Now showing 1 - 10 of 13
  • Publication
    Bionano Interactions: A Key to Mechanistic Understanding of Nanoparticle Toxicity
    The new paradigm in the assessment of toxicity of nanomaterials relies on a mechanistic understanding of the organism’s response to an exposure to foreign materials from the initial, molecular level interactions to signaling and regulatory cascades. Here, we present a methodology to quantify the essential interactions at the bionano interface, which can be used in combination with the adverse outcome pathway analysis to build mechanism-based predictive schemes for toxicity assessments. We introduce a set of new, advanced descriptors of the nanomaterials, which refer to their ability to bind biomolecules and trigger the pathways via the molecular initiating events.
      323
  • Publication
    EU US Roadmap Nanoinformatics 2030
    The Nanoinformatics Roadmap 2030 is a compilation of state-of-the-art commentaries from multiple interconnecting scientific fields, combined with issues involving nanomaterial (NM) risk assessment and governance. In bringing these issues together into a coherent set of milestones, the authors address three recognised challenges facing nanoinformatics: (1) limited data sets; (2) limited data access; and (3) regulatory requirements for validating and accepting computational models. It is also recognised that data generation will progress unequally and unstructured if not captured within a nanoinformatics framework based on harmonised, interconnected databases and standards. The implicit coordination efforts within such a framework ensure early use of the data for regulatory purposes, e.g., for the read-across method of filling data gaps.
      411
  • Publication
    Assessing the State of an Organism and Toxicity of Substances Using Biochemical Indicators
    (Karelian Research Centre of the Russian Academy of Sciences, 2017-10-01) ; ; ; ;
    Criteria for quantitatively describing the state and state variation of a living organism in various conditions are proposed using a set of its measurable characteristics (indicators, properties). The measurable indicators (of different origin and dimensionality) of an organism converted into a dimensionless form relative to their control values are combined into a single integral indicator showing relative deviation averaged over all the indicators. This integral indicator is useful to compare the results of different experiments. If the range of normal variability (reference range) is known for each indicator involved, the second integral indicator is proposed to be calculated as the normalized relative deviation from the middle of all reference ranges (in percentage) averaged over all the indicators. The calculation is performed in such a way that the range 0-100 % of this deviation of an indicator corresponds to the normal variability (where 0 corresponds to the middle of the reference range, 100 % - to its lower or upper limit), while the values over > 100 % represent a pathological response. The normalized relative deviation from the middle of all reference ranges (in percentage) averaged over all the indicators is an assessment of the state of a living organism (based on the given set of indicators) relative to the range of normal variability. The application of the suggested approach is illustrated by an example of a toxicological experiment.
      314
  • Publication
    Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?
    Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard.
      281Scopus© Citations 26
  • Publication
    Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations
    Molecular mechanisms play key roles at a fundamental and processing level, in innovative taste systems, functional and nutritional ingredients, and integrated solutions for the food, beverage and pharmaceutical markets. Incorporating a multiscale understanding of such mechanisms can provide greater insight into, and control of the relevant processes at play. Combining data from experiment, human panels and simulation through machine learning allows the construction of statistical models relating nano-scale properties to physiological outcomes and consumer tastes. This review will touch on several examples where advanced computer simulations at a molecular, meso- and multi-scale level can shed light into the mechanisms at play thereby facilitating their control. It includes a practical simulation toolbox for those new to in-silico modelling.
      373Scopus© Citations 30
  • Publication
    In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles
    The free energy of adsorption of proteins onto nanoparticles offers an insight into the biological activity of these particles in the body, but calculating these energies is challenging at the atomistic resolution. In addition, structural information of the proteins may not be readily available. In this work, we demonstrate how information about adsorption affinity of proteins onto nanoparticles can be obtained from first principles with minimum experimental input. We use a multiscale model of protein–nanoparticle interaction to evaluate adsorption energies for a set of 59 human blood serum proteins on gold and titanium dioxide (anatase) nanoparticles of various sizes. For each protein, we compare the results for 3D structures derived from experiments to those predicted computationally from amino acid sequences using the I-TASSER methodology and software. Based on these calculations and 2D and 3D protein descriptors, we develop statistical models for predicting the binding energy of proteins, enabling the rapid characterization of the affinity of nanoparticles to a wide range of proteins.
      156Scopus© Citations 14
  • Publication
    Translating Scientific Advances in the AOP Framework to Decision Making for Nanomaterials
    Much of the current innovation in advanced materials is occurring at the nanoscale, specifically in manufactured nanomaterials (MNs). MNs display unique attributes and behaviors, and may be biologically and physically unique, making them valuable across a wide range of applications. However, as the number, diversity and complexity of MNs coming to market continue to grow, assessing their health and environmental risks with traditional animal testing approaches is too time- and cost-intensive to be practical, and is undesirable for ethical reasons. New approaches are needed that meet current requirements for regulatory risk assessment while reducing reliance on animal testing and enabling safer-by-design product development strategies to be implemented. The adverse outcome pathway (AOP) framework presents a sound model for the advancement of MN decision making. Yet, there are currently gaps in technical and policy aspects of AOPs that hinder the adoption and use for MN risk assessment and regulatory decision making. This review outlines the current status and next steps for the development and use of the AOP framework in decision making regarding the safety of MNs. Opportunities and challenges are identified concerning the advancement and adoption of AOPs as part of an integrated approach to testing and assessing (IATA) MNs, as are specific actions proposed to advance the development, use and acceptance of the AOP framework and associated testing strategies for MN risk assessment and decision making. The intention of this review is to reflect the views of a diversity of stakeholders including experts, researchers, policymakers, regulators, risk assessors and industry representatives on the current status, needs and requirements to facilitate the future use of AOPs in MN risk assessment. It incorporates the views and feedback of experts that participated in two workshops hosted as part of an Organization for Economic Cooperation and Development (OECD) Working Party on Manufactured Nanomaterials (WPMN) project titled, “Advancing AOP Development for Nanomaterial Risk Assessment and Categorization”, as well as input from several EU-funded nanosafety research consortia.
      111Scopus© Citations 30
  • Publication
    Motion of Euglena gracilis: Active fluctuations and velocity distribution
    We study the velocity distribution of unicellular swimming algae Euglena gracilis using optical microscopy and active Brownian particle theory. To characterize a peculiar feature of the experimentally observed distribution at small velocities we use the concept of active fluctuations, which was recently proposed for the description of stochastically self-propelled particles [Romanczuk, P. and Schimansky-Geier, L., Phys. Rev. Lett. 106, 230601 (2011)]. In this concept, the fluctuating forces arise due to internal random performance of the propulsive motor. The fluctuating forces are directed in parallel to the heading direction, in which the propulsion acts. In the theory, we introduce the active motion via the depot model [Schweitzer, et al., Phys. Rev. Lett. 80(23), 5044 (1998)]. We demonstrate that the theoretical predictions based on the depot model with active fluctuations are consistent with the experimentally observed velocity distributions. In addition to the model with additive active noise, we obtain theoretical results for a constant propulsion with multiplicative noise.
      430Scopus© Citations 9
  • Publication
    Tricritical points in a Vicsek model of self-propelled particles with bounded confidence
    (American Physical Society, 2014-12-24) ; ;
    We study the orientational ordering in systems of self-propelled particles with selective interactions. To introduce the selectivity we augment the standard Vicsek model with a bounded-confidence collision rule: a given particle only aligns to neighbors who have directions quite similar to its own. Neighbors whose directions deviate more than a fixed restriction angle α are ignored. The collective dynamics of this system is studied by agent-based simulations and kinetic mean-field theory. We demonstrate that the reduction of the restriction angle leads to a critical noise amplitude decreasing monotonically with that angle, turning into a power law with exponent 3/2 for small angles. Moreover, for small system sizes we show that upon decreasing the restriction angle, the kind of the transition to polar collective motion changes from continuous to discontinuous. Thus, an apparent tricritical point with different scaling laws is identified and calculated analytically. We investigate the shifting and vanishing of this point due to the formation of density bands as the system size is increased. Agent-based simulations in small systems with large particle velocities show excellent agreement with the kinetic theory predictions. We also find that at very small interaction angles, the polar ordered phase becomes unstable with respect to the apolar phase. We derive analytical expressions for the dependence of the threshold noise on the restriction angle. We show that the mean-field kinetic theory also permits stationary nematic states below a restriction angle of 0.681 π. We calculate the critical noise, at which the disordered state bifurcates to a nematic state, and find that it is always smaller than the threshold noise for the transition from disorder to polar order. The disordered-nematic transition features two tricritical points: At low and high restriction angle, the transition is discontinuous but continuous at intermediate α. We generalize our results to systems that show fragmentation into more than two groups and obtain scaling laws for the transition lines and the corresponding tricritical points. A numerical method to evaluate the nonlinear Fredholm integral equation for the stationary distribution function is also presented. This method is shown to give excellent agreement with agent-based simulations, even in strongly ordered systems at noise values close to zero.
      389Scopus© Citations 31
  • Publication
    Coarse-grained model of adsorption of blood plasma proteins onto nanoparticles
    (AIP Publishing, 2015-12) ;
    We present a coarse-grained model for evaluation of interactions of globular proteins with nanoparticles (NPs). The protein molecules are represented by one bead per aminoacid and the nanoparticle by a homogeneous sphere that interacts with the aminoacids via a central force that depends on the nanoparticle size. The proposed methodology is used to predict the adsorption energies for six common human blood plasma proteins on hydrophobic charged or neutral nanoparticles of different sizes as well as the preferred orientation of the molecules upon adsorption. Our approach allows one to rank the proteins by their binding affinity to the nanoparticle, which can be used for predicting the composition of the NP-protein corona. The predicted ranking is in good agreement with known experimental data for proteinadsorption on surfaces.
      536Scopus© Citations 60