Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool "PaDEL-Descriptor"

Probing the bioconcentration and metabolism disruption of bisphenol A and its analogues in adult female zebrafish from integrated AutoQSAR and metabolomics studies

Plenty of emerging bisphenol A (BPA) substitutes rise to wait for assessment of bioconcentration and metabolism disruption. Computational methods are useful to fill the data gap in chemical risk assessment, such as automated quantitative structure-activity relationship (AutoQSAR). It is not clear how AutoQSAR performs in predicting the bioconcentration factor (BCF) in adult zebrafish. Herein, AutoQSAR was used to predict the logBCFs of BPA, bisphenol AF (BPAF), bisphenol B, bisphenol F and bisphenol S (BPS). For the test set, a linear relationship was shown between the observed and predicted logBCFs with a slope of 0.97. The predicted logBCFs of these five bisphenols were quite close to their experimental data with a slope of 0.94, suggesting better performance than directed message passing neural networks and EPI Suite with a slope of 0.69 and 0.61, respectively. Thus, AutoQSAR is powerful in modeling logBCFs in fish with minimal time and expertise. To link bioconcentration with metabolic effects, female zebrafish were exposed to BPA, BPAF and BPS for metabolomics analysis. BPA caused a significant disturbance in amino acid metabolism, while BPAF and BPS significantly altered another three metabolic pathways, showing chemical-specific responses. BPAF with the highest logBCF elicited the strongest metabolomic responses reflected by the metabolic effect level index, followed by BPA and BPS. Thus, BPAF and BPS elicited higher or similar metabolism disruption compared with BPA in female zebrafish, respectively, reflecting consequences of bioconcentration.

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

Bile acids are amphiphilic substances produced naturally in humans. In the context of drug delivery and dosage form design, it is critical to understand whether a drug interacts with bile inside the gastrointestinal (GI) tract or not. This study focuses on the identification of structural fingerprints/features important for bile interaction. Molecular modelling methods such as Bayesian classification and recursive partitioning (RP) studies are executed to find important fingerprints/features for the bile interaction. For the Bayesian classification study, the ROC score of 0.837 and 0.950 are found for the training set and the test set compounds, respectively. The fluorine-containing aliphatic/aromatic group, the branched chain of the alkyl group containing hydroxyl moiety and the phenothiazine ring etc. are identified as good fingerprints having a positive contribution towards bile interactions, whereas, the bad fingerprints such as free carboxylate group, purine, and pyrimidine ring etc. have a negative contribution towards bile interactions. The best tree (tree ID: 1) from the RP study classifies the bile interacting or non-interacting compounds with a ROC score of 0.941 for the training and 0.875 for the test set. Additionally, SARpy and QSAR-Co analyses are also been performed to classify compounds as bile interacting/non-interacting. Moreover, forty-six recently FDA-approved drugs have been screened by the developed SARpy and QSAR-Co models to assess their bile interaction properties. Overall, this attempt may facilitate the researchers to identify bile interacting/non-interacting molecules in a faster way and help in the design of formulations and target-specific drug development.

QSAR study of tetrahydropteridin derivatives as polo-like kinase 1(PLK1) Inhibitors with molecular docking and dynamics study

PLK1 is the key target for dealing with different cancer because it plays an important role in cell proliferation. According to the regulation of OECD, a QSAR model was developed from a dataset of 68 tetrahydropteridin derivatives. Three descriptors (maxHaaCH, ATSC7i, AATS7m) were considered for the development of the QSAR model. The reliability and predictability of the developed QSAR model were evaluated by various statistical parameters (r² = 0.8213, r²_ext = 0.8771 and CCC_ext = 0.9364). The maxHaaCH descriptor is positively correlated to pIC₅₀ whereas, the ATSC7i and AATS7m are negatively correlated with pIC₅₀. The QSAR model explains all the structural features and shows a good correlation with the activity. Based on molecular modelling techniques, five compounds (D1-D5) were designed. Molecular docking and dynamics studies of the most active compound were performed with PDB ID: 2RKU. The results of the present investigation may be employed to identify and develop effective inhibitors for the treatment of PLK1-related pathophysiological disorders.

Multiple machine learning methods aided virtual screening of NaV 1.5 inhibitors

Na_v 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na_v 1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na_v 1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na_v 1.5 ion channel and key privileged substructures with high affinity were also extracted.

Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors

Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; therefore, it is better to estimate BCF early in the chemical development process. The current research aims to evaluate the ecotoxicity potential of 122 pharmaceuticals and identify possible important structural attributes using BCF as the determining feature against a group of fish species. We have calculated the theoretical 2D descriptors from the OCHEM platform and SiRMS descriptor calculating software. The regression-based quantitative structure-property relationship (QSPR) modeling was used to identify the chemical features responsible for acute fish bioconcentration. Multiple models with the "intelligent consensus" algorithm were employed for the regression-based approach improving the predictive ability of the models. To ensure the robustness and interpretability of the developed models, rigorous validation was performed employing various statistical internal and external validation metrics. From the developed models, it can be specified that the presence of large lipophilic and electronegative moieties greatly enhances the bioaccumulative potential of pharmaceuticals, whereas the hydrophilic characteristics have shown a negative impact on BCF. Furthermore, the developed models were employed to screen the DrugBank database (https://go.drugbank.com/) for assessing the BCF properties of the entire database. The evidence acquired from the modeled descriptors might be used for aquatic risk assessment in the future, with the added benefit of providing an early caution of their probable negative impact on aquatic ecosystems for regulatory purposes.

Predicting the Bioconcentration Factor in Fish from Molecular Structures

The bioconcentration factor (BCF) is one of the metrics used to evaluate the potential of a substance to bioaccumulate into aquatic organisms. In this work, linear and non-linear regression QSARs were developed for the prediction of log BCF using different computational approaches, and starting from a large and structurally heterogeneous dataset. The new MLR-OLS and ANN regression models have good fitting with R2 values of 0.62 and 0.70, respectively, and comparable external predictivity with R2ext 0.64 and 0.65 (RMSEext of 0.78 and 0.76), respectively. Furthermore, linear and non-linear classification models were developed using the regulatory threshold BCF >2000. A class balanced subset was used to develop classification models which were applied to chemicals not used to create the QSARs. These classification models are characterized by external and internal accuracy up to 84% and 90%, respectively, and sensitivity and specificity up to 90% and 80%, respectively. QSARs presented in this work are validated according to regulatory requirements and their quality is in line with other tools available for the same endpoint and dataset, with the advantage of low complexity and easy application through the software QSAR-ME Profiler. These QSARs can be used as alternatives for, or in combination with, existing models to support bioaccumulation assessment procedures.

QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices

Octanol-water partition coefficient (logK_ow) and soil organic carbon content normalized sorption coefficient (logK_oc) values are two important physicochemical properties in the context of bioaccumulation and environmental fate of organic compounds and their environmental risk assessment. Simple, interpretable and easy-to-derive extended topochemical atom (ETA) indices obtained from 2D structural representation of compounds were used for quantitative structure-property relationship (QSPR) modeling of these two endpoints. Linear regression based models developed using only ETA indices show encouraging statistical and validation results. Based on the information obtained from developed QSPR models, we may conclude that molecular volume, branching pattern, presence of hydrophobic Cl atoms, cyclicity/fusion, polar environment, electron density, unsaturation content, hydrogen bonding propensity or hydrogen bond donor atoms, local topology, presence of heteroatoms and aromaticity are crucial factors in controlling the logK_ow and logK_oc values of the compounds. The suggested explanatory features for different classes of chemicals or the whole diverse set can help in safer designing of chemicals, which is one of the primary agenda of the "Green Chemistry" program.

QSAR modelling study of the bioconcentration factor and toxicity of organic compounds to aquatic organisms using machine learning and ensemble methods

Bioconcentration factors and median lethal concentrations (LC₅₀s) are important when assessing risks posed by organic pollutants to aquatic ecosystems. Various quantitative structure-activity relationship models have been developed to predict bioconcentration factors and classify acute toxicity. In the study, we developed a regression model using Recursive Feature Elimination (RFE) method combined with the Support Vector Machine (SVM) algorithm. We calculated 2D molecular descriptors from a dataset containing 450 diverse chemicals in our regression model. Then we built three ensemble models using three machine learning algorithms and calculated 12 molecular fingerprints from a dataset containing 400 diverse chemicals in our classification models. In the regression model, the R² and R_pred² for the regression model were 0.860 and 0.757, respectively. Other parameters indicated that the regression model made good predictions and could efficiently predict a new set of compounds following standards set by Golbraikh, Tropsha, and Roy. In the classification models, the ensemble-SVM classification model gave an overall accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 92.2, 95.1, 86.0, and 0.965, respectively, in a five-fold cross-validation and of 87.3, 92.6, 76.0, and 0.940, respectively, in an external validation. These parameters indicated that our ensemble-SVM model was more stable and gave more accurate predictions than previous models. The model could therefore be used to effectively predict aquatic toxicity and assess risks posed to aquatic ecosystems. We identified several structures most relevant to acute aquatic toxicity through predictions made by the two types of models, and this information may be important to aquatic toxicology experiments and aquatic system management.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.