@article{hughes-oliver_xu_baynes_2018, title={Skin Permeation of Solutes from Metalworking Fluids to Build Prediction Models and Test A Partition Theory}, volume={23}, ISSN={["1420-3049"]}, DOI={10.3390/molecules23123076}, abstractNote={Permeation of chemical solutes through skin can create major health issues. Using the membrane-coated fiber (MCF) as a solid phase membrane extraction (SPME) approach to simulate skin permeation, we obtained partition coefficients for 37 solutes under 90 treatment combinations that could broadly represent formulations that could be associated with occupational skin exposure. These formulations were designed to mimic fluids in the metalworking process, and they are defined in this manuscript using: one of mineral oil, polyethylene glycol-200, soluble oil, synthetic oil, or semi-synthetic oil; at a concentration of 0.05 or 0.5 or 5 percent; with solute concentration of 0.01, 0.05, 0.1, 0.5, 1, or 5 ppm. A single linear free-energy relationship (LFER) model was shown to be inadequate, but extensions that account for experimental conditions provide important improvements in estimating solute partitioning from selected formulations into the MCF. The benefit of the Expanded Nested-Solute-Concentration LFER model over the Expanded Crossed-Factors LFER model is only revealed through a careful leave-one-solute-out cross-validation that properly addresses the existence of replicates to avoid an overly optimistic view of predictive power. Finally, the partition theory that accompanies the MCF approach is thoroughly tested and found to not be supported under complex experimental settings that mimic occupational exposure in the metalworking industry.}, number={12}, journal={MOLECULES}, author={Hughes-Oliver, Jacqueline M. and Xu, Guangning and Baynes, Ronald E.}, year={2018}, month={Dec} }
 @article{tan_xu_orndorff_shirwaiker_2016, title={Effects of Electrically Activated Silver-Titanium Implant System Design Parameters on Time-Kill Curves Against Staphylococcus aureus}, volume={36}, ISSN={["2199-4757"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84978971017&partnerID=MN8TOARS}, DOI={10.1007/s40846-016-0136-x}, number={3}, journal={JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING}, author={Tan, Zhuo and Xu, Guangning and Orndorff, Paul E. and Shirwaiker, Rohan A.}, year={2016}, month={Jun}, pages={325–333} }
 @article{xu_hughes-oliver_brooks_baynes_2013, title={Predicting skin permeability from complex chemical mixtures: incorporation of an expanded QSAR model}, volume={24}, ISSN={1062-936X 1029-046X}, url={http://dx.doi.org/10.1080/1062936X.2013.792875}, DOI={10.1080/1062936x.2013.792875}, abstractNote={Quantitative structure–activity relationship (QSAR) models have been widely used to study the permeability of chemicals or solutes through skin. Among the various QSAR models, Abraham’s linear free-energy relationship (LFER) model is often employed. However, when the experimental conditions are complex, it is not always appropriate to use Abraham’s LFER model with a single set of regression coefficients. In this paper, we propose an expanded model in which one set of partial slopes is defined for each experimental condition, where conditions are defined according to solvent: water, synthetic oil, semi-synthetic oil, or soluble oil. This model not only accounts for experimental conditions but also improves the ability to conduct rigorous hypothesis testing. To more adequately evaluate the predictive power of the QSAR model, we modified the usual leave-one-out internal validation strategy to employ a leave-one-solute-out strategy and accordingly adjust the Q2 LOO statistic. Skin permeability was shown to have the rank order: water > synthetic > semi-synthetic > soluble oil. In addition, fitted relationships between permeability and solute characteristics differ according to solvents. We demonstrated that the expanded model (r2 = 0.70) improved both the model fit and the predictive power when compared with the simple model (r2 = 0.21).}, number={9}, journal={SAR and QSAR in Environmental Research}, publisher={Informa UK Limited}, author={Xu, G. and Hughes-Oliver, J.M. and Brooks, J.D. and Baynes, R.E.}, year={2013}, month={Sep}, pages={711–731} }
 @article{xu_hughes-oliver_brooks_yeatts_baynes_2013, title={Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design}, volume={24}, ISSN={1062-936X 1029-046X}, url={http://dx.doi.org/10.1080/1062936X.2012.742458}, DOI={10.1080/1062936x.2012.742458}, abstractNote={Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed ‘Baynes’ rule’, which is a modification of Lipinski's ‘rule of five’, was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].}, number={2}, journal={SAR and QSAR in Environmental Research}, publisher={Informa UK Limited}, author={Xu, G. and Hughes-Oliver, J.M. and Brooks, J.D. and Yeatts, J.L. and Baynes, R.E.}, year={2013}, month={Feb}, pages={135–156} }