Showing papers in "Sar and Qsar in Environmental Research in 2011"
TL;DR: A new model concept for plant uptake models was developed, approximating logistic growth and coupling transpiration to growing plant mass, which allows to mimic most input functions that are relevant in practice.
Abstract: Models for the prediction of chemical uptake into plants are widely applied tools for human and wildlife exposure assessment, pesticide design and for environmental biotechnology such as phytoremediation. Steady-state considerations are often applied, because they are simple and have a small data need. However, often the emission pattern is non-steady. Examples are pesticide spraying, or the application of manure and sewage sludge on agricultural fields. In these scenarios, steady-state solutions are not valid, and dynamic simulation is required. We compared different approaches for dynamic modelling of plant uptake in order to identify relevant processes and timescales of processes in the soil-plant-air system. Based on the outcome, a new model concept for plant uptake models was developed, approximating logistic growth and coupling transpiration to growing plant mass. The underlying system of differential equations was solved analytically for the inhomogenous case, i.e. for constant input. By superposition of the results of n periods, changes in emission and input data between periods are considered. This combination allows to mimic most input functions that are relevant in practice. The model was set up, parameterized and tested for uptake into growing crops. The outcome was compared with a numerical solution, to verify the mathematical structure.
69 citations
TL;DR: Additional novel parameters are defined to model n-octanol–water partition coefficient, water solubility, molar refractivity, and aromatic substituent constants to develop ETA and non-ETA models that have similar predictive capacity.
Abstract: Extended topochemical atom (ETA) indices developed by our group have been extensively applied in our previous reports for toxicity and ecotoxicity modelling in the field of quantitative structure–activity relationships (QSARs). In the present study these indices have been further explored by defining additional novel parameters to model n-octanol–water partition coefficient (two data sets; n = 168 and 139), water solubility (n = 193), molar refractivity (n = 166), and aromatic substituent constants π, MR, σ m, and σ p (n = 99). All the models developed in the present study have undergone rigorous internal and external validation tests and the models have high statistical significance and prediction potential. In terms of Q 2 and r 2 values the models developed for the datasets of whole molecules are better than those previously reported, with topochemically arrived unique (TAU) indices on the same datasets of chemicals. An attempt has also been made to develop models using non-ETA topological and informat...
57 citations
TL;DR: The Hierarchical Technology for Quantitative Structure–Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds tested for their toxicity and it was shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation.
Abstract: The Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC₅₀) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. The Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. The Partial Least Squares (PLS) statistical approach was then used to develop 2D QSAR models. Validated PLS models were explored to: (1) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (2) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; and (3) analyse the role of various physical-chemical factors responsible for compounds' toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (r²(ext) = 0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modelling and 76% for external set).
53 citations
TL;DR: Abiotic and biotic models implemented in CATALOGIC software based on unique mathematical formalism are described, which accounts more adequately than currently available approaches the multipathway metabolic logic in prokaryotes.
Abstract: The unprecedented pollution of the environment by xenobiotic compounds has provoked the need to understand the biodegradation potential of chemicals. Mechanistic understanding of microbial degradation is a premise for adequate modelling of the environmental fate of chemicals. The aim of the present paper is to describe abiotic and biotic models implemented in CATALOGIC software. A brief overview of the specificities of abiotic and microbial degradation is provided followed by detailed descriptions of models built in our laboratory during the last decade. These are principally new models based on unique mathematical formalism already described in the first paper of this series, which accounts more adequately than currently available approaches the multipathway metabolic logic in prokaryotes. Based on simulated pathways of degradation, the models are able to predict quantities of transformation products, biological oxygen demand (BOD), carbon dioxide (CO(2)) production, and primary and ultimate half-lives. Interpretation of the applicability domain of models is also discussed.
53 citations
TL;DR: A pharmacophore model using diverse classes of epidermal growth factor receptor (EGFR) tyrosine kinase (TK) inhibitors useful in the treatment of human tumours was developed and 10 hits were identified after virtual screening of ZINC database for EGFR TK inhibition.
Abstract: A pharmacophore model has been developed using diverse classes of epidermal growth factor receptor (EGFR) tyrosine kinase (TK) inhibitors useful in the treatment of human tumours. Among the top 10 generated hypotheses, the second hypothesis, with one hydrogen bond acceptor, one ring aromatic and three hydrophobic features, was found to be the best on the basis of Cat Scramble validation as well as test set prediction (r training = 0.89, r test = 0.82). The model also maps well to the external test set molecules as well as clinically active molecules and corroborates the docking studies. Finally, 10 hits were identified as potential leads after virtual screening of ZINC database for EGFR TK inhibition. The study may facilitate the designing and discovery of novel EGFR TK inhibitors.
45 citations
TL;DR: As the level of understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.
Abstract: Physiologically-based pharmacokinetic (PBPK) models are increasingly finding use in risk assessment applications of data-rich compounds. However, it is a challenge to determine the chemical-specific parameters for these models, particularly in time- and resource-limiting situations. In this regard, SARs, QSARs and QPPRs are potentially useful for computing the chemical-specific input parameters of PBPK models. Based on the frequency of occurrence of molecular fragments (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure) and exposure conditions, the available QSAR-PBPK models facilitate the simulation of tissue and blood concentrations for some inhaled volatile organic chemicals. The application domain of existing QSARs for developing PBPK models is limited, due to lack of relevant data for diverse chemicals and mechanisms. Even though this approach is conceptually applicable to non-volatile and high molecular weight organics as well, it is more challenging to predict the other PBPK model parameters required for modelling the kinetics of these chemicals (particularly tissue diffusion coefficients, association constants for binding and oral absorption rates). As the level of our understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.
44 citations
TL;DR: The present study indicates the potential usefulness of the QSAR–PBPK modelling approach to provide first-cut evaluations of the kinetics of chemicals in mixtures of increasing complexity, on the basis of chemical structure.
Abstract: The objective of this study was to predict the inhalation toxicokinetics of chemicals in mixtures using an integrated QSAR-PBPK modelling approach. The approach involved: (1) the determination of partition coefficients as well as V(max) and K(m) based solely on chemical structure for 53 volatile organic compounds, according to the group contribution approach; and (2) using the QSAR-driven coefficients as input in interaction-based PBPK models in the rat to predict the pharmacokinetics of chemicals in mixtures of up to 10 components (benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene, and styrene). QSAR-estimated values of V(max) varied compared with experimental results by a factor of three for 43 out of 53 studied volatile organic compounds (VOCs). K(m) values were within a factor of three compared with experimental values for 43 out of 53 VOCs. Cross-validation performed as a ratio of predicted residual sum of squares and sum of squares of the response value indicates a value of 0.108 for V(max) and 0.208 for K(m). The integration of QSARs for partition coefficients, V(max) and K(m), as well as setting the K(m) equal to K(i) (metabolic inhibition constant) within the mixture PBPK model allowed to generate simulations of the inhalation pharmacokinetics of benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene and styrene in various mixtures. Overall, the present study indicates the potential usefulness of the QSAR-PBPK modelling approach to provide first-cut evaluations of the kinetics of chemicals in mixtures of increasing complexity, on the basis of chemical structure.
39 citations
TL;DR: A knowledge-based formalism for the computer simulation of non-intermediary metabolism for untested chemicals, with an emphasis on qualitative and quantitative aspects of modelling metabolism.
Abstract: Information regarding the metabolism of xenobiotic chemicals plays a central role in regulatory risk assessments. In regulatory programmes where metabolism studies are required, the studies of metabolic pathways are often incomplete and the identification of activated metabolites and important degradation products are limited by analytical methods. Because so many more new chemicals are being produced than can be assessed for potential hazards, setting assessment priorities among the thousands of untested chemicals requires methods for predictive hazard identification which can be derived directly from chemical structure and their likely metabolites. In a series of papers we are sharing our experience in the computerized management of metabolic data and the development of simulators of metabolism for predicting the environmental fate and (eco)toxicity of chemicals. The first paper of the series presents a knowledge-based formalism for the computer simulation of non-intermediary metabolism for untested che...
39 citations
TL;DR: Quantitative structure–activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis, comparative molecular similarity analysis and hologram QSAR approaches, finding the best model was based on the naphthalene substructure alignment.
Abstract: Quantitative structure–activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA) and hologram QSAR (HQSAR) approaches. The database consisted of 36 active molecules featuring varied core structures. The model based on the naphthalene substructure alignment incorporating 19 molecules yielded the best model with a CoMFA cross validation value q2 of 0.667 and a Pearson correlation coefficient r2 of 0.976; a CoMSIA q2 value of 0.616 and r2 value of 0.985; and a HQSAR q2 value of 0.652 and r2 value of 0.917. A second model incorporating 34 molecules aligned using the benzene substructure yielded an acceptable CoMFA model with q2 value of 0.5 and r2 value of 0.991. Depending on the core structure of the molecule under consideration, new CYP1A2 inhibitors will be designed based on the results from these models.
32 citations
TL;DR: The reactivity of potential pre-electrophile polyphenolics was investigated using an in chemico assay based on glutathione depletion and the toxicity to Tetrahymena pyriformis was determined; no direct relationship between toxic potency and reactivity to GSH was obtained.
Abstract: Reactive toxicity encompasses important endpoints such as skin and respiratory sensitization, hepatotoxicity and elevated acute aquatic toxicity. These adverse effects are initiated by, among others, electrophilic chemicals and those transformed into electrophiles; i.e. non-reactive chemicals activated into reactive electrophilic species by either a biotransformation (pro-electrophiles) or abiotic mechanism (pre-electrophiles). The presence of pro- and pre-electrophiles is important when developing quantitative structure–activity relationships (QSARs). In this study, the reactivity of potential pre-electrophile polyphenolics was investigated using an in chemico assay based on glutathione (GSH) depletion; in addition, the toxicity to Tetrahymena pyriformis was determined. For pre-electrophiles, no direct relationship between toxic potency and reactivity to GSH was obtained. The structural determinants for the pre-electrophile domain were characterized qualitatively by assessing structure–activity relations...
30 citations
TL;DR: In order to gain insight into the applicability of the Benigni and Bossa list of structural alerts to the detection of potential carcinogens, about 200 pesticides and biocides showing a high structural diversity were screened.
Abstract: More than 20 years ago, Ashby and Tennant showed the interest of structural alerts for the prediction of the carcinogenicity of chemicals. These structural alerts are functional groups or structural features of various sizes that are linked to the level of carcinogenicity of chemicals. Since this pioneering work it has been possible to refine the alerts over time, as more experimental results have become available and additional mechanistic insights have been gained. To date, one of the most advanced lists of structural alerts for evaluating the carcinogenic potential of chemicals is the list proposed by Benigni and Bossa and that is implemented as a rule-based system in Toxtree and in the OECD QSAR Application Toolbox. In order to gain insight into the applicability of this system to the detection of potential carcinogens we screened about 200 pesticides and biocides showing a high structural diversity. Prediction results were compared with experimental data retrieved from an extensive bibliographical re...
TL;DR: The ECOTOX database was queried and analysed for available data and a homogenous subset of 253 compounds for the endpoint LC50 48 h was established, and the applicability domain was subsequently analysed and discussed.
Abstract: Quantitative structure–activity relationship analysis and estimation of toxicological effects at lower-mid trophic levels provide first aid means to understand the toxicity of chemicals. Daphnia magna serves as a good starting point for such toxicity studies and is also recognized for regulatory use in estimating the risk of chemicals. The ECOTOX database was queried and analysed for available data and a homogenous subset of 253 compounds for the endpoint LC50 48 h was established. A four-parameter quantitative structure–activity relationship was derived (coefficient of determination, r 2 = 0.740) for half of the compounds and internally validated (leave-one-out cross-validated coefficient of determination, = 0.714; leave-many-out coefficient of determination, = 0.738). External validation was carried out with the remaining half of the compounds (coefficient of determination for external validation, = 0.634). Two of the descriptors in the model (log P, average bonding information content) capture the s...
TL;DR: The expected behaviour of the particle size part is discussed from the point of view of catastrophe theory, providing a plausible general picture about the emergence of new properties of nanoparticles and holographic location of information content.
Abstract: The structure one can associate to coherent nano-quantitative structure–properties relationship (nano-QSPR) models is briefly discussed. Such nano-QSPR model functions are described as possessing three parts: a particle size polynomial; a typical QSPR function; and a special effects function. The expected behaviour of the particle size part is discussed from the point of view of catastrophe theory, in this way providing a plausible general picture about the emergence of new properties of nanoparticles and holographic location of information content.
TL;DR: Three modelling systems were used for construction of androgenic receptor antagonist models and different descriptors in the modelling systems are illustrated with hydroxyflutamide and dexamethasone as examples (a non-steroid and a steroid anti-androgen, respectively).
Abstract: Three modelling systems (MultiCase®, LeadScope® and MDL® QSAR) were used for construction of androgenic receptor antagonist models. There were 923-942 chemicals in the training sets. The models were cross-validated (leave-groups-out) with concordances of 77-81%, specificity of 78-91% and sensitivity of 51-76%. The specificity was highest in the MultiCase® model and the sensitivity was highest in the MDL® QSAR model. A complementary use of the models may be a valuable tool when optimizing the prediction of chemicals for androgenic receptor antagonism. When evaluating the fitness of the model for a particular application, balance of training sets, domain definition, and cut-offs for prediction interpretation should also be taken into account. Different descriptors in the modelling systems are illustrated with hydroxyflutamide and dexamethasone as examples (a non-steroid and a steroid anti-androgen, respectively). More research concerning the mechanism of anti-androgens would increase the possibility for further optimization of the QSAR models. Further expansion of the basis for the models is in progress, including the addition of more drugs.
TL;DR: A recently developed technique for selection of potential androgenically active chemicals was used to test the performance of the model in its applicability domain, and the experimental results confirmed the theoretical predictions.
Abstract: The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used to describe the structural requirements for eliciting rat androgen receptor (AR) binding affinity, accounting for molecular flexibility. Chemical affinity for AR binding was related to the distances between nucleophilic sites and structural features describing electronic and hydrophobic interactions between the receptor and ligands. Categorical models were derived for each binding affinity range in terms of specific distances, local (maximal donor delocalizability associated with the oxygen atom of the A ring), global nucleophilicity (partial positive surface areas and energy of the highest occupied molecular orbital) and hydrophobicity (log Kow) of the molecules. An integral screening tool for predicting binding affinity to AR was constructed as a battery of models, each associated with different activity bins. The quality of the screening battery of models was assessed using a high value (0.9) of the Pearson contingency coefficient. The predictability of the model was assessed by testing the model performance on external validation sets. A recently developed technique for selection of potential androgenically active chemicals was used to test the performance of the model in its applicability domain. Some of the selected chemicals were tested for AR transcriptional activation. The experimental results confirmed the theoretical predictions.
TL;DR: A three-dimensional pharmacophore model has been generated for protein ITK from its known inhibitors, and this model was employed for virtual screening (3D database searching), including Lipinsiki's filter, to obtain a pool of more drug-like molecules.
Abstract: Interleukin-2-inducible T-cell kinase (ITK) is a key member of the Tec family of non-receptor tyrosine kinases, and has been found to be a novel target for a number of inflammatory and autoimmune diseases. A three-dimensional pharmacophore model has been generated for protein ITK from its known inhibitors. The best HypoGen model consisted of four pharmacophore features: one hydrogen bond acceptor, one hydrogen bond donor and two hydrophobic rings. This model showed a correlation coefficient of 0.947, a root mean square deviation of 0.914 and a configuration cost of 16.866. The model was validated using test set prediction and Fischer's test. A test set containing 204 compounds showed an r 2 of 0.745 between estimated activity and activity measured experimentally. Fisher's test gave a confidence level of 95%. The best pharmacophore model (Hypo1) was then employed for virtual screening (3D database searching), including Lipinsiki's filter, to obtain a pool of more drug-like molecules. The molecular pool thu...
TL;DR: The model was adapted to become more robust, and predictions were made with an external validation set collected from several databases, and the long-term toxicity QSAR for fish can be applied with high certainty of a correct prediction within the limits of the inherent uncertainty of the model in cases where the substance falls within the applicability domain.
Abstract: This study concentrates on the external validation of an existing Quantitative Structure–Activity Relationship (QSAR) model widely used for long-term aquatic toxicity to fish. In the context of the REACH legislation, QSARs are used as an alternative for experimental data to achieve a complete environmental assessment without the need for animal testing. The predictivity of the model was evaluated in order to increase the reliability of the model. We assessed whether the model met all of the OECD principles. The model was adapted to become more robust, and predictions were made with an external validation set collected from several databases. For the internal validation of the QSAR, the r 2, and were used as validation criteria, and for the external validation r 2, , h and the validation ratio were used. A few substances were classified as outliers and therefore the applicability domain of the QSAR had to be adjusted. The QSAR passed all validation criteria and met all the OECD principles for QSAR validati...
TL;DR: A perspective of how some of the most promising alternative approaches to (Q)SARs have been utilized to address the authors' in-house REACH requirements is provided.
Abstract: Legislation such as REACH strongly advocates the use of alternative approaches including in vitro, (Q)SARs, and chemical categories as a means to satisfy the information requirements for risk assessment. One of the most promising alternative approaches is that of chemical categories, where the underlying hypothesis is that the compounds within the category are similar and therefore should have similar biological activities. The challenge lies in characterizing the chemicals, understanding the mode/mechanism of action for the activity of interest and deriving a way of relating these together to form inferences about the likely activity outcomes. (Q)SARs are underpinned by the same hypothesis but are packaged in a more formalized manner. Since the publication of the White Paper for REACH, there have been a number of efforts aimed at developing tools, approaches and techniques for (Q)SARs and read-across for regulatory purposes. While technical guidance is available, there still remains little practical guid...
TL;DR: SVRG scales were applied in three panels of peptide quantitative structure–activity relationships (QSARs) which were modelled by partial least squares regression (PLS) and Satisfactory results showed that SVRG contained much chemical information relating to bioactivities.
Abstract: In this work, a descriptor, SVRG (principal component scores vector of radial distribution function descriptors and geometrical descriptors), was derived from principal component analysis (PCA) of a matrix of two structural variables of coded amino acids, including radial distribution function index (RDF) and geometrical index. SVRG scales were then applied in three panels of peptide quantitative structure-activity relationships (QSARs) which were modelled by partial least squares regression (PLS). The obtained models with the correlation coefficient (R²(cum)), cross-validation correlation coefficient (Q²(LOO)) were 0.910 and 0.863 for 48 bitter-tasting dipeptides; 0.968 and 0.931 for 21 oxytocin analogues; and 0.992 and 0.954 for 20 thromboplastin inhibitors. Satisfactory results showed that SVRG contained much chemical information relating to bioactivities. The approach may be a useful structural expression methodology for studies on peptide QSAR.
TL;DR: The electron conformational–genetic algorithm (EC–GA), a sophisticated hybrid approach combining the GA and EC methods, has been employed for a 4D–QSAR procedure to identify the pharmacophore for benzotriazines as sarcoma inhibitors and for quantitative prediction of activity.
Abstract: The electron conformational–genetic algorithm (EC–GA), a sophisticated hybrid approach combining the GA and EC methods, has been employed for a 4D–QSAR procedure to identify the pharmacophore for benzotriazines as sarcoma inhibitors and for quantitative prediction of activity The calculated geometry and electronic structure parameters of every atom and bond of each molecule are arranged in a matrix described as the electron–conformational matrix of contiguity (ECMC) By comparing the ECMC of one of the most active compounds with other ECMCs we were able to obtain the features of the pharmacophore responsible for the activity, as submatrices of the template known as electron conformational submatrices of activity The GA was used to select the most important descriptors and to predict the theoretical activity of training and test sets The predictivity of the model was internally validated The best QSAR model was selected, having r 2 = 09008, standard error = 00510 and cross-validated squared correlati
TL;DR: The developed FS-QSAR method is proved to give more accurate predictions than the traditional and one-nearest-neighbour QSAR methods and can be a useful tool in the fragment-based drug discovery for ligand activity prediction.
Abstract: Quantitative structure–activity relationship (QSAR) studies are useful computational tools often used in drug discovery research and in many scientific disciplines. In this study, a robust fragment-similarity-based QSAR (FS-QSAR) algorithm was developed to correlate structures with biological activities by integrating fragment-based drug design concept and a multiple linear regression method. Similarity between any pair of training and testing fragments was determined by calculating the difference of lowest or highest eigenvalues of the chemistry space BCUT matrices of corresponding fragments. In addition to the BCUT-similarity function, molecular fingerprint Tanimoto coefficient (Tc) similarity function was also used as an alternative for comparison. For validation studies, the FS-QSAR algorithm was applied to several case studies, including a dataset of COX2 inhibitors and a dataset of cannabinoid CB2 triaryl bis-sulfone antagonist analogues, to build predictive models achieving average coefficient of d...
TL;DR: The pharmacophore and molecular shape-based QSAR scoring function now established can be used to predict the biological properties of virtual hits or untested compounds obtained from ligand-based virtual screenings.
Abstract: In order to build quantitative structure–activity relationship (QSAR) models for virtual screening of novel cannabinoid CB2 ligands and hit ranking selections, a new QSAR algorithm has been developed for the cannabinoid ligands, triaryl bis-sulfones, using a combined molecular morphological and pharmacophoric search approach. Both pharmacophore features and shape complementarity were considered using a number of molecular descriptors, including Surflex–Sim similarity and Unity Query fit, in addition to other molecular properties such as molecular weight, ClogP, molecular volume, molecular area, molecular polar volume, molecular polar surface area and dipole moment. Subsequently, partial least squares regression analyses were carried out to derive QSAR models linking bioactivity and the descriptors mentioned, using a training set of 25 triaryl bis-sulfones. Good prediction capability was confirmed for the best QSAR model by evaluation against a test set of a further 20 triaryl bis-sulfones. The pharmacopho...
TL;DR: The present tool allows to securely de-prioritize more than 50% chemicals of low concern with regard to the B criterion (BCF < 2000) and bioassays with compounds with these physico-chemical constraints may be waived.
Abstract: Physico-chemical properties related to the bioavailability of xenobiotics in aquatic environments have been tested for their ability to identify chemicals with low bioconcentration potential. Cut-offs in lipophilicity (log K OW 10), solubility and volatility (log Henry constant 5% ionisation at pH 7) have been adopted and combined into a decision tree based on 382 industrial chemicals. The five-parameter classification scheme was externally validated with 49 pesticides and successfully confirmed with 83 bioaccumulative compounds. The applicability domain of the model has been described in terms of chemical classes (excluding polybrominated compounds (>4 Br), organometallics, compounds with perfluorinated fragments, substances with an acyclic alkyl moiety (chain length > C7) and thiols) and ranges of physico-chemical properties. The present tool allows to securely de-prioritize more than 50% chemicals of l...
TL;DR: The results obtained revealed that PS is a suitable method for improving the ability of BRGNN to break out from the local minima and the proposed HGA technique is able to retrieve important variables from complex systems and nonlinear search spaces for optimisation.
Abstract: Bayesian regularised genetic neural network (BRGNN) has been used for modelling the inhibition activity of 141 biphenylalanine derivatives as integrin antagonists. Three local pattern search (PS) methods, simulated annealing and threshold acceptance were combined with BRGNN in the form of a hybrid genetic algorithm (HGA). The results obtained revealed that PS is a suitable method for improving the ability of BRGNN to break out from the local minima. The proposed HGA technique is able to retrieve important variables from complex systems and nonlinear search spaces for optimisation. Two models with 8-3-1 artificial neural network (ANN) architectures were developed for describing α 4 β 7 and α 4 β 1 modulatory activities of integrin antagonists. Monte Carlo cross-validation was performed to validate the models and Q 2 values of 0.75 and 0.74 were obtained for α 4 β 7 and α 4 β 1 inhibitory activities, respectively. The scrambling technique was used for sensitivity analysis of descriptors appearing in ANN mod...
TL;DR: ChemT, an easy-to-use open-source software tool that automates the process of preparing custom-made template-based chemical libraries, may be a valuable tool for investigators interested in using in silico virtual screening tools, such as quantitative structure-activity relationship modelling or molecular docking, in order to prioritize compounds for further chemical synthesis.
Abstract: In computational chemistry, vast quantities of compounds are generated, and there is a need for cheminformatic tools to efficiently build chemical compound libraries. Several software tools for drawing and editing compound structures are available, but they lack options for automatic generation of chemical libraries. We have implemented ChemT, an easy-to-use open-source software tool that automates the process of preparing custom-made template-based chemical libraries. ChemT automatically generates three-dimensional chemical libraries by inputting a chemical template and the functional groups of interest. The graphical user interface of ChemT is self-explanatory, and a complete tutorial is provided. Several file formats are accepted by ChemT, and it is possible to filter the generated compounds according to different physicochemical properties. The compounds can be subject to force field minimization, and the resulting three-dimensional structures recorded on commonly used file formats. ChemT may be a valuable tool for investigators interested in using in silico virtual screening tools, such as quantitative structure-activity relationship modelling or molecular docking, in order to prioritize compounds for further chemical synthesis. To demonstrate the usefulness of ChemT, we describe an example based on a thieno[3,2-b]pyridine template. ChemT is available free of charge from our website at http://www.esa.ipb.pt/~ruiabreu/chemt .
TL;DR: The results revealed that the developed CPANN models and decision tree can correctly classify the molecules according to their inhibition mechanisms and activities.
Abstract: The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure-activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm-pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the molecules according to their inhibition mechanisms and activities. Some general parameters such as molecular weight, average molecular weight, number of hydrogen atoms and number of hydroxyl groups were found to be important for describing the inhibition behaviour of anti-HIV agents. The developed classifier models in this work can be used to screen large libraries of compounds to identify those likely to display activity as anti-HIV agents.
TL;DR: 4-O-caffeoylquinic, naringin and lycopene stand out as the top-ranked potential inhibitors for aromatase, estrone sulfatase and 17β-HSD1, respectively, and the 3-D docked conformations for these compounds are discussed in detail.
Abstract: Mushrooms represent an unlimited source of compounds with anti-tumour and immunostimulating properties, and their intake has been shown to reduce the risk of breast cancer. A large number of low molecular weight (LMW) compounds present in mushrooms have been identified, including phenolic acids, flavonoids, tocopherols, carotenoids, sugars and fatty acids. In order to evaluate which wild mushroom LMW compounds may be involved in anti-breast cancer activity we selected a representative dataset of 43 LMW compounds and performed molecular docking against three known protein targets involved in breast cancer (aromatase, estrone sulfatase and 17β-HSD-1) using AutoDock4 as docking software. The estimated inhibition constants for all LMW compounds were determined, and the potential structure-activity relationships for the compounds with the best estimated inhibition constants are discussed for each compound family. 4-O-caffeoylquinic, naringin and lycopene stand out as the top-ranked potential inhibitors for aromatase, estrone sulfatase and 17β-HSD1, respectively, and the 3-D docked conformations for these compounds are discussed in detail. This information provides several interesting starting points for further development of aromatase, estrone sulfatase and 17β-HSD1 inhibitors.
TL;DR: A QSAR study of hSERT inhibitory and H3 antagonistic activity of piperazine and diazepane amide derivatives has been carried out using the combinatorial protocol in multiple linear regression (CP-MLR) with 0D- to 2D-Dragon descriptors, revealing their importance in modulating these activities.
Abstract: Selective human serotonin reuptake transporter (hSERT) inhibition is the first line of treatment to deal with the depression. In clinical practice for managing depression, the stimulants are co-prescribed to overcome cognitive impairment and fatigue. Recently, histamine H3 antagonists with serotonin reuptake inhibition activity have been proposed as alternative approach for the treatment of depression. In this context, a QSAR study of hSERT inhibitory and H3 antagonistic activity of piperazine and diazepane amide derivatives has been carried out using the combinatorial protocol in multiple linear regression (CP-MLR) with 0D- to 2D-Dragon descriptors. The derived QSAR models have provided a rational approach for the development of new piperazine and diazepane amide derivatives as hSERT inhibitors and H3 antagonists. In a concomitant partial least-squares (PLS) analysis of the hSERT and histamine H3 activities, the fraction contributions of identified descriptors revealed their importance in modulating thes...
TL;DR: It was concluded that the reaction mechanistic domains of toxic action for skin sensitisation could provide useful complementary information in predicting acute aquatic ecotoxicity, especially at the fish end-point.
Abstract: The validity of chemical reaction mechanistic domains defined by skin sensitisation in the Quantitative Structure–Activity Relationship (QSAR) ecotoxicity system, KAshinhou Tools for Ecotoxicity (KATE), March 2009 version, has been assessed and an external validation of the current KATE system carried out. In the case of the fish end-point, the group of chemicals with substructures reactive to skin sensitisation always exhibited higher root mean square errors (RMSEs) than chemicals without reactive substructures under identical C- or log P-judgements in KATE. However, in the case of the Daphnia end-point this was not so, and the group of chemicals with reactive substructures did not always have higher RMSEs: the Schiff base mechanism did not function as a high error detector. In addition to the RMSE findings, the presence of outliers suggested that the KATE classification rules needs to be reconsidered, particularly for the amine group. Examination of the dependency of the organism on the toxic action of ...
TL;DR: In this paper, the repeatability of replicate determinations was assessed and factors relating to repeatability were discussed and it was found that the mechanism of action is likely to be a modulating factor, despite the majority of compounds demonstrating excellent repeatability.
Abstract: Assessments necessary to ensure the safety of both humans and the environment are challenged by the sheer number of chemicals in use today. Chemical legislation, such as REACH, aims to use alternative methods to reduce the reliance on in vivo animal testing. Consequently, databases such as the TETRATOX database, containing data from the Tetrahymena pyriformis population growth impairment assay, have been used extensively to develop computational models which aid in priority setting and initial hazard assessments. To use any toxicological data, an assessment of quality is required. One important aspect of quality is the repeatability of the assay. This study considered TETRATOX assay data for 85 structurally and mechanistically diverse compounds. The repeatability of replicate determinations was assessed and factors relating to repeatability are discussed. Despite the majority of compounds demonstrating excellent repeatability, it was found that the mechanism of action is likely to be a modulating factor, ...