Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models

The study was aimed at investigating how the method of splitting data into a training set and a test set influences the external predictivity of quantitative structure-activity and/or structure-property relationships (QSAR/QSPR) models. Six models of good quality were collected from the literature and then redeveloped and validated on the basis of five alternative splitting algorithms, namely: (i) a commonly used algorithm ('Z:1'), in which every zth (e.g. third) from the compounds sorted ascending (according to the response values, y) is selected into the test set; (ii-iv) three variations of the Kennard-Stone algorithm; and (v) the duplex algorithm. The external validation statistics reported for each model served as a basis for the final comparison. We demonstrated that the splitting techniques utilizing the values of molecular descriptors alone (X) or in combination with the model response (y) always lead to the development of the models yielding better external predictivity in comparison with the models designed with methodologies based on the y-values only. Moreover, we showed that the external validation coefficient (Q2EXT) is more sensitive to the splitting technique than the root mean square error of prediction (RMSEP). This difference becomes especially important when the test set is relatively small (between 5-10 compounds). In the case of the models trained/validated with a small number of compounds, it is strongly recommended that both statistics (Q2EXT and RMSEP) are taken into account for the external predictivity evaluation.JRC.I.6-Systems toxicolog

Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across

Creating suitable chemical categories and developing read-across methods, supported by quantum mechanical calculations, can be an effective solution to solving key problems related to current scarcity of data on the toxicity of various nanoparticles. This study has demonstrated that by applying a nano-read-across, the cytotoxicity of nano-sized metal oxides could be estimated with a similar level of accuracy as provided by quantitative structure-activity relationship for nanomaterials (nano-QSAR model(s)). The method presented is a suitable computational tool for the preliminary hazard assessment of nanomaterials. It also could be used for the identification of nanomaterials that may pose potential negative impact to human health and the environment. Such approaches are especially necessary when there is paucity of relevant and reliable data points to develop and validate nano-QSAR model

Addressing a bottle neck for regulation of nanomaterials: quantitative read-across (Nano-QRA) algorithm for cases when only limited data is available

The number and variety of engineered nanoparticles have been growing exponentially. Since the experimental evaluation of nanoparticles causing public health concerns is expensive and time consuming, efficient computational tools are amongst the most suitable approaches to identifying potential negative impacts, to the human health and the environment, of new nanomaterials before their production. However, developing computational models complimentary to experiments is impossible without incorporating consistent and high quality experimental data. Although there are limited available data in the literature, one may apply read-across techniques that seem to be an attractive and pragmatic alternative way of predicting missing physico-chemical or toxicological data. Unfortunately, the existing methods of read-across are strongly dependent on the expert's knowledge. In consequence, the results of estimations may vary dependently on personal experience of expert conducting the study and as such cannot guarantee the reproducibility of their results. Therefore, it is essential to develop novel read-across algorithm(s) that will provide reliable predictions of the missing data without the need to for additional experiments. We proposed a novel quantitative read-across approach for nanomaterials (Nano-QRA) that addresses and overcomes a basic limitation of existing methods. It is based on: one-point-slope, two-point formula, or the equation of a plane passing through three points. The proposed Nano-QRA approach is a simple and effective algorithm for filling data gaps in quantitative manner providing reliable predictions of the missing data. © The Royal Society of Chemistry

Development of computational models for the prediction of the toxicity of nanomaterials

Extended abstrac

A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals

The objective of this work was to develop a QSBR model for the prioritization of organic pollutants based on biodegradation rates from a database containing globally harmonized biodegradation tests using relevant molecular descriptors. To do this, we first categorized the chemicals into three groups (Group 1: simple aromatic chemicals with a single ring, Group 2: aromatic chemicals with multiple rings and Group3: Group 1 plus Group 2) based on molecular descriptors, estimated the first order biodegradation rate of the chemicals using rating values derived from the BIOWIN3 model, and finally developed, validated and defined the applicability domain of models for each group using a multiple linear regression approach. All the developed QSBR models complied with OECD principles for QSAR validation. The biodegradation rate in the models for the two groups (Group 2 and 3 chemicals) are associated with abstract molecular descriptors that provide little relevant practical information towards understanding the relationship between chemical structure and biodegradation rates. However, molecular descriptors associated with the QSBR model for Group 1 chemicals (R2 = 0.89, Q2loo = 0.87) provided information on properties that can readily be scrutinised and interpreted in relation to biodegradation processes. In combination, these results lead to the conclusion that QSBRs can be an alternative tool to estimate the persistence of chemicals, some of which can provide further insights into those factors affecting biodegradation

Comparing the CORAL and random forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials

Nanotechnology is one of the most important technological developments of the twenty-first century. In silico methods such as quantitative structure-activity relationships (QSARs) to predict toxicity promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials was investigated to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called “nano-QSAR” models. “External” leave-one-out cross-validation (LOO) analysis was performed to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R2) between training and LOO (0.83 and 0.65 for training and LOO respectively) compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. The aspect ratio and zeta potential from amongst the nanomaterials’ physico-chemical properties were found to be the two most important variables for the Random Forest and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the dataset: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends

Structure-activity relationship models for hazard assessment and risk management of engineered nanomaterials

The widespread use of engineered nanomaterials (ENMs) for commercial purposes made human exposure to these materials almost inevitable. Moreover, the number of in vivo and in vitro studies reporting the potential adverse effects of exposure to ENMs is growing rapidly. Consequently, there is an urgent need to understand the interactions between ENMs and biological/environmental systems. Although the need to improve our understanding of the adverse health effects of ENMs has been recognised for some time, it has not been fully met to date. There are many reasons that have caused the hazard assessment of ENMs to fall behind the innovations in nanotechnology such as knowledge gaps exist in the field of nanotoxicology, difficulties in categorization of ENMs for toxicological considerations and uncertainties regarding the evaluation and regulation of potential risks of nanoparticles. The presence of a large number of ENMs with unknown risks has led to increased interest in the use of fast, cost-effective and efficient computational methods for predicting the toxic potential of ENMs. To that end, the potential use of in silico techniques, such as quantitative structure-activity relationship (QSAR), to model the relationship between biological activities and physicochemical characteristics of ENMs is investigated in this paper. The focus of this paper is on defining the current level of knowledge in (Q)SAR modeling of potential hazards of ENMs and demonstrating the use of (Q)SAR to predict the potential risks specific to ENMs with a case study. Moreover, it presents an overview of the (1) existing barriers currently limiting the development of robust nano-(Q)SAR models, (2) the current obstacles to regulatory acceptance of these models and (3) the integration of (Q)SARs into the risk assessment process. The result of this study demonstrated that the use of (Q)SAR modeling approach to model the toxicity of ENMs based on specific structural and compositional features greatly facilitates (1) filling knowledge gaps regarding the effect of specific parameters on the biological activities of ENMs, (2) predicting the potential risks associated with the exposure to ENMs, (3) classifying the ENMs according to their physicochemical properties and potential hazard degree and (4) reducing the risk by modifying ENMs based on the observed correlations between structural features and biological responses