scholarly journals QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 127
Author(s):  
Cosimo Toma ◽  
Alberto Manganaro ◽  
Giuseppa Raitano ◽  
Marco Marzo ◽  
Domenico Gadaleta ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Regression Models ◽  
External Validation ◽  
Inhalation Exposure ◽  
Quantitative Structure Activity Relationship ◽  
Learning Approaches ◽  
Chemical Safety ◽  
Qsar Models ◽  
Using Data

Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure–activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r2 values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online.

scholarly journals QSAR studies for the acute toxicity of nitrobenzenes to the Tetrahymena pyriformis

2014 ◽  
Vol 79 (9) ◽  
pp. 1111-1125 ◽  
Author(s):  
Dan-Dan Wang ◽  
Lin-Lin Feng ◽  
Guang-Yu He ◽  
Hai-Qun Chen
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Tetrahymena Pyriformis ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Molecular Structures ◽  
Least Square ◽  
Qsar Studies ◽  
Qsar Models ◽  
Leave One Out

Quantitative structure-activity relationship (QSAR) models play a key role in finding the relationship between molecular structures and the toxicity of nitrobenzenes to Tetrahymena pyriformis. In this work, genetic algorithm, along with partial least square (GA-PLS) was employed to select optimal subset of descriptors that have significant contribution to the toxicity of nitrobenzenes to Tetrahymena pyriformis. A set of five descriptors, namely G2, HOMT, G(Cl?Cl), Mor03v and MAXDP, was used for the prediction of the toxicity of 45 nitrobenzene derivatives and then were used to build the model by multiple linear regression (MLR) method. It turned out that the built model, whose stability was confirmed using the leave-one-out validation and external validation test, showed high statistical significance (R2=0.963, Q2LOO=0.944). Moreover, Y-scrambling test indicated there was no chance correlation in this model.

Chemometric Modeling of the Ecotoxicity of Industrial Chemicals to an Avian Species Anas Platyrhynchos

2020 ◽  
Vol 5 (2) ◽  
pp. 1-16
Author(s):  
Shinjita Ghosh ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Anas Platyrhynchos ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Avian Species ◽  
Industrial Chemicals ◽  
Linear Discriminant ◽  
Topological Distance ◽  
Qsar Models ◽  
Classification And Regression

Birds or avians have been imperative species in the ecology, having been evaluated in an effort to understand the toxic effects of endocrine disruption. The ecotoxicity of 56 industrial chemicals classified as endocrine disruptors were modeled employing classification and regression-based quantitative structure-activity relationship (QSAR) models to an important avian species, Anas platyrhynchos. The classification- and regression-based QSAR models were developed using linear discriminant analysis (LDA) and partial least squares (PLS) tools, respectively. All models were validated meticulously by employing internal and external validation metrics followed by randomization test, applicability domain (AD) study, and intelligent consensus prediction of all individual models. Features like topological distance of 1, 3, and 5 between atoms O-P, C-P, and N-S, correspondingly, along with the CR3X fragment, can be responsible for an increase in toxicity. On the contrary, the presence of S-Cl with topological distance 6 is accountable for lowering the toxicity of towards A. platyrhynchos. The developed chemometric models can offer significant evidence and guidance in the framework of virtual screening as well as a toxicity prediction of new and/or untested chemical libraries towards this specific avian species.

scholarly journals Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4795
Author(s):  
Ajaykumar Gandhi ◽  
Vijay Masand ◽  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Anis Ben Ghorbal ◽  
...  
Keyword(s):  
De Novo ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Structure Activity ◽  
Tnbc Cell ◽  
Qsar Models ◽  
Anti Tumor Activity

In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.

A QSAR STUDY OF SUBSTITUTED PYRAZOLINE DERIVATIVES AS POTENTIAL ANTI-TUBERCULOSIS AGENTS

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (04) ◽  
pp. 22-31
Author(s):  
M. C Sharma ◽  
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Least Square ◽  
Qsar Study ◽  
Partial Least Square Analysis ◽  
Qsar Models

A quantitative structure–activity relationship (QSAR) of a series of substituted pyrazoline derivatives, in regard to their anti-tuberculosis activity, has been studied using the partial least square (PLS) analysis method. QSAR model development of 64 pyrazoline derivatives was carried out to predict anti-tubercular activity. Partial least square analysis was applied to derive QSAR models, which were further evaluated for statistical significance and predictive power by internal and external validation. The best QSAR model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.7426 and 0.7903, respectively. Two-dimensional QSAR analyses of such pyrazoline derivatives provide important structural insights for designing potent antituberculosis drugs.

QSAR Models to Predict Effect of Concentration on the Adsorption of Phenolic Compounds onto XAD-4 and ZH-01

2011 ◽  
Vol 356-360 ◽  
pp. 340-344
Author(s):  
Yun Lan Gu ◽  
Zhen Xing Li ◽  
Zheng Hao Fei ◽  
Gen Cheng Zhang
Keyword(s):  
Phenolic Compounds ◽  
Adsorption Capacity ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Basis Set ◽  
Stepwise Multiple Regression ◽  
Background Concentration ◽  
Qsar Models ◽  
Predicted Values ◽  
Leave One Out

It is assumed that the experimental adsorption capacity of phenolic compounds onto resin depends upon the molecular properties as well as background concentration of the aquatic system. The utility of this concept has been demonstrated by incorporating concentration as a parameter in quantitative structure-activity relationship (QSAR). DFT-B3LYP method, with the basis set 6-311G **, was employed to calculate quantum mechanical and physicochemical descriptors of phenolic compounds. The logarithm of the adsorption capacity of phenolic compounds on XAD-4 and ZH-01 investigated from the static experiment along with the descriptors mentioned above were used to establish QSAR models. The variables were reduced using stepwise multiple regression method, and the statistical results indicated that the correlation coefficient in the multiple linear regression (MLR) and cross-validation using leave-one-out(LOO) were 0.966, 0.920, 0.905 and 0.797, respectively. To validate the predictive power of resulting models, external validation was performed with Qext2 values of 0.927 and 0.849, respectively. The developed models suggest that the adsorption mechanism of phenolic compounds onto XAD-4 and ZH-01 is different. Concentration, hydrophobic parameter are dominant factors governing the adsorption capacity of phenolic compounds onto XAD-4, while concentration and energy of the highest occupied molecular orbital are dominant factors controlling that of phenolic compounds on ZH-01. The consistency between experimental and predicted values indicates that the developed models can be used for estimating adsorption capacity of phenolic compounds onto XAD-4 and ZH-01.

Multilayer Perceptron Model for Predicting Acute Toxicity of Fungicides on Rats

2018 ◽  
Vol 3 (1) ◽  
pp. 100-118 ◽  
Author(s):  
Mabrouk Hamadache ◽  
Abdeltif Amrane ◽  
Salah Hanini ◽  
Othmane Benkortbi
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Acute Oral Toxicity ◽  
Oral Toxicity ◽  
Structure Activity ◽  
Marquardt Algorithm ◽  
Qsar Models ◽  
Validation Set

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, a QSAR model based on 10 molecular descriptors to predict acute oral toxicity of 91 fungicides to rats was developed and validated. Good results (PRESS/SSY = 0.085 and VIF < 5) were obtained, showing the validation of descriptors in the obtained model. The best results were obtained with a 10/11/1 Artificial Neural Network model trained with the Levenberg-Marquardt algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext which was equal to 0.960. Accordingly, the model developed in this study provided excellent predictions and can be used to predict the acute oral toxicity of fungicides, particularly for those that have not been tested as well as new fungicides.

scholarly journals Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

2020 ◽  
Vol 94 (10) ◽  
pp. 3581-3592
Author(s):  
M. J. Moné ◽  
G. Pallocca ◽  
S. E. Escher ◽  
T. Exner ◽  
M. Herzler ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Kinetic Modelling ◽  
Mitochondrial Respiratory Chain ◽  
Integrated Approach ◽  
Adverse Outcome Pathway ◽  
Next Generation ◽  
Chemical Safety ◽  
Advisory Boards ◽  
The Eu

Abstract In 2016, the European Commission launched the EU-ToxRisk research project to develop and promote animal-free approaches in toxicology. The 36 partners of this consortium used in vitro and in silico methods in the context of case studies (CSs). These CSs included both compounds with a highly defined target (e.g. mitochondrial respiratory chain inhibitors) as well as compounds with poorly defined molecular initiation events (e.g. short-chain branched carboxylic acids). The initial project focus was on developing a science-based strategy for read-across (RAx) as an animal-free approach in chemical risk assessment. Moreover, seamless incorporation of new approach method (NAM) data into this process (= NAM-enhanced RAx) was explored. Here, the EU-ToxRisk consortium has collated its scientific and regulatory learnings from this particular project objective. For all CSs, a mechanistic hypothesis (in the form of an adverse outcome pathway) guided the safety evaluation. ADME data were generated from NAMs and used for comprehensive physiological-based kinetic modelling. Quality assurance and data management were optimized in parallel. Scientific and Regulatory Advisory Boards played a vital role in assessing the practical applicability of the new approaches. In a next step, external stakeholders evaluated the usefulness of NAMs in the context of RAx CSs for regulatory acceptance. For instance, the CSs were included in the OECD CS portfolio for the Integrated Approach to Testing and Assessment project. Feedback from regulators and other stakeholders was collected at several stages. Future chemical safety science projects can draw from this experience to implement systems toxicology-guided, animal-free next-generation risk assessment.

scholarly journals Application of QSAR models in analysis of antibacterial activity of some benzimidazole derivatives against Sarcina lutea

2013 ◽  
Vol 67 (1) ◽  
pp. 27-33
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic ◽  
Lidija Jevric ◽  
Natasa Uzelac
Keyword(s):  
Antibacterial Activity ◽  
Inhibitory Activity ◽  
Antibacterial Activities ◽  
Quantitative Structure Activity Relationship ◽  
Benzimidazole Derivatives ◽  
Log P ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Qsar Models

In the present paper, a quantitative structure activity relationship (QSAR) has been carried out on a series of 2-methyl and 2-aminobenzimidazole derivatives to identify the lipophilicity requirements for their inhibitory activity against bacteria Sarcina lutea. The tested compounds displayed in vitro antibacterial activity and minimum inhibitory concentration (MIC) was determined for all compounds. The partition coefficients of the studied compounds were measured by the shake flask method (log P) and by theoretical calculation (Clog P). The relationships between lipophilicity descriptors and antibacterial activities were investigated and the mathematical models have been developed as a calibration models for predicting the inhibitory activity of this class of compounds. The models were validated by leave-one-out (LOO) technique as well as by the calculation of statistical parameters for the established models. Therefore, QSAR analysis reveals that lipophilicity descriptor govern the inhibitory activity of benzimidazoles studied against Sarcina lutea.

scholarly journals STUDI HUBUNGAN KUANTITATIF STRUKTUR AKTIVITAS SENYAWA TURUNAN MEISOINDIGO SEBAGAI INHIBITOR CDK4

2016 ◽  
Vol 1 (2) ◽  
pp. 129
Author(s):  
Muhammad Arba ◽  
Riki Andriansyah ◽  
Messi Leonita
Keyword(s):  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Heat Of Formation ◽  
Electronic Energy ◽  
Cyclin Dependent Kinase ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Multilinear Regression Analysis ◽  
Qsar Models ◽  
Better Than

ABSTRAKTelah dilakukan analisis Hubungan Kuantitatif Struktur-Aktivitas (HKSA) senyawa turunan meisoindigo sebagai inhibitor Cyclin Dependent Kinase-4 (CDK4) menggunakan regresi multi linear untuk pemilihan variabel. Hasil penelitian menyatakan bahwa aktivitas penghambatan CDK4 dari senyawa turunan mesoindigo bergantung pada beberapa parameter, yaitu momen dipol, energi total, energi elektronik, panas pembentukan, dan kelarutan. Akurasi model HKSA yang diusulkan divalidasi baik dengan teknik validasi silang maupun dengan validasi eksternal. Hasil penelitian ini dapat digunakan untuk desain senyawa inhibitor CDK4 yang lebih baik dari turunan meisoindigo. Kata kunci: HKSA, meisoindigo, kanker, CDK4 ABSTRACTCyclin-dependent kinase 4 (CDK4) is an important target in the treatment of cancer. Exploring of compounds that can inhibit the activity of CDK4 is actively performed worldwide. This research was conducted to do Quantitative Structure-Activity Relationship (QSAR) analysis of meisoindigo derivative compounds as inhibitor for CDK4 in order to get QSAR equation, then it was further used to design new inhibitor based meisoindigo which has more potent and selective for CDK4. Data compound is divided into training set to build QSAR models and the test set to validate the model. Calculation was done by MOE2009.10 descriptor and multilinear regression analysis, SPSS19.0. The results showed that the inhibitory activity of mesoindigo derived compounds toward CDK4 was depended on several dipole moment, total energy, electronic energy, heat of formation, and solubility. The accuracy of QSAR models proposed validated by cross validation techniques and with external validation. The results of this study can be used to design a new CDK4 inhibitor compound better than meisoindigo derivative Keywords: QSAR, meisoindigo, cancer, CDK4

scholarly journals Dibenzoylhydrazines as Insect Growth Modulators: Topology-Based QSAR Modelling

2020 ◽  
Vol 12 (1) ◽  
pp. 28-40
Author(s):  
J.P. Doucet ◽  
A. Doucet-Panaye
Keyword(s):  
External Validation ◽  
Projection Pursuit ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Learning Approaches ◽  
Least Squares Regression ◽  
Insect Growth Regulators ◽  
Multilinear Regression ◽  
Insect Growth ◽  
Structural Variables

Dibenzoylhydrazines Xa-(C6H5)a-CO-N-(t-Bu)-NH-CO-(C6H5)b-Yb are efficient insect growth regulators with high activity and selectivity toward lepidopteran and coleopteran pests. For 123 congeneric molecules, a quantitative structure activity relationship model was built in the framework of the QSARINS package using 2D, Topology-based, PaDEL descriptors. Variable selection by GA-MLR allows building an efficient multilinear regression linking pEC50 values to nine structural variables. Robustness and quality of the model were carefully examined at various levels: data-fitting (recall), leave-one (or some) - out, internal and external validation (including random splitting), points not in depth investigated in previous works. Various Machine Learning approaches (Partial Least Squares Regression, Projection Pursuit Regression, Linear Support Vector Machine or Three Layer Perceptron Artificial Neural Network) confirm the validity of the analysis, giving highly consistent results of comparable quality, with only a slight advantage for the three-layer perceptron.

Sign in / Sign up

Export Citation Format

Share Document