Activities ('Puzzles')

Computational algorithms will be developed within four work packages related to the following thematic areas (Fig. 1):
• Quality assessment of physicochemical and toxicological data available for nanomaterials and data exploration (NanoDATA),
• Development of novel descriptors for nanoparticles’ structure (NanoDESC),
• Simulating interactions of nanoparticles with biological systems (NanoINTER),
• Quantitative and qualitative structure-activity relationship modelling, grouping and read across (NanoQSAR).

NanoDATA (WP1)

Puzzle 1: NanoDATA

 The main objective of the first thematic area is to develop a framework for classified engineered nanoparticles based on the existing data utilising pattern recognition methods. This includes:

• Collecting and evaluating the existing data (physicochemical and toxicity data).
• Developing statistical procedures for data evaluation.
• Exploring the physico/chemical and toxicity data with pattern recognition techniques (the characterisation and classification techniques) to identify classes of similar properties/toxicity.
• Launching a publicly available database with high quality (evaluated) empirical data.

NanoDESC (WP2)

Puzzle 2: NanoDESC

The main objective of the second thematic area is to develop a framework for the optimal characterisation of the structure of engineered nanoparticles with use of appropriate descriptors and by categorising them according to structural similarities. This includes:

• Evaluation of the existing systems currently available for structural characterization of NPs.
• Development of simplified molecular models sufficient to characterize the whole structure
• Development of descriptors for the nanostructure (“nano-descriptors”) of four types:
(i) topological descriptors, which are calculated with molecular graphs, SMILES, InChI, SMART notations, and descriptors based on the technological and physicochemical parameters
(ii) descriptors derived from quantum-mechanical calculations,
(iii) descriptors derived from computational processing of microscopic (SEM/TEM/AFM) images of the particles,
(iv) descriptors based on the anisotropy dimensions (as proposed by Glotzer and Solomon).
• Development of databases of the physicochemical and biochemical properties of the nanomaterials which will be made available via the internet
• Development of freeware for calculating nanodescriptors.


Puzzle 3: NanoINTER

The objective of the third thematic area is to develop methods to predict and explain interactions of engineered nanoparticles with biological systems and small molecules. This includes:

• Development of a protocol which will provide the guidelines for developing or implementing a model  for the study of large interacting systems.
• Development of a hierarchy of computational models for the study of interacting systems involving NPs and biological molecules of varying size.
• Development of techniques for the study of the environment  (e.g. solvent) on the interacting system.
• Study of the effect of the computational model (e.g. level of quantum-mechanical theory) on the results.
• Implementation of techniques for the resolution of the interaction energy into various contributions (e.g. those due to electrostatic forces, dispersion etc).
• Design/recognition of  functional groups which seriously reduce the genotoxicity and increase the solubility of the considered NPs.
• Study of factors affecting the interaction of the selected systems, nanoparticles (NP) /{biological molecule}.

There are several important factors related with the NP. Among those we note: (i) the chemical composition of the NP (e.g. fullerene, CNT, etc.); (ii) the size and shapeof the NP; (iii) the particle aggregation; (iv) the surface charge of the NPs, which is known to affect their cellular uptake; (v) contamination. NPs (e.g. CNTs) may involve one or more toxic metals (e.g. Fe, Co, Ni) which may be considered as contaminants; (vi) functionalization. It is understood that functionalization may affect the toxicity of the NP as well as its solubility. We shall look for functional groups which seriously reduce the genotoxicity and increase the solubility of the considered NPs. Thus we propose to consider how the above factors affect the interaction of the selected systems: NP/{biological molecule}.

Moreover, engineered nanoparticles exposed to environment participate in reactions of other environmental pollutants (oxidation reactions etc.) and can change as reaction rates of degradation (oxidation) processes of those pollutants, as well as to change reaction pathways and produce new metabolites.

NanoQSAR (WP4)

Puzzle 4: NanoQSAR

The objective of this thematic area is to develop scientifically justified and technically viable methods of quantitative modelling relationships between chemical structure and toxicological targets which will extend understanding of toxicity and behaviour of emerging nanoparticles by establishing relations between experimental (based on available, validated data) and computational properties. This includes:

• Investigating the impact of size on the physico/chemical properties of NPs at the appropriate level of the quantum-mechanical theory.
• Developing NanoQSAR models of toxicity and environmentally relevant physico/chemical endpoints, based on reliable experimental data and appropriate nano-descriptors.
• Comparing the efficiency of CoMFA/CoMSIA and Hansch Analysis modelling schemes in NanoQSAR.
• Investigating the minimum requirements sufficient for successful validation of NanoQSAR models (minimal number of data, evaluation of the applicability domain etc.) in the light of the OECD Principles for the Validation of (Q)SARs.
• Development of procedures for validating QSPR/QSAR models using probabilistic principles: balance of correlations, balance of correlations with ideal slopes, and filtration of the rare attributes, which can lead to overtraining.
• Estimating the environmental behaviour of NPs based on the physico/chemical data predicted with NanoQSAR.
• Evaluation and publication of the NanoQSAR models and the results in scientific journals and with use of QSAR reporting formats (QMRFs) and QSAR prediction reporting formats (QPRFs).
• Update of the database (including the predicted results).
• Development of the conceptual framework for further grouping NPs based on chemical structure, physicochemical properties, interactions and toxicity.

This part of the NanoPUZZLES brings together all findings and summarizes the results of the project. High quality experimental physicochemical and toxicological data (from Puzzle 1: NanoDATA) and novel descriptors of nanostructure (developed in Puzzle 2: NanoDESC) will be utilized to develop mathematical models describing relationships between the structure and properties/activity. Information on the significance of structural factors responsible for the observed activity will be delivered by Puzzle 3: NanoINTER. The information about the character of interaction mechanisms will be important for an appropriate selection of nanodescriptors representing structural features of the studied nanoparticles.

WP5. Dissemination

The NanoPUZZLES project summary you can find in our Information Brochure.

WP6. Management