Activity landscape analysis of novel 5$$\upalpha $$-reductase inhibitors

Antitumor potential of novel 5α,6β-dibromo steroidal D-homo lactone

New steroidal D-homo androstane derivative, 5α,6β-dibromo-3β-hydroxy-17-oxa-17a-homoandrostan-16-one was synthesized and its structure was confirmed by NMR spectroscopy. In silico ADME properties of this compound were assessed using the SwissADME online prediction tool. Six human cancer cell lines (MDA-MB-231, MCF-7, PC3, HT-29, HeLa, and A549) and one human noncancerous cell line (MRC-5) were used for in vitro cytotoxicity testing. Novel steroidal dibromide was also tested for relative binding affinity for the ligand binding domain of estrogen receptor α and β or the androgen receptor using a published assay in yeast cells. Ligand binding domains of each steroid receptor were expressed in-frame with yellow fluorescent protein in yeast and the fluorescence intensity changes upon addition of test compound was measured. The new compound showed selective cytotoxic activity against HT-29 (colon adenocarcinoma) and A549 (lung adenocarcinoma) cell lines, as well as the potential to induce apoptosis in HT-29 cells, while results obtained from ligand binding assay in yeast suggested a lack of significant estrogenic or androgenic properties.

Finding Constellations in Chemical Space Through Core Analysis

Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of chemical space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of chemical space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in chemical space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in chemical space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.

Progress with modeling activity landscapes in drug discovery

INTRODUCTION

Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity. Areas covered: The concepts of AL modeling are introduced and its basis in quantitatively assessing molecular similarity is discussed. The different types of AL modeling approaches are introduced. AL designs can broadly be divided into three categories: compound-pair based, dimensionality reduction, and network approaches. Recent developments for each of these categories are discussed focusing on the application of mathematical, statistical, and machine learning tools for AL modeling. AL modeling using chemical space networks is covered in more detail. Expert opinion: AL modeling has remained a largely descriptive approach for the analysis of SARs. Beyond mere visualization, the application of analytical tools from statistics, machine learning and network theory has aided in the sophistication of AL designs and provides a step forward in transforming ALs from descriptive to predictive tools. To this end, optimizing representations that encode activity relevant features of molecules might prove to be a crucial step.

Synthesis and cytotoxic effect of pregnenolone derivatives with one or two α,β-unsaturated carbonyls and an ester moiety at C-21 or C-3

Four series of pregnenolone derivatives having one or two α,β-unsaturated carbonyls and an ester moiety at C-21 or C-3 were synthetized to compare their cytotoxicity effect. The final compounds were evaluated on three human cancer cell lines: PC-3 (prostate cancer), MCF-7 (breast cancer), SKLU-1 (lung cancer) and a noncancerous cell line HGF (human gingival fibroblast). Two steroids with a 4-fluorinated benzoic acid ester at C-21 were the most active against lung cancer cell line with IC₅₀ of 13.1 ± 1.2 and 12.8 ± 0.5 μM and showed a low percentage of cytotoxicity for noncancerous cells (27.63 ± 2.3 and 18.39 ± 1.2% in the screening at 50 μM).

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.