Application of acoustic spectroscopy and neural networks to on-line size measurement of pharmaceutical nanocrystals.

Acoustic spectroscopy and neural networks (NNs) are applied to on-line real-time measurement of particle size distribution (PSD) during wet milling of pharmaceutical nanocrystals. A method for modeling the relationship between acoustic attenuation spectra and PSD is proposed that is based on NNs and principal component analysis (PCA). PCA reduces the dimensions of both the spectra and the PSD; then, a neural network model of 2 × 2 × 2 (input, hidden, output layer nodes) with only eight connection weights is built. Compared with previous instrument models that could require as many as 14 physical properties, the current approach does not need any prior knowledge of the system's properties. In addition, the time taken to complete a PSD measurement is reduced from minutes to seconds and it always generates a single solution, rather than possible multiple PSD solutions as in early methods. Application to hydrotalcite nanomilling found good agreement between the on-line measurements and off-line analysis.

Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity.

Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches.

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure-activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure-property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models.

Nano(Q)SAR: Challenges, pitfalls and perspectives.

Regulation for nanomaterials is urgently needed, and the drive to adopt an intelligent testing strategy is evident. Such a strategy will not only provide economic benefits but will also reduce moral and ethical concerns arising from animal testing. For regulatory purposes, such an approach is promoted by REACH, particularly the use of quantitative structure-activity relationships [(Q)SAR] as a tool for the categorisation of compounds according to their physicochemical and toxicological properties. In addition to compounds, (Q)SAR has also been applied to nanomaterials in the form of nano(Q)SAR. Although (Q)SAR in chemicals is well established, nano(Q)SAR is still in early stages of development and its successful uptake is far from reality. This article aims to identify some of the pitfalls and challenges associated with nano-(Q)SARs in relation to the categorisation of nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predictions from such models. Three major barriers were identified: the need to improve quality of experimental data in which the models are developed from, the need to have practical guidelines for the development of the nano(Q)SAR models and the need to standardise and harmonise activities for the purpose of regulation. Of these three, the first, i.e. the need to improve data quality requires immediate attention, as it underpins activities associated with the latter two. It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.

Principal component and causal analysis of structural and acute in vitro toxicity data for nanoparticles.

Structure toxicity relationship analysis was conducted using principal component analysis (PCA) for a panel of nanoparticles that included dry powders of oxides of titanium, zinc, cerium and silicon, dry powders of silvers, suspensions of polystyrene latex beads and dry particles of carbon black, nanotubes and fullerene, as well as diesel exhaust particles. Acute in vitro toxicity was assessed by different measures of cell viability, apoptosis and necrosis, haemolytic effects and the impact on cell morphology, while structural properties were characterised by particle size and size distribution, surface area, morphology, metal content, reactivity, free radical generation and zeta potential. Different acute toxicity measures were processed using PCA that classified the particles and identified four materials with an acute toxicity profile: zinc oxide, polystyrene latex amine, nanotubes and nickel oxide. PCA and contribution plot analysis then focused on identifying the structural properties that could determine the acute cytotoxicity of these four materials. It was found that metal content was an explanatory variable for acute toxicity associated with zinc oxide and nickel oxide, while high aspect ratio appeared the most important feature in nanotubes. Particle charge was considered as a determinant for high toxicity of polystyrene latex amine.

High-throughput continuous hydrothermal synthesis of nanomaterials (part II): unveiling the as-prepared CexZryYzO2-δ phase diagram.

High-throughput continuous hydrothermal flow synthesis was used to manufacture 66 unique nanostructured oxide samples in the Ce-Zr-Y-O system. This synthesis approach resulted in a significant increase in throughput compared to that of conventional batch or continuous hydrothermal synthesis methods. The as-prepared library samples were placed into a wellplate for both automated high-throughput powder X-ray diffraction and Raman spectroscopy data collection, which allowed comprehensive structural characterization and phase mapping. The data suggested that a continuous cubic-like phase field connects all three Ce-Zr-O, Ce-Y-O, and Y-Zr-O binary systems together with a smooth and steady transition between the structures of neighboring compositions. The continuous hydrothermal process led to as-prepared crystallite sizes in the range of 2-7 nm (as determined by using the Scherrer equation).

RT-qPCR analysis of dexB and galE gene expression of Streptococcus alactolyticus in Astragalus membranaceus fermentation.

Streptococcus alactolyticus strain FGM, isolated from chicken cecum, was used to increase the extract yield of polysaccharides during Astragalus membranaceus fermentation. It was previously demonstrated that polysaccharides from fermented A. membranaceus by S. alactolyticus had some similar properties to those from A. membranaceus in terms of its ability to help heal hepatic fibrosis in rats and modulate immunopotentiation of broiler chicken. However, methods to increase the yield of the polysaccharides during fermentation of A. membranaceus are not well understood. In this paper, we investigated the involvement of uridine diphosphate (UDP)-glucose 4-epimerase (galE) and glucan-1,6-α-glucosidase (dexB) during A. membranaceus fermentation through real-time reverse transcription quantitative PCR. The galE and dexB genes of S. alactolyticus were cloned by homology-based cloning and the genome walking method for the first time, and the 3D structure of dexB was analyzed by Swiss-PdbViewer 4.0.1 software. The expression of both the galE and dexB genes in A. membranaceus fermentation was studied using the determined ideal reference gene ldh for transcript normalization. The results showed that these two genes were both highly induced and peaked after 12 h of fermentation. The expression level of galE was stepwise increased from 48 to 72 h, while dexB transcripts were markedly increased at 60 h and decreased by 72 h. These data suggested that dexB and galE of S. alactolyticus strain FGM were involved in the regulation of A. membranaceus fermentation and they might play some roles in the increase of polysaccharides.