Cytochrome P450 3A4 metabolizes nearly 50% of the drugs currently in clinical use with a broad range of substrate specificity. Early prediction of metabolites of xenobiotic compounds is crucial for cost efficient drug discovery and development. We developed a new combined model, MLite, for the prediction of regioselectivity in the cytochrome P450 3A4 mediated metabolism. In the model, the ensemble catalyticphore- based docking method was implemented for the accessibility prediction, and the activation energy estimation method of Korzekwa et al. was used for the reactivity prediction. Four major metabolic reactions, aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation reaction, were included and the reaction data, metabolite information, were collected for 72 well-known substrates. The 47 drug molecules were used as the training set, and the 25 well-known substrates were used as the test set for the ensemble catalyticphore-based docking method. MLite predicted correctly about 76% of the first two sites in the ranking list of the test set. This predictability is comparable with that of another combined model, MetaSite, and the recently published QSAR model proposed by Sheridan et al. MLite also offers information about binding configurations of the substrate-enzyme complex. This may be useful in drug modification by the structure-based drug design.

A kinetic, reactivity-binding model has been proposed to predict the regioselectivity of substrates meditated by the CYP1A2 enzyme, which is responsible for the metabolism of planar-conjugated compounds such as caffeine. This model consists of a docking simulation for binding energy and a semiempirical molecular orbital calculation for activation energy. Possible binding modes of CYP1A2 substrates were first examined using automated docking based on the crystal structure of CYP1A2, and binding energy was calculated. Then, activation energies for CYP1A2-mediated metabolism reactions were calculated using the semiempirical molecular orbital calculation, AM1. Finally, the metabolic probability obtained from two energy terms, binding and activation energies, was used for predicting the most probable metabolic site. This model predicted 8 out of 12 substrates accurately as the primary preferred site among all possible metabolic sites, and the other four substrates were predicted into the secondary preferred site. This method can be applied for qualitative prediction of drug metabolism mediated by CYP1A2 and other CYP450 family enzymes, helping to develop drugs efficiently.

In hot and humid climates, everyday, heat exposures continuously challenge the health of outdoor workers.

In an effort to improve drug design and predictions for pharmacokinetics (PK), an empirical model was developed to predict the activation energies (Ea) of cytochrome P450 (CYP450) mediated metabolism. The model, EaMEAD (Activation energy of Metabolism reactions with Effective Atomic Descriptors), predicts the Ea of four major metabolic reactions of the CYP450 enzyme: aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation. To build and validate the empirical model, the E(a) values of the substrates with diverse chemical structures (394 metabolic sites for aliphatic hydroxylation, 27 metabolic sites for N-dealkylation, 9 metabolic sites for O-dealkylation, and 85 metabolic sites for aromatic hydroxylation) were calculated by AM1 molecular orbital (MO). Empirical equations, Quantitative Structure Activity Relationship (QSAR) models, were derived using effective atomic charge, effective atomic polarizability, and bond dipole moments of the substrates as descriptors. EaMEAD is shown to accurately predict Ea with a correlation coefficient (R) of 0.94 and root-mean-square error (RMSE, unit is kcal/mol) of 0.70 for aliphatic hydroxylation, N-dealkylation, and O-dealkylation, and R of 0.83 and RMSE of 0.80 for aromatic hydroxylation, respectively. Physical origin and the role of the effective atomic descriptors of the models are presented in detail. With this model, the Ea of the metabolism can be rapidly predicted without any experimental parameters or time-consuming QM calculation. Regioselectivity prediction with our model is presented in the case of CYP3A4 metabolism. The reliability and ease of use of this model will greatly facilitate early stage PK predictions and rational drug design. Moreover, the model can be applied to develop the Ea prediction model of various types of chemical reactions.

Purpose of Review Increasing wildfire size and severity across the western United States has created an environmental and social crisis that must be approached from a transdisciplinary perspective. Climate change and more than a century of fire exclusion and wildfire suppression have led to contemporary wildfires with more severe environmental impacts and human smoke exposure. Wildfires increase smoke exposure for broad swaths of the US population, though outdoor workers and socially disadvantaged groups with limited adaptive capacity can be disproportionally exposed. Exposure to wildfire smoke is associated with a range of health impacts in children and adults, including exacerbation of existing respiratory diseases such as asthma and chronic obstructive pulmonary disease, worse birth outcomes, and cardiovascular events. Seasonally dry forests in Washington, Oregon, and California can benefit from ecological restoration as a way to adapt forests to climate change and reduce smoke impacts on affected communities. Recent Findings Each wildfire season, large smoke events, and their adverse impacts on human health receive considerable attention from both the public and policymakers. The severity of recent wildfire seasons has state and federal governments outlining budgets and prioritizing policies to combat the worsening crisis. This surging attention provides an opportunity to outline the actions needed now to advance research and practice on conservation, economic, environmental justice, and public health interests, as well as the trade-offs that must be considered. Summary Scientists, planners, foresters and fire managers, fire safety, air quality, and public health practitioners must collaboratively work together. This article is the result of a series of transdisciplinary conversations to find common ground and subsequently provide a holistic view of how forest and fire management intersect with human health through the impacts of smoke and articulate the need for an integrated approach to both planning and practice.

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BackgroundThe purpose of this study is to investigate the prevalence and utilization pattern of complementary and alternative medicine (CAM) administered by oneself or by non-institutional practitioners in a general population in South Korea.MethodsNationwide, face-to-face surveys were conducted from September 1, 2011 to October 5, 2011. We conveniently selected the participants by using a proportional allocation method according to age, gender, and region. The use of CAM in the last year, the patterns of use, sources of information, and counseling objects were investigated in addition to respondents’ demographic characteristics.ResultsAmong the 1284 people approached, 915 respondents (71.3%) reported having had at least one CAM therapy during the past 12 months. Natural products were used the most frequently (58.8%). Unexpectedly, 82.6% out of 1740 therapies reported were self-administered CAM. Healthcare professionals were the source of information on CAM in only 5.6% of all instances of use, and only 17.7% of participants had consulted with doctors regarding CAM use.ConclusionsOwing to the widespread use of CAM in South Korea, researchers should focus on the safety and potential effectiveness of CAM therapy when self-administered by users or by unauthorized CAM practitioners.

Social network services (SNSs) may benefit public health by augmenting surveillance and distributing information to the public. In this study, we collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, we examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets suggests that most jurisdictions provide heating instead of cooling energy assistance.