An Integrated Data-Driven Strategy for Safe-by-Design Nanoparticles: The FP7 MODERN Project.

The development and implementation of safe-by-design strategies is key for the safe development of future generations of nanotechnology enabled products. The safety testing of the huge variety of nanomaterials that can be synthetized is unfeasible due to time and cost constraints. Computational modeling facilitates the implementation of alternative testing strategies in a time and cost effective way. The development of predictive nanotoxicology models requires the use of high quality experimental data on the structure, physicochemical properties and bioactivity of nanomaterials. The FP7 Project MODERN has developed and evaluated the main components of a computational framework for the evaluation of the environmental and health impacts of nanoparticles. This chapter describes each of the elements of the framework including aspects related to data generation, management and integration; development of nanodescriptors; establishment of nanostructure-activity relationships; identification of nanoparticle categories; hazard ranking and risk assessment.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability.

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsets driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models.

A systems toxicology approach on the mechanism of uptake and toxicity of MWCNT in Caenorhabditis elegans.

The increased volumes of carbon nanotubes (CNTs) being utilized in industrial and biomedical processes carries with it an increased risk of unintentional release into the environment, requiring a thorough hazard and risk assessment. In this study, the toxicity of pristine and hydroxylated (OH-) multiwall CNTs (MWCNTs) was investigated in the nematode Caenorhabditis elegans using an integrated systems toxicology approach. To gain an insight into the toxic mechanism of MWCNTs, microarray and proteomics were conducted for C. elegans followed by pathway analyses. The results of pathway analyses suggested endocytosis, phagocytosis, oxidative stress and endoplasmic reticulum (ER) stress, as potential mechanisms of uptake and toxicity, which were subsequently investigated using loss-of-function mutants of genes of those pathways. The expression of phagocytosis related genes (i.e. ced-10 and rab-7) were significantly increased upon exposure to OH-MWCNT, concomitantly with the rescued toxicity by loss-of-function mutants of those genes, such as ced-10(n3246) and rab-7(ok511). An increased sensitivity of the hsp-4(gk514) mutant by OH-MWCNT, along with a decreased expression of hsp-4 at both gene and protein level suggests that MWCNTs may affect ER stress response in C. elegans. Collectively, the results implied phagocytosis to be a potential mechanism of uptake of MWCNTs, and ER and oxidative stress as potential mechanisms of toxicity. The integrated systems toxicology approach applied in this study provided a comprehensive insight into the toxic mechanism of MWCNTs in C. elegans, which may eventually be used to develop an "Adverse Outcome Pathway (AOP)", a recently introduced concept as a conceptual framework to link molecular level responses to higher level effects.

Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR).

Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical descriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selection were in line with already published literature, and the computational model constructed on these features had a good accuracy (R(2)LOO=0.76 and R(2)LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical descriptors were independent of the specific proteins that form the corona.

A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH.

This paper presents an inventory of in silico screening tools to identify substance properties of concern under the European chemicals' legislation REACH. The objective is to support the selection and implementation of appropriate tools as building blocks within integrated testing strategies (ITS). The relevant concerns addressed are persistence, bioaccumulation potential, acute and long-term aquatic toxicity, PBT/vPvB properties ((very) persistent, (very) bioaccumulative, toxic), CMR (carcinogenicity, mutagenicity, reproductive toxicity), endocrine disruption and skin sensitisation. The inventory offers a comparative evaluation of methods with respect to the underlying algorithms (how does the method work?) and the applicability domains (when does the method work?) as well as their limitations (when does the method not work?). The inventory explicitly addresses the reliability of predictions of different in silico models for diverse chemicals by applicability domain considerations. The confidence in predictions can be greatly improved by consensus modelling that allows for taking conflicting results into account. The inventory is complemented by a brief discussion of socio-economic tools for assessing the potential efficiency gains of using in silico methods compared to traditional in vivo testing of chemical hazards.

Quantitative consensus of bioaccumulation models for integrated testing strategies.

A quantitative consensus model based on bioconcentration factor (BCF) predictions obtained from five quantitative structure-activity relationship models was developed for bioaccumulation assessment as an integrated testing approach for waiving. Three categories were considered: non-bioaccumulative, bioaccumulative and very bioaccumulative. Five in silico BCF models were selected and included into a quantitative consensus model by means of the continuous formulation of Bayes' theorem. The discrete likelihoods commonly used in the qualitative Bayesian model were substituted by probability density functions to reduce the loss of information that occurred when continuous BCF values were distributed across the three bioaccumulation categories. Results showed that the continuous Bayesian model yielded the best classification predictions compared not only to the discrete Bayesian model, but also to the individual BCF models. The proposed quantitative consensus model proved to be a suitable approach for integrated testing strategies for continuous endpoints of environmental interest.

Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles.

The response of a murine macrophage cell line exposed to a library of seven metal and metal oxide nanoparticles was evaluated via High Throughput Screening (HTS) assay employing luciferase-reporters for ten independent toxicity-related signaling pathways. Similarities of toxicity response among the nanoparticles were identified via Self-Organizing Map (SOM) analysis. This analysis, applied to the HTS data, quantified the significance of the signaling pathway responses (SPRs) of the cell population exposed to nanomaterials relative to a population of untreated cells, using the Strictly Standardized Mean Difference (SSMD). Given the high dimensionality of the data and relatively small data set, the validity of the SOM clusters was established via a consensus clustering technique. Analysis of the SPR signatures revealed two cluster groups corresponding to (i) sublethal pro-inflammatory responses to Al2O3, Au, Ag, SiO2 nanoparticles possibly related to ROS generation, and (ii) lethal genotoxic responses due to exposure to ZnO and Pt nanoparticles at a concentration range of 25-100 µg/mL at 12 h exposure. In addition to identifying and visualizing clusters and quantifying similarity measures, the SOM approach can aid in developing predictive quantitative-structure relations; however, this would require significantly larger data sets generated from combinatorial libraries of engineered nanoparticles.

Multimedia environmental chemical partitioning from molecular information.

The prospect of assessing the environmental distribution of chemicals directly from their molecular information was analyzed. Multimedia chemical partitioning of 455 chemicals, expressed in dimensionless compartmental mass ratios, was predicted by SimpleBox 3, a Level III Fugacity model, together with the propagation of reported uncertainty for key physicochemical and transport properties, and degradation rates. Chemicals, some registered in priority lists, were selected according to the availability of experimental property data to minimize the influence of predicted information in model development. Chemicals were emitted in air or water in a fixed geographical scenario representing the Netherlands and characterized by five compartments (air, water, sediments, soil and vegetation). Quantitative structure-fate relationship (QSFR) models to predict mass ratios in different compartments were developed with support vector regression algorithms. A set of molecular descriptors, including the molecular weight and 38 counts of molecular constituents were adopted to characterize the chemical space. Out of the 455 chemicals, 375 were used for training and testing the QSFR models, while 80 were excluded from model development and were used as an external validation set. Training and test chemicals were selected and the domain of applicability (DOA) of the QSFRs established by means of self-organizing maps according to structural similarity. Best results were obtained with QSFR models developed for chemicals belonging to either the class [C] and [C; O], or the class with at least one heteroatom different than oxygen in the structure. These two class-specific models, with respectively 146 and 229 chemicals, showed a predictive squared coefficient of q(2) ≥ 0.90 both for air and water, which respectively dropped to q(2)≈ 0.70 and 0.40 for outlying chemicals. Prediction errors were of the same order of magnitude as the deviations associated to the uncertainty of the physicochemical and transport properties, and degradation rates.

Conscious worst case definition for risk assessment, part II: a methodological case study for pesticide risk assessment.

This paper illustrates, by a case study, how to apply the conceptual Worst-Case Definition (WCD) model, developed in the methodological paper in the current journal, by Sørensen et al. (2010-this issue). The case is about eco-toxicological risk assessment of pesticides under Danish conditions. Cumulative aspects are included on a conceptual basis as elements of the worst-case conditions. This defines factors that govern the risk assessment, including location in time and space of risk "hotspots". Two pillars of concern drive the conceptual modelling: (1) What to protect (denoted Protected Units (PUs)) and (2) the reason for increased risk level (denoted Causes of Risks (CRs)). Both PUs and CRs are analysed using hierarchical procedures that facilitate a complete listing of concrete factors governing increased risk for adverse effect due to agricultural usage of pesticide. The factors governing pesticide risk are combined in a context that combines the protection of relevant groupings of organisms with the factors for increased risk level for each of these. Identification of the most important relations between defined types of PUs and CRs is illustrated using expert knowledge. Existing databases are used to form spatial distributed risk indicators as estimators for a selection of important relations between PUs and CRs. This paper illustrates how the WCD model can break down the complex issue of uncertainty into fractions that are more open for evaluations. Finally, it shows application of risk indicators in a multi-criterion analysis using respectively self organizing mapping and partial order technique in a comparative analysis that highlights critical aspects of uncertainty, due to the ambiguity between single risk indicator rankings.

Uncertainty reduction in environmental data with conflicting information.

The assessment of ecotoxicological effects of chemicals for regulatory purposes requires large amounts of experimental data which are expensive to obtain and eventually might entail exhaustive animal testing. The required decision-making processes in this regulatory context, must often be carried out with limited or even contradictory sources of information. To benefit from all sources of information without compromising the quality of the decision process, uncertainty management and reduction techniques, such as the Dempster-Shafer theory of evidence, have to be applied. This theory was applied to both experimental and in silico biodegradation data sources to assess chemical persistence. Uncertainties of the initially lass uncertain estimates for biodegradation rates in water were reduced by as much as 20-60%. The analysis showed that conflicting evidence can be detected, quantified, and redistributed proportionally among all the feasible subsets of hypotheses. The advantages of the Dempster-Shafer theory over Bayesian approaches to represent evidence concerning hypotheses by assigning probabilities were also analyzed.

Bifurcation analysis of multiple steady flow patterns for Rayleigh-Bénard convection in a cubical cavity at Pr = 130.

The bifurcation diagram of steady convective flow patterns inside a cubical cavity with adiabatic lateral walls heated from below and filled with silicone oil (Pr = 130) was determined for values of the Rayleigh number (Ra) up to 1.5 x 10(5). A continuation procedure based on the Galerkin spectral method was used to determine the steady convective solutions as a function of Ra. Bifurcations leading to either new steady or time-dependent solutions were identified and new steady solution branches were also continued. A total of fifteen steady solutions were tracked and the stability analysis predicted that six flow patterns were stable and that two, three, or even four of these patterns coexisted over certain ranges of Ra in the studied domain. Predicted flow patterns and transitions are in agreement with flow visualizations previously reported in the literature. The variation of the Nusselt number (Nu) as a function of Pr was investigated for three of the stable flow patterns identified: a x or y roll, a diagonal oriented roll and a pattern formed by four connected half rolls. It was found that whereas the Nusselt changes within the region 0.71 < or = Pr < or = 10 it tends to an asymptotic value with increasing Pr.

A fuzzy ARTMAP-based quantitative structure-property relationship (QSPR) for the Henry's law constant of organic compounds.

Quantitative structure-property relationships (QSPRs) for estimating a dimensionless Henry's Law constant of organic compounds at 25 degrees C were developed based on a fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 495 organic compounds. A set of molecular descriptors developed from PM3 semiempirical MO-theory and topological descriptors (second-order molecular connectivity index) were used as input parameters to the neural networks. Quantum chemical input descriptors included average molecular polarizability, dipole moments (total point charge, total hybridization, and total sum), ionization potential, and heat of formation. The fuzzy ARTMAP/QSPR correlated Henry's Law constant for -6.72

Dynamical properties of model communication networks.

We study the dynamical properties of a collection of models for communication processes, characterized by a single parameter xi representing the relation between information load of the nodes and its ability to deliver this information. The critical transition to congestion reported so far occurs only for xi=1. This case is well analyzed for different network topologies. We focus on the properties of the order parameter, the susceptibility, and the time correlations when approaching the critical point. For xi<1, no transition to congestion is observed but it remains a crossover from a low-density to a high-density state. For xi>1, the transition to congestion is discontinuous and congestion nuclei arise.

Fuzzy ARTMAP and back-propagation neural networks based quantitative structure-property relationships (QSPRs) for octanol-water partition coefficient of organic compounds.

Quantitative structure-property relationships (QSPRs) for estimating the logarithm octanol/water partition coefficients, logK(ow), at 25 degrees C were developed based on fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 442 organic compounds. The set of molecular descriptors were derived from molecular connectivity indices and quantum chemical descriptors calculated from PM3 semiempirical MO-theory. Quantum chemical input descriptors include average polarizability, dipole moments, exchange energy, total electrostatic interaction energy, total two-center energy, and ionization potential. The fuzzy ARTMAP/QSPR performed, for a logK(ow) range of -1.6 to 7.9, with average absolute errors of 0.03 and 0.14 logK(ow) for the overall data and test sets, respectively. The optimal 12-11-1 back-propagation/QSPR model, for the same range of logK(ow), exhibited larger average absolute errors of 0.23 and 0.27 logK(ow) for the test and validation data sets, respectively, over the same range of logK(ow) values. The present results with the fuzzy ARTMAP-based QSPR are encouraging and suggest that high performance logK(ow) QSPR that encompasses a wider range of chemical groups could be developed, following the present approach, by training with a larger heterogeneous data set.

An integrated SOM-fuzzy ARTMAP neural system for the evaluation of toxicity.

Self-organized maps (SOM) have been applied to analyze the similarities of chemical compounds and to select from a given pool of descriptors the smallest and more relevant subset needed to build robust QSAR models based on fuzzy ARTMAP. First, the category maps for each molecular descriptor and for the target activity variable were created with SOM and then classified on the basis of topology and nonlinear distribution. The best subset of descriptors was obtained by choosing from each cluster the index with the highest correlation with the target variable and then in order of decreasing correlation. This process was terminated when a dissimilarity measure increased, indicating that the inclusion of more molecular indices would not add supplementary information. The optimal subset of descriptors was used as input to a fuzzy ARTMAP architecture modified to effect predictive capabilities. The performance of the integrated SOM-fuzzy ARTMAP approach was evaluated with the prediction of the acute toxicity LC50 of a homogeneous set of 69 benzene derivatives in the fathead minnow and the oral rat toxicity LD50 of a heterogeneous set of 155 organic compounds. The proposed methodology minimized the problem of misclassification of similar compounds and significantly enhanced the predictive capabilities of a properly trained fuzzy ARTMAP network.

Protein Adsorption onto Zirconia Modified with Terminally Grafted Polyvinylpyrrolidone.

The potential effectiveness of poly(vinyl pyrrolidone) (PVP) as a zirconia surface modifier for protein adsorption reduction was investigated using lysozyme (LYS). The relatively small size of LYS (45 x 30 x 30 Å) allowed for testing the adequacy of the graft polymerization method for producing a dense surface chain coverage to exclude LYS from direct interaction with the zirconia surface. The study demonstrated that a PVP brush layer is capable of reducing lysozyme adsorption. Overall, the maximum adsorption capacity decreased (by up to about 76%) due to surface modification with increasing polymer/silane surface coverage ratio (mol/mol). Adsorption reduction, due to protein exclusion from the surface by the tethered polymer layer, increased significantly when the distance between surface chains was less than the large axis of LYS (i.e., 45 Å). The present results are encouraging and suggest further consideration of polymer-modified ceramic surfaces for reducing fouling of ceramic membranes during protein ultrafiltration and producing ceramic biocompatible surfaces for biomedical applications. Copyright 2001 Academic Press.