Dan A. Buzatu

13C NMR Quantitative Spectrometric Data-Activity Relationship (QSDAR) Models of Steroids Binding the Aromatase Enzyme

Five quantitative spectroscopic data-activity relationships (QSDAR) models for 50 steroidal inhibitors binding to aromatase enzyme have been developed based on simulated (13)C nuclear magnetic resonance (NMR) data. Three of the models were based on comparative spectral analysis (CoSA), and the two other models were based on comparative structurally assigned spectral analysis (CoSASA). A CoSA QSDAR model based on five principal components had an explained variance (r(2)) of 0.78 and a leave-one-out (LOO) cross-validated variance (q(2)) of 0.71. A CoSASA model that used the assigned (13)C NMR chemical shifts from a steroidal backbone at five selected positions gave an r(2) of 0.75 and a q(2) of 0.66. The (13)C NMR chemical shifts from atoms in the steroid template position 9, 6, 3, and 7 each had correlations greater than 0.6 with the relative binding activity to the aromatase enzyme. All five QSDAR models had explained and cross-validated variances that were better than the explained and cross-validated variances from a five structural parameter quantitative structure-activity relationship (QSAR) model of the same compounds. QSAR modeling suffers from errors introduced by the assumptions and approximations used in partial charges, dielectric constants, and the molecular alignment process of one structural conformation. One postulated reason that the variances of QSDAR models are better than the QSAR models is that (13)C NMR spectral data, based on quantum mechanical principles, are more reflective of binding than the QSAR models calculated electrostatic potentials and molecular alignment process. The QSDAR models provide a rapid, simple way to model the steroid inhibitor activity in relation to the aromatase enzyme.

An Integrated Flow Cytometry-Based System for Real-Time, High Sensitivity Bacterial Detection and Identification

Foodborne illnesses occur in both industrialized and developing countries, and may be increasing due to rapidly evolving food production practices. Yet some primary tools used to assess food safety are decades, if not centuries, old. To improve the time to result for food safety assessment a sensitive flow cytometer based system to detect microbial contamination was developed. By eliminating background fluorescence and improving signal to noise the assays accurately measure bacterial load or specifically identify pathogens. These assays provide results in minutes or, if sensitivity to one cell in a complex matrix is required, after several hours enrichment. Conventional assessments of food safety require 48 to 56 hours. The assays described within are linear over 5 orders of magnitude with results identical to culture plates, and report live and dead microorganisms. This system offers a powerful approach to real-time assessment of food safety, useful for industry self-monitoring and regulatory inspection.

Combining NMR spectral and structural data to form models of polychlorinated dibenzodioxins, dibenzofurans, and biphenyls binding to the AhR.

A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) modeling technique which uses NMR spectral and structural information that is combined in a 3D-connectivity matrix has been developed. A 3D-connectivity matrix was built by displaying all possible assigned carbon NMR chemical shifts, carbon-to-carbon connections, and distances between the carbons. Two-dimensional 13C-13C COSY and 2D slices from the distance dimension of the 3D-connectivity matrix were used to produce a relationship among the 2D spectral patterns for polychlorinated dibenzofurans, dibenzodioxins, and biphenyls (PCDFs, PCDDs, and PCBs respectively) binding to the aryl hydrocarbon receptor (AhR). We refer to this technique as comparative structural connectivity spectral analysis (CoSCoSA) modeling. All CoSCoSA models were developed using forward multiple linear regression analysis of the predicted 13C NMR structure-connectivity spectral bins. A CoSCoSA model for 26 PCDFs had an explained variance (r2) of 0.93 and an average leave-four-out cross-validated variance (q42) of 0.89. A CoSCoSA model for 14 PCDDs produced an r2 of 0.90 and an average leave-two-out cross-validated variance (q22) of 0.79. One CoSCoSA model for 12 PCBs gave an r2 of 0.91 and an average q22 of 0.80. Another CoSCoSA model for all 52 compounds had an r2 of 0.85 and an average q42 of 0.52. Major benefits of CoSCoSA modeling include ease of development since the technique does not use molecular docking routines.

Computational identification of a phospholipidosis toxicophore using (13)C and (15)N NMR-distance based fingerprints.

Modified 3D-SDAR fingerprints combining (13)C and (15)N NMR chemical shifts augmented with inter-atomic distances were used to model the potential of chemicals to induce phospholipidosis (PLD). A curated dataset of 328 compounds (some of which were cationic amphiphilic drugs) was used to generate 3D-QSDAR models based on tessellations of the 3D-SDAR space with grids of different density. Composite PLS models averaging the aggregated predictions from 100 fully randomized individual models were generated. On each of the 100 runs, the activities of an external blind test set comprised of 294 proprietary chemicals were predicted and averaged to provide composite estimates of their PLD-inducing potentials (PLD+ if PLD is observed, otherwise PLD-). The best performing 3D-QSDAR model utilized a grid with a density of 8ppm×8ppm in the C-C region, 8ppm×20ppm in the C-N region and 20ppm×20ppm in the N-N region. The classification predictive performance parameters of this model evaluated on the basis of the external test set were as follows: accuracy=0.70, sensitivity=0.73 and specificity=0.66. A projection of the most frequently occurring bins on the standard coordinate space suggested a toxicophore composed of an aromatic ring with a centroid 3.5-7.5Å distant from an amino-group. The presence of a second aromatic ring separated by a 4-5Å spacer from the first ring and at a distance of between 5.5Å and 7Å from the amino-group was also associated with a PLD+ effect. These models provide comparable predictive performance to previously reported models for PLD with the added benefit of being based entirely on non-confidential, publicly available training data and with good predictive performance when tested in a rigorous, external validation exercise.

Improving proton MR spectroscopy of brain tissue for noninvasive diagnostics.

To examine preprocessing methods affecting the potential use of Magnetic Resonance Spectroscopy (MRS) as a noninvasive modality for detection and characterization of brain lesions and for directing therapy progress.

Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity

A diverse set of 154 chemicals that included US Food and Drug Administration-regulated compounds tested for their aquatic toxicity in Daphnia magna were modeled by a 3-dimensional quantitative spectral data-activity relationship (3D-QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity-based k-nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D-QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality-control criteria were imposed: 1) 100 randomized 20% hold-out test sets were generated and the average R(2) test of the respective models was used as a measure of their performance, and 2) a Y-scrambling procedure was used to identify chance correlations. A consensus between the best-performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average R(2) test  = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average R(2) test  = 0.801) offered an improvement of about 7.5% (R(2) test consensus  = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group-substituted aromatic systems-would result in an increased toxic effect in Daphnia. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity.

Electronic properties of single-wall carbon nanotubes and their dependence on synthetic methods

Since their discovery in 1991, carbon nanotubes have been intensively studied, and a number of new applications have been identified. Applications range from nanoelectronics to hydrogen absorption for battery electrodes and fuel cells. Because of their high electrical conductivity and strength, high sensitivity atomic force microscopes already use carbon nanotubes for their tips, and carbon nanostructures are also used as electron beam emitters for medical and scientific equipment. Electron emission is directly correlated with the work function and the ionization potential of carbon nanotubes. Gaussian 98 software was used to perform theoretical quantum calculations on a limited set of HyperChem 5.01 simulated metallic single-wall carbon nanotubes. These initial sets of calculations show that bandgaps and work functions of these small carbon nanostructures are dependent upon the diameter of the tubes, and to a lesser degree so is the ionization potential. In addition, we demonstrate how the manufacturing methods can directly affect the diameter of the nanotubes produced, and therefore directly influence the electrical properties of the nanotubes.

Thymol treatment of bacteria prior to matrix-assisted laser desorption/ionization time-of-flight mass spectrometric analysis aids in identifying certain bacteria at the subspecies level.

RATIONALE The identification of bacteria based on mass spectra produced by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) has become routine since its introduction in 1996. The major drawback is that bacterial patterns produced by MALDI are dependent on sample preparation prior to analysis. This results in poor reproducibility in identifying bacterial types and between laboratories. The need for a more broadly applicable and useful sample handling procedure is warranted. METHODS Thymol was added to the suspension solvent of bacteria prior to MALDI analysis. The suspension solvent consisted of ethanol, water and TFA. The bacterium was added to the thymol suspension solvent and heated. An aliquot of the bacterial suspension was mixed directly with the matrix solution at a 9:1 ratio, matrix/bacteria solution, respectively. The mixture was then placed on the MALDI plate and allowed to air dry before MALDI analysis. RESULTS The thymol method improved the quality of spectra and number of peaks when compared to other sample preparation procedures studied. The bacterium-identifying biomarkers assigned to four strains of E. coli were statistically 95% reproducible analyzed on three separate days. The thymol method successfully differentiated between the four E. coli strains. In addition, the thymol procedure could identify nine out of ten S. enterica serovars over a 3-day period and nine S. Typhimurium strains from the other ten serovars 90% of the time over the same period. CONCLUSIONS The thymol method can identify certain bacteria at the sub-species level and yield reproducible results over time. It improves the quality of spectra by increasing the number of peaks when compared to the other sample preparation methods assessed in this study. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutant—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D 13C and 1D 15N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D 13C-NMR and 15N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Use of carbon nanostructures for hydrogen storage for environmentally safe automotive applications

Carbon nanostructures are considered materials with a high potential for hydrogen storage. CCVD technique was used to grown nanotubes with diameters ranging from 50 to 100 nm on a Ni:Cu catalyst and nanofibers with diameters ranging from 10 to 100 nm on a Pd/La/sub 2/O/sub 3/ catalyst. The hydrogen uptake experiments performed volumetrically in a Sievert-type installation, showed the quantity of desorbed hydrogen (for pressure intervals ranging from 1 to 100 bars) by the nanotubes was less than 1 % by weight. The hydrogen sorption capacities of the carbon nanofibers show saturation value of about 1.5 wt. % and to be in a good correlation with the Pd/C ratio, revealing a catalytic effect of Pd that supplies atomic H.