SAR Methods Research Articles

Separación y caracterización de resinas y asfaltenos provenientes del crudo castilla. evaluación de su interacción molecular

The study of resins and asphaltenes, the heaviest fractions of oil, has become an area of interest due to the abundance of heavy crude oils in Colombia and Latin America. We studied the chemical composition of the heavy fractions of Castilla crude oil, evaluated some of its molecular parameters and found evidence of the interaction between the resin extracted from the crude with the asphaltenes of the original crude. With this objective, we carried out at the pilot plant level precipitation of the resin-asphaltene (R-A) aggregate by adding and mixing under controlled conditions, a paraffin solvent, from the Apiay refinery, called Apiasol. By extracting Soxhlet with the same solvent, resin I of aggregate R-A was separated. Resin II defined as the soluble fraction that is part of the maltenes, was separated from the deasphalted crude by open column chromatography, using alumina as support, according to the SAR method (Saturated, Aromatcis, Resins). The fractions of resins and the asphaltenes obtained, were characterized by: Nuclear Magnetic Resonance (NMR), FT-IR, DRX, elementary analysis (C, H, N, S), metal content (Ni and V), distribution of molecular weight by GPC, and average molecular weight by VPO. The results obtained show evidence that resin I which is part of the aggregate has less average molecular weight than resin II which is present in the fraction of maltenes. In addition, some changes were found in the elementary analysis of among the resins. On the one hand, and taking into account the existing theories of molecular interactions among these fractions, it was found that the resins I separated from the R-A aggregate, when added to the crude, they stabilize their asphaltenes. This evaluation was carried out by analyzing the flocculation point of the crude and its mixtures with 1,9% and 3,8% of resin I, when they are titrated with a pricipitating agent in an NIR cell that works with high pressure and temperature.

Luminescence chronology of Pleistocene loess deposits from Romania: testing methods of age correction for anomalous fading in alkali feldspars

The IRSL dating method is applied to silt-sized alkali feldspar grains (40–60 μm) from Upper and Middle Pleistocene loess deposits of southeastern Romania (sites of Tuzla and Giurgiu Malu-Rosu), using both the multiple aliquot γ dose technique and the SAR method. All samples show evidence of anomalous fading; the measured IRSL ages are in correct stratigraphic order but systematically underestimate the expected geological age (up to 40%). Three protocols of age correction for the observed fading have been tested: the correction of Huntley and Lamothe (Can. J. Earth Sci. 38 (2001) 1093), the correction of Mejdahl (Quat. Sci. Rev. 7 (1988) 357) and the correction model suggested by Wintle (Quat. Sci. Rev. 9 (1990) 385). The three protocols yield corrected IRSL ages that are in better agreement with the expected ages. This study demonstrates the potential of the IRSL dating method to provide chronological information on Upper and Middle Pleistocene loess deposits of Romania.

Applications of the CASE/MULTICASE SAR Method to Environmental and Public Health Situations

The availability of validated and characterized SAR models of toxicological phenomena provides a method to apply SAR technology to a variety of environmental, public health and industrial situations. These include (i) the prioritization of environmental pollutants for control and or regulation, (ii) the design of multi-action optimized therapeutics from which the potential for unwanted side-effects have been engineered out, (iii) the development of SAR-based computer-driven screening procedure to identify candidate therapeutics based upon combinatorial chemistry or compilations of molecular structures, (iv) the generation of toxicological profiles to be used in the selection of benign chemicals in the early stages of product development.

Testing by artificial intelligence: Computational alternatives to the determination of mutagenicity

In order to develop methods for evaluating the predictive performance of computer-driven structure—activity methods (SAR) as well as to determine the limits of predictivity, we investigated the behavior of two Salmonella mutagenicity data bases: (a) a subset from the Genetox Program and (b) one from the U.S. National Toxicology Program (NTP). For molecules common to the two data bases, the experimental concordance was 76% when “marginals” were included and 81% when they were excluded. Three SAR methods were evaluated: CASE, MULTICASE and CASE/Graph Indices (CASE/GI). The programs “learned” the Genetox data base and used it to predict NTP molecules that were not present in the Genetox compilation. The concordances were 72, 80 and 47% respectively. Obviously, the MULTICASE version is superior and approaches the 85% interlaboratory variability observed for the Salmonella mutagenicity assays when the latter was carried out under carefully controlled conditions.

Strategies for the use of computational SAR methods in assessing genotoxicity

The relationship between computational SAR studies and relevant data gathering and generation activities is complex. First, the chemical class to be studied is selected on the basis of information requirements for hazard identification and assessment. Membership in the class is determined by consideration of chemical structure and reactivity. Compilation of the existing bioassay data for this chemical class follows immediately from the specification of the class. Bioassay data, qualitative knowledge of general chemical reactivities in this class, and knowledge concerning potential interactions with biomolecular targets all contribute to the derivation of possible mechanisms for biological activity. Computational studies based on modeling the proposed mechanism of action and/or the existing data base can provide a quantitative basis for the differentiation between chemicals. There is the opportunity for continuing feedback between the quantitative computational studies and the development of a relevant bioassay data base for this chemical class. The qualitative and quantitative information on the potential biological responses obtained will provide a rational basis for extrapolation from the extant data base to the chemicals of interest, and to biological responses significant to the assessment for which complete data are unavailable. Knowledge concerning possible mechanisms of action and preexisting data determine the type of computational study that will be most useful.