Correlation of respiratory oscillometry with CT image analysis in a prospective cohort of idiopathic pulmonary fibrosis.

BACKGROUND: Markers of idiopathic pulmonary fibrosis (IPF) severity are based on measurements of forced vital capacity (FVC), diffusing capacity (DLCO) and CT. The pulmonary vessel volume (PVV) is a novel quantitative and independent prognostic structural indicator derived from automated CT analysis. The current prospective cross-sectional study investigated whether respiratory oscillometry provides complementary data to pulmonary function tests (PFTs) and is correlated with PVV. METHODS: From September 2019 to March 2020, we enrolled 89 patients with IPF diagnosed according to international guidelines. We performed standard spectral (5-37 Hz) and novel intrabreath tracking (10 Hz) oscillometry followed by PFTs. Patients were characterised with the gender-age-physiology (GAP) score. CT images within 6 months of oscillometry were analysed in a subgroup (26 patients) using automated lung texture analysis. Correlations between PFTs, oscillometry and imaging variables were investigated using different regression models. FINDINGS: The cohort (29F/60M; age=71.7±7.8 years) had mild IPF (%FVC=70±17, %DLCO=62±17). Spectral oscillometry revealed normal respiratory resistance, low reactance, especially during inspiration at 5 Hz (X5in), elevated reactance area and resonance frequency. Intrabreath oscillometry identified markedly low reactance at end-inspiration (XeI). XeI and X5in strongly correlated with FVC (r2=0.499 and 0.435) while XeI was highly (p=0.004) and uniquely correlated with the GAP score. XeI and PVV exhibited the strongest structural-functional relationship (r2=0.690), which remained significant after adjusting for %FVC, %DLCO and GAP score. INTERPRETATION: XeI is an independent marker of IPF severity that offers additional information to standard PFTs. The data provide a cogent rationale for adding oscillometry in IPF assessment.

Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials.

Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure-activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called 'nano-QSAR' models. 'External' leave-one-out cross-validation (LOO) analysis was performed, to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken, in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R²) for training and for LOO (0.83 and 0.65 for training and LOO, respectively), compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends.

Genotoxicity of metal oxide nanomaterials: review of recent data and discussion of possible mechanisms.

Nanotechnology has rapidly entered into human society, revolutionized many areas, including technology, medicine and cosmetics. This progress is due to the many valuable and unique properties that nanomaterials possess. In turn, these properties might become an issue of concern when considering potentially uncontrolled release to the environment. The rapid development of new nanomaterials thus raises questions about their impact on the environment and human health. This review focuses on the potential of nanomaterials to cause genotoxicity and summarizes recent genotoxicity studies on metal oxide/silica nanomaterials. Though the number of genotoxicity studies on metal oxide/silica nanomaterials is still limited, this endpoint has recently received more attention for nanomaterials, and the number of related publications has increased. An analysis of these peer reviewed publications over nearly two decades shows that the test most employed to evaluate the genotoxicity of these nanomaterials is the comet assay, followed by micronucleus, Ames and chromosome aberration tests. Based on the data studied, we concluded that in the majority of the publications analysed in this review, the metal oxide (or silica) nanoparticles of the same core chemical composition did not show different genotoxicity study calls (i.e. positive or negative) in the same test, although some results are inconsistent and need to be confirmed by additional experiments. Where the results are conflicting, it may be due to the following reasons: (1) variation in size of the nanoparticles; (2) variations in size distribution; (3) various purities of nanomaterials; (4) variation in surface areas for nanomaterials with the same average size; (5) differences in coatings; (6) differences in crystal structures of the same types of nanomaterials; (7) differences in size of aggregates in solution/media; (8) differences in assays; (9) different concentrations of nanomaterials in assay tests. Indeed, due to the observed inconsistencies in the recent literature and the lack of adherence to appropriate, standardized test methods, reliable genotoxicity assessment of nanomaterials is still challenging.

Evaluation of QSAR models for the prediction of ames genotoxicity: a retrospective exercise on the chemical substances registered under the EU REACH regulation.

We evaluated the performance of seven freely available quantitative structure-activity relationship models predicting Ames genotoxicity thanks to a dataset of chemicals that were registered under the EU Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation. The performance of the models was estimated according to Cooper's statistics and Matthew's Correlation Coefficients (MCC). The Benigni/Bossa rule base originally implemented in Toxtree and re-implemented within the Virtual models for property Evaluation of chemicals within a Global Architecture (VEGA) platform displayed the best performance (accuracy = 92%, sensitivity = 83%, specificity = 93%, MCC = 0.68) indicating that this rule base provides a reliable tool for the identification of genotoxic chemicals. Finally, we elaborated a consensus model that outperformed the accuracy of the individual models.

The acceptance of in silico models for REACH: Requirements, barriers, and perspectives.

In silico models have prompted considerable interest and debate because of their potential value in predicting the properties of chemical substances for regulatory purposes. The European REACH legislation promotes innovation and encourages the use of alternative methods, but in practice the use of in silico models is still very limited. There are many stakeholders influencing the regulatory trajectory of quantitative structure-activity relationships (QSAR) models, including regulators, industry, model developers and consultants. Here we outline some of the issues and challenges involved in the acceptance of these methods for regulatory purposes.

CAESAR models for developmental toxicity.

BACKGROUND: The new REACH legislation requires assessment of a large number of chemicals in the European market for several endpoints. Developmental toxicity is one of the most difficult endpoints to assess, on account of the complexity, length and costs of experiments. Following the encouragement of QSAR (in silico) methods provided in the REACH itself, the CAESAR project has developed several models. RESULTS: Two QSAR models for developmental toxicity have been developed, using different statistical/mathematical methods. Both models performed well. The first makes a classification based on a random forest algorithm, while the second is based on an adaptive fuzzy partition algorithm. The first model has been implemented and inserted into the CAESAR on-line application, which is java-based software that allows everyone to freely use the models. CONCLUSIONS: The CAESAR QSAR models have been developed with the aim to minimize false negatives in order to make them more usable for REACH. The CAESAR on-line application ensures that both industry and regulators can easily access and use the developmental toxicity model (as well as the models for the other four endpoints).