Nitrogen mustard (NM) is a toxic vesicant known to cause damage to the respiratory tract. Injury is associated with increased expression of inducible nitric oxide synthase (iNOS). In these studies we analyzed the effects of transient inhibition of iNOS using aminoguanidine (AG) on NM-induced pulmonary toxicity. Rats were treated intratracheally with 0.125 mg/kg NM or control. Bronchoalveolar lavage fluid (BAL) and lung tissue were collected 1 d - 28 d later and lung injury, oxidative stress and fibrosis assessed. NM exposure resulted in progressive histopathological changes in the lung including multifocal lesions, perivascular and peribronchial edema, inflammatory cell accumulation, alveolar fibrin deposition, bronchiolization of alveolar septal walls, and fibrosis. This was correlated with trichrome staining and expression of proliferating cell nuclear antigen (PCNA). Expression of heme oxygenase (HO)-1 and manganese superoxide dismutase (Mn-SOD) was also increased in the lung following NM exposure, along with levels of protein and inflammatory cells in BAL, consistent with oxidative stress and alveolar-epithelial injury. Both classically activated proinflammatory (iNOS+ and cyclooxygenase-2+) and alternatively activated profibrotic (YM-1+ and galectin-3+) macrophages appeared in the lung following NM administration; this was evident within 1 d, and persisted for 28 d. AG administration (50 mg/kg, 2x/day, 1 d - 3 d) abrogated NM-induced injury, oxidative stress and inflammation at 1 d and 3 d post exposure, with no effects at 7 d or 28 d. These findings indicate that nitric oxide generated via iNOS contributes to acute NM-induced lung toxicity, however, transient inhibition of iNOS is not sufficient to protect against pulmonary fibrosis.

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

Nitrogen mustard (NM) is a bifunctional alkylating agent that causes acute injury to the lung that progresses to fibrosis. This is accompanied by a prominent infiltration of macrophages into the lung and upregulation of proinflammatory/profibrotic cytokines including tumor necrosis factor (TNF)α. In these studies, we analyzed the ability of anti-TNFα antibody to mitigate NM-induced lung injury, inflammation, and fibrosis. Treatment of rats with anti-TNFα antibody (15 mg/kg, iv, every 9 days) beginning 30 min after intratracheal administration of NM (0.125 mg/kg) reduced progressive histopathologic alterations in the lung including perivascular and peribronchial edema, macrophage/monocyte infiltration, interstitial thickening, bronchiolization of alveolar walls, fibrin deposition, emphysema, and fibrosis. NM-induced damage to the alveolar-epithelial barrier, measured by bronchoalveolar lavage (BAL) protein and cell content, was also reduced by anti-TNFα antibody, along with expression of the oxidative stress marker, heme oxygenase-1. Whereas the accumulation of proinflammatory/cytotoxic M1 macrophages in the lung in response to NM was suppressed by anti-TNFα antibody, anti-inflammatory/profibrotic M2 macrophages were increased or unchanged. Treatment of rats with anti-TNFα antibody also reduced NM-induced increases in expression of the profibrotic mediator, transforming growth factor-β. This was associated with a reduction in NM-induced collagen deposition in the lung. These data suggest that inhibiting TNFα may represent an efficacious approach to mitigating lung injury induced by mustards.

In this paper, we present MolFind, a highly multi-threaded pipeline type software package for use as an aid in identifying chemical structures in complex biofluids and mixtures. MolFind is specifically designed for high performance liquid chromatography/mass spectrometry (HPLC/MS) data inputs typical of metabolomics studies where structure identification is the ultimate goal. MolFind enables compound identification by matching HPLC/MS based experimental data obtained for an unknown compound with computationally derived HPLC/MS values for candidate compounds downloaded from chemical databases such as PubChem. The downloaded “bins” consist of all compounds matching the monoisotopic molecular weight of the unknown. The computational HPLC/MS values predicted include retention index (RI), ECOM50 (energy required to fragment 50% of a selected precursor ion), drift time and collision induced dissociation (CID) spectrum. RI, ECOM50, and drift time models are used for filtering compounds downloaded from PubChem. The remaining candidates are then ranked based on CID spectra matching. Current RI and ECOM50 models allow for the removal of about 28% of compounds from PubChem bins. Our estimates suggest that this could be improved to as much as 87% with additional chemical structures included in the computational models. Quantitative structure property relationship based modeling of drift times showed a better correlation with experimentally determined drift times than did Mobcal cross sectional areas. In 23/35 example cases, filtering PubChem bins with RI and ECOM50 predictive models resulted in improved ranking of the unknown compound compared to previous studies using CID spectra matching alone. In 19/35 examples, the correct candidate was ranked within the top 20 compounds in bins containing an average of 1635 compounds.