NanoSAR: In Silico Modelling of Nanomaterial Toxicity

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties of ENMs. Clearly, it is important to understand and ameliorate any risks to health or the environment posed by the presence of ENMs. However, there still exists a critical gap in the literature on the (eco)toxicological properties of ENMs and the particular characteristics that influence their toxic effects. Given their increasing industrial and technological use, it is important to assess their potential health and environmental impacts in a time and cost effective manner. One strategy to alleviate the problem of a large number and variety of ENMs is through the development of data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics. Although such structure-activity relationship (SAR) methods have proven to be effective in predicting the toxicity of substances in bulk form, their practical application to ENMs requires more research and further development. This study aimed to address this research need by investigating the application of data-driven toxicity modelling approaches (e.g. SAR) that are beneficial over animal testing from a cost, time and ethical perspective to ENMs. A large amount of data on ENM toxicity and properties was collected and analysed using quantitative methods to explore and explain the relationship between ENM properties and their toxic outcomes, as a part of this study. More specifically, multi-dimensional data visualisation techniques including heat maps combined with hierarchical clustering and parallel co-ordinate plots, were used for data exploration purposes while classification and regression based modelling tools, a genetic algorithm based decision tree construction algorithm and partial least squares, were successfully applied to explain and predict ENMs’ toxicity based on physicochemical characteristics. As a next step, the implementation of risk reduction measures for risks that are outside the range of tolerable limits was investigated. Overall, the results showed that computational methods hold considerable promise in their ability to identify and model the relationship between physicochemical properties and biological effects of ENMs, to make it possible to reach a decision more quickly and hence, to provide practical solutions for the risk assessment problems caused by the diversity of ENMs

Causes of variability in latent phenotypes of childhood wheeze

Background Latent class analysis (LCA) has been used extensively to identify (latent) phenotypes of childhood wheezing. However, the number and trajectory of discovered phenotypes differed substantially between studies. Objective We sought to investigate sources of variability affecting the classification of phenotypes, identify key time points for data collection to understand wheeze heterogeneity, and ascertain the association of childhood wheeze phenotypes with asthma and lung function in adulthood. Methods We used LCA to derive wheeze phenotypes among 3167 participants in the ALSPAC cohort who had complete information on current wheeze recorded at 14 time points from birth to age 16½ years. We examined the effects of sample size and data collection age and intervals on the results and identified time points. We examined the associations of derived phenotypes with asthma and lung function at age 23 to 24 years. Results A relatively large sample size (>2000) underestimated the number of phenotypes under some conditions (eg, number of time points <11). Increasing the number of data points resulted in an increase in the optimal number of phenotypes, but an identical number of randomly selected follow-up points led to different solutions. A variable selection algorithm identified 8 informative time points (months 18, 42, 57, 81, 91, 140, 157, and 166). The proportion of asthmatic patients at age 23 to 24 years differed between phenotypes, whereas lung function was lower among persistent wheezers. Conclusions Sample size, frequency, and timing of data collection have a major influence on the number and type of wheeze phenotypes identified by using LCA in longitudinal data

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure–activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure–property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models

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

Distinguishing wheezing phenotypes from infancy to adolescence : A pooled analysis of five birth cohorts

The authors thank all the families who took part in this study; the midwives for their help in recruiting them; and the MAAS, IOW, Ashford, SEATON, and ALSPAC teams, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.Peer reviewedPublisher PD

(Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review

There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored. (Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models

Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice

Advances in big data analytics have created an opportunity for a step change in unraveling mechanisms underlying the development of complex diseases such as asthma, providing valuable insights that drive better diagnostic decision-making in clinical practice, and opening up paths to individualized treatment plans. However, translating findings from data-driven analyses into meaningful insights and actionable solutions requires approaches and tools which move beyond mining and patterning longitudinal data. The purpose of this review is to summarize recent advances in phenotyping of asthma, to discuss key hurdles currently hampering the translation of phenotypic variation into mechanistic insights and clinical setting, and to suggest potential solutions that may address these limitations and accelerate moving discoveries into practice. In order to advance the field of phenotypic discovery, greater focus should be placed on investigating the extent of within-phenotype variation. We advocate a more cautious modeling approach by “supervising” the findings to delineate more precisely the characteristics of the individual trajectories assigned to each phenotype. Furthermore, it is important to employ different methods within a study to compare the stability of derived phenotypes, and to assess the immutability of individual assignments to phenotypes. If we are to make a step change toward precision (stratified or personalized) medicine and capitalize on the available big data assets, we have to develop genuine cross-disciplinary collaborations, wherein data scientists who turn data into information using algorithms and machine learning, team up with medical professionals who provide deep insights on specific subjects from a clinical perspective

Causes of variability in latent phenotypes of childhood wheeze.

BACKGROUND Latent class analysis (LCA) has been used extensively to identify (latent) phenotypes of childhood wheezing. However, the number and trajectory of discovered phenotypes differed substantially between studies. OBJECTIVE To investigate sources of variability affecting the classification of phenotypes, identify key time points for data collection to understand wheeze heterogeneity, and ascertain the association of childhood wheeze phenotypes with asthma and lung function in adulthood. METHODS We used LCA to derive wheeze phenotypes among 3167 participants in the ALSPAC cohort who had complete information on current wheeze recorded at 14 time points from birth to age 16½ years. We examined the effects of sample size, data collection age and intervals on the results, and identified time points. We examined the associations of derived phenotypes with asthma and lung function at age 23-24 years. RESULTS A relatively large sample size (>2000) underestimated the number of phenotypes under some conditions (e.g. number of time points <11). Increasing the number of data points resulted in an increase in the optimal number of phenotypes, but an identical number of randomly selected follow-up points led to different solutions. A variable selection algorithm identified 8 informative time points (months 18, 42, 57, 81, 91, 140, 157 and 166). The proportion of asthmatics at age 23-24 years differed between phenotypes, while lung function was lower among persistent wheezers. CONCLUSIONS Sample size, frequency, and timing of data collection have a major influence on the number and type of wheeze phenotypes identified by LCA in longitudinal data

Causes of variability in latent phenotypes of childhood wheeze.

Background Latent class analysis (LCA) has been used extensively to identify (latent) phenotypes of childhood wheezing. However, the number and trajectory of discovered phenotypes differed substantially between studies. Objective We sought to investigate sources of variability affecting the classification of phenotypes, identify key time points for data collection to understand wheeze heterogeneity, and ascertain the association of childhood wheeze phenotypes with asthma and lung function in adulthood. Methods We used LCA to derive wheeze phenotypes among 3167 participants in the ALSPAC cohort who had complete information on current wheeze recorded at 14 time points from birth to age 16½ years. We examined the effects of sample size and data collection age and intervals on the results and identified time points. We examined the associations of derived phenotypes with asthma and lung function at age 23 to 24 years. Results A relatively large sample size (>2000) underestimated the number of phenotypes under some conditions (eg, number of time points <11). Increasing the number of data points resulted in an increase in the optimal number of phenotypes, but an identical number of randomly selected follow-up points led to different solutions. A variable selection algorithm identified 8 informative time points (months 18, 42, 57, 81, 91, 140, 157, and 166). The proportion of asthmatic patients at age 23 to 24 years differed between phenotypes, whereas lung function was lower among persistent wheezers. Conclusions Sample size, frequency, and timing of data collection have a major influence on the number and type of wheeze phenotypes identified by using LCA in longitudinal data

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

<p>The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure–activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure–property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and <i>interpretable</i> nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models.</p