Non-active Compounds Research Articles

Molecular determinant deciphering of MIC-guided RND efflux substrates in E. coli

Antimicrobial resistance poses an urgent and formidable global public health threat. The escalation of bacterial multidrug resistance to antibiotics has the potential to become a leading cause of global mortality if there is no substantial improvement in antimicrobial development and therapy protocols. In light of this, it is imperative to identify the molecular determinants responsible for the reduced antibiotic activity associated with RND efflux pumps. This comprehensive study meticulously examines Minimum Inhibitory Concentration (MIC) data obtained from in vitro tests for various antibiotic families and non-active dye compounds, sourced from diverse literature references. The primary focus of this study is to assess the susceptibility of these agents to efflux-resistant Escherichia coli strains, integrating both MIC data and relevant physicochemical properties. The central objective is to unveil the specific substituents that significantly influence the uptake process mediated by the AcrAB-TolC efflux system. This exploration seeks to reveal the consequences of these substituents on pharmacodynamic responses, providing valuable insights into Structure-Activity Relationships. It is noteworthy that this analysis represents a pioneering effort, with prospective implications for RND efflux pump-producing strains. Ultimately, deciphering efflux markers is crucial to effectively mitigate the emergence of specific resistance and to better monitor the role of this primary resistance mechanism in Gram-negative bacteria, particularly as observed in clinical antibiotic therapy practice.

Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle.

A Composite Decision Rule of CD8+ T-cell Density in Tumor Biopsies Predicts Efficacy in Early-stage, Immunotherapy Trials.

To examine whether CD8+ T-cell numbers in paired tumor biopsies in early-stage clinical trials can be used as an early indicator of clinical benefit for cancer immunotherapies. Paraffin sections of tumor biopsies were stained immunohistochemically for CD8+ T cells, which were digitally enumerated. The tumor biopsies were from cancer patients in early-phase trials testing novel immunotherapeutic agents. Paired biopsies taken before the start of treatment and on-treatment were compared. A total of 155 patients were used as the training set and an additional 221 patients were used as the validation set. Using the Cox proportional hazard model, a ≥0.9- increase in fold change (FC) on a ln scale in CD8+ T cells (corresponding to a 2.5-fold increase on the linear scale), from baseline, demonstrated a greater association with prolonged progression-free survival and allowed improved differentiation between groups above and below the threshold. Similarly, a ≥6.2 threshold in geometric mean of the on-treatment density (OTD) of T cells, which approximately corresponds to 500 cells/mm2, correlated with longer PFS. The combination of both criteria (FC and OTD) provided the best discrimination between clinically nonactive and active compounds. We propose that a composite score of CD8+ T-cell density in paired biopsies taken before and on-treatment may be a new biomarker to inform on clinical outcomes in early immunotherapy clinical trials.

In silico simulations and molecular descriptors to predict in vitro transactivation potencies of Baikal seal estrogen receptors by environmental contaminants

Baikal seals (Pusa sibirica) are vulnerable to high levels of organic pollutants. Here, we evaluated the transactivation potencies of bisphenols (BPs) and hydroxylated polychlorinated biphenyls (OH-PCBs) via the Baikal seal estrogen receptor α and β (bsERα and bsERβ) using in vitro and in silico approaches. In vitro reporter gene assays showed that most BPs and OH-PCBs exhibited estrogenic activity with bsER sub-type-specific potency. Among the BPs tested, bisphenol AF showed the lowest EC50 for both bsERs. 4′-OH-CB50 and 4′-OH-CB30 showed the lowest EC50 among OH-PCBs tested for bsERα and bsERβ, respectively. 4-((4-Isopropoxyphenyl)-sulfonyl)phenol, 4′-OH-CB72, and 4′-OH-CB121 showed weak bsERα-specific transactivation. Only 4-OH-CB107 did not affect both bsERs. In silico docking simulations revealed the binding affinities of these chemicals to bsERs and partially explained the in vitro results. Using the in silico simulations and molecular descriptors as explanatory variables and the in vitro results as objective variables, the quantitative structure-activity relationship (QSAR) models constructed for classification and regression accurately separated bsER-active compounds from non-active compounds and predicted the in vitro bsERα- and bsERβ-transactivation potencies, respectively. The QSAR models also suggested that chemical polarity, van der Waals surface area, bridging atom structure, position of the phenolic-OH group, and ligand interactions with key residues of the ligand binding pocket are critical variables to account for the bsER transactivation potency of the test compounds. We also succeeded in constructing computational models for predicting in vitro transactivation potencies of mouse ERs in the same manner, demonstrating the applicability of our approach independent of species-specific responses.

Antibacterial activity of withanolides and their structure-activity relationship

Two new withanolides, (17R,20S,22R)-4β-acetoxy-5β,6β-epoxy-19,27-dihydroxy-1-oxo-witha-2,24-dienolide (withalongolide A 4-acetate (5) and (17R,20S,22R)-5β,6β-epoxy-27-hydroxy-1,4-dioxo-witha-24-enolide (9), and seven known withanolides with normal structure (1–4, 6–8) were isolated from aerial parts of Cuatresia colombiana. Several semisynthetic derivatives were prepared from the natural metabolites withaferin A and jaborosalactone 38. The compounds were fully characterized by a combination of spectroscopic methods (1D and 2D NMR and MS). The compounds isolated from C. colombiana, sixteen withanolides previously isolated from different Solanaceae species with different skeletons and semisynthetic derivatives were evaluated for their antibacterial activity against a selected panel of Gram-positive and Gram-negative bacteria. According to the bioactivity against S. aureus and E. faecalis, the compounds evaluated were divided into three groups: compounds with high activity (MIC 0.063 mM), compounds with moderate activity (0.5 mM > MIC > 0.125 mM) and non-active compounds (MIC ≥1 mM); in addition, some structure-activity relationship keys could be inferred.

Inhibition of Acetylcholinesterase by Novel Lupinine Derivatives.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive memory loss and cognitive impairment due in part to a severe loss of cholinergic neurons in specific brain areas. AD is the most common type of dementia in the aging population. Although several acetylcholinesterase (AChE) inhibitors are currently available, their performance sometimes yields unexpected results. Thus, research is ongoing to find potentially therapeutic AChE inhibitory agents, both from natural and synthetic sources. Here, we synthesized 13 new lupinine triazole derivatives and evaluated them, along with 50 commercial lupinine-based esters of different carboxylic acids, for AChE inhibitory activity. The triazole derivative 15 [1S,9aR)-1-((4-(4-(benzyloxy)-3-methoxyphenyl)-1H-1,2,3-triazol-1-yl)methyl)octahydro-2H-quinolizine)] exhibited the most potent AChE inhibitory activity among all 63 lupinine derivatives, and kinetic analysis demonstrated that compound 15 was a mixed-type AChE inhibitor. Molecular docking studies were performed to visualize interaction between this triazole derivative and AChE. In addition, a structure-activity relationship (SAR) model developed using linear discriminant analysis (LDA) of 11 SwissADME descriptors from the 50 lupinine esters revealed 5 key physicochemical features that allowed us to distinguish active versus non-active compounds. Thus, this SAR model could be applied for design of more potent lupinine ester-based AChE inhibitors.

Screening of potent α-glucosidase inhibitory and antioxidant polyphenols in Prunella vulgaris L. by bioreaction-HPLC-quadrupole-time-of-flight-MS/MS and in silico analysis.

Prunella vulgaris L. is a well-known traditional Chinese medicine for blood glucose homeostasis and antioxidant potential. Ethyl acetate fraction of P. vulgaris L. demonstrated higher phenolic content (85.53 ± 6.74mg gallic acid equivalents per gram dry weight), α-glucosidase inhibitory (IC50 , 69.13 ± 2.86 μg/ml), and antioxidant (IC50 , 8.68 ± 1.01 μg/ml) activities. However, the bioactive polyphenols responsible for the beneficial properties remain unclear. Here, bioreaction-HPLC-quadrupole-time-of-flight-MS/MS method was developed for rapid, accurate, and efficient screening and identification of polyphenols with α-glucosidase inhibitory and antioxidant activities from P. vulgaris L. Bioactive polyphenols can specifically bind with α-glucosidase or react with 1,1-diphenyl-2-picryl-hydrazyl radical, which was easily discriminated from nonactive compounds. Subsequently, 20 bioactive polyphenols (16 phenyl propionic acid derivatives and four flavonoids) were screened and identified. Furthermore, molecular docking analysis revealed that screened 20 polyphenols bind with the active sites of α-glucosidase through hydrogen bonding and π-π stacking. Density functional theory calculations demonstrated their electron transport ability and chemical reactivity. The in silico analysis confirmed the screened results. In summary, this study provided a valuable strategy for rapid discovering bioactive compounds from complex natural products and offered scientific evidence for further development and application of P. vulgaris L.

Structure-Function Relationships in the Human P-Glycoprotein (ABCB1): Insights from Molecular Dynamics Simulations.

P-Glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette superfamily of transporters, and it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping compounds out of cells. P-gp contributes to a reduction in toxicity, and has broad substrate specificity. It is involved in the failure of many cancer and antiviral chemotherapies due to the phenomenon of multidrug resistance (MDR), in which the membrane transporter removes chemotherapeutic drugs from target cells. Understanding the details of the ligand–P-gp interaction is therefore critical for the development of drugs that can overcome the MDR phenomenon, for the early identification of P-gp substrates that will help us to obtain a more effective prediction of toxicity, and for the subsequent outdesign of substrate properties if needed. In this work, a series of molecular dynamics (MD) simulations of human P-gp (hP-gp) in an explicit membrane-and-water environment were performed to investigate the effects of binding different compounds on the conformational dynamics of P-gp. The results revealed significant differences in the behaviour of P-gp in the presence of active and non-active compounds within the binding pocket, as different patterns of movement were identified that could be correlated with conformational changes leading to the activation of the translocation mechanism. The predicted ligand–P-gp interactions are in good agreement with the available experimental data, as well as the estimation of the binding-free energies of the studied complexes, demonstrating the validity of the results derived from the MD simulations.

A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules.

Schistosomiasis is a chronic and painful disease of poverty caused by the flatworm parasite Schistosoma. Drug discovery for antischistosomal compounds predominantly employs in vitro whole organism (phenotypic) screens against two developmental stages of Schistosoma mansoni, post-infective larvae (somules) and adults. We generated two rule books and associated scoring systems to normalize 3898 phenotypic data points to enable machine learning. The data were used to generate eight Bayesian machine learning models with the Assay Central software according to parasite’s developmental stage and experimental time point (≤24, 48, 72, and >72 h). The models helped predict 56 active and nonactive compounds from commercial compound libraries for testing. When these were screened against S. mansoni in vitro, the prediction accuracy for active and inactives was 61% and 56% for somules and adults, respectively; also, hit rates were 48% and 34%, respectively, far exceeding the typical 1–2% hit rate for traditional high throughput screens.

Preclinical in vitro screening of newly synthesised amidino substituted benzimidazoles and benzothiazoles

Newly synthesised benzimidazole/benzotiazole derivatives bearing amidino, namely 3,4,5,6-tetrahydropyrimidin-1-ium chloride, substituents have been evaluated for their potential antitumor activity in vitro. Compounds and standard drugs (doxorubicin, staurosporine and vandetanib) were tested on three human lung cancer cell lines A549, HCC827 and NCI-H358. We tested compounds in MTS citotoxicity assay and in BrdU proliferative assay performed on 2 D and 3 D assay format. Because benzmidazole scaffold is similar to natural purines, we tested the most active compounds for ability to induce cell apoptosis of A549 by binding to DNA in comparison with doxorubicin and saturosporine. Additionally, the ADME properties of the most active benzothiazole/benzimidazole and non-active compounds were determined to see if the different ADME properties are the cause of different activity in 2 D and 3 D assays, as well as to see if the tested active compounds have drug like properties and potency for further profilation. ADME characterisation included solubility, lipophilicity, permeability, metabolic stability and binding to plasma proteins. In general, the benzothiazole derivatives were more active in comparison to their benzimidazole analogues. The exception was 2-phenyl substituted benzimidazole 6a being active with very pronounced activity especially towards HCC827 cells. All active compounds have similar mode of action on A549 cell line as standard compound doxorubicin, which binds to nucleic acids with the DNA double helix. Tested active benzothiazole compounds were characterised by moderate to good solubility, good metabolic stability, low permeability and high binding to plasma proteins. One tested active benzimidazole derivative showed ADME properties, but lower lipophilicity resulted in low PPB and higher metabolic instability. In addition, no significant difference was observed in ADME profile between active and non-active compounds.

A hydrogel-based in vitro assay for the fast prediction of antibiotic accumulation in Gram-negative bacteria.

The pipeline of antibiotics has been for decades on an alarmingly low level. Considering the steadily emerging antibiotic resistance, novel tools are needed for early and easy identification of effective anti-infective compounds. In Gram-negative bacteria, the uptake of anti-infectives is especially limited. We here present a surprisingly simple in vitro model of the Gram-negative bacterial envelope, based on 20% (w/v) potato starch gel, printed on polycarbonate 96-well filter membranes. Rapid permeability measurements across this polysaccharide hydrogel allowed to correctly predict either high or low accumulation for all 16 tested anti-infectives in living Escherichia coli. Freeze-fracture TEM supports that the macromolecular network structure of the starch hydrogel may represent a useful surrogate of the Gram-negative bacterial envelope. A random forest analysis of in vitro data revealed molecular mass, minimum projection area, and rigidity as the most critical physicochemical parameters for hydrogel permeability, in agreement with reported structural features needed for uptake into Gram-negative bacteria. Correlating our dataset of 27 antibiotics from different structural classes to reported MIC values of nine clinically relevant pathogens allowed to distinguish active from nonactive compounds based on their low in vitro permeability specifically for Gram-negatives. The model may help to identify poorly permeable antimicrobial candidates before testing them on living bacteria.

Floral scent evolution in the section Pseudophrys: pollinator-mediated selection or phylogenetic constraints?

Sexually deceptive orchid species from the Mediterranean genus Ophrys usually interact with one or a few pollinator species by means of specific floral scents. In this study, we investigated the respective role of pollinator-mediated selection and phylogenetic constraints in the evolution of floral scents in the section Pseudophrys. We built a phylogenetic tree of 19 Pseudophrys species based on three nuclear loci; we gathered a dataset on their pollination interactions from the literature and from our own field data; and we extracted and analysed their floral scents using solid phase microextraction and gas chromatography-mass spectrometry. We then quantified the phylogenetic signal carried by floral scents and investigated the link between plant-pollinator interactions and floral scent composition using phylogenetic comparative methods. We confirmed the monophyly of the section Pseudophrys and demonstrated the existence of three main clades within this section. We found that floral scent composition is affected by both phylogenetic relationships among Ophrys species and pollination interactions, with some compounds (especially fatty acid esters) carrying a significant phylogenetic signal and some (especially alkenes and alkadienes) generating dissimilarities between closely related Pseudophrys pollinated by different insects. Our results show that in the section Pseudophrys, floral scents are shaped both by pollinator-mediated selection and by phylogenetic constraints, but that the relative importance of these two evolutionary forces differ among compound classes, probably reflecting distinct selective pressures imposed upon behaviourally active and non-active compounds.

Influence of the oxidation in the aromatic composition and sensory profile of Rioja red aged wines

Rioja is one of the most important Spanish wine regions, and commonly produces wines with good aging potential. The quality of Rioja aged wines is well appreciated by tasters and consumers in the international wine market. In this study, the influence of the aromatic composition and oxidation on their sensory profile of different high-quality long-aged wines produced in Rioja was evaluated. The aromatic compounds that can participate actively on the aromatic profile of this type of wines have been identified. In this way, 3-methyl-2,4-nonanedione and 2-phenylacetaldehyde were the most sensory active compounds. At lesser extent, 3-methylbutanal, (E)-2-nonenal, and (E)-2-octenal were also important volatile compounds in this type of wines. Surprisingly, sotolon was only detected as active compounds in the oldest wines with lower price. Contrary, methional mainly appeared as active compound in the youngest samples of wines with higher price. 2-methylpropanal was a non-active compound in wines correctly aged and stored. This work contributes to an improved understanding about the key odorants in Rioja high-quality old wines.

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands.

Atom based linear index descriptors in QSAR-machine learning classifiers for the prediction of ubiquitin-proteasome pathway activity

This report showed the use of the atom-based linear index together with different classic and machine learning classification techniques in a QSAR (quantitative structure-activity relationship) study. A PubChem BioAssay DataSet composed by 705 compounds with inhibitory (258 chemicals) and non-inhibitory (447 compounds) activity against the ubiquitin-proteasome pathway were used. The classification models were developed using the linear discriminant analysis, support vector machine, Bayesian networks, k-nearest neighbor, and random forest techniques. In this sense, all the QSAR models show accuracies above 85% in the training set and values of the Matthews correlation coefficient ranging from 0.68 to 0.83. The external validation set shows adequate classifications between 81.25 and 86.36% and Matthews’s correlation coefficient values ranging from 0.59 to 0.70. The present approach contributes as a useful tool for the early detection of novel UPP inhibitors for the treatment of the multiple myeloma and related diseases. A dataset of 705 compounds was extracted from PubChem, with 258 active and 447 non-active compounds in ubiquitin-proteasome pathway inhibitory activity. Later this dataset was divided in training and set, consisting of 529 and 176 compounds, respectively. The quality of the QSAR models developed was proved using the validation set and also checking the applicability domain.

Emulsification efficacy of Quillaja saponins at very low concentration: Model development and role of alcohols

The present study aims at quantifying interfacial coverage of a biosurfactant (Quillaja saponins) and understanding the impact of flavor and fragrance alcohols on emulsification efficacy of the biosurfactant in a surfactant-oil-matrix system. Emulsions were prepared using limonene, alkanes (C8, C12, and C16) or limonene ̶ alcohol (linalool and C6C10 alcohols) mixtures at different ratios as oil phase stabilized by Quillaja saponins at very low concentrations (0.005–0.05% w/w). Droplet size was measured and size distributions were numerized to determine surface and volume average droplet diameters of bimodal emulsions. Using a model developed in the present study, Quillaja saponins showed an interfacial coverage of 5.0×106cm2/g and a head surface of 1.37nm2 with a lay-on configuration at interface. The model proved to discriminate between surface active (alcohols) and non-active (alkanes) compounds. The apparent interfacial coverage of saponins increased linearly with increasing alcohol concentration. The type of alcohol (terpene alcohol vs. medium chain alcohols) and alcohol chain length (C6C10) showed little impact on emulsification efficacy of Quillaja saponins. The molar ratio of heptanol to saponin at interface increased from 0 to 8.6 corresponding to 0–30% w/w heptanol in limonene. This study revealed that the distribution of alcohol at interface was mainly driven by partitioning in the surfactant-oil-matrix system. The practical implication of the present study is to enhance emulsification efficacy of Quillaja Saponins at very low concentration by incorporating surface active compounds, i.e. flavor or fragrance alcohols.

Cytochrome P450 enzyme RosC catalyzes a multistep oxidation reaction to form the non-active compound 20-carboxyrosamicin.

The cytochrome P450 enzyme RosC catalyzes a two-step, hydroxylation and alcohol oxidation, oxidation reaction to form the C-20 formyl group in the biosynthesis of a 16-membered macrolide antibiotic rosamicin produced by Micromonospora rosaria IFO13697. RosC is presumed to be involved in the formation of 20-carboxyrosamicin because it has been isolated from the culture broth of M. rosaria. Here, we confirmed that RosC has catalytic activity, with E. coli expressing RosC converting rosamicin into 20-carboxyrosamicin. Therefore, it was revealed that RosC is a multifunctional P450 that catalyzes a three-step oxidation reaction that leads to the formation of the hydroxyl group, formyl group and carboxyl group at C-20 on the macrolactone ring in the rosamicin biosynthetic pathway. Moreover, the cytochrome P450 enzyme TylI, which is involved in formation of the formyl group of a 16-membered macrolide antibiotic tylosin produced by Streptomyces fradiae ATCC 19609, also converted rosamicin into 20-carboxyrosamicin.

Integrated Computational Approach for Virtual Hit Identification against Ebola Viral Proteins VP35 and VP40.

The Ebola virus (EBOV) has been recognised for nearly 40 years, with the most recent EBOV outbreak being in West Africa, where it created a humanitarian crisis. Mortalities reported up to 30 March 2016 totalled 11,307. However, up until now, EBOV drugs have been far from achieving regulatory (FDA) approval. It is therefore essential to identify parent compounds that have the potential to be developed into effective drugs. Studies on Ebola viral proteins have shown that some can elicit an immunological response in mice, and these are now considered essential components of a vaccine designed to protect against Ebola haemorrhagic fever. The current study focuses on chemoinformatic approaches to identify virtual hits against Ebola viral proteins (VP35 and VP40), including protein binding site prediction, drug-likeness, pharmacokinetic and pharmacodynamic properties, metabolic site prediction, and molecular docking. Retrospective validation was performed using a database of non-active compounds, and early enrichment of EBOV actives at different false positive rates was calculated. Homology modelling and subsequent superimposition of binding site residues on other strains of EBOV were carried out to check residual conformations, and hence to confirm the efficacy of potential compounds. As a mechanism for artefactual inhibition of proteins through non-specific compounds, virtual hits were assessed for their aggregator potential compared with previously reported aggregators. These systematic studies have indicated that a few compounds may be effective inhibitors of EBOV replication and therefore might have the potential to be developed as anti-EBOV drugs after subsequent testing and validation in experiments in vivo.

The Joint Action of Sesquiterpene Lactones from Leaves as an Explanation for the Activity of Cynara cardunculus.

The work described herein is a continuation of a previous study centered on the bioprospect of cardoon (Cynara cardunculus) leaf extracts through the isolation of secondary metabolites with phytotoxic activity. Chromatographic fractionations of the ethyl acetate extract and spectroscopic analysis showed that the majority of the components were sesquiterpene lactones. Of these compounds, aguerin B, grosheimin, and cynaropicrin were very active on etiolated wheat coleoptile, standard target species, and weed growth. The joint action of binary mixtures of these three active sesquiterpene lactones and one nonactive compound (11,13-dihydroxy-8-desoxygrosheimin) was studied. The activities of fixed-ratio mixtures were assessed on wheat coleoptile. The results can be interpreted with respect to a reference model by considering dose-response analyses and isobolograms with linear regression analyses. A total of 17 binary mixtures at different levels of inhibition (ED25, ED50, and ED75) were studied, and predominantly they responded additively (25). Deviations from additivity included seven synergistic responses and two antagonistic responses. The joint action of major sesquiterpene lactones isolated from C. cardunculus can explain the activities observed in extracts and fractions. The results reported here reiterate the utility of the wheat coleoptile bioassay as a quick tool to detect potential synergistic effects in binary mixtures.

Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II

Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrödinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q2=0.78–0.86 when cross-validation procedures were carried out; and R2=0.81–0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8–1.1kcalmol−1. Also, 76–80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments.