How decision analysis can further nanoinformatics

A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints

The increasing use of nanoparticles (NPs) in a wide range of consumer and industrial applications has necessitated significant effort to address the challenge of characterizing and quantifying the underlying nanostructure - biological response relationships to ensure that these novel materials can be exploited responsibly and safely. Such efforts demand reliable experimental data not only in terms of the biological dose-response, but also regarding the physicochemical properties of the NPs and their interaction with the biological environment. The latter has not been extensively studied, as a large surface to bind biological macromolecules is a unique feature of NPs that is not relevant for chemicals or pharmaceuticals, and thus only limited data have been reported in the literature quantifying the protein corona formed when NPs interact with a biological medium and linking this with NP cellular association/uptake. In this work we report the development of a predictive model for the assessment of the biological response (cellular association, which can include both internalized NPs and those attached to the cell surface) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs. Cellular association was chosen as the end-point for the original experimental study due to its relevance to inflammatory responses, biodistribution, and toxicity in vivo. The validated predictive model is freely available online through the Enalos Cloud Platform ( http://enalos.insilicotox.com/NanoProteinCorona/ ) to be used as part of a regulatory or NP safe-by-design decision support system. This online tool will allow the virtual screening of NPs, based on a list of the significant NP descriptors, identifying those NPs that would warrant further toxicity testing on the basis of predicted NP cellular association.

Co-constructive development of a green chemistry-based model for the assessment of nanoparticles synthesis

Nanomaterials (materials at the nanoscale, 10-9m) are extensively used in several industry sectors due to the improved properties they empower commercial products with. There is a pressing need to produce these materials more sustainably. This paper proposes a MCDA approach to assess the implementation of green chemistry principles as applied to the protocols for nanoparticles synthesis. In the presence of multiple green and environmentally oriented criteria, decision aiding is performed with a synergy of ordinal regression methods; preference information in the form of desired assignment for a subset of reference protocols is accepted. The classification models, indirectly derived from such information, are composed of an additive value function and a vector of thresholds separating the pre-defined and ordered classes. The method delivers a single representative model that is used to assess the relative importance of the criteria, identify the possible gains with improvement of the protocol's evaluations and classify the non-reference protocols. Such precise recommendation is validated against the outcomes of robustness analysis exploiting the sets of all classification models compatible with all maximal subsets of consistent assignment examples. The introduced approach is used with real-world data concerning silver nanoparticles. It is proven to effectively resolve inconsistency in the assignment examples, tolerate ordinal and cardinal measurement scales, differentiate between inter- and intra-criteria attractiveness and deliver easily interpretable scores and class assignments. This work thoroughly discusses the learning insights that MCDA provided during the co-constructive development of the classification model, distinguishing between problem structuring, preference elicitation, learning, modeling and problem-solving stages.

Robustness analysis of a green chemistry-based model for the classification of silver nanoparticles synthesis processes

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and/or decision-makers with diverse backgrounds.