Richard D Wood

Robert Wood Johnson University Hospital, New Brunswick, New Jersey, United States

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Publications (5)21.21 Total impact

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    ABSTRACT: Toxoplasma gondii ( T. gondii ) is an apicomplexan parasite that can cause eye disease, brain disease, and death, especially in congenitally infected and immune-compromised people. Novel medicines effective against both active and latent forms of the parasite are greatly needed. The current study focused on the discovery of such medicines by exploring a family of potential inhibitors whose antiapicomplexan activity has not been previously reported. Initial screening efforts revealed that niclosamide, a drug approved for anthelmintic use, possessed promising activity in vitro against T. gondii . This observation inspired the evaluation of the activity of a series of salicylanilides and derivatives. Several inhibitors with activities in the nanomolar range with no appreciable in vitro toxicity to human cells were identified. An initial structure-activity relationship was explored. Four compounds were selected for evaluation in an in vivo model of infection, and two derivatives with potentially enhanced pharmacological parameters demonstrated the best activity profiles.
    Journal of Medicinal Chemistry 09/2012; 55(19):8375-91. · 5.61 Impact Factor
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    ABSTRACT: Chemometric analysis of a set of one-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and/or unacceptable levels of dermatan sulfate (DS) impurity. Three chemometric pattern recognition approaches were implemented: classification and regression tree (CART), artificial neural network (ANN), and support vector machine (SVM). Heparin sodium samples from various manufacturers were analyzed in 2008 and 2009 by 1D (1)H NMR, strong anion-exchange high-performance liquid chromatography, and percent galactosamine in total hexosamine tests. Based on these data, the samples were divided into three groups: Heparin, DS ≤ 1.0% and OSCS = 0%; DS, DS > 1.0% and OSCS = 0%; and OSCS, OSCS > 0% with any content of DS. Three data sets corresponding to different chemical shift regions (1.95-2.20, 3.10-5.70, and 1.95-5.70 ppm) were evaluated. While all three chemometric approaches were able to effectively model the data in the 1.95-2.20 ppm region, SVM was found to substantially outperform CART and ANN for data in the 3.10-5.70 ppm region in terms of classification success rate. A 100% prediction rate was frequently achieved for discrimination between heparin and OSCS samples. The majority of classification errors between heparin and DS involved cases where the DS content was close to the 1.0% DS borderline between the two classes. When these borderline samples were removed, nearly perfect classification results were attained. Satisfactory results were achieved when the resulting models were challenged by test samples containing blends of heparin APIs spiked with non-, partially, or fully oversulfated chondroitin sulfate A, heparan sulfate, or DS at the 1.0%, 5.0%, and 10.0% (w/w) levels. This study demonstrated that the combination of 1D (1)H NMR spectroscopy with multivariate chemometric methods is a nonsubjective, statistics-based approach for heparin quality control and purity assessment that, once standardized, minimizes the need for expert analysts.
    Analytical and Bioanalytical Chemistry 06/2011; 401(3):939-55. · 3.66 Impact Factor
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    ABSTRACT: Heparin is a naturally produced, heterogeneous compound consisting of variably sulfated and acetylated repeating disaccharide units. The structural complexity of heparin complicates efforts to assess the purity of the compound, especially when differentiating between similar glycosaminoglycans. Recently, heparin sodium contaminated with oversulfated chondroitin sulfate A (OSCS) has been associated with a rapid and acute onset of an anaphylactic reaction. In addition, naturally occurring dermatan sulfate (DS) was found to be present in these and other heparin samples as an impurity due to incomplete purification. The present study was undertaken to determine whether chemometric analysis of these NMR spectral data would be useful for discrimination between USP-grade samples of heparin sodium API and those deemed unacceptable based on their levels of DS, OSCS, or both. Several multivariate chemometric methods for clustering and classification were evaluated; specifically, principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and the k-nearest-neighbor (kNN) method. Data dimension reduction and variable selection techniques, implemented to avoid over-fitting the training set data, markedly improved the performance of the classification models. Under optimal conditions, a perfect classification (100% success rate) was attained on external test sets for the Heparin vs OSCS model. The predictive rates for the Heparin vs DS, Heparin vs [DS+OSCS], and Heparin vs DS vs OSCS models were 89%, 93%, and 90%, respectively. In most cases, misclassifications can be ascribed to the similarity in NMR chemical shifts of heparin and DS. Among the chemometric methods evaluated in this study, we found that the LDA models were superior to the PLS-DA and kNN models for classification. Taken together, the present results demonstrate the utility of chemometric methods when applied in combination with (1)H NMR spectral analysis for evaluating the quality of heparin APIs.
    Journal of pharmaceutical and biomedical analysis 04/2011; 54(5):1020-9. · 2.45 Impact Factor
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    ABSTRACT: To differentiate heparin samples with varying amounts of dermatan sulfate (DS) impurities and oversulfated chondroitin sulfate (OSCS) contaminants, proton NMR spectral data for heparin sodium active pharmaceutical ingredient samples from different manufacturers were analyzed using multivariate chemometric techniques. A total of 168 samples were divided into three groups: (a) Heparin, [DS] ≤ 1.0% and [OSCS] = 0%; (b) DS, [DS] > 1.0% and [OSCS] = 0%; (c) OSCS, [OSCS] > 0% with any content of DS. The chemometric models were constructed and validated using two well-established methods: soft independent modeling of class analogy (SIMCA) and unequal class modeling (UNEQ). While SIMCA modeling was conducted using the entire set of variables extracted from the NMR spectral data, UNEQ modeling was combined with variable reduction using stepwise linear discriminant analysis to comply with the requirement that the number of samples per class exceed the number of variables in the model by at least 3-fold. Comparison of the results from these two modeling approaches revealed that UNEQ had greater sensitivity (fewer false positives) while SIMCA had greater specificity (fewer false negatives). For Heparin, DS, and OSCS, respectively, the sensitivity was 78% (56/72), 74% (37/50), and 85% (39/46) from SIMCA modeling and 88% (63/72), 90% (45/50), and 91% (42/46) from UNEQ modeling. Importantly, the specificity of both the SIMCA and UNEQ models was 100% (46/46) for Heparin with respect to OSCS; no OSCS-containing sample was misclassified as Heparin. The specificity of the SIMCA model (45/50, or 90%) was superior to that of the UNEQ model (27/50, or 54%) for Heparin with respect to DS samples. However, the overall prediction ability of the UNEQ model (85%) was notably better than that of the SIMCA model (76%) for the Heparin vs DS vs OSCS classes. The models were challenged with blends of heparin spiked with nonsulfated, partially sulfated, or fully oversulfated chondroitin sulfate A, dermatan sulfate, or heparan sulfate at the 1.0, 5.0, and 10.0 wt % levels. The results from the present study indicate that the combination of (1)H NMR spectral data and class modeling techniques (viz., SIMCA and UNEQ) represents a promising strategy for assessing the quality of commercial heparin samples with respect to impurities and contaminants. The methodologies show utility for applications beyond heparin to other complex products.
    Analytical Chemistry 02/2011; 83(3):1030-9. · 5.82 Impact Factor
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    ABSTRACT: Heparin, a widely used anticoagulant primarily extracted from animal sources, contains varying amounts of galactosamine impurities. Currently, the United States Pharmacopeia (USP) monograph for heparin purity specifies that the weight percent of galactosamine (%Gal) may not exceed 1%. In the present study, multivariate regression (MVR) analysis of (1)H NMR spectral data obtained from heparin samples was employed to build quantitative models for the prediction of %Gal. MVR analysis was conducted using four separate methods: multiple linear regression, ridge regression, partial least squares regression, and support vector regression (SVR). Genetic algorithms and stepwise selection methods were applied for variable selection. In each case, two separate prediction models were constructed: a global model based on dataset A which contained the full range (0-10%) of galactosamine in the samples and a local model based on the subset dataset B for which the galactosamine level (0-2%) spanned the 1% USP limit. All four regression methods performed equally well for dataset A with low prediction errors under optimal conditions, whereas SVR was clearly superior among the four methods for dataset B. The results from this study show that (1)H NMR spectroscopy, already a USP requirement for the screening of contaminants in heparin, may offer utility as a rapid method for quantitative determination of %Gal in heparin samples when used in conjunction with MVR approaches.
    Analytical and Bioanalytical Chemistry 10/2010; 399(2):635-49. · 3.66 Impact Factor