Dual and triple activity-difference (DAD/TAD) maps are tools for the systematic characterization of structure-activity relationships (SAR) of compound data sets screened against two or three targets. DAD and TAD maps are two- and three- dimensional representations of the pairwise activity differences of compound data sets, respectively. Adding pairwise structural similarity information into these maps readily reveals activity cliff regions in the SAR for one, two, or three targets. In addition, pairs of compounds in the smooth regions of the SAR and scaffold hops are also easily identified in these maps. Herein, DAD and TAD maps are employed for the systematic characterization of the SAR of a benchmark set of 299 compounds screened against dopamine, norepinephrine, and serotonin transporters. To reduce the well-known dependence of the activity landscape on the structural representation, five selected 2D and 3D structure representations were used to characterize the SAR. Systematic analysis of the DAD and TAD maps reveals regions in the landscape with similar SAR for two or the three targets as well as regions with inverse SAR, i.e., changes in structure that increase activity for one target, but decrease activity for the other target. Focusing the analysis on pairs of compounds with high structure similarity revealed the presence of single-, dual-, and triple-target activity cliffs, i.e., small changes in structure with high changes in potency for one, two, or the three targets, respectively. Triple-target scaffold hops are also discussed. Activity cliffs and scaffold hops were also quantified and represented using two recently proposed approaches namely, mean Structure Activity Landscape Index (mean SALI) and Consensus Structure-Activity Similarity (SAS) maps.
Indazole is considered a very important scaffold in medicinal chemistry. It is commonly found in compounds with diverse biological activities, e.g., antimicrobial and anti-inflammatory agents. Considering that infectious diseases are associated to an inflammatory response, we designed a set of 2H-indazole derivatives by hybridization of cyclic systems commonly found in antimicrobial and anti-inflammatory compounds. The derivatives were synthesized and tested against selected intestinal and vaginal pathogens, including the protozoa Giardia intestinalis, Entamoeba histolytica, and Trichomonas vaginalis; the bacteria Escherichia coli and Salmonella enterica serovar Typhi; and the yeasts Candida albicans and Candida glabrata. Biological evaluations revealed that synthesized compounds have antiprotozoal activity and, in most cases, are more potent than the reference drug metronidazole, e.g., compound 18 is 12.8 times more active than metronidazole against G. intestinalis. Furthermore, two 2,3-diphenyl-2H-indazole derivatives (18 and 23) showed in vitro growth inhibition against Candida albicans and Candida glabrata. In addition to their antimicrobial activity, the anti-inflammatory potential for selected compounds was evaluated in silico and in vitro against human cyclooxygenase-2 (COX-2). The results showed that compounds 18, 21, 23, and 26 display in vitro inhibitory activity against COX-2, whereas docking calculations suggest a similar binding mode as compared to rofecoxib, the crystallographic reference.
Structure-activity relationships (SAR) of compound databases play a key role in hit identification and lead optimization. In particular, activity cliffs, defined as a pair of structurally similar molecules that present large changes in potency, provide valuable SAR information. Herein, we introduce the concept of activity cliff generator, defined as a molecular structure that has a high probability to form activity cliffs with molecules tested in the same biological assay. To illustrate this concept, we discuss a case study where Structure-Activity Similarity maps were used to systematically identify and analyze activity cliff generators present in a dataset of 168 compounds tested against three peroxisome-proliferator-activated receptor (PPAR) subtypes. Single-target and dual-target activity cliff generators for PPARα and δ were identified. In addition, docking calculations of compounds that were classified as cliff generators helped to suggest a hot spot in the target protein responsible of activity cliffs and to analyze its implication in ligand-enzyme interaction.
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