Inhalation TTC values: A new integrative grouping approach considering structural, toxicological and mechanistic features.

The present publication describes an integrative grouping concept to derive threshold values for inhalation exposure. The classification scheme starts with differences in toxicological potency and develops criteria to group compounds into two potency classes, namely toxic (T-group) or low toxic (L-group). The TTC concept for inhalation exposure is based on the TTC RepDose data set, consisting of 296 organic compounds with 608 repeated-dose inhalation studies. Initially, 21 structural features (SFs) were identified as being characteristic for compounds of either high or low NOEC values (Schüürmann et al., 2016). In subsequent analyses these SF groups were further refined by taking into account structural homogeneity, type of toxicological effect observed, differences in absorption, metabolism and mechanism of action (MoA), to better define their structural and toxicological boundaries. Differentiation of a local or systemic mode of action did not improve the classification scheme. Finally, 28 groups were discriminated: 19 T-groups and 9 L-groups. Clearly distinct thresholds were derived for the T- and L-toxicity groups, being 2 × 10(-5) ppm (2 µg/person/day) and 0.05 ppm (4260 µg/person/day), respectively. The derived thresholds and the classification are compared to the initial mainly structure driven grouping (Schüürmann et al., 2016) and to the Cramer classification.

Identification of oxidative coupling products of xylenols arising from laboratory-scale phytoremediation.

Oxidative coupling reactions take place during the passage of xylenols through a laboratory-scale helophyte-based constructed wetland system. Typical coupling product groups including tetramethyl-[1,1'-biphenyl] diols and tetramethyl diphenylether monools as stable organic intermediates could be identified by a combination of pre-chromatographic derivatization and GC/MS analysis. Structural assignment of individual analytes was performed by an increment system developed by Zenkevich to pre-calculate retention sequences. The most abundant analyte turned out to be 3,3',5,5'-tetramethyl-[1,1'-biphenyl]-4,4'-diol, which can be formed by a combination of radicals based on 2,6-xylenol or by an attack of a 2,6-xylenol-based radical on 2,6-xylenol. Organic intermediates originating from oxidative coupling could also be identified in anaerobic constructed wetland systems. This finding suggested the presence of (at least partly) oxic conditions in the rhizosphere.

Application of neural networks to modeling and estimating temperature-dependent liquid viscosity of organic compounds.

Back-propagation neural network models for correlating and predicting the viscosity-temperature behavior of a large variety of organic liquids were developed. Experimental values for the liquid viscosity for 1229 data points from 440 compounds containing C, H, N, O, S, and all halogens have been collected from the literature. The data ranges covered are from -120 to 160 degrees C for temperature and from 0.164 (trans-2-pentene at 20 degrees C) to 1.34 x 10(5) (glycerol at -20 degrees C) mPa.s for viscosity value. After dividing the total database of 440 compounds into training (237 with 673 data points), validation (124 with 423 data points), and test (79 with 133 data points) sets, the modeling performance of two separate neural network models with different architectures, one based on a compound-specific temperature dependence and the second based on a compound-independent one, has been examined. The resulting former model showed somewhat better modeling performance than latter, and the model gave squared correlation coefficients of 0.956, 0.932, and 0.884 and root mean-squares errors of 0.122, 0.134, and 0.148 log units for the training, validation, and test sets, respectively. The input descriptors include molar refraction, critical temperature, molar magnetic susceptibility, cohesive energy, temperatures, and five kinds of indicator variables for functionalities, alcohols/phenols, nitriles, amines, amides, and aliphatic ring. The reliability of the proposed model was assessed by comparing the results against calculated viscosities by two existing group-contribution approaches, the method of van Velzen et al. and the Joback and Reid method.