Designing antibacterial compounds through a topological substructural approach

A novel application of TOPological Substructural MOlecular DEsign (TOPS-MODE) was carried out in antibacterial drugs using computer-aided molecular design. Two series of compounds, one containing antibacterial and the other containing non-antibacterial compounds, were processed by a k-means cluster analysis in order to design training and predicting series. All clusters had a p-level < 0.005. Afterward, a linear classification function has been derived toward discrimination between antibacterial and non-antibacterial compounds. The model correctly classifies 94% of active and 86% of inactive compounds in the training series. More specifically, the model showed a global good classification of 91%, i.e., 263 cases out of 289. In predicting series, the model has shown overall predictabilities of 91 and 83% for active and inactive compounds, respectively. Thereby, the model has a global percentage of good classification of 89%. The TOPS-MODE approach, also, similarly compares with respect to one of the most useful models for antimicrobials selection reported to date.     

In silico studies toward the discovery of new anti-HIV nucleoside compounds through the use of TOPS-MODE and 2D/3D connectivity indices. 2. Purine derivatives

The TOPological Substructural MOlecular DEsign (TOPS-MODE) approach has been used to predict the anti-HIV activity in MT-4 assays (Estrada et al., 2002) of a diverse range of purine-based nucleosides. A database of 206 nucleosides has been selected from the literature and a theoretical virtual screening model has been developed. The model is able of discriminating between compounds that have anti-HIV activity and those that do not, with a good classification level of 85% in the training and 82.8% in the cross-validation series. On the basis of the information generated by the model, the correct classification of practically 80% of compounds from an external prediction set has been achieved using the theoretical model. Furthermore, the contribution of a range of molecular fragments to the pharmacological action has been calculated and this could provide a powerful tool in the design of nucleoside analogues that show activity against the HIV. Finally, a QSAR model has been developed that allows quantitative data to be obtained regarding the pharmacological potency shown by this type of compound.     

Development of an information-intensive structure-activity relationship model and its application to human respiratory chemical sensitizers

Structure-activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

Development of an information-intensive structure–activity relationship model and its application to human respiratory chemical sensitizers

Structure–activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

Betaine and Carnitine Derivatives as Herbicidal Ionic Liquids

This study focused on the synthesis and subsequent characterization of herbicidal ionic liquids based on betaine and carnitine, two derivatives of amino acids, which were used as cations. Four commonly used herbicides (2,4‐D, MCPA, MCPP and Dicamba) were used as anions in simple (single anion) and oligomeric (two anions) salts. The obtained salts were subjected to analyzes regarding physicochemical properties (density, viscosity, refractive index, thermal decomposition profiles and solubility) as well as evaluation of their herbicidal activity under greenhouse and field conditions, toxicity towards rats and biodegradability. The obtained results suggest that the synthesized herbicidal ionic liquids displayed low toxicity (classified as category 4 compounds) and showed similar or improved efficacy against weed compared to reference herbicides. The highest increase was observed during field trials for salts containing 2,4‐D as the anion, which also exhibited the highest biodegradability (>75 %).     

Tomocomd-Cardd, a novel approach for computer-aided ‘ rational’ drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds

Summary In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silicoscreening and several compounds from the Merck Index, Negwers handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.     

Synthesis and biological activity of novel phenyltriazolinone derivatives

Phenyltriazolinones are one of the most important classes of herbicides targeting the protoporphyrinogen oxidase enzyme. A series of triazolinone derivatives containing a strobilurin pharmacophore were designed and synthesized with the aim of discovering new phenyltriazolinone analogues with high activity. The herbicidal activity of the synthesized compounds was assayed and some of the test compounds displayed moderate herbicidal activity at 150 g ai/ha.     

Quantitative structure-toxicity relationships using TOPS-MODE. 2. Neurotoxicity of a non-congeneric series of solvents

Neurotoxicities of a series of solvents in rats and mice have been modeled by means of the TOPS-MODE approach. Two quantitative structure-toxicity relationship (QSTR) models were obtained explaining more than 80% of the variance in the experimental values of neurotoxicity of 45 solvents. Only one compound was detected as statistical outlier for these models. In contrast, previous models explained less than 60% of the variance in this property for 44 solvents. Finally, the contributions to neurotoxicity in rats and mice for a series of structural fragments were found. Structural characteristics of chlorinated fragments responsible for their different neurotoxicities were analyzed. The differences in neurotoxic behavior of some fragments in rats and mice were also analyzed, which could give insights on the toxicological mechanism of action of solvents studied.     

Design,Synthesis and Herbicidal Activity of Novel Sulfonylureas Containing Triazole and Oxadiazole Moieties

Three novel series of 5-substituted sulfonylurea derivatives were designed and synthesized via introducing a triazole or oxadiazole ring at the 5th position of the benzene ring in classical sulfonylurea herbicides. All the target compounds were confirmed by means of ¹H nuclear magnetic resonance(NMR), ¹³C NMR and elemental analysis. The bioassay results show that the target compounds containing an oxadiazole ring at the 5th position display moderate to excellent herbicidal activities against Brassica campestris and Amaranthus retroflexus under soil treatment. Especially, compounds zdk20, zdk21 and zdk22 possess 98.6%, 96.5% and 94.5% inhibition rates, respectively, against Amaranthus retroflexus at a concentration of 75 g/ha(1 ha=1×10⁴ m²) under soil treatment, which approach to those of the commercial herbicide chlorsulfuron.     

磺酰脲类化合物在水稻田中的除草性能

水稻是我国重要的粮食作物,但杂草对水稻的产量和品质产生了严重影响。 化学防除是治理水稻田杂草最有效的途径。 文中设计合成了苯环2,6-取代和2,5-取代两个系列磺酰脲类化合物,并通过核磁共振波谱仪(NMR)和高分辨质谱仪(HRMS)等对其进行了结构表征。 通过水稻田除草活性和安全性测试发现化合物在水稻田中具有较好的除草活性,尤其是化合物10a对水稻田中的主要杂草稗草和醴肠除草活性(目测初筛防效大于90%)优于对照药醚苯磺隆和氯磺隆,安全性与之相当。     

Predictive activity profiling of drugs by topological-fragment-spectra-based support vector machines

Aiming at the prediction of pleiotropic effects of drugs, we have investigated the multilabel classification of drugs that have one or more of 100 different kinds of activity labels. Structural feature representation of each drug molecule was based on the topological fragment spectra method, which was proposed in our previous work. Support vector machine (SVM) was used for the classification and the prediction of their activity classes. Multilabel classification was carried out by a set of the SVM classifiers. The collective SVM classifiers were trained with a training set of 59,180 compounds and validated by another set (validation set) of 29,590 compounds. For a test set that consists of 9,864 compounds, the classifiers correctly classified 80.8% of the drugs into their own active classes. The SVM classifiers also successfully performed predictions of the activity spectra for multilabel compounds.     

From knowledge generation to knowledge archive. A general strategy using TOPS-MODE with DEREK to formulate new alerts for skin sensitization

A general strategy for knowledge flow concerning skin sensitization based on the combined use of TOPS-MODE and DEREK expert system is proposed. TOPS-MODE is used as a knowledge generator, while DEREK represents the knowledge archive. A TOPS-MODE classification model allows the identification of structural fragments and groups responsible for strong/moderate skin sensitization. These structural contributions are sorted, analyzed, and graphically displayed in an appropriate way allowing the identification of several structural alerts for skin sensitization. Nine structural alerts already implemented in DEREK are identified using this strategy. They comprise, among others, alkyl halides, aldehydes, alpha,beta-unsaturated compounds, aromatic amines, phenols, hydroquinone, isothiazolinone, and alkyl sulfonates. Four new hypotheses are generated using TOPS-MODE structural contributions to skin sensitization, which are not recognized as structural alerts by DEREK. They include the reduction of aromatic nitro groups and epoxidation reaction of double bonds as metabolic activation steps that can lead to reactive haptens which can trigger the skin sensitization mechanism. Another new alert is based on 1,2,5-thiadiazole-1,1-dioxide for which we have identified a possible mechanism explaining its strong skin sensitization profile. It is based on the existence of a tautomeric equilibrium and further reaction with nucleophiles, which are both supported by experimental evidence. Finally, we have identified a possible new mechanism for the skin sensitization of nonreactive compounds, which involves the formation of noncovalent complexes with proteins in a processing- and metabolism-independent way.     

Exploiting the Evolutionary Relationship between Malarial Parasites and Plants To Develop New Herbicides

Herbicide resistance is driving a need to develop new herbicides. The evolutionary relationship between apicomplexan parasites, such as those causing malaria, and plants is close enough that many antimalarial drugs are herbicidal and so represent novel scaffolds for herbicide development. Using a compound library from the Medicines for Malaria Venture, the model plant Arabidopsis thaliana, and a physicochemical database of known herbicides, a compound was discovered that showed post‐emergence herbicidal activity equal to commercial herbicides. Using structure–activity analysis, important points for its potency were found. The compound was also tested and found to be active against common crop weeds. Physiological profiling suggested the compound was a photosystem II inhibitor, representing a new scaffold for herbicide development. Overall this approach demonstrates the viability of using antimalarial compounds as lead compounds for the development of much needed new herbicides.