Hydrocarbon molar water solubility predicts NMDA vs. GABAA receptor modulation.

BackgroundMany anesthetics modulate 3-transmembrane (such as NMDA) and 4-transmembrane (such as GABAA) receptors. Clinical and experimental anesthetics exhibiting receptor family specificity often have low water solubility. We hypothesized that the molar water solubility of a hydrocarbon could be used to predict receptor modulation in vitro.MethodsGABAA (α1β2γ2s) or NMDA (NR1/NR2A) receptors were expressed in oocytes and studied using standard two-electrode voltage clamp techniques. Hydrocarbons from 14 different organic functional groups were studied at saturated concentrations, and compounds within each group differed only by the carbon number at the ω-position or within a saturated ring. An effect on GABAA or NMDA receptors was defined as a 10% or greater reversible current change from baseline that was statistically different from zero.ResultsHydrocarbon moieties potentiated GABAA and inhibited NMDA receptor currents with at least some members from each functional group modulating both receptor types. A water solubility cut-off for NMDA receptors occurred at 1.1 mM with a 95% CI = 0.45 to 2.8 mM. NMDA receptor cut-off effects were not well correlated with hydrocarbon chain length or molecular volume. No cut-off was observed for GABAA receptors within the solubility range of hydrocarbons studied.ConclusionsHydrocarbon modulation of NMDA receptor function exhibits a molar water solubility cut-off. Differences between unrelated receptor cut-off values suggest that the number, affinity, or efficacy of protein-hydrocarbon interactions at these sites likely differ

Comparison of Genetic Algorithm Based Support Vector Machine and Genetic Algorithm Based RBF Neural Network in Quantitative Structure-Property Relationship Models on Aqueous Solubility of Polycyclic Aromatic Hydrocarbons

AbstractA modified method to develop quantitative structure-property relationship (QSPR) models of organic contaminants was proposed based on genetic algorithm (GA) and support vector machine (SVM). GA was used to perform the variable selection and SVM was used to construct QSPR model. In this study, GA-SVM was applied to develop the QSPR model for aqueous solubility (Sw, mg•l-1) of polycyclic aromatic hydrocarbons (PAHs). The R2 (0.980), SSE (2.84), and RMSE (0.25) values of the model developed by GA-SVM indicated a good predictive capability for logSw values of PAHs. Based on leave-one-out cross validation, the results of GA-SVM were compared with those of genetic algorithm-radial based function neural network (GA-RBFNN). The comparison showed that the R2 (0.923) and RMSE (0.485) values of GA-SVM were higher and lower, respectively, which illustrated GA-SVM was more suitable to develop QSPR model for the logSw values of PAHs than GA-RBFNN

A screening method for ranking chemicals by their fate and behaviour in the environment and potential toxic effects in humans following non-occupational exposure

A large number of chemicals are released intentionally or unintentionally into the environment each year. These include thousands of substances that are currently listed worldwide and several hundred new substances added annually (Mücke et al., 1986). When these compounds are used, they can reach microorganisms, plants, animals and man either in their original state or in the form of reaction and degradation products via air, water, soil or foodstuffs. Hence environmental chemicals can occur in practically all environmental compartments and ecosystems. It is not feasible to conduct assessments of human exposure and possible associated health effects for all chemicals. Even if the necessary resources were available, reliable data for a quantitative evaluation are likely to be absent in most cases. This has led to the development of schemes for prioritising compounds likely to be of environmental significance. Such schemes can be used to direct future research efforts towards the prioritised compounds. This study was commissioned by the Department of Health (DH) as part of a broader research activity that aims to identify key priority chemicals of concern to human health at routine levels of environmental exposure. The main pathways of human exposure are shown in Figure 1.1. A review of the principal prioritisation schemes used by different organisations to assess the significance of chemical release into the environment has been conducted by the MRC Institute for Environment and Health (IEH, 2003). This review showed that the approaches used by different organisations vary widely, depending on the initial reasons for which the schemes were developed. The basic information presented in the review was used to develop a simple screening method for ranking chemicals. The model used in this prioritisation scheme is outlined in Figure 1.2. The main purpose in developing the prioritisation scheme for DH was to develop a dedicated priority setting method capable of identifying chemicals in air, water, soil and foodstuffs that might pose a significant risk to human health following low level environmental exposure. The methodology was developed in order to identify compounds that required further assessment and those that had data gaps. More detailed risk assessments were conducted at a later stage on those compounds prioritised as being of high importancea. The screening methodology was developed for ‘existing chemicals’ as these are of greatest concern because data on their toxicity and/or fate and behaviour are often unknownb. The production of a priority list was designed to highlight compounds that required further regulatory measures to reduce exposure of the general population and for which an in-depth risk characterisation would be necessary to assist in the evaluation and implementation of activities for reducing environmental risks. This might include an assessment of the costs of such risks to human health and the costs of reduction measures. As the scheme also aimed to identify data gaps that might warrant further investigation, the application of default categories for chemicals with no data was also considered. The overall aim was to develop a screening methodology that is quick, clear and simple to use and that can easily be revised to take into account new information on compounds as and when it becomes available. a Benzene (IEH Report on Benzene in the Environment, R12); 4,6-dichlorocresol, hexachloro-1,3-butadiene, tetrachlorobenzene, 2,4,6-trichlorophenol (reports to DH; available from MRC Institute for Environment and Health b ‘Existing Substances’ are those that were placed in the European Union (EU) market before 1981. Prior to 1981 regulatory requirements were related to products intended for certain uses (e.g. veterinary medicines) and did not require assessment of the hazardous properties of any substance before they were released into the market. For substances placed on the market after 1981 (classified as ‘New Substances’) there is a legal requirement to conduct such assessments. Regulatory agencies require the collection of extensive documentation for safety before a chemical, for example, can be used in foods or commercial products. IEH Web Report W14, posted March 2004 at http://www.le.ac.uk/ieh/ 4 This report describes how physicochemical properties and toxicological data were incorporated into a screening model to assess the potential fate and transfer of chemicals between different environmental compartments and to predict the potential human exposure to toxic chemicals through the inhalation of contaminated air and the ingestion of water and food. It must be stressed, however, that the method devised is a simple screening process and that a more detailed assessment is necessary to determine the potential transfer through the foodchain of a chemical and the full extent of any adverse health effects. Sections 2 and 4 present the physicochemical properties, toxicological data and algorithms used to screen the compounds. Section 3 summarises the groups of chemicals that were included in the screening process. The results of the prioritisation scheme and comments on their limitations and constraints are presented in Section 5

Assessment of effects on vegetation of degradation products from alternative fluorocarbons

Concern with the effects of fluorides on plants has been devoted to that resulting from dry deposition (mainly with reference to gaseous HF and secondarily with particulate forms). The occurrence of precipitation as rain or mist and the presence of dew or free water on the foliage has mainly been considered with respect to their effects on the accumulation of air-borne fluoride and not with fluoride in wet deposition. That is, precipitation has been viewed primarily with respect to its facilitation of the solution and subsequent absorption of deposits by the foliar tissues or its elution of deposited fluoride from foliage. Accordingly, our evaluation of inorganic fluoride from fluorocarbon degradation rests upon a comparison with what is known about the effects of industrial emissions and what could be considered the natural condition

The Modeling of Solubility

In this work the solubilities of gases in liquids and liquids in liquids were modeled using both physical properties and topological descriptors of the solutes. Quantitative structure-activity relationship (QSAR) methods were employed to create single-linear regression (SLR) and multiple-linear regression (MLR) models of the solubilities. Factor analysis was employed to determine the number of significant factors present in the solubilities. The solubilities of monoalcohols in water, halogenated alkanes in water, gases in water, gases in alkanes, and gases in alcohols were examined and modeled

In Silico Prediction of Physicochemical Properties

This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance

Long term monitoring of trichloroethylene degradation indicator parameters using sensors

Past operations at Savannah River Site (SRS) have resulted in significant amount of groundwater contamination with trichloroethylene. Natural attenuation of chlorinated solvents via reductive dechlorination is one of the most important processes occurring at SRS, which requires monitoring. Many traditional monitoring techniques require manual sampling and analysis at an onsite or offsite laboratory, which is costly and time consuming. Therefore the need for a system, which can accurately and cost-effectively conduct real-time analysis using automated sensors, is important. There are several characteristics of groundwater like pH, ORP, conductivity and chloride that may be monitored to assess the TCE degradation. To evaluate the effectiveness of the sensors to measure the required parameters, a series of tests were conducted by varying the parameters that can affect the performance of the sensors. Interference by the other ions is neither strong nor permanent but can cause interference during measurement. So a thorough testing of the ISE is necessary to obtain reliable data