of more than 40 years. The flavor descriptors were referenced against a detailed and authoritative sensory lexicon (ASTM, American Society for Testing and Materials publication DS 66) comprising 662 flavor attributes. Comparison of clustering analysis, principal component analysis, and descriptor associations provided similar conclusions for various mutually correlated descriptors. Regarding analysis of the flavor similarity of the molecules, the clustering performed provided a means for the quick selection of molecules with either high or low flavor similarity description. Preliminary comparison of the chemical structures to the flavor description demonstrated the feasibility but also the complexity of this task. Additional studies including different structural representations, careful selection of subsets from this data set, as well as the use of a number of classification methods will demonstrate the utility of structure–flavor associations. This work shows that the flavor information contained in databases, such as that used in the present study, can be analyzed following standard chemoinformatics methods. Copyright

Selective Agonists and Antagonists of Formylpeptide Receptors: Duplex Flow Cytometry and Mixture-Based Positional Scanning Libraries

The formylpeptide receptor (FPR1) and formylpeptide-like 1 receptor (FPR2) are G protein–coupled receptors that are linked to acute inflammatory responses, malignant glioma stem cell metastasis, and chronic inflammation. Although several N-formyl peptides are known to bind to these receptors, more selective small-molecule, high-affinity ligands are needed for a better understanding of the physiologic roles played by these receptors. High-throughput assays using mixture-based combinatorial libraries represent a unique, highly efficient approach for rapid data acquisition and ligand identification. We report the superiority of this approach in the context of the simultaneous screening of a diverse set of mixture-based small-molecule libraries. We used a single cross-reactive peptide ligand for a duplex flow cytometric screen of FPR1 and FPR2 in color-coded cell lines. Screening 37 different mixture-based combinatorial libraries totaling more than five million small molecules (contained in 5,261 mixture samples) resulted in seven libraries that significantly inhibited activity at the receptors. Using positional scanning deconvolution, selective high-affinity (low nM Ki) individual compounds were identified from two separate libraries, namely, pyrrolidine bis-diketopiperazine and polyphenyl urea. The most active individual compounds were characterized for their functional activities as agonists or antagonists with the most potent FPR1 agonist and FPR2 antagonist identified to date with an EC50 of 131 nM (4 nM Ki) and an IC50 of 81 nM (1 nM Ki), respectively, in intracellular Ca2+ response determinations. Comparative analyses of other previous screening approaches clearly illustrate the efficiency of identifying receptor selective, individual compounds from mixture-based combinatorial libraries.

Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches.

We present a general approach to describe the structure-activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses dual-activity difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries.

Scaffold Ranking and Positional Scanning Utilized in the Discovery of nAChR-Selective Compounds Suitable for Optimization Studies

Nicotine binds to nicotinic acetylcholine receptors (nAChR), which can exist as many different subtypes. The α4β2 nAChR is the most prevalent subtype in the brain and possesses the most evidence linking it to nicotine seeking behavior. Herein we report the use of mixture based combinatorial libraries for the rapid discovery of a series of α4β2 nAChR selective compounds. Further chemistry optimization provided compound 301, which was characterized as a selective α4β2 nAChR antagonist. This compound displayed no agonist activity but blocked nicotine-induced depolarization of HEK cells with an IC50 of approximately 430 nM. 301 demonstrated nearly 500-fold selectivity for binding and 40-fold functional selectivity for α4β2 over α3β4 nAChR. In total over 5 million compounds were assessed through the use of just 170 samples in order to identify a series of structural analogues suitable for future optimization toward the goal of developing clinically relevant smoking cessation medications.

Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities.

Structure-property relationships and structure-activity relationships play an important role in many research areas, such as medicinal chemistry and drug discovery. Such methods, however, have focused on providing post-hoc descriptions of such relationships based on known data. The ability for these descriptions to remain relevant when considering compounds of unknown activity, and thus the prediction of activity and property landscapes using existing data, remains little explored. In this study, we present a novel method of evaluating the ability of a compound comparison methodology to provide accurate information about a set of unknown compounds and also explore the ability of these predicted activity landscapes to prioritize active compounds over inactive. These methods are applied to three distinct and diverse sets of compounds, each with activity data for multiple targets, for a total of eight target-compound set pairs. Six methodologically distinct compound comparison methods were evaluated. We show that overall, all compound comparison methods provided an improvement in structure-activity relationship prediction over random and were able to prioritize compounds in a superior manner to random sampling, but the degree of success and therefore applicability varied markedly.

GM-CSF Production Allows the Identification of Immunoprevalent Antigens Recognized by Human CD4+ T Cells Following Smallpox Vaccination

The threat of bioterrorism with smallpox and the broad use of vaccinia vectors for other vaccines have led to the resurgence in the study of vaccinia immunological memory. The importance of the role of CD4+ T cells in the control of vaccinia infection is well known. However, more CD8+ than CD4+ T cell epitopes recognized by human subjects immunized with vaccinia virus have been reported. This could be, in part, due to the fact that most of the studies that have identified human CD4+ specific protein-derived fragments or peptides have used IFN-γ production to evaluate vaccinia specific T cell responses. Based on these findings, we reasoned that analyzing a large panel of cytokines would permit us to generate a more complete analysis of the CD4 T cell responses. The results presented provide clear evidence that TNF-α is an excellent readout of vaccinia specificity and that other cytokines such as GM-CSF can be used to evaluate the reactivity of CD4+ T cells in response to vaccinia antigens. Furthermore, using these cytokines as readout of vaccinia specificity, we present the identification of novel peptides from immunoprevalent vaccinia proteins recognized by CD4+ T cells derived from smallpox vaccinated human subjects. In conclusion, we describe a “T cell–driven” methodology that can be implemented to determine the specificity of the T cell response upon vaccination or infection. Together, the single pathogen in vitro stimulation, the selection of CD4+ T cells specific to the pathogen by limiting dilution, the evaluation of pathogen specificity by detecting multiple cytokines, and the screening of the clones with synthetic combinatorial libraries, constitutes a novel and valuable approach for the elucidation of human CD4+ T cell specificity in response to large pathogens.