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1.
J Agric Food Chem ; 67(45): 12382-12392, 2019 Nov 13.
Artigo em Inglês | MEDLINE | ID: mdl-31635461

RESUMO

Protoporphyrinogen oxidase (PPO, EC 1.3.3.4) is a promising target for herbicide discovery. Search for new compounds with novel chemotypes is a key objective for agrochemists. Here, we describe the discovery and systematic SAR-based structure optimization of novel N-isoxazolinylphenyltriazinones 5-9 as PPO inhibitors. The in vivo herbicidal activity and in vitro Nicotiana tabacum PPO (NtPPO) inhibitory activity were explored in detail. A number of the new synthetic compounds displayed strong PPO inhibitory activity with Ki values in the nanomolar range. Some compounds exhibited excellent and broad-spectrum weed control at the rate of 9.375-37.5 g ai/ha by postemergence application and showed improved monocotyledonous weed control compared to saflufenacil. Most promisingly, ethyl 3-(2-chloro-5-(3,5-dimethyl-2,6-dioxo-4-thioxo-1,3,5-triazinan-1-yl)-4-fluorophenyl)-5-methyl-4,5-dihydroisoxazole-5-carboxylate, 5a, with a Ki value of 4.9 nM, displayed over 2- and 6-fold higher potency than saflufenacil (Ki = 10 nM) and trifludimoxazin (Ki = 31 nM), respectively. Moreover, 5a showed excellent and broad-spectrum weed control against 32 kinds of weeds at 37.5-75 g ai/ha. Rice exhibited relative tolerance to 5a at 150 g ai/ha by postemergence application, indicating that 5a could be a potential herbicide candidate for weed control in paddy fields.


Assuntos
Inibidores Enzimáticos/farmacologia , Herbicidas/química , Herbicidas/farmacologia , Proteínas de Plantas/antagonistas & inibidores , Protoporfirinogênio Oxidase/antagonistas & inibidores , Descoberta de Drogas , Inibidores Enzimáticos/química , Cinética , Proteínas de Plantas/química , Proteínas de Plantas/metabolismo , Plantas Daninhas/química , Plantas Daninhas/efeitos dos fármacos , Plantas Daninhas/enzimologia , Protoporfirinogênio Oxidase/química , Protoporfirinogênio Oxidase/metabolismo , Relação Quantitativa Estrutura-Atividade , Tabaco/química , Tabaco/efeitos dos fármacos , Tabaco/enzimologia , Controle de Plantas Daninhas
2.
Prog Chem Org Nat Prod ; 110: 177-238, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31621014

RESUMO

Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.


Assuntos
Produtos Biológicos/farmacologia , Biologia Computacional , Canais de Potássio Éter-A-Go-Go/antagonistas & inibidores , Bloqueadores dos Canais de Potássio/farmacologia , Química Farmacêutica , Humanos , Aprendizado de Máquina , Modelos Moleculares , Relação Quantitativa Estrutura-Atividade
3.
Water Res ; 166: 115083, 2019 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-31541794

RESUMO

Hydroxyl radicals (·OH) initiated degradation is an important process governing fate of aquatic organic micropollutants (OMPs). However, rate constants for aqueous reaction of OMPs with ·OH (kOH) are available only for a limited number of OMPs, which complicates fate assessment of OMPs. Furthermore, molecular structures of many OMPs contain ionizable groups, and the OMPs may dissociate into different anionic/cationic species with different reactivity towards ·OH. Therefore, it is of importance to determine kOH of ionizable OMPs, and to develop quantitative structure-activity relationship (QSAR) models for predicting kOH of OMPs at different ionization forms. Herein kOH values of 9 fluoroquinolones (FQs) and 11 sulfonamides (SAs) at 3 dissociation forms (FQ±/FQ+/FQ-, SA0/SA+/SA-) were determined by competition kinetics experiments. A QSAR model using theoretical molecular structural descriptors was subsequently developed. The QSAR model successfully corroborated previous experimental results, exhibited good statistical performance, and is capable to predict kOH for FQs and SAs with different dissociation forms at environmentally relevant pH conditions. As organic ions have rarely been included in previous QSAR studies, the newly developed model that covers both neutral molecules and ions is of significance for future QSAR development as well as fate assessment of ionizable OMPs.


Assuntos
Radical Hidroxila , Poluentes Químicos da Água , Fluoroquinolonas , Relação Quantitativa Estrutura-Atividade , Sulfonamidas
4.
SAR QSAR Environ Res ; 30(8): 561-585, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31535949

RESUMO

Current guidance for the estimation of dermal absorption (DA) of pesticides recommends the use of default values, read-across of information between formulations and in vitro testing. While QSARs exist to estimate percutaneous absorption, their use is currently not encouraged. Therefore, the potential of publicly available models for DA estimation was investigated based on data from 564 human in vitro DA experiments on pesticides. The classic Potts Guy model, the correction of Cleek Bunge for highly lipophilic chemicals, the mechanistic model of Mitragotri, and the COSMOS model were used to estimate the permeability coefficient kp. Different approaches were explored to calculate the percentage of external dose absorbed. IH SkinPerm was examined as stand-alone model. The models generally failed to accurately predict experimental values. For 30-40% of the predictions, there was overestimation by one order of magnitude. Three models underpredicted >10% of the cases, the remaining models <5%. DA of hydrophilic substances was typically underpredicted. Overprediction was more prominent for solid preparations and suspensions. The molecular weight, irritation potential and skin thickness did not correlate with the models' predictivity. Of the models investigated, IH SkinPerm performed best with 38% of the predictions within one order of magnitude and 2% underpredicted cases.


Assuntos
Praguicidas/metabolismo , Absorção Cutânea , Simulação por Computador , Humanos , Técnicas In Vitro , Modelos Biológicos , Relação Quantitativa Estrutura-Atividade
5.
Food Chem ; 301: 125229, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31377620

RESUMO

Capsaicinoids and capsinoids from dietary peppers have promising sensory properties and bioactivity, but the molecular basis of their penetration mechanism through cell lipid bilayers and its relationship to their bioavailability as food constituents are still poorly understood. Herein, statistically significant linear and quadratic quantitative structure-activity relationships were constructed to derive the essential structural elements required for their bioactivity against the elongation of etiolated wheat coleoptiles that mainly occurs via penetration. The resultant optimal models had high predictivity and reliability (r2 > 0.825 and r2pred > 0.950), which elucidate the importance of steric structural elements. Besides, their mechanistic hypothesis and rational design strategy were proposed, and the correlation between this bioactivity and their food-sensory properties was supposed. Finally, the bioactivity of newly designed analogs with methyl terminals and/or conjugated CC links was screened. Hopefully, this work would benefit the better understanding of their penetration mechanism and facile identification of bioactive analogs for designing food/drug formulations.


Assuntos
Capsaicina/química , Capsaicina/farmacologia , Cotilédone/metabolismo , Estiolamento/efeitos dos fármacos , Alimentos , Triticum/efeitos dos fármacos , Triticum/crescimento & desenvolvimento , Catecóis/metabolismo , Ácidos Graxos Monoinsaturados/metabolismo , Relação Quantitativa Estrutura-Atividade , Reprodutibilidade dos Testes , Triticum/metabolismo
6.
SAR QSAR Environ Res ; 30(8): 587-615, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31469296

RESUMO

The rivality index (RI) is a normalized distance measurement between a molecule and their first nearest neighbours providing a robust prediction of the activity of a molecule based on the known activity of their nearest neighbours. Negative values of the RI describe molecules that would be correctly classified by a statistic algorithm and, vice versa, positive values of this index describe those molecules detected as outliers by the classification algorithms. In this paper, we have described a classification algorithm based on the RI and we have proposed four weighted schemes (kernels) for its calculation based on the measuring of different characteristics of the neighbourhood of molecules for each molecule of the dataset at established values of the threshold of neighbours. The results obtained have demonstrated that the proposed classification algorithm, based on the RI, generates more reliable and robust classification models than many of the more used and well-known machine learning algorithms. These results have been validated and corroborated by using 20 balanced and unbalanced benchmark datasets of different sizes and modelability. The classification models generated provide valuable information about the molecules of the dataset, the applicability domain of the models and the reliability of the predictions.


Assuntos
Aprendizado de Máquina , Relação Quantitativa Estrutura-Atividade , Algoritmos , Modelos Teóricos , Reprodutibilidade dos Testes
7.
Ecotoxicol Environ Saf ; 182: 109429, 2019 Oct 30.
Artigo em Inglês | MEDLINE | ID: mdl-31323522

RESUMO

Both water and non-water soluble ionic liquids (ILs) may be toxic where ILs soluble in water can be released into aquatic ecosystems. Toxicity of ILs can be determined as effective nominal concentration EC50 because it is important to assess the toxicity of ILs by an inhibition assay, which can evaluate the toxicological danger of ILs. A novel model is introduced for desk calculations of chemical toxicity of ILs based on Vibrio fischeri with more reliance on their answers as one could attach to the more complex outputs. It requires only specific elemental compositions of cations and anions as well as the presence of some molecular fragments in cations with particular anions. The measured values of logEC50(/µM) for 187 ILs corresponding to 250 experimental data were used to derive and test of the new model. For 153 ILs (203 datapoints), where the reported values of logEC50(/µM) as training and test sets by one of the best quantitative structure-activity relationship (QSAR) were available, the new method gives more reliable predictions. The present simple method is also tested with further 34 (47 datapoints), which confirm good forecasting reliability of the new model.


Assuntos
Líquidos Iônicos/toxicidade , Testes de Toxicidade/métodos , Aliivibrio fischeri/efeitos dos fármacos , Ânions , Cátions/química , Líquidos Iônicos/química , Relação Quantitativa Estrutura-Atividade , Reprodutibilidade dos Testes
8.
J Agric Food Chem ; 67(33): 9254-9264, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31356740

RESUMO

In continuation of our search for potent protoporphyrinogen IX oxidase (PPO, EC 1.3.3.4) inhibitors, we designed and synthesized a series of novel herbicidal cycloalka[d]quinazoline-2,4-dione-benzoxazinones. The bioassay results of these synthesized compounds indicated that most of the compounds exhibited very strong Nicotiana tabacum PPO (NtPPO) inhibition activity. More than half of the 37 synthesized compounds displayed over 80% control of all three tested broadleaf weeds at 37.5-150 g ai/ha by postemergent application, and a majority of them showed no phytotoxicity toward at least one kind of crop at 150 g ai/ha. Promisingly, 17i (Ki = 6.7 nM) was 6 and 4 times more potent than flumioxazin (Ki = 46 nM) and trifludimoxazin (Ki = 31 nM), respectively. Moreover, 17i displayed excellent, broad-spectrum herbicidal activity, even at levels as low as 37.5 g ai/ha, and it was determined to be safe for wheat at 150 g ai/ha in postemergent application, indicating the great potential for 17i development as a herbicide for weed control in wheat fields.


Assuntos
Benzoxazinas/química , Inibidores Enzimáticos/química , Inibidores Enzimáticos/farmacologia , Herbicidas/química , Herbicidas/farmacologia , Proteínas de Plantas/antagonistas & inibidores , Protoporfirinogênio Oxidase/antagonistas & inibidores , Quinazolinas/química , Benzoxazinas/farmacologia , Desenho de Drogas , Cinética , Proteínas de Plantas/química , Plantas Daninhas/efeitos dos fármacos , Plantas Daninhas/enzimologia , Protoporfirinogênio Oxidase/química , Relação Quantitativa Estrutura-Atividade , Quinazolinas/farmacologia , Tabaco/efeitos dos fármacos , Tabaco/enzimologia , Controle de Plantas Daninhas
9.
AAPS PharmSciTech ; 20(7): 268, 2019 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-31350676

RESUMO

Chemoinformatics is emerging as a new trend to set drug discovery which correlates the relationship between structure and biological functions. The main aim of chemoinformatics refers to analyzing the similarity among molecules, searching the molecules in the structural database, finding potential drug molecule and their property. One of the key fields in chemoinformatics is quantitative structure-property relationship (QSPR), which is an alternative process to predict the various physicochemical and biopharmaceutical properties. This methodology expresses molecules via various numerical values or properties (descriptors), which encodes the structural characteristics of molecules and further used to calculate physicochemical properties of the molecule. The established QSPR model could be used to predict the properties of compounds that have been measured or even have been unknown, which ultimately accelerates the development process of a new molecule or the product. The formulation characteristics (drug release, transportability, bioavailability) can be predicted with the integration of QSPR approach. Therefore, QSPR modeling is an emerging trend to skip conventional drug as well as formulation development process. The current review highlights the overall process involved in the application of the QSPR approach in formulation development.


Assuntos
Composição de Medicamentos , Descoberta de Drogas , Liberação Controlada de Fármacos , Relação Quantitativa Estrutura-Atividade
10.
Environ Pollut ; 253: 29-38, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31302400

RESUMO

Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to in vitro methods to predict the toxicological effects of chemicals on human health. In this study, we assessed a deep-learning based QSAR (DL-QSAR) model to predict the qualitative and the quantitative effects of EDCs on the human endocrine system, and especially sex-hormone binding globulin (SHBG) and estrogen receptor (ER). Statistical analyses of the qualitative responses indicated that the accuracies of all three DL-QSAR methods were above 90%, and greater than the other statistical and machine learning models, indicating excellent classification performance. The quantitative analyses, as assessed using deep-neural-network-based QSAR (DNN-QSAR), resulted in a coefficient of determination (R2) of 0.80 and predictive square correlation coefficient (Q2) of 0.86, which implied satisfactory goodness of fit and predictive ability. Thus, DNN was able to transform sparse molecular descriptors into higher dimensional spaces, and was superior for assessment qualitative responses. Moreover, DNN-QSAR demonstrated excellent performance in the discipline of computational chemistry by handling multicollinearity and overfitting problems.


Assuntos
Aprendizado Profundo , Ecotoxicologia , Disruptores Endócrinos/toxicidade , Poluentes Ambientais/toxicidade , Relação Quantitativa Estrutura-Atividade , Biologia Computacional , Disruptores Endócrinos/metabolismo , Poluentes Ambientais/metabolismo , Humanos , Redes Neurais (Computação) , Receptores Estrogênicos/metabolismo , Globulina de Ligação a Hormônio Sexual
11.
SAR QSAR Environ Res ; 30(8): 543-560, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31328578

RESUMO

The large collection of known and experimentally verified compounds from the ChEMBL database was used to build different classification models for predicting the antimalarial activity against Plasmodium falciparum. Four different machine learning methods, namely the support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN) and XGBoost have been used for the development of models using the diverse antimalarial dataset from ChEMBL. A well-established feature selection framework was used to select the best subset from a larger pool of descriptors. Performance of the models was rigorously evaluated by evaluation of the applicability domain, Y-scrambling and AUC-ROC curve. Additionally, the predictive power of the models was also assessed using probability calibration and predictiveness curves. SVM and XGBoost showed the best performances, yielding an accuracy of ~85% on the independent test set. In term of probability prediction, SVM and XGBoost were well calibrated. Total gain (TG) from the predictiveness curve was more related to SVM (TG = 0.67) and XGBoost (TG = 0.75). These models also predict the high-affinity compounds from PubChem antimalarial bioassay (as external validation) with a high probability score. Our findings suggest that the selected models are robust and can be potentially useful for facilitating the discovery of antimalarial agents.


Assuntos
Antimaláricos/química , Desenho de Drogas , Aprendizado de Máquina , Antimaláricos/análise , Antimaláricos/farmacologia , Área Sob a Curva , Relação Quantitativa Estrutura-Atividade , Curva ROC
12.
SAR QSAR Environ Res ; 30(8): 525-541, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31331203

RESUMO

Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r2 = 0.8129, CCC = 0.8979 and Q2 = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.


Assuntos
Diacilglicerol O-Aciltransferase/antagonistas & inibidores , Desenho de Drogas , Simulação por Computador , Modelos Moleculares , Estrutura Molecular , Método de Monte Carlo , Relação Quantitativa Estrutura-Atividade
13.
Anticancer Res ; 39(7): 3507-3518, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31262875

RESUMO

BACKGROUND/AIM: Very few studies of anticancer activity of azulene amides led us to investigate the cytotoxicity of 21 N-alkylazulene-1-carboxamides introduced either with 3-methyl [1-7], 7-isopropyl-3-methyl [8-14] or 2-methoxy group [15-21] Materials and Methods: Tumor-specificity (TS) was calculated by the ratio of mean 50% cytotoxic concentration (CC50) against three normal human oral mesenchymal cells to that against four human oral squamous cell carcinoma (OSCC) cell lines. Potency-selectivity expression (PSE) was calculated by dividing TS value by CC50 value against OSCC cell lines. Apoptosis-inducing activity was evaluated by caspase-3 activation and appearance of subG1 cell population. RESULTS: [8-14] showed higher TS and PSE values, than [1-7] and [15-21] The most active compound [8-14] induced apoptosis in C9-22 OSCC cells at 4-times higher CC50 Quantitative structure-activity relationship analysis of [1-14] demonstrated that their tumor-specificity was correlated with chemical descriptors that explain the molecular shape and hydrophobicity. CONCLUSION: 7-Isopropyl-3-methyl-N-propylazulene-1-carboxamide [8] can be a potential candidate of lead compound for manufacturing new anticancer drug.


Assuntos
Amidas/farmacologia , Antineoplásicos/farmacologia , Azulenos/farmacologia , Carcinoma de Células Escamosas/tratamento farmacológico , Neoplasias Bucais/tratamento farmacológico , Amidas/química , Antineoplásicos/química , Apoptose/efeitos dos fármacos , Azulenos/química , Linhagem Celular , Sobrevivência Celular/efeitos dos fármacos , Humanos , Relação Quantitativa Estrutura-Atividade
14.
Chemosphere ; 235: 719-725, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31279122

RESUMO

UV direct photolysis has been used as a promising process to remove halogenated disinfection byproducts (DBPs) generated in water. In this study, experimental studies and modeling approaches were applied to investigate the UV direct photolysis rate constants for 40 kinds of halogenated DBPs. The fluence-based pseudo-first-order rate constants for the removal of halogenated DBPs under UV photolysis spanned more than 2 orders of magnitude, with a range of (0.23-29.84) × 10-4 cm2 mJ-1. DBPs with higher number of halogenated substituents featured higher photolysis rate constants. The degradation efficiencies of DBPs were also affected by the species of halogen substituents, which followed the trend of iodo- > bromo- > chloro- DBPs. A quantitative structure-activity relationship (QSAR) model was established on the basis of the observed degradation rate constant values, which contained a quantum-chemical descriptor (ELUMO-EHOMO) and a molecular descriptor (Eta_C). The calculated parameters of the developed model indicated its good robustness and high reliability. The developed QSAR model can predict the degradation rate constants for DBPs within factors of 1/3 to 3. The model was validated using application domain and visualized in a Williams plot. The selected descriptors for QSAR model can explain the reaction mechanism for UV direct photolysis.


Assuntos
Desinfetantes/química , Relação Quantitativa Estrutura-Atividade , Poluentes Químicos da Água/química , Desinfecção , Halogenação , Halogênios , Fotólise , Reprodutibilidade dos Testes , Raios Ultravioleta , Água/química , Poluentes Químicos da Água/análise
15.
Environ Sci Pollut Res Int ; 26(23): 23763-23776, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31209750

RESUMO

Red tides that occur off coasts have become a worldwide phenomenon over the past decades. In order to mitigate the damage of the red tides on the aquatic ecosystems, it is crucial to develop a method for predicting algicidal activities that requires less labor and time, and most importantly, this method can quickly screen potential algicides to control red tides. In this study, we have investigated the algicidal activity of 19 natural flavonoids against a typical red tide alga, Phaeocystis globosa. Our results indicate that after 5 days of flavonoid exposure, the half inhibition concentrations (IC50) ranged from 0.068 to 3.065 mg L-1, which showed the strong algicidal activities of the flavonoids. Furthermore, quantitative structure activity relationship (QSAR) model has been carried out between negative scale logarithm (pIC50) of the flavonoids and the corresponding molecular descriptors. The developed model was validated, both internally and externally, which displayed statistical robustness (R2 = 0.867, p = 0.0002, Q2LOO = 0.825, RMSEC = 0.182, Q2extF3 = 0.896, RMSEP = 0.161, CCC = 0.935). This indicates that the developed model was obtained successfully with satisfactory predictability and robustness for the future rapid screening of natural flavonoids with high inhibition activity on the red tide alga growth. Moreover, the main descriptors in the QSAR model were the molar refractivity, partition coefficient, lowest unoccupied molecular orbital, and highest occupied molecular orbital, illustrating that the molecular electro-chemical characteristics are significant in the algicidal actions of the flavonoids. Graphical abstract Red tides frequently occur worldwide and have become a global environment problem. Flavonoids showed great potential in allelopathic control of the excessive growth of red tide algae. In this study, the algicidal activity of 19 natural flavonoids was investigated on a typical red tide organism Phaeocystis globosa. Futhermore, we applied the quantitative structure-activity relationship (QSAR) model to the experimental data. The model between molecular descriptors of flavonoids and their antialgal activity displays statistical robustness, and 4 of 45 selected molecular descriptors were obtained by regression of training set. The numbers in the figure represent the half inhibition concentration (IC50) of flavonoids. Our results show that the algicidal activity of flavonoids is closely related to molar refraction, partition coefficient, lowest unoccupied molecular orbital, and highest occupied molecular orbital. The QSAR model can efficaciously predict the algicidal activity and provide insights into the inhibitory mechanisms of flavonoids.


Assuntos
Flavonoides/toxicidade , Haptófitas/efeitos dos fármacos , Proliferação Nociva de Algas/efeitos dos fármacos , Herbicidas/toxicidade , Ecossistema , Relação Quantitativa Estrutura-Atividade
16.
Curr Top Med Chem ; 19(13): 1121-1128, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31210111

RESUMO

BACKGROUND: The discovery of novel potent molecules for both cancer prevention and treatment has been continuing over the past decade. In recent years, identification of new, potent, and safe anticancer agents through drug repurposing has been regarded as an expeditious alternative to traditional drug development. The cyclooxygenase-2 is known to be over-expressed in several types of human cancer. For this reason cyclooxygenase-2 inhibition may be useful tool for cancer chemotherapy. OBJECTIVE: The first aim of the study was to develop a validated linear model to predict antitumor activity. Subsequently, applicability of the model for repurposing these cyclooxygenase-2 inhibitors as antitumor compounds to abridge drug development process. METHODS: We performed a quantitative structure-toxicity relationship (QSTR) study on a set of coumarin derivatives using a large set of molecular descriptors. A linear model predicting growth inhibition on leukemia CCRF cell lines was developed and consequently validated internally and externally. Accordingly, the model was applied on a set of 143 cyclooxygenase-2 inhibitor coumarin derivatives to explore their antitumor activity. RESULTS: The results indicated that the developed QSAR model would be useful for estimating inhibitory activity of coumarin derivatives on leukemia cell lines. Electronegativity was found to be a prominent property of the molecules in describing antitumor activity. The applicability domain of the developed model highlighted the potential antitumor compounds. CONCLUSION: The promising results revealed that applied integrated in silico approach for repurposing by combining both the biological activity similarity and the molecular similarity via the computational method could be efficiently used to screen potential antitumor compounds among cyclooxygenase-2 inhibitors.


Assuntos
Antineoplásicos/farmacologia , Cumarínicos/farmacologia , Inibidores de Ciclo-Oxigenase 2/farmacologia , Ciclo-Oxigenase 2/metabolismo , Reposicionamento de Medicamentos , Relação Quantitativa Estrutura-Atividade , Antineoplásicos/síntese química , Antineoplásicos/química , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Cumarínicos/síntese química , Cumarínicos/química , Inibidores de Ciclo-Oxigenase 2/síntese química , Inibidores de Ciclo-Oxigenase 2/química , Relação Dose-Resposta a Droga , Ensaios de Seleção de Medicamentos Antitumorais , Humanos , Modelos Moleculares , Estrutura Molecular , Reprodutibilidade dos Testes
17.
Environ Sci Process Impacts ; 21(7): 1099-1114, 2019 Jul 17.
Artigo em Inglês | MEDLINE | ID: mdl-31179481

RESUMO

Endocrine active contaminants (EACs) in environmental samples can pose a range of toxicological threats to ecosystems, especially through their impacts on reproductive pathways mediated by the estrogen receptor. The physicochemical properties of known organic EACs vary greatly and typically require different sample preparation techniques to identify different classes of compounds. EAC sources are similarly diverse, including both endogenous compounds and anthropogenic chemicals found in personal care products, pharmaceuticals, and their transformation products, which are often disposed of to sewers at their end of use. Looking for EACs in sewage sludge proposes a bottom-up, or end-of-use and treatment approach to discover environmentally relevant EACs, since many EACs accumulate in sludges even after application of robust wastewater treatment processes. This study demonstrates an extraction and analytical method capable of detecting a broad spectrum of known and suspected EACs via High Resolution Liquid Chromatography Quadropole Time-of-Flight Mass Spectrometry (LC-QTOF-MS) suspect screening of fourteen California sewage sludge samples. Spike-recovery experiments were performed using twelve carefully selected surrogates to assess different extraction solvents, sample weights, extraction pH values, procedures for combining extracts with different extraction pH's, and solid phase extraction cartridges. Using LC-QTOF-MS, identifications of several other organic compounds in the samples were made, a goal unachievable with unit resolution mass spectrometry. Suspect screening of California sludge samples discovered 118 compounds including hormones, pharmaceuticals, phosphate flame retardants, recreational drugs, antimicrobials, and pesticides. Additionally, 22 of these identified compounds are predicted to interfere with estrogen receptors or other reproductive/developmental pathways based on the VEGA QSAR toxicity prediction model.


Assuntos
Disruptores Endócrinos/análise , Disruptores Endócrinos/toxicidade , Modelos Teóricos , Esgotos/química , Poluentes Químicos da Água/análise , Poluentes Químicos da Água/toxicidade , California , Cromatografia Líquida de Alta Pressão , Valor Preditivo dos Testes , Relação Quantitativa Estrutura-Atividade , Extração em Fase Sólida , Espectrometria de Massas em Tandem , Águas Residuárias/análise , Purificação da Água/métodos
18.
SAR QSAR Environ Res ; 30(7): 477-490, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31155931

RESUMO

Selection of key descriptors is very important in QSPR analysis. Presence of noise in the subset of descriptors reduces the quality of predictions. A complete set is considered as perfect when it does not include irrelevant or redundant elements. This paper reports complete sets of descriptors used to develop QSPR models for 1786 13C NMR chemical shifts (δC parameters) of carbon atoms in 125 diverse chemical compounds. PBE1PBE/6-311G(2d,2p) and B3LYP/6-31G(d) basis sets were used for quantum chemistry calculations after the molecular structures were optimized with semi-empirical AM1 and B3LYP/6-31G(d). The two complete sets consisting of magnetic shielding elements (σXX, σYY, σZZ) and the chemical shift principal values (σ11, σ22, σ33) were used as the inputs for support vector machine (SVM) models of δC parameters. The four SVM models obtained have the mean root mean square (rms) errors of about 4.5-4.6 ppm. The results suggest that SVM models are accurate and acceptable compared with previous models, although our models are based on a relatively large set of compounds. Our approach is valuable in the selection of important descriptors for QSPR studies of δC parameters.


Assuntos
Espectroscopia de Ressonância Magnética Nuclear de Carbono-13/métodos , Compostos Orgânicos/química , Relação Quantitativa Estrutura-Atividade , Algoritmos , Modelos Moleculares , Estrutura Molecular , Teoria Quântica , Máquina de Vetores de Suporte
19.
SAR QSAR Environ Res ; 30(7): 457-475, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31157558

RESUMO

ABCG2 is the principal ABC transporter involved in the multidrug resistance of breast cancer. Looking at the current demand in the development of ABCG2 inhibitors for the treatment of multidrug-resistant cancer, we have explored structural requirements of phenyltetrazole derivatives for ABCG2 inhibition by combining classical QSAR, Bayesian classification modelling and molecular docking studies. For classical QSAR, structural descriptors were calculated from the free software tool PaDEL-descriptor. Stepwise multiple linear regression (SMLR) was used for model generation. A statistically significant model was generated and validated with different parameters (For training set: r = 0.825; Q2 = 0.570 and for test set: r = 0.894, r2pred = 0.783). The predicted model was found to satisfy the Golbraikh and Trospha criteria for model acceptability. Bayesian classification modelling was also performed (ROC scores were 0.722 and 0.767 for the training and test sets, respectively). Finally, the binding interactions of phenyltetrazole type inhibitor with the ABCG2 receptor were mapped with the help of molecular docking study. The result of the docking analysis is aligned with the classical QSAR and Bayesian classification studies. The combined modelling study will guide the medicinal chemists to act faster in the drug discovery of ABCG2 inhibitors for the management of resistant breast cancer.


Assuntos
Subfamília G de Transportadores de Cassetes de Ligação de ATP/antagonistas & inibidores , Proteínas de Neoplasias/antagonistas & inibidores , Tetrazóis/química , Animais , Teorema de Bayes , Neoplasias da Mama/tratamento farmacológico , Cães , Desenho de Drogas , Resistência a Múltiplos Medicamentos/efeitos dos fármacos , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Modelos Lineares , Células Madin Darby de Rim Canino , Simulação de Acoplamento Molecular , Relação Quantitativa Estrutura-Atividade , Tetrazóis/farmacologia
20.
SAR QSAR Environ Res ; 30(7): 491-505, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31219354

RESUMO

The usefulness of five specific information-theoretical molecular descriptors was investigated for predicting the gas phase entropy of selected classes of acyclic and cyclic compounds. Among them, total information on atomic number (TIZ), graph vertex complexity (HV) and total information on bonds (TIBAT), considered together showed the best correlation along with a low standard deviation (r2 = 0.97, s = 21.14) with gas phase entropy values of 130 compounds. The multiple regression equation treating these three indices as independent variables was statistically highly significant which was evident from the F-statistics. In particular, very small difference between r2 and r2-pred values indicates that the regression model is not overfitted and is, therefore, suitable for prediction purposes. When truly used as a training set to predict (from regression equation) 40 additional compounds we get a very high correlation (r2 = 0.975), which remains almost identical (r2 = 0.97) for the combined data set of 170 compounds. The three indices appear to be useful descriptors producing correlation that remains stable with the change in the size of the data set. Also, the information-theoretical measures appear to capture an additive-cum-constitutive nature of gas phase entropy yielding an acceptable statistical fit.


Assuntos
Entropia , Hidrocarbonetos/química , Estrutura Molecular , Relação Quantitativa Estrutura-Atividade , Análise de Regressão
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