349257 Utilizing Predictive Fields of Space for Organic Compound Solubilities to Analyze Toxicology of Pharmaceuticals

Monday, November 4, 2013
Grand Ballroom B (Hilton)
Amanda N. Quay, Chemical Engineering, Stanford University, Stanford, CA; Chemistry, University of North Texas, Denton, TX

Amanda N. Quay


Predictive Spaces for the Solubilities of Organic Compounds Pertaining to Toxicology

As global life expectancy increases, amplified drug consumption has led to recent discoveries of pharmaceutical concentrations in water. This has caused urgent investigations of drug effects on aquatic species. Finding predictive spaces for the solubilities of organic compounds, particularly in ionic liquids for drug formulations, addresses the toxicity levels of profuse pharmaceutical waste in streams

In this study, I validated the Abraham solvation parameter model for prediction of new data for pharmaceutical compounds. I utilized the Computer Aided Statistical Programs Mathematica and SPSS, for predictive field calculations and matrix-guided statistics, respectively. I then enhanced the pharmaceutical solubility database using the Group Contribution Approach (GCA) and outlier organic data points, and resolved several inconsistencies in the GCA by modifying and adding carboxylic acid and nitrogenous functional groups and calculating new groups for the method. My work with the five-dimensional Abraham model will prioritize which drugs to eliminate from wastewater of the targeted the 83 drugs that are most likely to be in hospital wastewater. I then discuss the viability of photocatalytic reactions to degrade

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