Endocrine disruption: the noise in available data adversely impacts the models' performance.

This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption (ED): the oestrogen (ER) and the androgen (AR) receptors. The ED-relevant data were retrieved from multiple sources, including the CERAPP, CoMPARA, and the Tox21 projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of generative topographic mapping revealed the problem of low agreement between experimental values from different sources. Collected data were used to train both classification models for ER and AR binding activities and regression models for relative binding affinity (RBA) and median inhibition concentration (IC50). These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient r 2 for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models' performance resulted from misinterpreted experimental endpoints or wrongly reported values, thus confirming the observations reported in CERAPP and CoMPARA studies. Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds, which are freely available.

Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish.

We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSEext = 0.56-0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/ColorAtom utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracy and data coverage. Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending the applicability domain and relevance for application in an industrial context. Generated models and collected public data are made freely available.

Publicly available QSPR models for environmental media persistence.

The evaluation of persistency of chemicals in environmental media (water, soil, sediment) is included in European Regulations, in the context of the Persistence, Bioaccumulation and Toxicity (PBT) assessment. In silico predictions are valuable alternatives for compounds screening and prioritization. However, already existing prediction tools have limitations: narrow applicability domains due to their relatively small training sets, and lack of medium-specific models. A dataset of 1579 unique compounds has been collected, merging several persistence data sources annotated by, at least, one experimental dissipation half-life value for the given environmental medium. This dataset was used to train binary classification models discriminating persistent/non-persistent (P/nP) compounds based on REACH half-life thresholds on sediment, water and soil compartments. Models were built using ISIDA (In SIlico design and Data Analysis) fragment descriptors and support vector regression, random forest and naïve Bayesian machine-learning methods. All models scored satisfactory performances: sediment being the most performing one (BAext = 0.91), followed by water (BAext = 0.77) and soil (BAext = 0.76). The latter suffer from low detection of persistent ('P') compounds (Snext = 0.50), reflecting discrepancies in reported half-life measurements among the different data sources. Generated models and collected data are made publicly available.

Modelling of ready biodegradability based on combined public and industrial data sources.

The European Registration, Evaluation, Authorization and Restriction of Chemical Substances Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB), considering in silico prediction as valid alternative to experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues. In this work, we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (Balance Accuracy (BA) = 0.74-0.79) and data coverage (83-91%). The Generative Topographic Mapping approach identified several chemotypes and structural motifs unique to the industrial dataset, highlighting for which chemical classes currently available models may have less reliable predictions. Finally, public and industrial data were merged into global dataset containing 3146 compounds. This is the biggest dataset reported in the literature so far, covering some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has larger applicability domain than the existing ones.

Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context.

We report predictive models of acute oral systemic toxicity representing a follow-up of our previous work in the framework of the NICEATM project. It includes the update of original models through the addition of new data and an external validation of the models using a dataset relevant for the chemical industry context. A regression model for LD50 and multi-class classification model for toxicity classes according to the Global Harmonized System categories were prepared. ISIDA descriptors were used to encode molecular structures. Machine learning algorithms included support vector machine (SVM), random forest (RF) and naïve Bayesian. Selected individual models were combined in consensus. The different datasets were compared using the generative topographic mapping approach. It appeared that the NICEATM datasets were lacking some relevant chemotypes for chemical industry. The new models trained on enlarged data sets have applicability domains (AD) sufficiently large to accommodate industrial compounds. The fraction of compounds inside the models' AD increased from 58% (NICEATM model) to 94% (new model). The increase of training sets improved models' prediction performance: RMSE values decreased from 0.56 to 0.47 and balanced accuracies increased from 0.69 to 0.71 for NICEATM and new models, respectively.

Biomarkers of early respiratory effects in smoking adolescents.

Noninvasive biomarkers can be used to evaluate airways damage caused by tobacco smoke, but studies so far have only involved adult smokers. In this study, we evaluated whether such biomarkers can detect early respiratory effects in adolescents passively or actively exposed to tobacco smoke. In a cross-sectional study of 845 adolescents (mean age 16 yrs), we measured exhaled nitric oxide (NO) and various epithelial markers in nasal lavage fluid (NALF) and serum, including Clara cell protein (CC16) and surfactant protein (SP)-D. Information about smoking habits and potential confounders was collected by questionnaire. Four groups of equal size (n = 36), of nonsmokers, passive smokers, light smokers (<5 cigarettes · day(-1), median 0.08 pack-yrs) and heavy smokers (≥ 5 cigarettes · day(-1), median 0.35 pack-yrs), were matched using an automated procedure. The levels of exhaled NO and of CC16 in NALF were significantly decreased in the group of heavy smokers. A trend towards lower levels of CC16 in NALF was observed in passive smokers. There were no significant changes in serum CC16 and SP-D, which suggests that the deep lung epithelium had not yet been affected by smoking. In conclusion, tobacco smoke can cause early changes in the airways of adolescents with a cumulative smoking history of <1 pack-yr.

CC16 as a marker of lung epithelial hyperpermeability in an acute model of rats exposed to mainstream cigarette smoke.

The Clara cell secretory protein (CC16), which is produced along the tracheal-bronchial tree, has been shown to be a sensitive marker for the detection of lung hyperpermeability. Cigarette smoke inhalation has been associated with increased lung epithelial permeability. In this study we investigated the changes in CC16 in serum and bronchoalveolar lavage fluid (BALF) from female Sprague Dawley rats after a single exposure (2 x 1 h) to diluted mainstream cigarette smoke (MS) from the Reference Cigarette 2R4F. Rats were nose-only exposed to MS at concentrations of 0 (sham exposure), 250, 500, 750, 1000 or 1250 microg total particulate matter per liter. At 2, 4, 15 and 24h after exposure, serum and BALF-samples were collected. CC16 was determined in BALF and serum. Albumin in BALF, another marker for lung permeability was also determined. A trend towards a lower CC16 recovery was observed in BALF from smoke-exposed rats. The CC16 concentration in serum showed a marked (up to five-fold) concentration- and time-dependent increase after MS exposure. The increase of CC16 in serum was most prominent at the early timepoints, i.e. 2 and 4 h after exposure, and a return to baseline concentrations was obvious at 24 h after exposure. The effect of MS exposure on the amount of albumin in BALF was limited (up to 60% increase). This study clearly showed that CC16 is a good marker for the assessment of the increased permeability of the lung/blood barrier after MS-exposure.

Identification of RELMgamma, a novel resistin-like molecule with a distinct expression pattern.

We have identified RELMgamma, a novel member of the resistin-like molecule/found in inflammatory zone (RELM/FIZZ) family in mice and rats. Microarray and real-time RT-PCR experiments revealed a repression of RELMgamma mRNA in nasal respiratory epithelium of cigarette smoke-exposed versus untreated rats. The analysis of the physiological tissue-specific expression revealed highest expression in hematopoietic tissues, suggesting a cytokine-like role for RELMgamma. RELMgamma is most closely related to RELMalpha/FIZZ1. Despite the high similarity, the expression properties of the two genes are clearly distinct. While RELMgamma (approved symbol retnlg) is expressed in rat white adipose tissue, minute to no expression of RELMalpha was detected in that system. Thus, previous reports analyzing RELMalpha expression in rat adipose tissue might have been influenced by cross-hybridization with RELMgamma. Finally we could demonstrate that white adipose tissue of mice shows strong RELMalpha expression but only low levels of RELMgamma, indicating a species-specific gene regulation.

Intracranial hemorrhage: principles of CT and MRI interpretation.

Accurate diagnosis of intracranial hemorrhage represents a frequent challenge for the practicing radiologist. The purpose of this article is to provide the reader with a synoptic overview of the imaging characteristics of intracranial hemorrhage, using text, tables, and figures to illustrate time-dependent changes. We examine the underlying physical, biological, and biochemical factors of evolving hematoma and correlate them with the aspect on cross-sectional imaging techniques. On CT scanning, the appearance of intracranial blood is determined by density changes which occur over time, reflecting clot formation, clot retraction, clot lysis and, eventually, tissue loss. However, MRI has become the technique of choice for assessing the age of an intracranial hemorrhage. On MRI the signal intensity of intracranial hemorrhage is much more complex and is influenced by multiple variables including: (a) age, location, and size of the lesion; (b) technical factors (e.g., sequence type and parameters, field strength); and (c) biological factors (e.g., pO2, arterial vs. venous origin, tissue pH, protein concentration, presence of a blood-brain barrier, condition of the patient). We discuss the intrinsic magnetic properties of sequential hemoglobin degradation products. The differences in evolution between extra- and intracerebral hemorrhages are addressed and illustrated.

Regulation of squalene synthetase in human hepatoma cell line Hep G2 by sterols, and not by mevalonate-derived non-sterols.

Incubations of Hep G2 cells for 18 h with human low-density lipoprotein (LDL) resulted in a decrease of squalene synthetase activity, whereas heavy high-density lipoprotein (hHDL) stimulated the activity. Simultaneous addition of LDL abolished the hHDL-induced stimulation, indicating that manipulating the regulatory sterol pool within the cells influenced the enzyme activity. Blocking the endogenous cholesterol synthesis either at the 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase site with compactin or at the 2,3-oxidosqualene cyclase site with the inhibitor U18666A gave rise to an elevation of the squalene synthetase activity. Simultaneous addition of mevalonate abolished the compactin-induced increase. However, at total blockade of sterol synthesis by 30 microM U18666A, added compactin and/or mevalonate did not change the enzyme activity further. It was concluded that sterols regulate the squalene synthetase activity, whereas, in contrast with the regulation of the HMG-CoA reductase activity in Hep G2 cells, mevalonate-derived non-sterols did not influence this enzyme.