Cheminformatics Analysis of the Multitarget Structure-Activity Landscape of Environmental Chemicals Binding to Human Endocrine Receptors

In human exposome, environmental chemicals can target and disrupt different endocrine axes, ultimately leading to several endocrine disorders. Such chemicals, termed endocrine disrupting chemicals, can promiscuously bind to different endocrine receptors and lead to varying biological end points. Thus, understanding the complexity of molecule-receptor binding of environmental chemicals can aid in the development of robust toxicity predictors. Toward this, the ToxCast project has generated the largest resource on the chemical-receptor activity data for environmental chemicals that were screened across various endocrine receptors. However, the heterogeneity in the multitarget structure-activity landscape of such chemicals is not yet explored. In this study, we systematically curated the chemicals targeting eight human endocrine receptors, their activity values, and biological end points from the ToxCast chemical library. We employed dual-activity difference and triple-activity difference maps to identify single-, dual-, and triple-target cliffs across different target combinations. We annotated the identified activity cliffs through the matched molecular pair (MMP)-based approach and observed that a small fraction of activity cliffs form MMPs. Further, we structurally classified the activity cliffs and observed that R-group cliffs form the highest fraction among the cliffs identified in various target combinations. Finally, we leveraged the mechanism of action (MOA) annotations to analyze structure-mechanism relationships and identified strong MOA-cliffs and weak MOA-cliffs, for each of the eight endocrine receptors. Overall, insights from this first study analyzing the structure-activity landscape of environmental chemicals targeting multiple human endocrine receptors will likely contribute toward the development of better toxicity prediction models for characterizing the human chemical exposome.

Analysis of structure-activity and structure-mechanism relationships among thyroid stimulating hormone receptor binding chemicals by leveraging the ToxCast library

The thyroid stimulating hormone receptor (TSHR) is crucial in thyroid hormone production in humans, and dysregulation in TSHR activation can lead to adverse health effects such as hypothyroidism and Graves' disease. Further, animal studies have shown that binding of endocrine disrupting chemicals (EDCs) with TSHR can lead to developmental toxicity. Hence, several such chemicals have been screened for their adverse physiological effects in human cell lines via high-throughput assays in the ToxCast project. The invaluable data generated by the ToxCast project has enabled the development of toxicity predictors, but they can be limited in their predictive ability due to the heterogeneity in structure-activity relationships among chemicals. Here, we systematically investigated the heterogeneity in structure-activity as well as structure-mechanism relationships among the TSHR binding chemicals from ToxCast. By employing a structure-activity similarity (SAS) map, we identified 79 activity cliffs among 509 chemicals in TSHR agonist dataset and 69 activity cliffs among 650 chemicals in the TSHR antagonist dataset. Further, by using the matched molecular pair (MMP) approach, we find that the resultant activity cliffs (MMP-cliffs) are a subset of activity cliffs identified via the SAS map approach. Subsequently, by leveraging ToxCast mechanism of action (MOA) annotations for chemicals common to both TSHR agonist and TSHR antagonist datasets, we identified 3 chemical pairs as strong MOA-cliffs and 19 chemical pairs as weak MOA-cliffs. In conclusion, the insights from this systematic investigation of the TSHR binding chemicals are likely to inform ongoing efforts towards development of better predictive toxicity models for characterization of the chemical exposome.

Design, synthesis of benzimidazole tethered 3,4-dihydro-2H-benzo[e] [1, 3] oxazines as anticancer agents

A series of novel 3-(1H-benzo[d]imidazol-2-yl)-3,4-dihydro-2H-benzo[e][1,3] oxazine analogues synthesized through a two-step synthetic protocol. The structure of the compounds were established by interpretation ¹H NMR, ¹³C NMR and Mass spectral data recorded after purification. All the title compounds 4a-k were screened for their in vitro anti-cancer activity against two breast cancer cell lines MCF 7 and MDA-MB-231 by using Doxorubicin as standard reference. Compound 4e displayed superior activity against both the cell lines MCF-7 and MDA-MB-231 with IC₅₀ values of 8.60 ± 0.75 and 6.30 ± 0.54 µM respectively, compared to the Doxorubicin IC₅₀ value of 9.11 ± 0.54 and 8.47 ± 0.47 µM. Compound 4i also indicated good activity with IC₅₀ value of 9.85 ± 0.69 μM on par with Doxorubicin against MCF-7 cells. Compound 4g demonstrated best activity on par with standard reference to IC₅₀ value of 8.52 ± 0.62 μM against MDA-MB-231 cell line. And all other compounds demonstrated good to moderate activity compared to Doxorubicin. Docking studies against EGFR showed that all the compounds have very good binding affinities towards the target. The predicted drug-likeness properties of all compounds enable them to be used as therapeutic agents.

Design and Syntheses of Ruthenium ENE (E = S, Se) Pincer Complexes: A Versatile System for Catalytic and Biological Applications

This report describes synthesis of two ruthenium(II) ENE pincer complexes (E = S, C1 and E = Se, C2 ) by the reaction of bis(2-(phenylchalcogenyl)ethyl)amine ( L1 , L2 ) with RuCl 2 (PPh 3 ) 3 . The complexes were characterized with the help of 1 H and 13 C{ 1 H} NMR, FTIR, HRMS, cyclic voltammetry and elemental analysis techniques. The structure and bonding mode of ligand with ruthenium in C2 was established with the help of single crystal X-ray diffraction. The complex showed distorted octahedral geometry with two chlorine atoms trans to each other. The Ru-Se bond distances (Å) are 2.4564(3)-2.4630(3), Ru-N distance is 2.181(2), Ru-P distance is 2.2999(6), and Ru-Cl distances are 2.4078(6)-2.4314(6). The complexes showed good to excellent catalytic activity for the N -alkylation of 1,2-phenylenediamine with benzyl alcohol derivatives to synthesize 1,2-disubstituted benzimidazole derivatives. The complexes were also found to be efficient for aerobic oxidation of benzyl alcohols to corresponding aldehydes which are precursors to the bisimines generated in situ during the synthesis of 1,2-disubstituted benzimidazole derivatives. Complex C2 where selenium is coordinated with ruthenium was found to be more efficient as compared to sulfur coordinated ruthenium complex C1 . Since ruthenium complexes are getting increasing attention for developing new anticancer agents, the preliminary studies like binding behavior of both the complexes towards CT-DNA were studied by competitive binding with ethidium bromide (EthBr) using emission spectroscopy. In addition, the interactions of C1-C2 were also studied with bovine serum albumin (BSA) using steady state fluorescence quenching and synchronous fluorescence studies. A good stability of Ru(II) state was observed by cyclic voltammetric studies of C1-C2 . Overall these molecules are good examples of bio-organometallic systems for catalytic and biological applications.

Synthesis and Evaluation of a Non-Peptide Small-Molecule Drug Conjugate Targeting Integrin αVβ3

An integrin α_Vβ₃-targeting linear RGD mimetic containing a small-molecule drug conjugate (SMDC) was synthesized by combining the antimitotic agent monomethyl auristatin E (MMAE), an enzymatically cleavable Val-Ala-PABC linker with a linear conjugable RGD mimetic. The structure proposal for the conjugable RGD mimetic was suggested upon the DAD mapping analysis of a previously synthesized small-molecule RGD mimetic array based on a tyrosine scaffold. Therefore, a diversifying strategy was developed as well as a novel method for the partial hydrogenation of pyrimidines in the presence of the hydrogenolytically cleavable Cbz group. The small-molecule RGD mimetics were evaluated in an ELISA-like assay, and the structural relationships were analyzed by DAD mapping revealing activity differences induced by structural changes as visualized in dependence on special structural motifs. This provided a lead structure for generation of an SMDC containing the antimitotic drug MMAE. The resulting SMDC containing a linear RGD mimetic was tested in a cell adhesion and an in vitro cell viability assay in comparison to reference SMDCs containing cRGDfK or cRADfK as the homing device. The linear RGD SMDC and the cRGDfK SMDC inhibited adhesion of α_Vβ₃-positive WM115 cells to vitronectin with IC₅₀ values in the low µM range, while no effect was observed for the α_Vβ₃-negative M21-L cell line. The cRADfK SMDC used as a negative control was about 30-fold less active in the cell adhesion assay than the cRGDfK SMDC. Conversely, both the linear RGD SMDC and the cRGDfK SMDC are about 55-fold less cytotoxic than MMAE against the α_Vβ₃-positive WM115 cell line with IC50 values in the nM range, while the cRADfK SMDC is 150-fold less cytotoxic than MMAE. Hence, integrin binding also influences the antiproliferative activity giving a targeting index of 2.8.

Synthesis, Antiprotozoal Activity, and Cheminformatic Analysis of 2-Phenyl-2H-Indazole Derivatives

Indazole is an important scaffold in medicinal chemistry. At present, the progress on synthetic methodologies has allowed the preparation of several new indazole derivatives with interesting pharmacological properties. Particularly, the antiprotozoal activity of indazole derivatives have been recently reported. Herein, a series of 22 indazole derivatives was synthesized and studied as antiprotozoals. The 2-phenyl-2H-indazole scaffold was accessed by a one-pot procedure, which includes a combination of ultrasound synthesis under neat conditions as well as Cadogan's cyclization. Moreover, some compounds were derivatized to have an appropriate set to provide structure-activity relationships (SAR) information. Whereas the antiprotozoal activity of six of these compounds against E. histolytica, G. intestinalis, and T. vaginalis had been previously reported, the activity of the additional 16 compounds was evaluated against these same protozoa. The biological assays revealed structural features that favor the antiprotozoal activity against the three protozoans tested, e.g., electron withdrawing groups at the 2-phenyl ring. It is important to mention that the indazole derivatives possess strong antiprotozoal activity and are also characterized by a continuous SAR.

Reaching for the bright StARs in chemical space

Visualization of activity data in chemical space is common in drug discovery. Navigating the space in a systematic manner is not trivial, given its size and huge coverage. To this end, methods for data visualization have been developed charting biological activity into chemical space. Herein, we review the progress in different visualization approaches to explore the chemical space aiming at reaching insightful structure-activity relationships (SARs) in the chemical space. We discuss recent methods including consensus diversity plots, ChemMaps, and constellation plots. Several of the methods we review can be extended to analyze other properties of interest in medicinal chemistry, such as structure-toxicity relationships, and can be adapted to postprocess results of virtual screening (VS) of large compound libraries.

Developments towards synthesis of N-heterocycles from amidines via C–N/C–C bond formation

This review focuses on the synthesis of N-heterocycles using amidines as starting materials, with an emphasis on the mechanisms of these reactions via C–N/C–C bond formation.

Activity Landscape and Molecular Modeling to Explore the SAR of Dual Epigenetic Inhibitors: A Focus on G9a and DNMT1

In this work we discuss the insights from activity landscape, docking and molecular dynamics towards the understanding of the structure-activity relationships of dual inhibitors of major epigenetic targets: lysine methyltransferase (G9a) and DNA methyltranferase 1 (DNMT1). The study was based on a novel data set of 50 published compounds with reported experimental activity for both targets. The activity landscape analysis revealed the presence of activity cliffs, e.g., pairs of compounds with high structure similarity but large activity differences. Activity cliffs were further rationalized at the molecular level by means of molecular docking and dynamics simulations that led to the identification of interactions with key residues involved in the dual activity or selectivity with the epigenetic targets.

Complexes of (η5-Cp*)Ir(iii) with 1-benzyl-3-phenylthio/selenomethyl-1,3-dihydrobenzoimidazole-2-thione/selenone: catalyst for oxidation and 1,2-substituted benzimidazole synthesis

The treatment of 1-benzyl-3-phenylthio/selenomethyl-1,3-dihydrobenzoimidazole-2-thione/selenone [L1-L4] with [(η⁵-Cp*)IrCl(μ-Cl)]₂ at 25 °C followed by NH₄PF₆ results in [(η⁵-Cp*)Ir(L)Cl][PF₆] (1-4 for L = L1 to L4), authenticated with high-resolution mass spectrometry (HR-MS) and multi-nuclei nuclear magnetic resonance (NMR) imaging (¹H, ¹³C{¹H} and ⁷⁷Se{¹H}). The structures of 1-4, established with single-crystal X-ray diffraction, reveal a "piano-stool" geometry around the Ir. The Ir-thio/selenoether (Ir-S/Ir-Se) bond distances (Å) are 2.347(18)-2.355(4)/2.4663(12)-2.4663(13) and Ir-thione/selenone (Ir-S/Ir-Se) distances are 2.4146(19)-2.417(2)/2.5141(16)-2.5159(12). The reaction of 1,2-phenylenediamine with benzylic alcohols and furfuryl alcohol under mild and ambient conditions, catalyzed efficiently with complexes 1-4, generates bisimine in situ. Cyclization and rearrangement via 1,3-hydride shift triggered by its electrophilic activation with Ir(iii) species finally results in 1,2-disubstituted benzimidazole. The yield of the heterocycles in this one-pot synthesis is excellent to good. The aldehydes generated in situ by aerial oxidation of alcohols in the presence of 1-4 as catalysts are precursors to the bisimine as the protocols of this heterocycle synthesis carried out in the absence of 1,2-phenylenediamine give them in excellent-to-good yield. The oxidation of alcohols by hydrogen transfer to acetone was catalyzed efficiently with complexes 1-4 and resulted in aldehyde/ketone in excellent-to-good yield. Each catalytic process is marginally more efficient with 1 than its counterparts.

Chemoinformatics: a perspective from an academic setting in Latin America

This perspective discusses the current progress of a chemoinformatics group in a major university in Latin America. Three major aspects are discussed in a critical manner: research, education, and collaboration with industry and other public research networks. It is also presented an overview of the progress in applied research and development of research concepts. Efforts to teach chemoinformatics at the undergraduate and graduate levels are discussed. It is addressed how the partnership with industry and other not-for-profit research institutions not only brings additional sources of funding but, more importantly, increases the impact of the multidisciplinary work and offers the students to be exposed to other research environments. We also discuss the main perspectives and challenges that remain to be addressed in these settings.

Synthesis, antiprotozoal activity, and chemoinformatic analysis of 2-(methylthio)-1H-benzimidazole-5-carboxamide derivatives: Identification of new selective giardicidal and trichomonicidal compounds

A series of twelve new 2-(methylthio)-1H-benzimidazole-5-carboxamide derivatives (1-12) were synthesized and their antiparasitic activity was tested in vitro against Giardia intestinalis, Trichomonas vaginalis and Entamoeba histolytica. Experimental evaluations showed IC₅₀ values within the nanomolar range for all tested compounds, some showing higher activity than metronidazole and albendazole. A chemoinformatic study was used to compare the structure-activity relationship of the synthesized carboxamides with those of 91 previously studied benzimidazoles, and with some Nitazoxanide-N-methylbenzimidazole hybrids recently synthetized by our group. Compounds 1 and 3 were identified as prominent selective compounds against T. vaginalis and G. intestinalis, respectively, while compound 4 was found to be of broad spectrum against the three protozoans.

Activity Landscape Plotter: A Web-Based Application for the Analysis of Structure-Activity Relationships

Activity landscape modeling is a powerful method for the quantitative analysis of structure-activity relationships. This cheminformatics area is in continuous growth, and several quantitative and visual approaches are constantly being developed. However, these approaches often fall into disuse due to their limited access. Herein, we present Activity Landscape Plotter as the first freely available web-based tool to automatically analyze structure-activity relationships of compound data sets. Based on the concept of activity landscape modeling, the online service performs pairwise structure and activity relationships from an input data set supplied by the user. For visual analysis, Activity Landscape Plotter generates Structure-Activity Similarity and Dual-Activity Difference maps. The user can interactively navigate through the maps and export all the pairwise structure-activity information as comma delimited files. Activity Landscape Plotter is freely accessible at https://unam-shiny-difacquim.shinyapps.io/ActLSmaps /.

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery.

Synthesis and Biological Evaluation of Benzimidazole Phenylhydrazone Derivatives as Antifungal Agents against Phytopathogenic Fungi

A series of benzimidazole phenylhydrazone derivatives (6a–6ai) were synthesized and characterized by ¹H-NMR, ESI-MS, and elemental analysis. The structure of 6b was further confirmed by single crystal X-ray diffraction as (E)-configuration. All the compounds were screened for antifungal activity against Rhizoctonia solani and Magnaporthe oryzae employing a mycelium growth rate method. Compound 6f exhibited significant inhibitory activity against R. solani and M. oryzae with the EC₅₀ values of 1.20 and 1.85 μg/mL, respectively. In vivo testing demonstrated that 6f could effectively control the development of rice sheath blight (RSB) and rice blast (RB) caused by the above two phytopathogens. This work indicated that the compound 6f with a benzimidazole phenylhydrazone scaffold could be considered as a leading structure for the development of novel fungicides.

Accessing Benzimidazoles via a Ring Distortion Strategy: An Oxone Mediated Tandem Reaction of 2-Aminobenzylamines

An exceptional oxone mediated tandem transformation of 2-aminobenzylamines to 2-substituted benzimidazoles is reported. It occurs at room temperature with aromatic, heteroaromatic, and aliphatic aldehydes. In this reaction initial condensation of 2-aminobenzylamine with appropriate aldehydes afforded a tetrahydroquinazoline intermediate which underwent oxone-mediated ring distortion to afford the desired compounds in moderate to excellent yields.

Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry

Property landscape modeling (PLM) methods are at the interface of experimental sciences and computational chemistry. PLM are becoming a common strategy to describe systematically structure-property relationships of datasets. Thus far, PLM have been used mainly in medicinal chemistry and drug discovery. Herein, we survey advances on key topics on PLM with emphasis on questions often raised regarding the outcomes of the property landscape studies. We also emphasize on concepts of PLM that are being extended to other experimental areas beyond drug discovery. Topics discussed in this paper include applications of PLM to further characterize protein-ligand interactions, the utility of PLM as a quantitative and descriptive approach, and the statistical validation of property cliffs.

ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling

Background

Determination of acute toxicity, expressed as median lethal dose (LD₅₀), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity.

Results

In this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD₅₀ values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by q_ext² for the test set containing 2376 molecules, ranged from 0.572 to 0.659.

Conclusion

Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (q_ext² = 0.669–0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity. Graphical abstract

Activity cliffs and activity cliff generators based on chemotype-related activity landscapes

Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized. Compounds in each database were grouped into chemotype classes. Then, pairwise comparisons of structure similarities and activity differences were calculated for each compound and used to construct chemotype-based structure-activity similarity (SAS) maps. Different landscape distributions among four major regions of the SAS maps were observed for different subsets of molecules grouped in chemotypes. Based on this observation, the activity cliff enrichment factor was calculated to numerically detect chemotypes enriched in activity cliffs. Several chemotype classes were detected having major proportion of activity cliffs than the entire database. In addition, some chemotype classes comprising compounds with smooth structure activity relationships (SAR) were detected. Finally, the activity cliff generator concept was applied to compounds grouped in chemotypes to extract valuable SAR information.

On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?

BACKGROUND

Most work on the topic of activity landscapes has focused on their quantitative description and visual representation, with the aim of aiding navigation of SAR. Recent developments have addressed applications such as quantifying the proportion of activity cliffs, investigating the predictive abilities of activity landscape methods and so on. However, all these publications have worked under the assumption that the activity landscape models are "real" (i.e., statistically significant).

RESULTS

The current study addresses for the first time, in a quantitative manner, the significance of a landscape or individual cliffs in the landscape. In particular, we question whether the activity landscape derived from observed (experimental) activity data is different from a randomly generated landscape. To address this we used the SALI measure with six different data sets tested against one or more molecular targets. We also assessed the significance of the landscapes for single and multiple representations.

CONCLUSIONS

We find that non-random landscapes are data set and molecular representation dependent. For the data sets and representations used in this work, our results suggest that not all representations lead to non-random landscapes. This indicates that not all molecular representations should be used to a) interpret the SAR and b) combined to generate consensus models. Our results suggest that significance testing of activity landscape models and in particular, activity cliffs, is key, prior to the use of such models.

Chemoinformatic characterization of activity and selectivity switches of antiprotozoal compounds

BACKGROUND

Benzimidazole derivatives are promising compounds for the treatment of parasitic infections. The structure-activity relationships of 91 benzimidazoles with activity against Trichomonas vaginalis and Giardia intestinalis were analyzed using a novel activity landscape modeling approach.

RESULTS

We identified two prominent cases of 'activity switches' and 'selectivity switches' where two R group substitutions in the benzimidazole scaffold completely invert the activity and selectivity pattern for T. vaginalis and G. intestinalis.

CONCLUSION

A chemoinformatic methodology was used to rapidly identify discrete structural changes around the central scaffold that are associated with large changes in biological activity for each parasite. The structure-activity relationships for the benzimidazole derivatives is smooth for both protozoan with few but markedly important activity cliffs.

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.

Large-scale SAR analysis

The analysis of structure–activity relationships (SARs) is a central task in medicinal chemistry. Traditionally, SAR exploration has concentrated on individual compound series. This conventional approach is complemented by large-scale SAR analysis, which puts strong emphasis on data mining and SAR visualization. This contribution reviews recent concepts for large-scale SAR analysis including numerical functions to characterize global and local SAR information content of compound data sets, alternative activity landscape representations and data mining strategies.

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.

Synthesis and antiprotozoal activity of novel 2-{[2-(1H-imidazol-1-yl)ethyl]sulfanyl}-1H-benzimidazole derivatives

A series of 19 new 2-{[2-(1H-imidazol-1-yl)ethyl]sulfanyl}-1H-benzimidazole derivatives was synthesized starting from the properly substituted 1,2-phenylendiamine. These compounds have hydrogen or methyl at position 1; while hydrogen, chlorine, ethoxy or methoxycarbonyl group is at position 5 and/or 6. The novel compounds were tested against protozoa Trichomonas vaginalis, Giardia intestinalis and Entamoeba histolytica. Experimental evaluations revealed strong activity for all tested compounds, having IC50 values in the nanomolar range, which were even better than metronidazole, the drug of choice for these parasites.

Systematic characterization of structure-activity relationships and ADMET compliance: a case study

Traditionally, activity landscape modeling has been focused on analyzing SAR, despite the fact that lead optimization in drug discovery involves concurrent enhancements of activity and ADMET properties of leads. As a case study, we discuss the systematic analysis of activity landscapes, incorporating ADMET considerations, using a dataset of 166 compounds screened for kappa-opioid receptor activity. Pairwise MACCS/Tanimoto structure similarities, property similarities utilizing 33 ADMET descriptors and a 35-dimensional 'violation bit vector' representing drug-likeness are analyzed. We address the question about the range of ADMET property violations that arise from structural changes, subtle and significant. Pairs of compounds are identified bearing identical, comparable and significantly different drug-likeness in the three informative regions of structure-activity landscapes.