<i>Ortho</i>- and <i>Para</i>-Selective Ruthenium-Catalyzed C(sp²)–H Oxygenations of Phenol Derivatives

Versatile ruthenium catalysts allowed for efficient direct oxygenations of aryl carbamates under remarkably mild reaction conditions. In addition to chelation-assisted C–H activation, the optimized ruthenium catalyst proved amenable to <i>para</i>-selective hydroxylations of anisoles without Lewis basic directing groups

<i>In Silico</i> Prediction of Cytochrome P450-Mediated Biotransformations of Xenobiotics: A Case Study of Epoxidation

Predicting the biotransformation of xenobiotics is important in toxicology; however, as more compounds are synthesized than can be investigated experimentally, powerful computational methods are urgently needed to prescreen potentially useful candidates. Cytochrome P450 enzymes (P450s) are the major enzymes involved in xenobiotic metabolism, and many substances are bioactivated by P450s to form active compounds. An example is the conversion of olefinic substrates to epoxides, which are intermediates in the metabolic activation of many known or suspected carcinogens. We have calculated the activation energies for epoxidation by the active species of P450 enzymes (an iron-oxo porphyrin cation radical oxidant, compound I) for a diverse set of 36 olefinic substrates with state-of-the-art density functional theory (DFT) methods. Activation energies can be estimated by the computationally less demanding method of calculating the ionization potentials of the substrates, which provides a useful and simple predictive model based on the reaction mechanism; however, the preclassification of these diverse substrates into weakly polar and strongly polar groups is a prerequisite for the construction of specific predictive models with good predictability for P450 epoxidation. This approach has been supported by both internal and external validations. Furthermore, the relation between the activation energies for the regioselective epoxidation and hydroxylation reactions of P450s and experimental data has been investigated. The results show that the computational method used in this work, single-point energy calculations with the B3LYP functional including zero-point energy and solvation and dispersion corrections based on B3LYP-optimized geometries, performs well in reproducing the experimental trends of the epoxidation and hydroxylation reactions

Atomic Insights into Distinct Hormonal Activities of Bisphenol A Analogues toward PPARγ and ERα Receptors

Bisphenol A analogues (BPAs) belong to a wide variety of large volume chemicals with diverse applications yet emerging environmental concerns. Limited experimental data have demonstrated that BPAs with different halogenation patterns distinctly affect the agonistic activities toward proliferator-activated receptor (PPAR)­γ and estrogen receptors (ER)­α. Understanding the modes of action of BPAs toward different receptors is essential, however, the underlying molecular mechanism is still poorly understood. Here we probed the molecular recognition process of halogenated BPAs including TBBPA, TCBPA, BPAF, BPC, triBBPA, diBBPA, and monoBBPA toward PPARγ and ERα by molecular modeling, especially the impact of different halogen patterns. Increasing bromination at phenolic rings of BPAs was found highly correlated with electrostatic interactions (<i>R</i>² = 0.978 and 0.865 toward PPARγ and ERα, respectively) and van der Waals interactions (<i>R</i>² = 0.995 and 0.994 toward PPARγ and ERα, respectively). More halogenated phenolic rings at 3,5-positions of BPAs increase the shielding of the hormonally active phenolic OH and markedly decrease electrostatic interactions favorable for agonistic activities toward PPARγ, but unfavorable for agonistic activities toward ERα. The halogenation at the phenolic rings of BPAs exerts more impact on molecular electrostatic potential distribution than halogenation at the bridging alkyl moiety. Different halogenations further alter hydrogen bond interactions of BPAs and induce conformational changes of PPARγ ligand binding domain (LBD) and ERα LBD, specifically affecting the stabilization of helix H12 attributable to the different agonistic activities. Our results indicate that structural variations in halogenation patterns result in different interactions of BPAs with PPARγ LBD and ERα LBD, potentially causing distinct agonistic/antagonistic toxic effects. The various halogenation patterns should be fully considered for the design of future environmentally benign chemicals with reduced toxicities and desired properties

Probing the Molecular Interaction of Triazole Fungicides with Human Serum Albumin by Multispectroscopic Techniques and Molecular Modeling

Triazole fungicides, one category of broad-spectrum fungicides, are widely applied in agriculture and medicine. The extensive use leads to many residues and casts potential detrimental effects on aquatic ecosystems and human health. After exposure of the human body, triazole fungicides may penetrate into the bloodstream and interact with plasma proteins. Whether they could have an impact on the structure and function of proteins is still poorly understood. By using multispectroscopic techniques and molecular modeling, the interaction of several typical triazole fungicides with human serum albumin (HSA), the major plasma protein, was investigated. The steady-state and time-resolved fluorescence spectra manifested that static type, due to complex formation, was the dominant mechanism for fluorescence quenching. Structurally related binding modes speculated by thermodynamic parameters agreed with the prediction of molecular modeling. For triadimefon, hydrogen bonding with Arg-218 and Arg-222 played an important role, whereas for imazalil, myclobutanil, and penconazole, the binding process was mainly contributed by hydrophobic and electrostatic interactions. Via alterations in three-dimensional fluorescence and circular dichroism spectral properties, it was concluded that triazoles could induce slight conformational and some microenvironmental changes of HSA. It is anticipated that these data can provide some information for possible toxicity risk of triazole fungicides to human health and be helpful in reinforcing the supervision of food safety

Basic statistical features of the data sets.

<p>Basic statistical features of the data sets.</p

Exposure to Organochlorine Pollutants and Type 2 Diabetes: A Systematic Review and Meta-Analysis

<div><p>Objective</p><p>Though exposure to organochlorine pollutants (OCPs) is considered a risk factor for type 2 diabetes (T2DM), epidemiological evidence for the association remains controversial. A systematic review and meta-analysis was applied to quantitatively evaluate the association between exposure to OCPs and incidence of T2DM and pool the inconsistent evidence.</p><p>Design and Methods</p><p>Publications in English were searched in MEDLINE and WEB OF SCIENCE databases and related reference lists up to August 2013. Quantitative estimates and information regarding study characteristics were extracted from 23 original studies. Quality assessments of external validity, bias, exposure measurement and confounding were performed, and subgroup analyses were conducted to examine the heterogeneity sources.</p><p>Results</p><p>We retrieved 23 eligible articles to conduct this meta-analysis. OR (odds ratio) or RR (risk ratio) estimates in each subgroup were discussed, and the strong associations were observed in PCB-153 (OR, 1.52; 95% CI, 1.19–1.94), PCBs (OR, 2.14; 95% CI, 1.53–2.99), and <i>p,p′</i>-DDE (OR, 1.33; 95% CI, 1.15–1.54) based on a random-effects model.</p><p>Conclusions</p><p>This meta-analysis provides quantitative evidence supporting the conclusion that exposure to organochlorine pollutants is associated with an increased risk of incidence of T2DM.</p></div

The performance of different methods on RYM data set.

<p>The parameters are set as: for HHM; for GHHM; for WGHC; for uKNN; for iKNN.</p

How to add a ground user to user-item bipartite network.

<p>Graph (a) is the original network. Graph (b) is the network after adding the ground user presented by filled circle. The red lines are the new added links between ground user and all the items.</p

Modeling of Toxicity-Relevant Electrophilic Reactivity for Guanine with Epoxides: Estimating the Hard and Soft Acids and Bases (HSAB) Parameter as a Predictor

According to the electrophilic theory in toxicology, many chemical carcinogens in the environment and/or their active metabolites are electrophiles that exert their effects by forming covalent bonds with nucleophilic DNA centers. The theory of hard and soft acids and bases (HSAB), which states that a toxic electrophile reacts preferentially with a biological macromolecule that has a similar hardness or softness, clarifies the underlying chemistry involved in this critical event. Epoxides are hard electrophiles that are produced endogenously by the enzymatic oxidation of parent chemicals (e.g., alkenes and PAHs). Epoxide ring opening proceeds through a S_N2-type mechanism with hard nucleophile DNA sites as the major facilitators of toxic effects. Thus, the quantitative prediction of chemical reactivity would enable a predictive assessment of the molecular potential to exert electrophile-mediated toxicity. In this study, we calculated the activation energies for reactions between epoxides and the guanine N7 site for a diverse set of epoxides, including aliphatic epoxides, substituted styrene oxides, and PAH epoxides, using a state-of-the-art density functional theory (DFT) method. It is worth noting that these activation energies for diverse epoxides can be further predicted by quantum chemically calculated nucleophilic indices from HSAB theory, which is a less computationally demanding method than the exacting procedure for locating the transition state. More importantly, the good qualitative/quantitative correlations between the chemical reactivity of epoxides and their bioactivity suggest that the developed model based on HSAB theory may aid in the predictive hazard evaluation of epoxides, enabling the early identification of mutagenicity/carcinogenicity-relevant S_N2 reactivity

Study characteristics.

a<p>Different models adjusted by confounding, such as sex, age, BMI, total cholesterol and triglycerides, and various compounds.</p>b<p>stratified by BMI.</p>c<p>stratified by the years diagnosed after the baseline investigation.</p><p>Study characteristics.</p