General Quantitative Structure-activity Relationship Research Articles

Assessment of abiotic reduction rates of organic compounds by interpretable structural factors and experimental conditions in anoxic water environments

For organic contaminants in lake sediments, aquifers, and anaerobic bioreactors, their reduction is one of the primary transformation paths in these anoxic water environments. A simple model is introduced to predict pseudo-first order rate constants (kobs) for the abiotic reduction of organic compounds featuring diverse reducible functional groups. It utilizes the largest experimental dataset of –log kobs, encompassing 59 organic compounds (278 data points). Unlike available complex quantitative structure–activity relationship (QSAR) methods, the novel approach requires both experimental conditions and structural parameters. In comparison to one of the available general QSAR methods, the new model demonstrates favorable performance. The average absolute deviation (AAD), absolute maximum deviation (ADmax), average absolute relative deviation (AARD%), and R-squared (R2) values of the estimated outputs for 54/5 training/test data sets of the new model are 0.641/1.761, 1.761/1.417, 20.52/83.87, and 0.797/0.949, respectively. On the other hand, the available general comparative QSAR method shows the AAD: 1.311/2.301, ADmax: 3.795/3.732, AARD%: 641.0/821.2, and R2: 0.003/0.447. For the test set, AAD, AARD%, ADmax, and R2 values for the new/comparative models are 0.649/2.403, 62.20/190.5, 1.215/3.732 and 0.974/0.789, respectively. In summary, the new model offers a straightforward approach for the manual calculation of –log kobs, demonstrating excellent goodness-of-fit, reliability, precision, and accuracy.

Finding a Novel Chalcone-Cinnamic Acid Chimeric Compound with Antiproliferative Activity against MCF-7 Cell Line Using a Free-Wilson Type Approach.

In this work, we carried out the design and synthesis of new chimeric compounds from the natural cytotoxic chalcone 2',4'-dihydroxychalcone (2',4'-DHC, A) in combination with cinnamic acids. For this purpose, a descriptive and predictive quantitative structure-activity relationship (QSAR) model was developed to study the chimeric compounds' anti-cancer activities against human breast cancer MCF-7, relying on the presence or absence of structural motifs in the chalcone structure, like in a Free-Wilson approach. For this, we used 207 chalcone derivatives with a great variety of structural modifications over the α and β rings, such as halogens (F, Cl, and Br), heterocyclic rings (piperazine, piperidine, pyridine, etc.), and hydroxyl and methoxy groups. The multilinear equation was obtained by the genetic algorithm technique, using logIC50 as a dependent variable and molecular descriptors (constitutional, topological, functional group count, atom-centered fragments, and molecular properties) as independent variables, with acceptable statistical parameter values (R2 = 86.93, Q2LMO = 82.578, Q2BOOT = 80.436, and Q2EXT = 80.226), which supports the predictive ability of the model. Considering the aromatic and planar nature of the chalcone and cinnamic acid cores, a structural-specific QSAR model was developed by incorporating geometrical descriptors into the previous general QSAR model, again, with acceptable parameters (R2 = 85.554, Q2LMO = 80.534, Q2BOOT = 78.186, and Q2EXT = 79.41). Employing this new QSAR model over the natural parent chalcone 2',4'-DHC (A) and the chimeric compound 2'-hydroxy,4'-cinnamate chalcone (B), the predicted cytotoxic activity was achieved with values of 55.95 and 17.86 µM, respectively. Therefore, to corroborate the predicted cytotoxic activity compounds A and B were synthesized by two- and three-step reactions. The structures were confirmed by 1H and 13C NMR and ESI+MS analysis and further evaluated in vitro against HepG2, Hep3B (liver), A-549 (lung), MCF-7 (breast), and CasKi (cervical) human cancer cell lines. The results showed IC50 values of 11.89, 10.27, 56.75, 14.86, and 29.72 µM, respectively, for the chimeric cinnamate chalcone B. Finally, we employed B as a molecular scaffold for the generation of cinnamate candidates (C-K), which incorporated structural motifs that enhance the cytotoxic activity (pyridine ring, halogens, and methoxy groups) according to our QSAR model. ADME/tox in silico analysis showed that the synthesized compounds A and B, as well as the proposed chalcones C and G, are the best candidates with adequate drug-likeness properties. From all these results, we propose B (as a molecular scaffold) and our two QSAR models as reliable tools for the generation of anti-cancer compounds over the MCF-7 cell line.

How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models?

One of the important applications of quantitative structure-activity relationship (QSAR) models is to fill data gaps by predicting a given response property or activity from known molecular features or physicochemical properties of new compounds which might not have been tested experimentally. The general QSAR users are now already aware that the new compounds which will be predicted from a given QSAR model should be similar in structural and/or physicochemical property space to the training set compounds so as to be included in the chemical applicability domain of the model. It is also well-established, at least among some groups of QSAR scientists, that performance of a model should be evaluated based on the quality of predictions from the test set and not from the training set to obviate any overfitting problem. It is also important to analyze prediction errors of test set compounds in order to search for presence of any systematic error or bias in model predictions. A simple residual plot can provide sufficient information about the type of error present in model predictions. As the test set compounds are structurally and physico-chemically similar to the training set compounds, the model prediction errors for the test set also should obey, at least approximately, the basic assumptions of the least-squares regression under the best linear unbiased estimator (BLUE) framework, provided that a sufficient number of test set compounds is available allowing an acceptable degree of freedom. The present article explains the importance of analysis of prediction errors to check for the presence of systematic error and/or violation of basic assumptions of the least-squares regression models under the BLUE framework with suitable examples using real QSAR model-derived quantitative predictions for test sets and simulated prediction data. The intention of the present authors is neither to make a comparison of performances of different validation metrics for quantitative predictions (eventually favoring or disfavoring one or the other validation metric) nor to compare quality of different models but to indicate the situations where such comparison should not be made due to inappropriate functional form of a model making it unsuitable for quantitative predictions of a set of compounds and also due to the use of unrealistic and biased prediction pattern that never happens in real QSAR problems, thus making the situation unsuitable for making any generalized conclusion. This article also alerts the QSAR users to the importance of “statistical applicability domain” of QSAR models before their application for quantitative prediction of a response of test set compounds in order to compare performance of different validation approaches.

Behavior and rejection mechanisms of fruit juice phenolic compounds in model solution during nanofiltration

Behavior and rejection mechanisms of typical phenolic compounds in fruit juice during nanofiltration (NF) are not clear. Because the system of actual fruit juice is very complex, in this study, rejection mechanisms of six typical fruit juice phenolic compounds in model solutions with two NF membranes were investigated. Molecular structure parameters of these phenolics, such as hydrophobic properties, electrical parameters, and spatial parameters on the process were considered in a general quantitative structure activity relationship (QSAR) model. Principal component analysis and partial least square regression were used to find the relationships between rejection and molecular parameters. Results showed that NF performances of phenolics could be affected by filtration operating conditions, membrane properties and molecular parameters. In QSAR models, dipole moment, acidity coefficient, Octanol-water partition coefficient and fifth-order path cluster molecules connectivity index were the most important molecular parameters, and correlation coefficients for two membranes were 0.895 and 0.833, respectively. QSAR models can be successfully applied to predict the rejection for NF of fruit juice phenolics model solution.

Framework for Ecotoxicological Risk Assessment of Ionic Liquids

Although ionic liquids (ILs) hold salient features and considered as a replacement of conventional organic solvents, but their possible toxicological and ecotoxicological impacts must be considered equally in the design and development stage. Predicted no-effect concentration (PNEC) plays a vital role in assessing the ecotoxicological hazards in the ILs. A systematic methodological framework is presented for assessing the predicted environmental concentration (PEC) of ionic liquids. Initially, an effect level is predicted with a general quantitative structure–activity relationship (QSAR). Secondly, predicted no-effect concentration (PNEC) is calculated by using assessment factor (AF). The PEC is compared to the PNEC to characterize the risk for the freshwater aquatic organisms. If the ratio continued to exceed one, ionic liquids under investigation will need further aquatic risk assessment procedures, which involve iterative steps to refine the PEC and PNEC.

Predicting reaction rate constants of ozone with organic compounds from radical structures

Abstract The reaction rate constants of ozone with organic compounds in the atmosphere were predicted by a quantitative structure–activity relationship (QSAR) model. Density functional theory (DFT) calculations, for the first time, were carried out on the radicals from organic compounds, at the UB3LYP level of theory with 6-31G(d) basis set. A set of quantum chemical descriptors calculated from the radicals, the energy of the highest occupied molecular orbital of beta spin states (EβHOMO), the molecular average polarizability (α), and the total energy (ET), were used to build the general QSAR model for aliphatic compounds, applying the genetic algorithm (GA) technique and support vector machine (SVM) regression. The root mean square errors (RMSE) are 0.680 for the training set (68 compounds), 0.777 for the validation set (36 compounds) and 0.709 for the test set (35 compounds). Investigated results indicate that the SVM model given here has good predictivity for aliphatic compounds.

A QSAR (quantitative structure-activity relationship) approach for modelling and prediction of rejection of emerging contaminants by NF membranes

Principal component analysis (PCA) and multivariate regressions were used to find a quantitative structure-activity relationship (QSAR) model equation that combines interactions between membrane characteristics and solute properties for predicting rejection. An internal experimental database that accounts rejections of contaminants by two nanofiltration membranes (NF-90, NF-200) was used to develop the QSAR model equation. Membrane characteristics related to hydrophobicity (contact angle), salt rejection, and surface charge (zeta potential [ZP]); compound properties describing hydrophobicity (log Kow, log D), polarity (dipole moment), and size (molar volume, molecular length, molecular depth, equivalent width, molecular weight); and operating conditions (flux, pressure, cross flow velocity) were identified and evaluated as candidate variables for rejection prediction. Subsequently, using the QSAR model, rejection predictions were made for an external experimental database. Measured rejections were compared against predicted rejections to determine the best model; an acceptable R2 (0.93) correlation coefficient was found for the best model with a standard deviation of error of 7% for predicted rejections. In conclusion, a general QSAR model equation was able to model and predict rejections of emerging contaminants during nanofiltration.

A QSAR model for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors) by nanofiltration membranes

A quantitative structure activity relationship (QSAR) model has been produced for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors, pesticides and other organic compounds) by polyamide nanofiltration (NF) membranes. Principal component analysis, partial least square regression and multiple linear regressions were used to find a general QSAR equation that combines interactions between membrane characteristics, filtration operating conditions and compound properties for predicting rejection. Membrane characteristics related to hydrophobicity (contact angle), salt rejection, and surface charge (zeta potential); compound properties describing hydrophobicity (log K ow, log D), polarity (dipole moment), and size (molar volume, molecular length, molecular depth, equivalent width, molecular weight); and operating conditions namely flux, pressure, cross flow velocity, back diffusion mass transfer coefficient, hydrodynamic ratio ( J o/ k), and recovery were identified as candidate variables for rejection prediction. An experimental database produced by the authors that accounts for 106 rejection cases of emerging contaminants by NF membranes as result of eight experiments with clean and fouled membranes (NF-90, NF-200) was used to produce the QSAR model. Subsequently, using the QSAR model, rejection predictions were made for external experimental databases. Actual rejections were compared against predicted rejections and acceptable R 2 correlation coefficients were found (0.75 and 0.84) for the best models. Additionally, leave-one-out cross-validation of the models achieved a Q 2 of 0.72 for internal validation. In conclusion, a unified general QSAR equation was able to predict rejections of emerging contaminants during nanofiltration; moreover the present approach is a basis to continue investigation using multivariate analysis techniques for understanding membrane rejection of organic compounds.

A framework for prioritizing fragrance materials for aquatic risk assessment

More than 2,100 chemically defined organic chemicals are listed in the Research Institute of Fragrance Materials/Flavor and Extract Manufacturers' Association (RIFM/FEMA) Database that are used as ingredients of fragrances for consumer products. An approach was developed for prioritizing these fragrance materials for aquatic risk assessment by first estimating the predicted environmental concentration (PEC) of these fragrance materials in the aquatic environment based upon their physicochemical properties and annual volume of use. Subsequently, an effect level was predicted with a general quantitative structure-activity relationship (QSAR) for aquatic toxicity, and a predicted no-effect concentration (PNEC) was calculated from this effect level by using an assessment factor (AF) that accounts for uncertainty in the toxicity QSAR prediction. A conservative AF of 10(6) was applied to the endpoint predicted by the QSAR to provide an adequate margin of safety in the calculation of the PNEC. The PEC was compared to the PNEC to characterize the risk to freshwater aquatic organisms (e.g., Daphnia magna and Pimephales promelas). If the ratio of PEC to PNEC was below one, the material was considered to have negligible environmental risk and to be acceptable for the aquatic environment at current use levels. If this ratio exceeded one, the PNEC was refined by using more specific QSAR models (Ecological Structure-Activity Relationships [ECOSAR]). If the ratio continued to exceed one, the material became a candidate for further aquatic risk assessment procedures, which involve iterative steps to refine the PEC, the PNEC, or both by using measured ecotoxicological endpoints. Prioritization for this latter process can be based upon the magnitudes of the estimated PEC:PNEC ratios. When using the first tier of this approach, only 568 of 2,141 fragrance materials (26.5%) in the RIFM/FEMA Database had PEC:PNEC ratios greater than one. This percentage decreased to only 164 materials (7.7%) when PNECs were derived with ECOSAR. Comparison of predicted PECs and PNECs with those based upon measured data confirmed the conservatism and low risk for type I errors associated with the framework. These combined exercises demonstrated the ability of this highly precautionary risk-based screening approach to quickly prioritize a large number of materials without benefit of experimental ecotoxicological or fate data.

A FRAMEWORK FOR PRIORITIZING FRAGRANCE MATERIALS FOR AQUATIC RISK ASSESSMENT

More than 2,100 chemically defined organic chemicals are listed in the Research Institute of Fragrance Materials/Flavor and Extract Manufacturers' Association (RIFM/FEMA) Database that are used as ingredients of fragrances for consumer products. An approach was developed for prioritizing these fragrance materials for aquatic risk assessment by first estimating the predicted environmental concentration (PEC) of these fragrance materials in the aquatic environment based upon their physicochemical properties and annual volume of use. Subsequently, an effect level was predicted with a general quantitative structure—activity relationship (QSAR) for aquatic toxicity, and a predicted no-effect concentration (PNEC) was calculated from this effect level by using an assessment factor (AF) that accounts for uncertainty in the toxicity QSAR prediction. A conservative AF of 106 was applied to the endpoint predicted by the QSAR to provide an adequate margin of safety in the calculation of the PNEC. The PEC was compared to the PNEC to characterize the risk to freshwater aquatic organisms (e.g., Daphnia magna and Pimephales promelas). If the ratio of PEC to PNEC was below one, the material was considered to have negligible environmental risk and to be acceptable for the aquatic environment at current use levels. If this ratio exceeded one, the PNEC was refined by using more specific QSAR models (Ecological Structure—Activity Relationships [ECOSAR]). If the ratio continued to exceed one, the material became a candidate for further aquatic risk assessment procedures, which involve iterative steps to refine the PEC, the PNEC, or both by using measured ecotoxicological endpoints. Prioritization for this latter process can be based upon the magnitudes of the estimated PEC:PNEC ratios. When using the first tier of this approach, only 568 of 2,141 fragrance materials (26.5%) in the RIFM/FEMA Database had PEC:PNEC ratios greater than one. This percentage decreased to only 164 materials (7.7%) when PNECs were derived with ECOSAR. Comparison of predicted PECs and PNECs with those based upon measured data confirmed the conservatism and low risk for type I errors associated with the framework. These combined exercises demonstrated the ability of this highly precautionary risk-based screening approach to quickly prioritize a large number of materials without benefit of experimental ecotoxicological or fate data.

A General QSAR Model for Predicting the Acute Toxicity of Pesticides to Lepomis Macrochirus

A Quantitative Structure-Activity Relationship (QSAR) model was derived for estimating the acute toxicity of pesticides against Lepomis macrochirus under varying experimental conditions. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H0 to H5) and the H-bonding acceptor ability (HBA0) and H-bonding donor ability (HBD0) of the pesticides. A three-layer feedforward neural network trained by the back-propagation algorithm was used as statistical engine for deriving a powerful QSAR model accounting for the weight of the fish, time of exposure, temperature, pH, and water hardness.

A General QSAR Model for Predicting the Acute Toxicity of Pesticides to Oncorhynchus mykiss

A Quantitative Structure-Activity Relationship (QSAR) model was derived for estimating the acute toxicity of pesticides against Oncorhynchus mykiss under varying experimental conditions. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H0 to H5) and the H-bonding acceptor ability (HBA0) and H-bonding donor ability (HBD0) of the pesticides. A three-layer feedforward neural network trained by the back-propagation algorithm was used as statistical engine for deriving a powerful QSAR model accounting for the weight of the fish, time of exposure, temperature, pH, and hardness.

A Noncongeneric Model for Predicting Toxicity of Organic Molecules to Vibrio Fischeri

A general Quantitative Structure-Activity Relationship (QSAR) model on Vibrio fischeri (Microtox™ test) was derived using the autocorrelation method for describing the molecules and a neural network as statistical tool. From a training set of 1068 organic chemicals described by means of four different autocorrelation vectors, it was possible to obtain valuable models but presenting some large outliers. Addition of the time of exposure as variable allowed us to derive a more powerful model from 2795 toxicity results. The predictive power of this 36/26/1 neural network model was tested on an external testing set of 385 toxicity data and compared with the performances of linear models designed for polar narcotic amines and for weak acid respiratory uncouplers.

Membrane-interaction QSAR analysis: application to the estimation of eye irritation by organic compounds.

The purpose of this study was to explore a potential mechanism of eye irritation, and to construct a corresponding general quantitative structure-activity relationship (QSAR) model, in terms of diversity of irritant chemical structure, based on the Draize eye irritation ECETOC data set. Molecular dynamic simulation (MDS) was used to generate intermolecular membrane-solute interaction properties. These intermolecular properties were combined with intramolecular physicochemical properties and features of the solute (irritant) to construct QSAR models using multi-dimensional linear regression and the Genetic Function Approximation (GFA) algorithm. Significant QSAR models for estimating eye irritation potential were constructed in which solute aqueous solvation free energy and solute-membrane interaction energies are the principle correlation descriptors. These physicochemical descriptors were selected from a trial set of 95 descriptors for 18 structurally diverse compounds fully representative of the ECETOC set of 38 compounds. Combining intermolecular solute-membrane interaction descriptors with intramolecular solute descriptors yields statistically significant eye irritation QSAR models. The resultant QSAR models support an eye irritation mechanism of the action in which increased aqueous solubility of the irritant and its strength of binding to the membrane both increase eye irritation.

A general QSAR for dihydrofolate reductase inhibition by 2,4-diaminotriazines based upon molecular shape analysis

A general quantitative structure-activity relationship (QSAR) is proposed to explain the 50% in vitro inhibition of dihydrofolate reductases from Walker 256 tumor, and/or L1210 leukemia cells, bovine liver, and rat liver by any reported 2,4-diaminotriazine. A maximum of four molecular descriptors are needed in the QSAR. Three of these descriptors characterize molecular shape and are determined from conformational analysis. The fourth descriptor is the sum of the water/octanol fragment constants for 3- and 4-substituents on the phenyl ring of a triazine molecule. The need for specific descriptors, and their corresponding linear or parabolic behavior in a correlation equation, depends upon the choice of the triazine compound data base, and the species source of the dihydrofolate reductase. Correlation coefficients, R, in the range 0.92 to 0.96 have been achieved for the QSAR equations derived from data bases containing compounds that had to be deleted in previous structure-activity studies.