The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. A key step in this process is the quantification of spectral features, which is commonly accomplished by dividing an NMR spectrum into several hundred integral regions or bins. Binning attempts to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition, while reducing the dimensionality for multivariate statistical analyses. Herein we develop an improved novel spectral quantification technique, dynamic adaptive binning. With this technique, bin boundaries are determined by optimizing an objective function using a dynamic programming strategy. The objective function measures the quality of a bin configuration based on the number of peaks per bin. This technique shows a significant improvement over both traditional uniform binning and other adaptive binning techniques. This improvement is quantified via synthetic validation sets by analyzing an algorithm's ability to create bins that do not contain more than a single peak and that maximize the distance from peak to bin boundary. The validation sets are developed by characterizing the salient distributions in experimental NMR spectroscopic data. Further, dynamic adaptive binning is applied to a 1 H NMRbased experiment to monitor rat urinary metabolites to empirically demonstrate improved spectral quantification.
Perchlorate (ClO4-), a contaminant in drinking water, competitively inhibits active uptake of iodide (I-) into various tissues, including mammary tissue. During postnatal development, inhibition of I- uptake in the mammary gland and neonatal thyroid and the active concentration ClO4- in milk indicate a potentially increased susceptibility of neonates to endocrine disruption. A physiologically based pharmacokinetic (PBPK) model was developed to reproduce measured ClO4- distribution in the lactating and neonatal rat and predict resulting effects on I- kinetics from competitive inhibition at the sodium iodide symporter (NIS). Kinetic I- and ClO4- behavior in tissues with NIS (thyroid, stomach, mammary gland, and skin) was simulated with multiple subcompartments, Michaelis-Menten (M-M) kinetics and competitive inhibition. Physiological and kinetic parameters were obtained from literature and experiment. Systemic clearance and M-M parameters were estimated by fitting simulations to tissue and serum data. The model successfully describes maternal and neonatal thyroid, stomach, skin, and plasma, as well as maternal mammary gland and milk data after ClO4- exposure (from 0.01 to 10 mg/kg-day ClO4-) and acute radioiodide (2.1 to 33,000 ng/kg I-) dosing. The model also predicts I- uptake inhibition in the maternal thyroid, mammary gland, and milk. Model simulations predict a significant transfer of ClO4- through milk after maternal exposure; approximately 50% to 6% of the daily maternal dose at doses ranging from 0.01 to 10.0 mg ClO4-/kg-day, respectively. Comparison of predicted dosimetrics across life-stages in the rat indicates that neonatal thyroid I- uptake inhibition is similar to the adult and approximately tenfold less than the fetus.
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