2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma

Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

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.

QSAR models reveal new EPAC-selective allosteric modulators

Exchange proteins directly activated by cAMP (EPAC) are guanine nucleotide exchange factors for the small GTPases, Rap1 and Rap2. They regulate several physiological functions and mitigation of their activity has been suggested as a possible treatment for multiple diseases such as cardiomyopathy, diabetes, chronic pain, and cancer. Several EPAC-specific modulators have been developed, however studies that quantify their structure–activity relationships are still lacking. Here we propose a quantitative structure–activity relationship (QSAR) model for a series of EPAC-specific compounds. The model demonstrated high reproducibility and predictivity and the predictive ability of the model was tested against a series of compounds that were unknown to the model. The compound with the highest predicted affinity was validated experimentally through fluorescence-based competition assays and NMR experiments revealed its mode of binding and mechanism of action as a partial agonist. The proposed QSAR model can, therefore, serve as an effective screening tool to identify promising EPAC-selective drug leads with enhanced potency.

QSAR models of EPAC-specific allosteric ligands predict the affinity of a promising analogue.

Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds

Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (K_PE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the K_PE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (K_mic-w) and the LDPE-micelle partition coefficient (K_PE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the K_PE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental K_PE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.

Effective Search of Triterpenes with Anti-HSV-1 Activity Using a Classification Model by Logistic Regression

Natural products are an excellent source of skeletons for medicinal seeds. Triterpenes and saponins are representative natural products that exhibit anti-herpes simplex virus type 1 (HSV-1) activity. However, there has been a lack of comprehensive information on the anti-HSV-1 activity of triterpenes. Therefore, expanding information on the anti-HSV-1 activity of triterpenes and improving the efficiency of their exploration are urgently required. To improve the efficiency of the development of anti-HSV-1 active compounds, we constructed a predictive model for the anti-HSV-1 activity of triterpenes by using the information obtained from previous studies using machine learning methods. In this study, we constructed a binary classification model (i.e., active or inactive) using a logistic regression algorithm. As a result of the evaluation of predictive model, the accuracy for the test data is 0.79, and the area under the curve (AUC) is 0.86. Additionally, to enrich the information on the anti-HSV-1 activity of triterpenes, a plaque reduction assay was performed on 20 triterpenes. As a result, chikusetsusaponin IVa (11: IC₅₀ = 13.06 μM) was found to have potent anti-HSV-1 with three potentially anti-HSV-1 active triterpenes. The assay result was further used for external validation of predictive model. The prediction of the test compounds in the activity test showed a high accuracy (0.83) and AUC (0.81). We also found that this predictive model was found to be able to successfully narrow down the active compounds. This study provides more information on the anti-HSV-1 activity of triterpenes. Moreover, the predictive model can improve the efficiency of the development of active triterpenes by integrating many previous studies to clarify potential relationships.

Computational study on subfamilies of piperidine derivatives: QSAR modelling, model external verification, the inter-subset similarity determination, and structure-based drug designing

A new subset of furan-pyrazole piperidine derivatives was used for QSAR model development. These compounds exhibit good Akt1 inhibitory activity; moreover, antiproliferative activities in vitro against OVCAR-8 (Human ovarian carcinoma cells) and HCT116 (human colon cancer cells), were confirmed for them. Based on the relevant three-dimensional (3D) and 2D autocorrelation descriptors, selected by genetic algorithm (GA), multiple linear regression (MLR) was established on half maximal-inhibitory concentration (IC₅₀), in Akt1 and cancer cell lines independently. Robustness, stability, and predictive ability of the models were evaluated using external and internal validation (r²: 0.742-0.832, Q²_LOO: 0.684-0.796, RMSE: 0.247-0.299, F: 32.283-57.578, and r²_y-random: 0.049-0.080). Furthermore, in the new strategy, each of the evaluated models was generalized to two other subfamilies of piperidines to simultaneously compare the activities and structural similarity of these three subsets. Probably, structural similarity can be more considered as a criterion of similarity in the mechanism of action. Also, external verification of suggested predictive models was performed by another subset. Finally, by focusing on M64 as the most potent in vivo antitumor compound, 15 new derivatives were designed and six potent candidates were proposed for further investigation.

Antiprotozoal QSAR modelling for trypanosomiasis (Chagas disease) based on thiosemicarbazone and thiazole derivatives

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, remains a neglected endemic infection that affects around 8 million people worldwide and causes 12,000 premature deaths per year. Traditional chemotherapy is limited to the nitro-antiparasitic drugs Benznidazole and Nifurtimox, which present serious side effects and low long-term efficacy. Several research efforts have been made over the last decade to find new chemical structures with better effectiveness and tolerance than standard anti-Chagas drugs. Among these, new sets of thiosemicarbazone and thiazole derivatives have exhibited potent in vitro activity against T. cruzi, especially for its extracellular forms (epimastigote and trypomastigote). In this work, we have developed three antiprotozoal quantitative structure-relationship (QSAR) models for Chagas disease based on the in vitro activity data reported as IC₅₀ (μM) and CC₅₀ (μM) over the last decade, particularly by Lima-Leite's group in Brazil. The models were developed using the replacement method (RM), a technique based on Multivariable Linear Regression (MLR), and external and internal validation methodologies, like the use of a test set, Leave-one-Out (LOO) cross-validation and Y-Randomization. Two of these QSAR models were developed for trypomastigotes form of the parasite Trypanosoma cruzi, one based on IC₅₀ and the other on CC₅₀ data; while the third QSAR model was developed for its epimastigotes form based on CC₅₀ activity. Our models presented sound statistical parameters that endorses their prediction capability. Such capability was tested for a set of 13 hitherto-unknown structurally related aromatic cyclohexanone derivatives.

Occurrence and Distribution of Photoinitiator Additives in Paired Maternal and Cord Plasma in a South China Population

Photoinitiators (PIs) are widely used in industrial polymerization and have been detected as emerging contaminants in environmental matrixes. It has been reported that humans are exposed to PIs, but the maternal-fetal transmission of PIs has not been documented. In this study, we analyzed 21 PIs (9 benzophenones, BZPs; 8 amine co-initiators, ACIs; and 4 thioxanthones, TXs) in matched maternal-cord plasma samples from 49 pregnant women in South China. Sixteen of the 21 target PIs were found in maternal plasma at concentrations of ∑PIs (sum of the detected PIs) from 303 to 3500 pg/mL. Meanwhile, 12 PIs were detected in cord plasma with ∑PIs from 104 to 988 pg/mL. The PIs detected in both maternal and cord plasma samples were dominated by BZPs, followed by ACIs and TXs. Different groups of PIs showed structure-dependent placental transfer efficiencies (PTEs). The PTEs were generally less than 100% for BZPs but greater than 100% for ACIs and TXs. By further theoretical calculation, we revealed the critical structural features of PIs that affect PTEs. This is the first study to investigate the occurrence and distribution of PIs in paired maternal and cord plasma, and it sheds light on the potential mechanism of structure-dependent placental transfer.

Insight into structural features of phenyltetrazole derivatives as ABCG2 inhibitors for the treatment of multidrug resistance in cancer

ABCG2 is the principal ABC transporter involved in the multidrug resistance of breast cancer. Looking at the current demand in the development of ABCG2 inhibitors for the treatment of multidrug-resistant cancer, we have explored structural requirements of phenyltetrazole derivatives for ABCG2 inhibition by combining classical QSAR, Bayesian classification modelling and molecular docking studies. For classical QSAR, structural descriptors were calculated from the free software tool PaDEL-descriptor. Stepwise multiple linear regression (SMLR) was used for model generation. A statistically significant model was generated and validated with different parameters (For training set: r = 0.825; Q² = 0.570 and for test set: r = 0.894, r²_pred = 0.783). The predicted model was found to satisfy the Golbraikh and Trospha criteria for model acceptability. Bayesian classification modelling was also performed (ROC scores were 0.722 and 0.767 for the training and test sets, respectively). Finally, the binding interactions of phenyltetrazole type inhibitor with the ABCG2 receptor were mapped with the help of molecular docking study. The result of the docking analysis is aligned with the classical QSAR and Bayesian classification studies. The combined modelling study will guide the medicinal chemists to act faster in the drug discovery of ABCG2 inhibitors for the management of resistant breast cancer.

Implementation of a dynamic intestinal gut-on-a-chip barrier model for transport studies of lipophilic dioxin congeners

Novel microfluidic technologies allow the manufacture of in vitro organ-on-a-chip systems that hold great promise to adequately recapitulate the biophysical and functional complexity of organs found in vivo. In this study, a gut-on-a-chip model was developed aiming to study the potential cellular association and transport of food contaminants. Intestinal epithelial cells (Caco-2) were cultured on a porous polyester membrane that was tightly clamped between two glass slides to form two separate flow chambers. Glass syringes, polytetrafluoroethylene tubing and glass microfluidic chips were selected to minimize surface adsorption of the studied compounds (i.e. highly lipophilic dioxins), during the transport studies. Confocal microscopy studies revealed that, upon culturing under constant flow for 7 days, Caco-2 cells formed complete and polarized monolayers as observed after culturing for 21 days under static conditions in Transwells. We exposed Caco-2 monolayers in the chip and Transwell to a mixture of 17 dioxin congeners (7 polychlorinated dibenzo-p-dioxins and 10 polychlorinated dibenzofurans) for 24 h. Gas chromatography-high resolution mass spectrometry was used to assess the cellular association and transport of individual dioxin congeners across the Caco-2 cell monolayers. After 24 h, the amount of transported dioxin mixture was similar in both the dynamic gut-on-a-chip model and the static Transwell model. The transport of individual congeners corresponded with their number of chlorine atoms and substitution patterns as revealed by quantitative structure–property relationship modelling. These results show that the gut-on-a-chip model can be used, as well as the traditional static Transwell system, to study the cellular association and transport of lipophilic compounds like dioxins.

Novel microfluidic technologies allow the manufacture of in vitro organ-on-a-chip systems that hold great promise to adequately recapitulate the biophysical and functional complexity of organs found in vivo.

In silico evaluation, molecular docking and QSAR analysis of quinazoline-based EGFR-T790M inhibitors

Mutated epidermal growth factor receptor (EGFR-T790M) inhibitors hold promise as new agents against cancer. Molecular docking and QSAR analysis were performed based on a series of fifty-three quinazoline derivatives to elucidate key structural and physicochemical properties affecting inhibitory activity. Molecular docking analysis identified the true conformations of ligands in the receptor's active pocket. The structural features of the ligands, expressed as molecular descriptors, were derived from the obtained docked conformations. Non-linear and spline QSAR models were developed through novel genetic algorithm and artificial neural network (GA-ANN) and multivariate adaptive regression spline techniques, respectively. The former technique was employed to consider non-linear relation between molecular descriptors and inhibitory activity of quinazoline derivatives. The later technique was also used to describe the non-linearity using basis functions and sub-region equations for each descriptor. Our QSAR model gave a high predictive performance [Formula: see text] and [Formula: see text]) using diverse validation techniques. Eight new compounds were designed using our QSAR model as potent EGFR-T790M inhibitors. Overall, the proposed in silico strategy based on docked derived descriptor and non-linear descriptor subset selection may help design novel quinazoline derivatives with improved EGFR-T790M inhibitory activity.

Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations

Fullerene and its derivatives have potential antiviral activity due to their specific binding interactions with biological molecules. In this study fullerene derivatives were investigated by the synergic combination of three approaches: quantum-mechanical calculations, protein-ligand docking and quantitative structure-activity relationship methods. The protein-ligand docking studies and improved structure-activity models have been able both to predict binding affinities for the set of fullerene-C60 derivatives and to help in finding mechanisms of fullerene derivative interactions with human immunodeficiency virus type 1 aspartic protease, HIV-1 PR. Protein-ligand docking revealed several important molecular fragments that are responsible for the interaction with HIV-1 PR. In addition, a density functional theory method has been utilized to identify the optimal geometries and predict physico-chemical parameters of the studied compounds. The 5-variable GA-MLRA based model showed the best predictive ability (r(2)training = 0.882 and r(2)test = 0.738), with high internal and external correlation coefficients.

Molecular Descriptors in Modelling the Tumour Necrosis Factor-α Converting Enzyme Inhibition Activity of Novel Tartrate-Based Analogues

The tumour necrosis factor-α converting enzyme inhibition activity of a series comprising of novel tartrate-based analogues has been quantitatively analysed in terms of molecular descriptors. The statistically validated quantitative structure-activity relationship models provided rationales to explain the inhibition activity of these congeners. The descriptors identified through combinatorial protocol in multiple linear regression analysis have highlighted the role of Moran autocorrelation of lag 7, weighted by atomic van der Waals volume, presence of both prime and nonprime amide carbonyl oxygen in the tartrate moiety and occurrence of five membered ring bearing substituents at varying sites. A few potential novel tartrate-based analogues have been suggested for further investigation.

Retrospective group fusion similarity search based on eROCE evaluation metric

In this study, a simple evaluation metric, denoted as eROCE was proposed to measure the early enrichment of predictive methods. We demonstrated the superior robustness of eROCE compared to other known metrics throughout several active to inactive ratios ranging from 1:10 to 1:1000. Group fusion similarity search was investigated by varying 16 similarity coefficients, five molecular representations (binary and non-binary) and two group fusion rules using two reference structure set sizes. We used a dataset of 3478 actives and 43,938 inactive molecules and the enrichment was analyzed by means of eROCE. This retrospective study provides optimal similarity search parameters in the case of ALDH1A1 inhibitors.

A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest

Experimental pEC₅₀s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development.

A quantitative structure-activity relationship study on serotonin 5-HT6) receptor ligands: indolyl and piperidinyl sulphonamides

The serotonin 5-HT(6) binding affinity of indolyl- and piperidinyl-sulphonamide derivatives has been analysed with topological and molecular features with DRAGON software. Analysis of the structural features in conjunction with the biological endpoints in combinatorial protocol in multiple linear regression (CP-MLR) led to the identification of 25 descriptors for modelling the activity. The study clearly suggested the role of an average Randic-type eigenvector-based index from adjacency matrix, VRA2, number of secondary aliphatic amines, nNHR, the sum of the topological distance between N and O, T(N...O), ring tertiary carbon atoms, nCrHR, and CH2RX type fragment, C-006, in a molecular structure to optimize the 5-HT(6) binding affinities of titled compounds. The PLS analysis confirmed the dominance of information content of CP-MLR identified descriptors for modelling the activity when compared with those of leftover ones.

A QCAR model for predicting antioxidant activity of wild mushrooms

Wild mushrooms have been described as sources of natural antioxidants, particularly phenolic compounds. However, many other compounds present in wild mushrooms can also act as antioxidants (reducers), so whole extracts from a wide range of species need to be examined. To gain further knowledge in this area, the relationship between the antioxidant potential (scavenging effect and reducing power) and chemical composition of twenty three samples from seventeen Portuguese wild mushroom species was investigated. A wide range of analytical parameters reported by our research group (including ash, carbohydrates, proteins, fat, monounsaturated fatty acids, polyunsaturated fatty acids, saturated fatty acids, phenolics, flavonoids, ascorbic acid and beta-carotene) were studied and the data were analysed by partial least squares (PLS) regression analysis to allow correlation of all the parameters. Antioxidant activity correlated well with phenolic and flavonoid contents. A QCAR (Quantitative Composition-Activity Relationships) model was constructed, using the PLS method, and its robustness and predictability were verified by internal and external cross-validation methods. Finally, this model proved to be a useful tool in the prediction of mushrooms' reducing power.

Data mining the NCI60 to predict generalized cytotoxicity

Elimination of cytotoxic compounds in the early and later stages of drug discovery can help reduce the costs of research and development. Through the application of principal components analysis (PCA), we were able to data mine and prove that approximately 89% of the total log GI 50 variance is due to the nonspecific cytotoxic nature of substances. Furthermore, PCA led to the identification of groups of structurally unrelated substances showing very specific toxicity profiles, such as a set of 45 substances toxic only to the Leukemia_SR cancer cell line. In an effort to predict nonspecific cytotoxicity on the basis of the mean log GI 50, we created a decision tree using MACCS keys that can correctly classify over 83% of the substances as cytotoxic/noncytotoxic in silico, on the basis of the cutoff of mean log GI 50 = -5.0. Finally, we have established a linear model using least-squares in which nine of the 59 available NCI60 cancer cell lines can be used to predict the mean log GI 50. The model has R (2) = 0.99 and a root-mean-square deviation between the observed and calculated mean log GI 50 (RMSE) = 0.09. Our predictive models can be applied to flag generally cytotoxic molecules in virtual and real chemical libraries, thus saving time and effort.

Topological research on diamagnetic susceptibilities of organic compounds

A novel molecular connectivity index, (m)chi('), based on the adjacency matrix of molecular graphs and novel atomic valence connectivities, delta(i)(') for predicting the molar diamagnetic susceptibilities of organic compounds is proposed. The delta(i)(') is defined as: delta(i)(') = delta(i)(nu) x Ei=12:625, where delta(i)(nu) and E(i) are the atomic valence connectivity and the valence orbital energy of atom i, respectively. A good QSPR model for molar diamagnetic susceptibilities can be constructed from (0)chi('), (1)chi('), (2)chi(') and (4)chi(p)(') using multivariate linear regression (MLR). The correlation coefficient r, standard error, and average absolute deviation of the MLR model are 0.9918, 5.56 cgs, and 4.26 cgs, respectively, for the 721 organic compounds tested (training set). Cross-validation using the leave-one-out method demonstrates that the MLR model is highly reliable statistically. Using the MLR model, the average absolute deviations of the predicted values of molar diamagnetic susceptibility of another 360 organic compounds (test set) is 4.34 cgs. The results show that the current method is more effective than literature methods for estimating the molar diamagnetic susceptibility of an organic compound. The MLR method thus provides an acceptable model for the prediction of molar diamagnetic susceptibilities of organic compounds.