Monte Carlo method for predicting of cardiac toxicity: hERG blocker compounds

Using the Correlation Intensity Index to Build a Model of Cardiotoxicity of Piperidine Derivatives

The assessment of cardiotoxicity is a persistent problem in medicinal chemistry. Quantitative structure-activity relationships (QSAR) are one possible way to build up models for cardiotoxicity. Here, we describe the results obtained with the Monte Carlo technique to develop hybrid optimal descriptors correlated with cardiotoxicity. The predictive potential of the cardiotoxicity models (pIC50, Ki in nM) of piperidine derivatives obtained using this approach provided quite good determination coefficients for the external validation set, in the range of 0.90-0.94. The results were best when applying the so-called correlation intensity index, which improves the predictive potential of a model.

Acute and developmental toxicity of embelin isolated from Embelia schimperi Vatke fruit: In vivo and in silico studies

Background

Embelin is a hydroxybenzoquinone constituent of the Embelia species that has anti-disease properties. However, its toxicity, particularly the in silico, acute, and developmental toxicity profiles, has yet to be thoroughly investigated. Hence, this study aims to assess these toxicity profiles.

Materials and Methods

In silico and in vivo experimental studies were conducted on embelin isolated from the fruits of Embelia schimperi Vatke. In silico toxicity predictions were computed using the ProTox model. The in vivo experiment was done by administering 5000 mg/kg of embelin to a single female albino Wistar rat, followed by three female rats in the absence of death, to determine the mean lethal dose (LD50). Afterwards, three groups of pregnant rats were treated with embelin at doses of 250 mg/kg, 500 mg/kg, and 1000 mg/kg for the developmental toxicity test. Vehicle and ad libitum control groups were used to compare the acute and developmental toxicity variables.

Results

In silico toxicity predicted that embelin is free from hepatotoxic, carcinogenic, mutagenic, and cytotoxic effects. No inhibitory effect on hERG channels was observed. It has an immunotoxic property and an inhibitory effect on the CYP2D6 enzyme. Since mortality and signs of toxicities were not observed after treatment with 5000 mg/kg, the mean lethal dose (LD₅₀) is determined to be > 5000 mg/kg. There was no significant difference in the morphological scores or number of somites among experimental animals. None of the embryonic systems possessed developmental delays. Nevertheless, the crown-rump length of the high-dose group became significantly shorter. Maternal food intake and weight gain exhibited significant dose-dependent differences between embelin-treated animals and controls. The number of implantations was significantly low in the treatment group, accompanied by a higher frequency of prior resorption.

Conclusion

Embelin is predicted to have a high probability of immunotoxicity potential and affect drug metabolism by inhibiting CYP2D6. In addition, it affects food intake, weight gain, and the number of implantations in pregnant rats. Therefore, it is highly recommended not to take embelin and embelin-rich plants during pregnancy. Further in vitro and in vivo studies need to be conducted to understand the mechanism behind the toxicity of embelin.

Machine-learning technique, QSAR and molecular dynamics for hERG-drug interactions

One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-dependent hERG K + channel, which is well-known for its crucial involvement in cardiac action potential repolarization. Torsades de Pointes, QT prolongation, and sudden death are all caused by hERG (the human Ether-à-go-go-Related Gene) inhibition. There is great interest in creating predictive computational (in silico) tools to identify and weed out potential hERG blockers early in the drug discovery process because testing for hERG liability and the traditional experimental screening are complicated, expensive and time-consuming. This study used 2D descriptors of a large curated dataset of 6766 compounds and machine learning approaches to build robust descriptor-based QSAR and predictive classification models for KCNH2 liability. Decision Tree, Random Forest, Logistic Regression, Ada Boosting, kNN, SVM, Naïve Bayes, neural network and stochastic gradient classification classifier algorithms were used to build classification models. If a compound's IC50 value was between 10 μM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew's correlation coefficient formula and F1score were applied to compare and track the developed models' performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG-gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG-ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns.Communicated by Ramaswamy H. Sarma.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

CO2 uptake prediction of metal–organic frameworks using quasi-SMILES and Monte Carlo optimization

The first report of quasi-SMILES-based QSPR models for CO2 capture of MOFs based on experimental data.

The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR

Background:

The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.

Methods:

Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model.

Results:

The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated.

Conclusion:

The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria

Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class of materials in the pharmaceutical industry and human health. The wide use of MO-NPs forces an enhanced understanding of their potential impact on human health and the environment. The research aims to investigate and develop a nano-QFAR (nano-quantitative feature activity relationship) model applying the quasi-SMILES such as cell line, assay, time exposition, concentration, nanoparticles size and metal oxide type for prediction of cell viability (%) of MO-NPs. The total set of 83 quasi-SMILES of MO-NPs divided into training, validation and test sets randomly three times. The statistical model results based on the balance of correlation target function (TF₁) and index of ideality correlation target function (TF₂) and the Monte Carlo optimization were compared. The comparison of two target function results indicated that TF₂ improves the predictability of models. The significance of various eclectic features of both increase and decrease of cell viability (%) is provided. Mechanistic interpretation of significant factors for the model are proposed as well. The sufficient statistical quality of three nano-QFAR models based on TF₂ reveals that the developed models can be efficiency for predictions of the cell viability (%) of MO-NPs.

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.

The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models

Acetylcholinesterase (AChE) inhibitors, dihydrofolate reductase inhibitors (DHFR), Toxicity in Tetrahymena pyriformis (TP), Acute Toxicity in fathead minnow (TFat), Water solubility (WS), and Acute Aquatic Toxicity in Daphnia magna (DM) are examined as endpoints to establish quantitative structure - property/activity relationships (QSPRs/QSARs). The Index of Ideality of Correlation (IIC) is a measure of predictive potential. The IIC has been studied in a few recent works. The comparison of models for the six endpoints above confirms that the index can be a useful tool for building up and validation of QSPR/QSAR models. All examined endpoints are important from an ecologic point of view. The diversity of examined endpoints confirms that the IIC is real criterion of the predictive potential of a model.

Mutagenicity, anticancer activity and blood brain barrier: similarity and dissimilarity of molecular alerts

The aim of the present work is an attempt to define computable measure of similarity between different endpoints. The similarity of structural alerts of different biochemical endpoints can be used to solve tasks of medicinal chemistry. Optimal descriptors are a tool to build up models for different endpoints. The optimal descriptor is calculated with simplified molecular input-line entry system (SMILES). A group of elements (single symbol or pair of symbols) can represent any SMILES. Each element of SMILES can be represented by so-called correlation weight i.e. coefficient that should be used to calculate descriptor. Numerical data on the correlation weights are calculated by the Monte Carlo method, i.e. by optimization procedure, which gives maximal correlation coefficient between the optimal descriptor and endpoint for the training set. Statistically stable correlation weights observed in several runs of the optimization can be examined as structural alerts, which are promoters of the increase or the decrease of a biochemical activity of a substance. Having data on several runs of the optimization correlation weights, one can extract list of promoters of increase and list of promoters of decrease for an endpoint. The study of similarity and dissimilarity of the above lists has been carried out for the following pairs of endpoints: (i) mutagenicity and anticancer activity; (ii) mutagenicity and blood brain barrier; and (iii) blood brain barrier and anticancer activity. The computational experiment confirms that similarity and dissimilarity for pairs of endpoints can be measured.

Dual Action of Mexiletine and Its Pyrroline Derivatives as Skeletal Muscle Sodium Channel Blockers and Anti-oxidant Compounds: Toward Novel Therapeutic Potential

Mexiletine (Mex) has been recently appointed as an orphan-drug in myotonic-syndromes, being a potent use-dependent blocker of skeletal-muscle sodium channels (Na_V1.4). Available evidences about a potential anti-oxidant effect of Mex and its tetramethyl-pyrroline-derivatives in vivo, suggest the possibility to further enlarge the therapeutic potential of Mex-like compounds in myopathies in which alteration of excitation-contraction coupling is paralleled by oxidative stress. In line with this and based on our previous structure-activity-relationship studies, we synthesized new compounds with a tetramethyl-pyrroline-ring on the amino-group of both Mex (VM11) and of its potent use-dependent isopropyl-derivative (CI16). The compounds were tested for their ability to block native Na_V1.4 and to exert cyto-protective effects against oxidative-stress injury in myoblasts. Voltage-clamp-recordings on adult myofibers were performed to assess the tonic and use-dependent block of peak sodium-currents (I_Na) by VM11 and CI16, as well as Mex, VM11 and CI16 were 3 and 6-fold more potent than Mex in producing a tonic-block of peak sodium-currents (I_Na), respectively. Interestingly, CI16 showed a 40-fold increase of potency with respect to Mex during high-frequency stimulation (10-Hz), resulting the strongest use-dependent Mex-like compound so far. The derivatives also behaved as inactivated channel blockers, however the voltage dependent block was modest. The experimental data fitted with the molecular-modeling simulation based on previously proposed interaction of main pharmacophores with Na_V1.4 binding-site. CI16 and VM11 were then compared to Mex and its isopropyl derivative (Me5) for the ability to protect C₂C₁₂-cells from H₂O₂-cytotoxicity in the concentration range effective on Na_v1.4. Mex and Me5 showed a moderate cyto-protective effect in the presence of H₂O₂, Importantly, CI16 and VM11 showed a remarkable cyto-protection at concentrations effective for use-dependent block of Na_V1.4. This effect was comparable to that of selected anti-oxidant drugs proved to exert protective effect in preclinical models of progressive myopathies such as muscular dystrophies. Then, the tetramethyl-pyrroline compounds have increased therapeutic profile as sodium channel blockers and an interesting cyto-protective activity. The overall profile enlarges therapeutic potential from channelopathies to myopathies in which alteration of excitation-contraction coupling is paralleled by oxidative-stress, i.e., muscular dystrophies.

Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results

Quantitative structure-activity relationships (QSARs) for prediction of toxicological endpoints built up with the CORAL software are discussed. Prejudices related to these QSAR models are listed. Possible ways to improve the software are discussed.

CORAL: Binary classifications (active/inactive) for drug-induced liver injury

INTRODUCTION

The data on human hepatotoxcity (drug-induced liver injury) is extremely important information from point of view of drug discovery. Experimental clinical data on this endpoint is scarce. Experimental way to extend databases on this endpoint is extremely difficult. Quantitative structure - activity relationships (QSAR) is attractive alternative of the experimental approach.

METHODS

Predictive models for human hepatotoxicity (drug-induced liver injury) have been built up by the Monte Carlo method with using of the CORAL software (http://www.insilico.eu/coral). These models are the binary classifications into active class and inactive class. These models are calculated with so-called "semi correlations" described in this work. The Mattews correlation coefficient of these models for external validation sets ranged from 0.52 to 0.62.

RESULTS DISCUSSION

The approach has been checked up with a group of random splits into the training and validation sets. These stochastic experiments have shown the stability of results: predictability of the models for various splits. Thus, the attempt to build up the classification QSAR model by means of the Monte Carlo technique, based on representation of the molecular structure via simplified molecular input line entry systems (SMILES) and hydrogen suppressed graph (HSG) using the CORAL software (http://www.insilico.eu/coral) has shown ability of this approach to provide quite good prediction of the examined endpoint (drug-induced liver injury).

Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors

P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.

Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method

By optimization of so-called correlation weights of attributes of simplified molecular input-line entry system (SMILES) quantitative structure - activity relationships (QSAR) for toxicity towards Pimephales promelas are established. A new SMILES attribute has been utilized in this work. This attribute is a molecular descriptor, which reflects (i) presence of different kinds of bonds (double, triple, and stereo chemical bonds); (ii) presence of nitrogen, oxygen, sulphur, and phosphorus atoms; and (iii) presence of fluorine, chlorine, bromine, and iodine atoms. The statistical characteristics of the best model are the following: n=226, r²=0.7630, RMSE=0.654 (training set); n=114, r²=0.7024, RMSE=0.766 (calibration set); n=226, r²=0.6292, RMSE=0.870 (validation set). A new criterion to select a preferable split into the training and validation sets are suggested and discussed.

Cardamonin, a Novel Antagonist of hTRPA1 Cation Channel, Reveals Therapeutic Mechanism of Pathological Pain

The increasing demand for safe and effective treatments of chronic pain has promoted the investigation of novel analgesic drugs. Some herbals have been known to be able to relieve pain, while the chemical basis and target involved in this process remained to be clarified. The current study aimed to find anti-nociceptive candidates targeting transient receptor potential ankyrin 1 (TRPA1), a receptor that implicates in hyperalgesia and neurogenic inflammation. In the current study, 156 chemicals were tested for blocking HEK293/TRPA1 ion channel by calcium-influx assay. Docking study was conducted to predict the binding modes of hit compound with TRPA1 using Discovery Studio. Cytotoxicity in HEK293 was conducted by Cell Titer-Glo assay. Additionally, cardiotoxicity was assessed via xCELLigence RTCA system. We uncovered that cardamonin selectively blocked TRPA1 activation while did not interact with TRPV1 nor TRPV4 channel. A concentration-dependent inhibitory effect was observed with IC₅₀ of 454 nM. Docking analysis of cardamonin demonstrated a compatible interaction with A-967079-binding site of TRPA1. Meanwhile, cardamonin did not significantly reduce HEK293 cell viability, nor did it impair cardiomyocyte constriction. Our data suggest that cardamonin is a selective TRPA1 antagonist, providing novel insight into the target of its anti-nociceptive activity.