Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: the case of a group of ZnO and TiO2 nanoparticles

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.

Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment

Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.

Topological QSAR Modelling of Carboxamides Repellent Activity to Aedes Aegypti

Aedes aegypti vector control is of paramount importance owing to the damages induced by the various severe diseases that these insects may transmit, and the increasing risk of important outbreaks of these pathologies. Search for new chemicals efficient against Aedes aegypti, and devoid of side-effects, which have been associated to the currently most used active substance i. e. N,N-diethyl-m-toluamide (DEET), is therefore an important issue. In this context, we developed various Quantitative Structure Activity Relationship (QSAR) models to predict the repellent activity against Aedes aegypti of 43 carboxamides, by using Multiple Linear Regression (MLR) and various machine learning approaches. The easy computation of the four topological descriptors selected in this study, compared to the CODESSA descriptors used in the literature, and the predictive ability of the here proposed MLR and machine learning models developed using the software QSARINS and R, make the here proposed QSARs attractive. As demonstrated in this study, these models can be applied at the screening level, to guide the design of new alternatives to DEET.

Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials

A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-QSAR models were developed using CORAL software (www.insilico.eu/coral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (R_adj² for the training dataset: 0.71-0.73; R_adj² for the calibration dataset: 0.74-0.82; and R_adj² for the validation dataset: 0.70-0.76).

CORAL: Predictive models for cytotoxicity of functionalized nanozeolites based on quasi-SMILES

Unlike the well-known simplified molecular input-line entry system (SMILES), the so-called quasi-SMILES contains information related to physicochemical and biochemical conditions by a special additional symbols (codes), each standing for different conditions (time exposure, concentration, type of cell, etc.). Thus, quasi-SMILES can be used to build up models for cytotoxicity of functionalized nanozeolites using a mathematical function of eclectic information. These calculations were done with the Monte Carlo CORAL software. The statistical quality of models based on quasi-SMILES was usually considerably better than the statistical quality of models based on traditional SMILES.

Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells

Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R² for internal validation data sets: 0.60-0.80; R²_pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 μm; surface area, 11.3-380.0 m²/g; and dose, 0-200 ppm.

QSAR models for predicting the toxicity of piperidine derivatives against Aedes aegypti

QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives against Aedes aegypti. From 2D topological descriptors, calculated with the PaDEL software, ordinary least squares multilinear regression (OLS-MLR) treatment from the QSARINS software and machine learning and related approaches including linear and radial support vector machine (SVM), projection pursuit regression (PPR), radial basis function neural network (RBFNN), general regression neural network (GRNN) and k-nearest neighbours (k-NN), led to four-variable models. Their robustness and predictive ability were evaluated through both internal and external validation. Determination coefficients (r²) greater than 0.85 on the training sets and 0.8 on the test sets were obtained with OLS-MLR and linear SVM. They slightly outperform PPR, radial SVM and RBFNN, whereas GRNN and k-NN showed lower performance. The easy availability of the involved structural descriptors and the simplicity of the MLR model make the corresponding model attractive at an exploratory level for proposing, from this limited dataset, guidelines in the design of new potentially active molecules.

CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2)

Quantitative feature - activity relationships (QFAR) approach was applied to prediction of bioavailability of metal oxide nanoparticles. ZnO, CuO, Co₃O₄, and TiO₂ nanoxides were considered. The computational model for bioavailability of investigated species is asserted. The model was calculated using the Monte Carlo method. The CORAL free software (http://www.insilico.eu/coral) was used in this study. The developed model was tested by application of three different splits of data into the training and validation sets. So-called, quasi-SMILES are used to represent the conditions of action of metal oxide nanoparticles. A new paradigm of building up predictive models of endpoints related to nanomaterials is suggested. The paradigm is the following "An endpoint is a mathematical function of available eclectic data (conditions)". Recently, the paradigm has been checked up with endpoints related to metal oxide nanoparticles, fullerenes, and multi-walled carbon-nanotubes.

Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions

The experimental data on the bacterial reverse mutation test (under various conditions) on C60 nanoparticles for the cases (i) TA100, and (ii) WP2uvrA/pkM101 are examined as endpoints. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of these endpoints has been built up. The models are a mathematical function of eclectic data such as (i) dose (g/plate); (ii) metabolic activation (i.e. with mix S9 or without mix S9); and (iii) illumination (i.e. darkness or irradiation). The eclectic data on different conditions were represented by so-called quasi-SMILES. In contrast to the traditional SMILES which are representation of molecular structure, the quasi-SMILES are representation of conditions by sequence of symbols. The calculations were carried out with the CORAL software, available on the Internet at http://www.insilico.eu/coral. The main idea of the suggested descriptors is the accumulation of all available eclectic information in the role of logical and digital basis for building up a model. The computational experiments have shown that the described approach can be a tool to build up models of mutagenicity of fullerene under different conditions.

Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions

Simplified molecular input-line entry system (SMILES) are a tool to represent molecular features of various compounds. Quasi-SMILES is a tool to represent various eclectic features of interaction between complex substances and bio targets (cells, organs, organisms). The construction and the application of quasi-SMILES in order to build up a model for prediction of mutagenicity of fullerene and multi-walled carbon-nanotubes (MWCNTs) are described in this work: instead of paradigm "endpoint is a mathematical function of molecular structure", the paradigm "endpoint is a mathematical function of eclectic data (features)" is used.

Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms

Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests.

Linear and non-linear modelling of the cytotoxicity of TiO2 and ZnO nanoparticles by empirical descriptors

Titanium oxide (TiO2) and zinc oxide (ZnO) nanoparticles are among the most widely used in different applications in daily life. In this study, local regression and classification models were developed for a set of ZnO and TiO2 nanoparticles tested at different concentrations for their ability to disrupt the lipid membrane in cells. Different regression techniques were applied and compared by checking the robustness of the models and their external predictive ability. Additionally, a simple classification model was developed, which predicts the potential for disruption of the studied nanoparticles with good accuracy (overall accuracy, specificity, and sensitivity >80%) on the basis of two empirical descriptors. The present study demonstrates that empirical descriptors, such as experimentally determined size and tested concentrations, are relevant to modelling the activity of nanoparticles. This information may be useful to screen the potential for harmful effect of nanoparticles in different experimental conditions and to optimize the design of toxicological tests. Results from the present study are useful to support and refine the future application of in silico tools to nanoparticles, for research and regulatory purposes.

Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides

Systematization of knowledge on nanomaterials has become a necessity with the fast growth of applications of these species. Building up predictive models that describe properties (both beneficial and hazardous) of nanomaterials is vital for computational sciences. Classic quantitative structure-property/activity relationships (QSPR/QSAR) are not suitable for investigating nanomaterials because of the complexity of their molecular architecture. However, some characteristics such as size, concentration, and exposure time can influence endpoints (beneficial or hazardous) related to nanoparticles and they can therefore be involved in building a model. Application of the optimal descriptors calculated with the so-called correlation weights of various concentrations and different exposure times are suggested in order to build up a predictive model for cell membrane damage caused by a series of nano metal-oxides. The numerical data on correlation weights are calculated by the Monte Carlo method. The obtained results are in good agreement with the experimental data.