CORAL: Building up QSAR models for the chromosome aberration test

Semi-Correlations for Building Up a Simulation of Eye Irritation

The OECD recognizes that data on a compound's ability to treat eye irritation are essential for the assessment of new compounds on the market. In silico models are frequently used to provide information when experimental data are lacking. Semi-correlations, as they are called, can be useful to build up categorical models for eye irritation. Semi-correlations are latent regressions that can be used when the endpoint is expressed by two values: 1 for an active molecule and 0 for an inactive molecule. The regression line is based on the descriptor values which serve to distribute the data into four classes: true positive, true negative, false positive, and false negative. These values are applied to calculate the corresponding statistical criterion for assessing the predictive potential of the categorical model. In our model, the descriptor is the sum of what are termed correlation weights. These are defined by optimization using the Monte Carlo method. The target function of the optimization is related to the determination coefficient and the mean absolute error for the training set. Our model gives results that are better than those previously reported for the same endpoint.

HDAC6 detector: online application for evaluating compounds as potential histone deacetylase 6 inhibitors

The HDAC6 (histone deacetylase 6) enzyme plays a key role in many biological processes, including cell division, apoptosis, and immune response. To date, HDAC6 inhibitors are being developed as effective drugs for the treatment of various diseases. In this work, adequate QSAR models of HDAC6 inhibitors are proposed. They are integrated into the developed application HDAC6 Detector, which is freely available at https://ovttiras-hdac6-detector-hdac6-detector-app-yzh8y5.streamlit.app/. The web application HDAC6 Detector can be used to perform virtual screening of HDAC6 inhibitors by dividing the compounds into active and inactive ones relative to the reference vorinostat compound (IC50 = 10.4 nM). The web application implements a structural interpretation of the developed QSAR models. In addition, the application can evaluate the compliance of a compound with Lipinski's rule. The developed models are used for virtual screening of a series of 12 new hydroxamic acids, namely, the derivatives of 3-hydroxyquinazoline-4(3H)-ones and 2-aryl-2,3-dihydroquinazoline-4(1H)-ones. In vitro evaluation of the inhibitory activity of this series of compounds against HDAC6 allowed us to confirm the results of virtual screening and to select promising compounds V-6 and V-11, the IC50 of which is 0.99 and 0.81 nM, respectively.

MicotoXilico: An Interactive Database to Predict Mutagenicity, Genotoxicity, and Carcinogenicity of Mycotoxins

Mycotoxins are secondary metabolites produced by certain filamentous fungi. They are common contaminants found in a wide variety of food matrices, thus representing a threat to public health, as they can be carcinogenic, mutagenic, or teratogenic, among other toxic effects. Several hundreds of mycotoxins have been reported, but only a few of them are regulated, due to the lack of data regarding their toxicity and mechanisms of action. Thus, a more comprehensive evaluation of the toxicity of mycotoxins found in foodstuffs is required. In silico toxicology approaches, such as Quantitative Structure-Activity Relationship (QSAR) models, can be used to rapidly assess chemical hazards by predicting different toxicological endpoints. In this work, for the first time, a comprehensive database containing 4360 mycotoxins classified in 170 categories was constructed. Then, specific robust QSAR models for the prediction of mutagenicity, genotoxicity, and carcinogenicity were generated, showing good accuracy, precision, sensitivity, and specificity. It must be highlighted that the developed QSAR models are compliant with the OECD regulatory criteria, and they can be used for regulatory purposes. Finally, all data were integrated into a web server that allows the exploration of the mycotoxin database and toxicity prediction. In conclusion, the developed tool is a valuable resource for scientists, industry, and regulatory agencies to screen the mutagenicity, genotoxicity, and carcinogenicity of non-regulated mycotoxins.

The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity

Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the active (1)-inactive (0) format. The so-called system of self-consistent models has been suggested as an approach for two tasks: (i) building up a model and (ii) estimating its predictive potential. However, this approach has been tested so far for regression models. Here, the approach is applied to building up and estimating a categorical hepatotoxicity model using the CORAL software. This new process yields good results: sensitivity = 0.77, specificity = 0.75, accuracy = 0.76, and Matthew correlation coefficient = 0.51 (all compounds) and sensitivity = 0.83, specificity = 0.81, accuracy = 0.83 and Matthew correlation coefficient = 0.63 (validation set).

Safety evaluation of kaempferol glycosides-rich standardized roasted goji berry leaf extract

Goji berry leaf (GL) has been used for medicinal foods for its pharmacological effects, including anti-oxidative and anti-obesity activities. Nevertheless, toxicological information on GL is limited for developing health functional ingredient. The aim of the research was to evaluate the single dose acute, 14-day repeated oral toxicity, and genotoxicity of standardized roasted GL extract (rGL) rich in kaempferol-3-O-sophoroside-7-O-glucoside. Tested rGL was found to be stable as kaempferol-3-O-sophoroside-7-O-glucoside, showing 0.7-2.1% of analytical standard variance. According to the single dose toxicity for 14 days, the lethal dose of rGL was determined to be ≥ 2000 mg/kg. Repeated doses of 0-1000 mg/kg of rGL per day for 14 days did not show any toxicity signs or gross pathological abnormalities. No genotoxic signs for the rGL treatment appeared via bacterial reverse mutation up to 5000 μg/plate. There was no significant increase in chromosomal aberration of rGL irrespective of metabolic activation by using CHO-K1 cells (p > 0.05). Regarding carcinogenic toxicity, chromosomal aberrations were not induced at 2000 mg of rGL/kg by using the in vivo bone marrow micronucleus test (p > 0.05). Results from the current study suggest that rGL could be used as a functional ingredient to provide various effects with safety assurance.

Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants?

The toxicological profile of any chemical is defined by multiple endpoints and testing procedures, including representative test species from different trophic levels. While computer-aided methods play an increasingly important role in supporting ecotoxicology research and chemical hazard assessment, most of the recently developed machine learning models are directed towards a single, specific endpoint. To overcome this limitation and accelerate the process of identifying potentially hazardous environmental pollutants, we are introducing an effective approach for quantitative, multi-species modeling. The proposed approach is based on canonical correlation analysis that finds a pair(s) of uncorrelated, linear combinations of the original variables that best defines the overall variability within and between multiple biological responses and predictor variables. Its effectiveness was confirmed by the machine learning model for estimating acute toxicity of diverse organic pollutants in aquatic species from three trophic levels: algae (Pseudokirchneriella subcapitata), daphnia (Daphnia magna), and fish (Oryzias latipes). The multi-species model achieved a favorable predictive performance that were in line with predictive models derived for the aquatic organisms individually. The chemical bioavailability and reactivity parameters (n-octanol/water partition coefficient, chemical potential, and molecular size and volume) were important to accurately predict acute ecotoxicity to the three aquatic organisms. To facilitate the use of this approach, an open-source, Python-based script, named qMTM (quantitative Multi-species Toxicity Modeling) has been provided.

Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R², Q², and IIC for the validation set of splits 1 to 3 are in the range of 0.7180-0.7755, 0.6891-0.7561, and 0.4431-0.8611 respectively. The numerical result of [Formula: see text] > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC₅₀ are recognized and used for the mechanistic interpretation of structural attributes.

A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians

Amphibian populations are undergoing a global decline worldwide. Such decline has been attributed to their unique physiology, ecology, and exposure to multiple stressors including chemicals, temperature, and biological hazards such as fungi of the Batrachochytrium genus, viruses such as Ranavirus, and habitat reduction. There are limited toxicity data for chemicals available for amphibians and few quantitative structure-activity relationship (QSAR) models have been developed and are publicly available. Such QSARs provide important tools to assess the toxicity of chemicals particularly in a data poor context. QSARs provide important tools to assess the toxicity of chemicals particularly when no toxicological data are available. This manuscript provides a description and validation of a regression-based QSAR model to predict, in a quantitative manner, acute lethal toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica). QSAR models for acute median lethal molar concentrations (LC50-12 h) of waterborne chemicals using the Monte Carlo method were developed. The statistical characteristics of the QSARs were described as average values obtained from five random distributions into training and validation sets. Predictions from the model gave satisfactory results for the overall training set (R² = 0.72 and RMSE = 0.33) and were even more robust for the validation set (R² = 0.96 and RMSE = 0.11). Further development of QSAR models in amphibians, particularly for other life stages and species, are discussed.

In Silico Methods for Chromosome Damage

Due to the link with serious adverse health effects, genotoxicity is an important toxicological endpoint in each regulatory setting with respect to human health, including for pharmaceuticals. To this extent, a compound potential to induce gene mutations as well as chromosome damage needs to be addressed. For chromosome damage, i.e., the induction of structural or numerical chromosome aberrations, several in vitro and in vivo test methods are available. In order to rapidly collect toxicological data without the need for test material, several in silico tools for chromosome damage have been developed over the last years. In this chapter, a battery of freely available in silico chromosome damage prediction tools for chromosome damage is applied on a dataset of pharmaceuticals. Examples of the different outcomes obtained with the in silico battery are provided and briefly discussed. Furthermore, results for coumarin are presented in more detail as a case study. Overall, it can be concluded that although they are in general less developed than those for mutagenicity, in silico tools for chromosome damage can provide valuable information, especially when combined in a battery.

Quantitative structure-activity relationship models for genotoxicity prediction based on combination evaluation strategies for toxicological alternative experiments

Mutagenicity exerts adverse effects on humans. Conventional methods cannot simultaneously predict the toxicity of a large number of compounds. Most mutagenicity prediction models are based on a single experimental type and lack other experimental combination data as support, resulting in limited application scope and predictive ability. In this study, we partitioned data from GENE-TOX, CPDB, and Chemical Carcinogenesis Research Information System according to the weight-of-evidence method for modelling. In our data set, in vivo and in vitro experiments in groups as well as prokaryotic and eukaryotic cell experiments were included in accordance with the ICH guideline. We compared the two experimental combinations mentioned in the weight-of-evidence method and reintegrated the experimental data into three groups. Nine sub-models and three fusion models were established using random forest (RF), support vector machine (SVM), and back propagation (BP) neural network algorithms. When fusing base models under the same algorithm according to the ensemble rules, all models showed excellent predictive performance. The RF, SVM, and BP fusion models reached a prediction accuracy rate of 83.4%, 80.5%, 79.0% respectively. The area under the curve (AUC) reached 0.853, 0.897, 0.865 respectively. Therefore, the established fusion QSAR models can serve as an early warning system for mutagenicity of compounds.

Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors

As the world struggles against current global pandemic of novel coronavirus disease (COVID-19), it is challenging to trigger drug discovery efforts to search broad-spectrum antiviral agents. Thus, there is a need of strong and sustainable global collaborative works especially in terms of new and existing data analysis and sharing which will join the dots of knowledge gap. Our present chemical-informatics based data analysis approach is an attempt of application of previous activity data of SARS-CoV main protease (Mpro) inhibitors to accelerate the search of present SARS-CoV-2 Mpro inhibitors. The study design was composed of three major aspects: (1) classification QSAR based data mining of diverse SARS-CoV Mpro inhibitors, (2) identification of favourable and/or unfavourable molecular features/fingerprints/substructures regulating the Mpro inhibitory properties, (3) data mining based prediction to validate recently reported virtual hits from natural origin against SARS-CoV-2 Mpro enzyme. Our Structural and physico-chemical interpretation (SPCI) analysis suggested that heterocyclic nucleus like diazole, furan and pyridine have clear positive contribution while, thiophen, thiazole and pyrimidine may exhibit negative contribution to the SARS-CoV Mpro inhibition. Several Monte Carlo optimization based QSAR models were developed and the best model was used for screening of some natural product hits from recent publications. The resulted active molecules were analysed further from the aspects of fragment analysis. This approach set a stage for fragment exploration and QSAR based screening of active molecules against putative SARS-CoV-2 Mpro enzyme. We believe the future in vitro and in vivo studies would provide more perspectives for anti-SARS-CoV-2 agents.

Cytotoxicity of quantum dots: Use of quasiSMILES in development of reliable models with index of ideality of correlation and the consensus modelling

The assessment of cytotoxicity of quantum dots is very essential for environmental and health risk analysis. In the present work we have modelled HeLa cell cytotoxicity of sixty one CdSe quantum dots with ZnS shell as a function of its experimental conditions and molecular construction using quasiSMILES representations. The index of ideality of correlation helps in the building of ten statistically significant models having good fitting ability with value of R² ranging from 0.8414 to 0.9609 for the training set. The split 5 model is rated as the best model with values of R², Q²_F1, Q²_F2 and Q²_F3 as 0.8964, 0.8267, 0.8264 and 0.8777 respectively for the calibration set. The extraction of features causing increase and decrease of cytotoxicity of quantum dots indicates importance of neutral surface charge, surface modified with protein, 72 h exposure time, combination of MTT assay with surface protein in decreasing the cytotoxicity. Amphiphilic polymer, polyol ligand with neutral charge, 0.5 - 0.6 nm quantum dot diameter with lipid ligand and unmodified positively charged surface are grouped in toxicity enhancer features. Further, consensus modelling using split 5 and 8 patterns enhances the prediction quality by increasing the R²_val to 0.9361 and 0.9656 respectively.

Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening

Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models' accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.

In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation

Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We have employed two approaches, namely balance of correlation without IIC (TF₁) and balance of correlation with IIC (TF₂), to generate 16 QSAR models from 8 splits. The determination coefficient of calibration and validation set was found higher when the QSPR models were developed using the index of ideality correlation (IIC) parameter (TF₂). The model developed with TF₂ for split 3 was considered as a prominent model because the determination coefficient of the validation set was maximum (r ² = 0.9468). The applicability domain (AD) was also analysed based on 'statistical defect', d(A) for a SMILES attribute. The mechanistic interpretation was done by identifying the SMILES attributes responsible for the promoter of endpoint increase and promoter of endpoint decrease. These SMILES attributes were applied to design 15 new dyes with higher affinity for cellulose fibre.

In silico exploration of the fingerprints triggering modulation of glutaminyl cyclase inhibition for the treatment of Alzheimer's disease using SMILES based attributes in Monte Carlo optimization

Alzheimer's disease is the most common neurodegenerative disorder and being a social burden Alzheimer's has become an economic liability on developing countries. With limited understanding regarding the cause of disease, it is commonly identified by extracellular deposit of amyloid β (Aβ) peptides as senile plaques. Pyroglutamated Aβ is identified from the brain of AD patients and constituted the majority of total Aβ present. The formation of Pyroglutamated Aβ could be hindered by the use of Glutaminyl cyclase inhibitors and could efficiently improve the symptoms of Alzheimer's. The literature revealed the competence of quantitative structure activity/property relationship studies in drug discovery. The present work explores the efficiency of Monte Carlo based QSAR modelling studies on a dataset of 125 Glutaminyl cyclase inhibitors with pKi taken as the endpoint for QSAR analysis. The dataset is divided into training, subtraining, calibration and validation sets resulting in the generation of five random splits. The validation is performed in accordance with the Organization of Economic Corporation and Development principles. The values of R², Q², index of ideality of correlation, concordance correlation coefficient, av. rm2 and delta rm2 of calibration set of the best split are found to be 0.9012, 0.8775, 0.9479, 0.9435, 0.8347 and 0.0847, respectively. The structural features responsible for increasing the inhibitory activity are identified. These structural features are added to a base compound from the dataset to design six novel molecules. These new molecules possess improved inhibitory activity as compare to the base compound. The results are further supported by docking studies.Communicated by Vsevolod Makeev.

Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors

World Health Organization characterized novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) as world pandemic. This infection has been spreading alarmingly by causing huge social and economic disruption. In order to response quickly, the inhibitors already designed against different targets of previous human coronavirus infections will be a great starting point for anti-SARS-CoV-2 inhibitors. In this study, our approach integrates different ligand based drug design strategies of some in-house chemicals. The study design was composed of some major aspects: (a) classification QSAR based data mining of diverse SARS-CoV papain-like protease (PLpro) inhibitors, (b) QSAR based virtual screening (VS) to identify in-house molecules that could be effective against putative target SARS-CoV PLpro and (c) finally validation of hits through receptor-ligand interaction analysis. This approach could be used to aid in the process of COVID-19 drug discovery. It will introduce key concepts, set the stage for QSAR based screening of active molecules against putative SARS-CoV-2 PLpro enzyme. Moreover, the QSAR models reported here would be of further use to screen large database. This study will assume that the reader is approaching the field of QSAR and molecular docking based drug discovery against SARS-CoV-2 PLpro with little prior knowledge.

Communicated by Ramaswamy H. Sarma

The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification

Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.

Exploration of good and bad structural fingerprints for inhibition of indoleamine-2,3-dioxygenase enzyme in cancer immunotherapy using Monte Carlo optimization and Bayesian classification QSAR modeling

Indoleamine-2,3-dioxygenase 1 (IDO1) is an extrahepatic, heme-containing and tryptophan-catalyzing enzyme responsible for causing blockade of T-cell proliferation and differentiation by depleting tryptophan level in cancerous cells. Therefore, inhibition of IDO1 may be a useful strategy for immunotherapy against cancer. In this study, 448 structurally diverse IDO1 inhibitors with a wide range of activity has been taken into consideration for classification QSAR analysis through Monte Carlo Optimization by using different splits as well as different combinations of SMILES-based, graph-based and hybrid descriptors. The best model from Monte Carlo optimization was interpreted to find out the good and bad structural fingerprints for IDO1 and further justified by using Bayesian classification QSAR modeling. Among the three splits in Monte Carlo optimization, the statistics of the best model was obtained from Split 3: sensitivity = 0.87, specificity = 0.91, accuracy = 0.89 and MCC = 0.78. In Bayesian classification modeling, the ROC scores for training and test set were found to be 0.91 and 0.86, respectively. The combined modeling analysis revealed that the presence of aryl hydrazyl sulphonyl moiety, furazan ring, halogen substitution, nitro group and hetero atoms in aromatic system can be very useful in designing IDO1 inhibitors. All the good and bad structural fingerprints for IDO1 were identified and are justified by correlating these fragments to the inhibition of IDO1 enzyme. These structural fingerprints will guide the researchers in this field to design better inhibitors against IDO1 enzyme for cancer immunotherapy.Communicated by Ramaswamy H. Sarma.