<scp>NAD(P)H</scp>:Quinone Oxidoreductases

Ross, David

doi:10.1002/0471203076.emm0590

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…Treatment at high temperatures of PtRu alloys typically results in Pt segregation on the catalyst surface. 22 In this section, the influence of heat-treatment on the prepared catalyst powders is discussed to ensure that suitable catalyst preparation conditions are used. PtRu alloy catalysts of 54:46 atom % composition were heated in air for 1 h at either ͑a͒ 600, ͑b͒ 450, ͑c͒ 350, ͑d͒ 250, or ͑e͒ 100°C.…”

Section: Catalyst Preparation Parameters and Resulting Catalystmentioning

confidence: 99%

Dependence of CH[sub 3]OH Oxidation Activity for a Wide Range of PtRu Alloys

Bock

MacDougall

LePage

2004

J. Electrochem. Soc.

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Unsupported PtRu alloy powders of a wide range of compositions were prepared at low temperatures by carefully adjusting the preparation procedure. PtRu alloys of essentially the same surface, nominal and bulk composition, were formed up to ca. 46 atom % Ru content. Adsorbed CO stripping voltammetry and CH 3 OH oxidation characteristics, namely, i-V curves and pseudo-steadystate current density values recorded at constant potentials, were the same as reported for corresponding bulk alloys, suggesting that the electrocatalytic activities of the powders can be compared to bulk alloys. CH 3 OH oxidation activities obtained for PtRu alloy, Pt, and Ru powders showed the PtRu alloy of 70:30 atom % Pt:Ru composition to exhibit the highest activity independent of the temperature and potential tested ͑0.3 and 0.4 V vs. a reversible hydrogen electrode͒. The experimental pseudo-steady-state current density values for the CH 3 OH oxidation reaction were found to show the same dependence on Ru content as theoretical values calculated assuming that an assembly of three neighboring Pt and one Ru site are involved in the oxidation of a CH 3 OH molecule.

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Section: Catalyst Preparation Parameters and Resulting Catalystmentioning

confidence: 99%

Dependence of CH[sub 3]OH Oxidation Activity for a Wide Range of PtRu Alloys

Bock

MacDougall

LePage

2004

J. Electrochem. Soc.

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“…A strong candidate enzyme for this differential reactivity is NAD(P)H:quinone oxidoreductase 1 (QRI, NQO1, also DT-diaphorase; EC 1.6.99.2). This enzyme is a homodimeric flavoprotein, mainly cytosolic in location, that is expressed in normal cells and at high levels in many types of tumors …”

Section: Resultsmentioning

confidence: 99%

“…This enzyme is a homodimeric flavoprotein, mainly cytosolic in location, that is expressed in normal cells and at high levels in many types of tumors. 16 Two electron reduction of a variety of substrates is catalyzed by this enzyme with the requirement for a pyridine nucleotide cofactor. The most efficient substrates are quinones.…”

Section: Identified Asmentioning

confidence: 99%

Data Mining the NCI Cancer Cell Line Compound GI₅₀ Values: Identifying Quinone Subtypes Effective Against Melanoma and Leukemia Cell Classes

Marx¹,

O'Neil²,

Hoffman³

et al. 2003

J. Chem. Inf. Comput. Sci.

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Using data mining techniques, we have studied a subset (1400) of compounds from the large public National Cancer Institute (NCI) compounds data repository. We first carried out a functional class identity assignment for the 60 NCI cancer testing cell lines via hierarchical clustering of gene expression data. Comprised of nine clinical tissue types, the 60 cell lines were placed into six classes-melanoma, leukemia, renal, lung, and colorectal, and the sixth class was comprised of mixed tissue cell lines not found in any of the other five classes. We then carried out supervised machine learning, using the GI(50) values tested on a panel of 60 NCI cancer cell lines. For separate 3-class and 2-class problem clustering, we successfully carried out clear cell line class separation at high stringency, p < 0.01 (Bonferroni corrected t-statistic), using feature reduction clustering algorithms embedded in RadViz, an integrated high dimensional analytic and visualization tool. We started with the 1400 compound GI(50) values as input and selected only those compounds, or features, significant in carrying out the classification. With this approach, we identified two small sets of compounds that were most effective in carrying out complete class separation of the melanoma, non-melanoma classes and leukemia, non-leukemia classes. To validate these results, we showed that these two compound sets' GI(50) values were highly accurate classifiers using five standard analytical algorithms. One compound set was most effective against the melanoma class cell lines (14 compounds), and the other set was most effective against the leukemia class cell lines (30 compounds). The two compound classes were both significantly enriched in two different types of substituted p-quinones. The melanoma cell line class of 14 compounds was comprised of 11 compounds that were internal substituted p-quinones, and the leukemia cell line class of 30 compounds was comprised of 6 compounds that were external substituted p-quinones. Attempts to subclassify melanoma or leukemia cell lines based upon their clinical cancer subtype met with limited success. For example, using GI(50) values for the 30 compounds we identified as effective against all leukemia cell lines, we could subclassify acute lymphoblastic leukemia (ALL) origin cell lines from non-ALL leukemia origin cell lines without significant overlap from non-leukemia cell lines. Based upon clustering using GI(50) values for the 60 cancer cell lines laid out by the RadViz algorithm, these two compound subsets did not overlap with clusters containing any of the NCI's 92 compounds of known mechanism of action, a few of which are quinones. Given their structural patterns, the two p-quinone subtypes we identified would clearly be expected to possess different redox potentials/substrate specificities for enzymatic reduction in vivo. These two p-quinone subtypes represent valuable information that may be used in the elucidation of pharmacophores for the design of compounds to treat these two cancer tissue types...

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NAD(P)H:Quinone Oxidoreductases

Cited by 2 publications

References 25 publications

Dependence of CH[sub 3]OH Oxidation Activity for a Wide Range of PtRu Alloys

Dependence of CH[sub 3]OH Oxidation Activity for a Wide Range of PtRu Alloys

Data Mining the NCI Cancer Cell Line Compound GI₅₀ Values: Identifying Quinone Subtypes Effective Against Melanoma and Leukemia Cell Classes

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NAD(P)H:Quinone Oxidoreductases

Cited by 2 publications

References 25 publications

Dependence of CH[sub 3]OH Oxidation Activity for a Wide Range of PtRu Alloys

Dependence of CH[sub 3]OH Oxidation Activity for a Wide Range of PtRu Alloys

Data Mining the NCI Cancer Cell Line Compound GI50 Values: Identifying Quinone Subtypes Effective Against Melanoma and Leukemia Cell Classes

Contact Info

Product

Resources

About

Data Mining the NCI Cancer Cell Line Compound GI₅₀ Values: Identifying Quinone Subtypes Effective Against Melanoma and Leukemia Cell Classes