Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Point mutants of α1-antitrypsin form ordered polymers that are retained as inclusions within the endoplasmic reticulum (ER) of hepatocytes in association with neonatal hepatitis, cirrhosis and hepatocellular carcinoma. These inclusions cause cell damage and predispose to ER stress in the absence of the classical unfolded protein response (UPR). The pathophysiology underlying this ER stress was explored by generating cell models that conditionally express wildtype α1-antitrypsin, two mutants that cause polymer-mediated inclusions and liver disease (E342K [the Z allele] and H334D) and a truncated mutant (Null Hong Kong, NHK) that induces classical ER stress and is removed by ER associated degradation. Expression of the polymeric mutants resulted in gross changes in the ER luminal environment that recapitulated the changes seen in liver sections from individuals with PI*ZZ α1-antitrypsin deficiency. In contrast expression of NHK α1-antitrypsin caused electron lucent dilatation and expansion of the ER throughout the cell. Photobleaching microscopy in live cells demonstrated a decrease in the mobility of soluble luminal proteins in cells that express E342K and H334D α1-antitrypsin when compared to those that express wildtype and NHK α1-antitrypsin (0.34±0.05, 0.22±0.03, 2.83±0.30 and 2.84±0.55 μm2/s respectively). There was no effect on protein mobility within ER membranes indicating that cisternal connectivity was not disrupted. Polymer expression alone was insufficient to induce the UPR but the resulting protein overload rendered cells hypersensitive to ER stress induced by either tunicamycin or glucose depletion. Conclusion Changes in protein diffusion provide an explanation for the cellular consequences of ER protein overload in mutants that cause inclusion body formation and α1-antitrypsin deficiency.

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BackgroundFlat epithelial atypia (FEA) of the breast is characterised by a few layers of mildly atypical luminal epithelial cells. Genetic changes found in ductal carcinoma in situ (DCIS) and invasive ductal breast cancer (IDC) are also found in FEA, albeit at a lower concentration. So far, miRNA expression changes associated with invasive breast cancer, like miR-21, have not been studied in FEA.MethodsWe performed miRNA in-situ hybridization (ISH) on 15 cases with simultaneous presence of normal breast tissue, FEA and/or DCIS and 17 additional cases with IDC. Expression of the miR-21 targets PDCD4, TM1 and PTEN was investigated by immunohistochemistry.ResultsTwo out of fifteen cases showed positive staining for miR-21 in normal breast ductal epithelium, seven out of fifteen cases were positive in the FEA component and nine out of twelve cases were positive in the DCIS component. A positive staining of miR-21 was observed in 15 of 17 IDC cases. In 12 cases all three components were present in one tissue block and an increase of miR-21 from normal breast to FEA and to DCIS was observed in five cases. In three cases the FEA component was negative, whereas the DCIS component was positive for miR-21. In three other cases, normal, FEA and DCIS components were negative for miR-21 and in the last case all three components were positive. Overall we observed a gradual increase in percentage of miR-21 positive cases from normal, to FEA, DCIS and IDC. Immunohistochemical staining for PTEN revealed no obvious changes in staining intensities in normal, FEA, DCIS and IDC. Cytoplasmic staining of PDCD4 increased from normal to IDC, whereas, the nuclear staining decreased. TM1 staining decreased from positive in normal breast to negative in most DCIS and IDC cases. In FEA, the staining pattern for TM1 was similar to normal breast tissue.ConclusionUpregulation of miR-21 from normal ductal epithelial cells of the breast to FEA, DCIS and IDC parallels morphologically defined carcinogenesis. No clear relation was observed between the staining pattern of miR-21 and its previously reported target genes.