Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models. Our studies prove the power of a collaborative and consensual approach to QSAR model development. The best validated models of aquatic toxicity developed by our collaboratory (both individual and consensus) can be used as reliable computational predictors of aquatic toxicity and are available from any of the participating laboratories.     

Phenolic compounds from the insect Blaps japanensis with inhibitory activities towards cancer cells,COX-2, ROCK1 and JAK3

The insect Blaps japanensis have been used as an ethnomedicine in China for the treatment of several disorders such as cancer and inflammation. Our investigation with this insect led to the isolation of eight new and two known phenolic compounds. The structures of the new compounds, blapsins C?J (1–8), were identified by spectroscopic data. The inhibitory activities of all the compounds except of 9 against human cancer cells (A549, K562 and Huh-7), COX-2, ROCK1, and JAK3 were evaluated. Several compounds were found to be biologically active in these assays.     

Amino Alcohols from Eugenol as Potential Semisynthetic Insecticides: Chemical,Biological, and Computational Insights

A series of β-amino alcohols were prepared by the reaction of eugenol epoxide with aliphatic and aromatic amine nucleophiles. The synthesized compounds were fully characterized and evaluated as potential insecticides through the assessment of their biological activity against Sf9 insect cells, compared with a commercial synthetic pesticide (chlorpyrifos, CHPY). Three derivatives bearing a terminal benzene ring, either substituted or unsubstituted, were identified as the most potent molecules, two of them displaying higher toxicity to insect cells than CHPY. In addition, the most promising molecules were able to increase the activity of serine proteases (caspases) pivotal to apoptosis and were more toxic to insect cells than human cells. Structure-based inverted virtual screening and molecular dynamics simulations demonstrate that these molecules likely target acetylcholinesterase and/or the insect odorant-binding proteins and are able to form stable complexes with these proteins. Encapsulation assays in liposomes of DMPG and DPPC/DMPG (1:1) were performed for the most active compound, and high encapsulation efficiencies were obtained. A thermosensitive formulation was achieved with the compound release being more efficient at higher temperatures.     

Multiresidue determination of abamectin, doramectin, ivermectin, and moxidectin in milk using liquid chromatography and fluorescence detection

Abamectin, doramectin, ivermectin, and moxidectin are macrocyclic lactones derived from soil dwelling actinomycetes, and are very effective against nematode, insect, and arthropod infestations. These compounds, known as endectocides, have been approved for use in beef cattle in the United States; however, they are currently not approved for use in dairy cattle. Abamectin, doramectin, ivermectin, and moxidectin residues were isolated from milk by a series of liquid-liquid extraction steps, derivatized with trifluoroacetic anhydride, and determined by liquid chromatography with fluorescence detection. Recovery studies were performed in 2 laboratories. Recoveries of > 80% (1-30 ng/mL) were achieved for all 4 compounds.     

Synthesis and Insecticidal Activity of Novel Dithiophosphonates

A new series of dithiophosphonates has been synthesized using cycloiminium halides of pyridine, thiazole, and benzothiazole as starting material, dichlorophenylphosphine as phosphorylating agent, and 2-propanethiol as nucleophilic substituent. These compounds were tested for their insecticidal activity against a polyphagous insect plutella xylostella and found to exhibit potent pesticidal activity.     

ANTIOXIDANT DEFENSE OF THE CABBAGE LOOPER, Trichoplusia ni: ENZYMATIC RESPONSES TO THE SUPEROXIDE-GENERATING FLAVONOID, QUERCETIN, and PHOTODYNAMIC FURANOCOUMARIN, XANTHOTOXIN

Abstract— Superoxide dismutase (SOD) activity was induced by ca 2-fold (to5–6 U) when Trichoplusia ni midfifth-instar larvae were exposed to two toxic oxygen species generating plant pro-oxidants, quercetin (a flavonoid) and xanthotoxin (8-methoxypsoralen; a photoactive furanocoumarin). Very high catalase (CAT) activity ( ca 300 U) of this insect was not affected by 8-methoxypsoralen, but was slightly decreased by quercetin. No Se-dependent glutathione peroxidase (GPOX) activity was observed, but high glutathione transferase (GST) peroxidase activity (over 50 U) in this insect was slightly induced by 8-methoxypsoralen (8-MOP), and was partially inhibited by quercetin, 8-Methoxypsoralen induced the activity of glutathione reductase (GR), but quercetin partially inhibited the activity of this enzyme. An increase in SOD activity appears to be the main response of this insect to dietary exposure to pro-oxidant compounds. High CAT activity guarantees the destruction of large cellular increases in H₂O₂, a product of rapid dismutation of superoxide from induced activity of SOD. Moreover, GST with its peroxidase activity apparently substitutes for GPOX, forming a GST/GR enzyme pair as a primary line of defense against deleterious organic hydroperoxides. These studies clearly point out the key role for an insect's antioxidant enzymatic countermeasures against defensive pro-oxidant compounds produced by plants.     

Selective small molecule peptidomimetic ligands of TrkC and TrkA receptors afford discrete or complete neurotrophic activities

We designed a minilibrary of 55 small molecule peptidomimetics based on beta-turns of the neurotrophin growth factor polypeptides neurotrophin-3 (NT-3) and nerve growth factor (NGF). Direct binding, binding competition, and biological screens identified agonistic ligands of the ectodomain of the neurotrophin receptors TrkC and TrkA. Agonism is intrinsic to the peptidomimetic ligand (in the absence of neurotrophins), and/or can also be detected as potentiation of neurotrophin action. Remarkably, some peptidomimetics afford both neurotrophic activities of cell survival and neuronal differentiation, while others afford discrete signals leading to either survival or differentiation. The high rate of hits identified suggests that focused minilibraries may be desirable for developing bioactive ligands of cell surface receptors. Small, selective, proteolytically stable ligands with defined biological activity may have therapeutic potential.     

Data mining the NCI cancer cell line compound GI(50) values: identifying quinone subtypes effective against melanoma and leukemia cell classes

Using data mining techniques, we have studied a subset (1400) of compounds from the large public National Cancer Institute (NCI) compounds data repository. We first carried out a functional class identity assignment for the 60 NCI cancer testing cell lines via hierarchical clustering of gene expression data. Comprised of nine clinical tissue types, the 60 cell lines were placed into six classes-melanoma, leukemia, renal, lung, and colorectal, and the sixth class was comprised of mixed tissue cell lines not found in any of the other five classes. We then carried out supervised machine learning, using the GI(50) values tested on a panel of 60 NCI cancer cell lines. For separate 3-class and 2-class problem clustering, we successfully carried out clear cell line class separation at high stringency, p < 0.01 (Bonferroni corrected t-statistic), using feature reduction clustering algorithms embedded in RadViz, an integrated high dimensional analytic and visualization tool. We started with the 1400 compound GI(50) values as input and selected only those compounds, or features, significant in carrying out the classification. With this approach, we identified two small sets of compounds that were most effective in carrying out complete class separation of the melanoma, non-melanoma classes and leukemia, non-leukemia classes. To validate these results, we showed that these two compound sets' GI(50) values were highly accurate classifiers using five standard analytical algorithms. One compound set was most effective against the melanoma class cell lines (14 compounds), and the other set was most effective against the leukemia class cell lines (30 compounds). The two compound classes were both significantly enriched in two different types of substituted p-quinones. The melanoma cell line class of 14 compounds was comprised of 11 compounds that were internal substituted p-quinones, and the leukemia cell line class of 30 compounds was comprised of 6 compounds that were external substituted p-quinones. Attempts to subclassify melanoma or leukemia cell lines based upon their clinical cancer subtype met with limited success. For example, using GI(50) values for the 30 compounds we identified as effective against all leukemia cell lines, we could subclassify acute lymphoblastic leukemia (ALL) origin cell lines from non-ALL leukemia origin cell lines without significant overlap from non-leukemia cell lines. Based upon clustering using GI(50) values for the 60 cancer cell lines laid out by the RadViz algorithm, these two compound subsets did not overlap with clusters containing any of the NCI's 92 compounds of known mechanism of action, a few of which are quinones. Given their structural patterns, the two p-quinone subtypes we identified would clearly be expected to possess different redox potentials/substrate specificities for enzymatic reduction in vivo. These two p-quinone subtypes represent valuable information that may be used in the elucidation of pharmacophores for the design of compounds to treat these two cancer tissue types in the clinic.     

Exploring polypharmacology using a ROCS-based target fishing approach

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.     

Aristolochic Acids as a Defensive Substance for the Aristolochiaceous Plant‐Feeding Swallowtail Butterfly,Pachliopta aristolochiae interpositus

The chemical constituents isolated from every stage of the Taiwanese swallowtail butterfly, Pachliopta aristolochiae interpositus, were studied and compared with the constituents of the insect feeding plant, A. cucurbitifolia. Two aristolochic acid derivatives ( 1 and 4 ) and three aristolactam analogues ( 5, 6 , and 7 ) were isolated from the larval osmeterial fluid and larvae of the insect. In addition, four purines ( 2, 3, 13 , and 14 ), one indole alkaloid ( 11 ), one steroid ( 10 ), two benzenoids ( 8 and 9 ) and allantoin ( 12 ) were also isolated from the insect. Among these compounds, aristolochic acid ‐I (AA‐I, 1 ) was detected in all life stages of the insect, especially in the larval osmeterial fluid. The studies confirm that the toxic AAs are used as larval feeding stimulants and deterrent allomones against birds.     

Chemoinformatics-based classification of prohibited substances employed for doping in sport

Representative molecules from 10 classes of prohibited substances were taken from the World Anti-Doping Agency (WADA) list, augmented by molecules from corresponding activity classes found in the MDDR database. Together with some explicitly allowed compounds, these formed a set of 5245 molecules. Five types of fingerprints were calculated for these substances. The random forest classification method was used to predict membership of each prohibited class on the basis of each type of fingerprint, using 5-fold cross-validation. We also used a k-nearest neighbors (kNN) approach, which worked well for the smallest values of k. The most successful classifiers are based on Unity 2D fingerprints and give very similar Matthews correlation coefficients of 0.836 (kNN) and 0.829 (random forest). The kNN classifiers tend to give a higher recall of positives at the expense of lower precision. A na?ve Bayesian classifier, however, lies much further toward the extreme of high recall and low precision. Our results suggest that it will be possible to produce a reliable and quantitative assignment of membership or otherwise of each class of prohibited substances. This should aid the fight against the use of bioactive novel compounds as doping agents, while also protecting athletes against unjust disqualification.     

Bioassay-Guided Isolation of Iridoid Glucosides from Stenaria nigricans,Their Biting Deterrence against Aedes aegypti (Diptera: Culicidae), and Repellency Assessment against Imported Fire Ants (Hymenoptera: Formicidae)

In our natural product screening program, we screened natural products for their repellency and toxicity against insect vectors. Methanolic extract of aerial parts of Stenaria nigricans (Lam.), with no published chemistry, was tested for repellency against mosquitoes and imported hybrid fire ants. Methanolic extracts showed biting deterrence similar to DEET (N,N-diethyl-3-methylbenzamide) against Aedes aegypti L. Based on this activity, the crude extract was fractionated into chloroform, ethyl acetate, and methanol subfractions. The active methanolic subfraction was further fractionated into 13 subfractions. These fractions were tested for their biting deterrence against Ae. Aegypti. Active subfractions were further characterized to identify the compounds responsible for this activity. Four undescribed iridoid glucosides (1–4) and three previously reported compounds (5–7) were isolated from active subfractions and tested for their biting deterrent activity. Based on BDI values, compounds 2, 3, 6, and 7, with biting deterrence similar to DEET, showed the potential to be used as repellents against mosquitoes. In an in vitro digging bioassay, none of these compounds showed any repellency against hybrid imported fire ants at a dose of 125 µg/g. This is the first report of biting deterrence and repellency of S. nigricans extract and its pure compounds, iridoid glucosides against mosquitoes and imported fire ants. Further studies will be conducted to explore the repellent potential of these compounds in different formulations under field conditions.     

Design,synthesis and insecticidal activity of biphenyl-diamide derivatives

Diamides acting on insect ryanodine receptors are an intensive research area now. In order to search for novel candidates, a series of diamides containing biphenyl substructure were designed and synthesized.Their insecticidal activities against armyworms(Mythimna sepatara) and aphis(Aphis craccivora) were screened. The compounds with 3,5-dichloro-4-(1,1,2,2-tetrafluoroethoxy)phenyl substituent were found to be insecticidal to armyworms with the similar symptoms to poisoning by flubendiamide. In this research, we presented a novel type of diamide insecticide as a lead compound for further optimization.     

Design,Synthesis and Biological Evaluation of Potent Azadipeptide Nitrile Inhibitors and Activity‐Based Probes as Promising Anti‐Trypanosoma brucei Agents

Trypanosoma cruzi and Trypanosoma brucei are parasites that cause Chagas disease and African sleeping sickness, respectively. There is an urgent need for the development of new drugs against both diseases due to the lack of adequate cures and emerging drug resistance. One promising strategy for the discovery of small‐molecule therapeutics against parasitic diseases has been to target the major cysteine proteases such as cruzain for T. cruzi, and rhodesain/TbCatB for T. brucei. Azadipeptide nitriles belong to a novel class of extremely potent cysteine protease inhibitors against papain‐like proteases. We herein report the design, synthesis, and evaluation of a series of azanitrile‐containing compounds, most of which were shown to potently inhibit both recombinant cruzain and rhodesain at low nanomolar/picomolar ranges. A strong correlation between the potency of rhodesain inhibition (i.e., target‐based screening) and trypanocidal activity (i.e., whole‐organism‐based screening) of the compounds was observed. To facilitate detailed studies of this important class of inhibitors, selected hit compounds from our screenings were chemically converted into activity‐based probes (ABPs), which were subsequently used for in situ proteome profiling and cellular localization studies to further elucidate potential cellular targets (on and off) in both the disease‐relevant bloodstream form (BSF) and the insect‐residing procyclic form (PCF) of Trypanosoma brucei. Overall, the inhibitors presented herein show great promise as a new class of anti‐trypanosome agents, which possess better activities than existing drugs. The activity‐based probes generated from this study could also serve as valuable tools for parasite‐based proteome profiling studies, as well as bioimaging agents for studies of cellular uptake and distribution of these drug candidates. Our studies therefore provide a good starting point for further development of these azanitrile‐containing compounds as potential anti‐parasitic agents.     

Lead hopping using SVM and 3D pharmacophore fingerprints

The combination of 3D pharmacophore fingerprints and the support vector machine classification algorithm has been used to generate robust models that are able to classify compounds as active or inactive in a number of G-protein-coupled receptor assays. The models have been tested against progressively more challenging validation sets where steps are taken to ensure that compounds in the validation set are chemically and structurally distinct from the training set. In the most challenging example, we simulate a lead-hopping experiment by excluding an entire class of compounds (defined by a core substructure) from the training set. The left-out active compounds comprised approximately 40% of the actives. The model trained on the remaining compounds is able to recall 75% of the actives from the "new" lead series while correctly classifying >99% of the 5000 inactives included in the validation set.     

Metabolic fate of dietary volatile compounds in Pieris brassicae

In this work, the evolution of the qualitative and quantitative profile of the volatile fraction of Pieris brassicae after feeding on Brassica oleracea var. acephala (kale) was monitored through time. HS-SPME/GC-MS was applied to both the host plant and the living insect and its excrements. A total of seventy seven compounds (lipoxygenase pathway by-products, nitrogen compounds, norisoprenoids, sulphur compounds, terpenes, among others) were identified. Thirty eight compounds were identified in insect after 2 h of starvation and forty eight compounds in excrements. Qualitative and quantitative changes were detected along time. Dimethyldisulfide, dimethyltrisulfide, limonene and eugenol were major compounds for all analysed times in both matrices, being limonene an important compound in insect after starvation. The accumulation by P. brassicae of some compounds, such as limonene, was verified, suggesting a mechanism by which the insect can take benefit from bioactive constituents from the diet. Along with accumulation, complete excretion of some compounds, including nitrogen bearing compounds, by-products of glucosinolates was detected. These results reflect one of the strategies used to overcome plant barriers, namely detoxification of toxic compounds. The findings contribute to the knowledge of the metabolization of the volatile compounds in insects and contribute to the body of knowledge of this ecologic system.     

Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.