Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform

In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation

The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.

Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size

A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.

Sustainable lignin nanoparticles from coconut fiber waste for enhancing multifunctional properties of macroalgae biofilms

Multifunctional and sustainable packaging biofilms felicitous to changeable conditions are in large demand as substitutes to petroleum-derived synthetic films. Macroalgae with noticeable film-formation, abundant, low-cost, and edible properties is a promising bioresource for sustainable and eco-friendly packaging materials. However, the poor hydrophobicity and mechanical properties of sustainable macroalgae biofilms seriously impede their practical applications. Herein, lignin nanoparticles (LNPs) produced by a sustainable approach from black liquor of coconut fiber waste were incorporated in the macroalgae matrix to improve the water tolerance and mechanical characteristics of the biofilms. The effect of different LNPs loadings on the performance of biofilms, such as physical, morphological, surface roughness, structural, water resistance, mechanical, and thermal behaviors, were systematically evaluated and found to be considerably improved. Biofilm with 6 % LNPs presented the optimum enhancement in most ultimate performances. The optimized biofilm exhibited great hydrophobic features with a water contact angle of over 100° and high enhancement in the tensile strength of >60 %. This study proposes a facile and sustainable approach for designing and developing LNPs-macroalgae biofilms with excellent and multifunctional properties for sustainable high-performance packaging materials.

Deimos: A novel automated methodology for optimal grouping. Application to nanoinformatics case studies

In this study we present deimos, a computational methodology for optimal grouping, applied on the read-across prediction of engineered nanomaterials' (ENMs) toxicity-related properties. The method is based on the formulation and the solution of a mixed-integer optimization program (MILP) problem that automatically and simultaneously performs feature selection, defines the grouping boundaries according to the response variable and develops linear regression models in each group. For each group/region, the characteristic centroid is defined in order to allocate untested ENMs to the groups. The deimos MILP problem is integrated in a broader optimization workflow that selects the best performing methodology between the standard multiple linear regression (MLR), the least absolute shrinkage and selection operator (LASSO) models and the proposed deimos multiple-region model. The performance of the suggested methodology is demonstrated through the application to benchmark ENMs datasets and comparison with other predictive modelling approaches. However, the proposed method can be applied to property prediction of other than ENM chemical entities and it is not limited to ENMs toxicity prediction.

Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power (Q²_EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics–AI systems, limitations thereof and recent tools were also discussed.

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

Using the Isalos platform to develop a (Q)SAR model that predicts metal oxide toxicity utilizing facet-based electronic, image analysis-based, and periodic table derived properties as descriptors

Engineered nanoparticles (NPs) are being studied for their potential to harm humans and the environment. Biological activity, toxicity, physicochemical properties, fate, and transport of NPs must all be evaluated and/or predicted. In this work, we explored the influence of metal oxide nanoparticle facets on their toxicity towards bronchial epithelial (BEAS-2B), Murine myeloid (RAW 264.7), and E. coli cell lines. To estimate the toxicity of metal oxide nanoparticles grown to a low facet index, a quantitative structure–activity relationship ((Q)SAR) approach was used. The novel model employs theoretical (density functional theory calculations) and experimental studies (transmission electron microscopy images from which several particle descriptors are extracted and toxicity data extracted from the literature) to investigate the properties of faceted metal oxides, which are then utilized to construct a toxicity model. The classification mode of the k-nearest neighbour algorithm (EnaloskNN, Enalos Chem/Nanoinformatics) was used to create the presented model for metal oxide cytotoxicity. Four descriptors were identified as significant: core size, chemical potential, enthalpy of formation, and electronegativity count of metal oxides. The relationship between these descriptors and metal oxide facets is discussed to provide insights into the relative toxicities of the nanoparticle. The model and the underpinning dataset are freely available on the NanoSolveIT project cloud platform and the NanoPharos database, respectively.

Ecotoxicological read-across models for predicting acute toxicity of freshly dispersed versus medium-aged NMs to Daphnia magna

Nanoinformatics models to predict the toxicity/ecotoxicity of nanomaterials (NMs) are urgently needed to support commercialization of nanotechnologies and allow grouping of NMs based on their physico-chemical and/or (eco)toxicological properties, to facilitate read-across of knowledge from data-rich NMs to data-poor ones. Here we present the first ecotoxicological read-across models for predicting NMs ecotoxicity, which were developed in accordance with ECHA's recommended strategy for grouping of NMs as a means to explore in silico the effects of a panel of freshly dispersed versus environmentally aged (in various media) Ag and TiO₂ NMs on the freshwater zooplankton Daphnia magna, a keystone species used in regulatory testing. The dataset used to develop the models consisted of dose-response data from 11 NMs (5 TiO₂ NMs of identical cores with different coatings, and 6 Ag NMs with different capping agents/coatings) each dispersed in three different media (a high hardness medium (HH Combo) and two representative river waters containing different amounts of natural organic matter (NOM) and having different ionic strengths), generated in accordance with the OECD 202 immobilization test. The experimental hypotheses being tested were (1) that the presence of NOM in the medium would reduce the toxicity of the NMs by forming an ecological corona, and (2) that environmental ageing of NMs reduces their toxicity compared to the freshly dispersed NMs irrespective of the medium composition (salt only or NOM-containing). As per the ECHA guidance, the NMs were grouped into two categories - freshly dispersed and 2-year-aged and explored in silico to identify the most important features driving the toxicity in each group. The final predictive models have been validated according to the OECD criteria and a QSAR model report form (QMRF) report included in the supplementary information to support adoption of the models for regulatory purposes.

Surface Functionalization of Graphene-Based Materials: Biological Behavior, Toxicology, and Safe-By-Design Aspects

The increasing exploitation of graphene-based materials (GBMs) is driven by their unique properties and structures, which ignite the imagination of scientists and engineers. At the same time, the very properties that make them so useful for applications lead to growing concerns regarding their potential impacts on human health and the environment. Since GBMs are inert to reaction, various attempts of surface functionalization are made to make them reactive. Herein, surface functionalization of GBMs, including those intentionally designed for specific applications, as well as those unintentionally acquired (e.g., protein corona formation) from the environment and biota, are reviewed through the lenses of nanotoxicity and design of safe materials (safe-by-design). Uptake and toxicity of functionalized GBMs and the underlying mechanisms are discussed and linked with the surface functionalization. Computational tools that can predict the interaction of GBMs behavior with their toxicity are discussed. A concise framing of current knowledge and key features of GBMs to be controlled for safe and sustainable applications are provided for the community.