Biomedical subjects

T Wayne Schultz

Publications and source records attributed to T Wayne Schultz.

16 recordsLinked to original sources

PubMed2004

Population growth impairment of aliphatic alcohols to Tetrahymena.

The toxicity of a series of 120 aliphatic alcohols was evaluated using the Tetrahymena pyriformis population growth impairment assay. For tertiary propargylic alcohols; primary, secondary, and tertiary homopropargylic alcohols; allylic alcohols; and saturated alcohols, a statistically robust structure-activity model was developed for toxicity data [log (IGC(50) (-1))] using the 1-octanol/water partition coefficient (log K(ow)) as the lone descriptor [log (IGC(50))(-1) = 0.74 (log K(ow)) - 1.73; n = 97; r(2) (adj.) = 0.933; r(2) (pred.) = 0.932; s = 0.298; F = 1328; Pr > F = 0.0001]. Analysis of data for the primary propargylic alcohols yielded a separate, high-quality log K(ow)-dependent quantitative structure-activity relationship (QSAR) [log (IGC(50))(-1) = 0.65 (log K(ow)) - 1.22; n = 10; r(2) (adj.) = 0.969; r(2) (pred.) = 0.964; s = 0.222; F = 254; Pr > F = 0.0001]. A comparison of the observed toxicity and that predicted by the first QSAR showed that the primary propargylic alcohols with log K(ow) values < 2.00 exhibited enhanced toxicity and that this increased toxicity was inversely related to hydrophobicity. In sharp contrast, analysis of the data for the secondary propargylic alcohols exhibited little relationship with log K(ow) (r(2) = 0.339). Although the initial QSAR can be used to model the toxicity of any aliphatic alcohol for the T. pyriformis population growth impairment end point, the estimated potency would be underestimated for primary propargylic alcohols with log K(ow) values < 2.00. Moreover, estimates of toxic potency of secondary propargylic alcohols based on this QSAR should be viewed with limited confidence. The findings for beta-unsaturated alcohols in Tetrahymena were sharply different from that reported for fathead minnow acute mortality; this difference in toxicity is a result of a difference in the protocol used rather than in metabolism.

Alcohols↗

PubMed2003

Estrogenicity and acute toxicity of selected anilines using a recombinant yeast assay.

Suspected estrogen modulators include industrial organic chemicals (i.e., xenoestrogens), and have been shown to consist of alkylphenols, bisphenols, biphenylols, and some hydroxy-substituted polycyclic aromatic hydrocarbons. The most prominent structural feature identified to be important for estrogenic activity is a polar group capable of donating hydrogen bonds (i.e., hydroxyl) on an aromatic system. The present study was undertaken to explore the estrogenic activity and acute toxicity of chemicals containing a weaker hydrogen bond donor group on aromatic systems, i.e., the amino substituent. There is a great deal of chemical similarity between aromatic amines (anilines) and aromatic alcohols (phenols). The chemicals chosen for the current study contained an amino-substituted benzene ring with hydrophobic constituents varying in size and shape. Thus, 37 substituted aromatic amines were assayed for estrogenic activity EC50 and acute toxicity LC50 using the Saccharomyces cerevisiae recombinant yeast assay. While the EC50 of 17-beta-estradiol occurs at the 10(-10) range, the aniline with the greatest activity had an EC50 of 10(-6) M. Thus, anilines, in general, are capable only of very weak estrogenic activity in this assay. A comparison of estrogenic potency between the present group of anilines and a set of previously tested analogous phenols indicated that anilines are consistently less estrogenic than phenols. A comparison of hazard indices (EC50/LC50) of these chemicals revealed that, for the vast majority of anilines, the EC50 and LC50 were in the same order of magnitude. More specifically, estrogenic activity of para-substituted alkylanilines increases with alkyl group size up to 5 carbons in length, after which the acute toxicity of the larger alkyl-substituents precluded the ability of the compound to induce the estrogenic response.

Aniline Compounds↗

PubMed2003

An exploratory study of the use of multivariate techniques to determine mechanisms of toxic action.

The most successful quantitative structure-activity relationships have been developed by separating compounds by their mechanisms of toxic action (MOAs). However, to correctly determine the MOA of a compound is often not easy. We investigated the usefulness of discriminant analysis and logistic regression in determining MOAs. The discriminating variables used were the logarithm of octanol-water partition coefficients (logKow) and the experimental toxicity data obtained from Pimephales promelas and Tetrahymena pyriformis assays. Small total error rates were obtained when separating nonpolar narcotic compounds from other compounds, however, relatively high total error rates were obtained when separating less reactive compounds (polar, ester, and amine narcotics) from more reactive compounds (electrophiles, proelectrophiles, and nucleophiles).

Animals↗

PubMed2003

Housing and husbandry of Xenopus for oocyte production.

Despite their importance as a research model, particularly in developmental toxicology investigations, there are few established standards for maintaining Xenopus spp. frogs in the laboratory. The authors review the literature on handling, housing, nutrition, and breeding of Xenopus spp. for optimal oocyte production.

Animal Husbandry↗

PubMed2003

Overview of data and conceptual approaches for derivation of quantitative structure-activity relationships for ecotoxicological effects of organic chemicals.

The use of quantitative structure-activity relationships (QSARs) in assessing potential toxic effects of organic chemicals on aquatic organisms continues to evolve as computational efficiency and toxicological understanding advance. With the ever-increasing production of new chemicals, and the need to optimize resources to assess thousands of existing chemicals in commerce, regulatory agencies have turned to QSARs as essential tools to help prioritize tiered risk assessments when empirical data are not available to evaluate toxicological effects. Progress in designing scientifically credible QSARs is intimately associated with the development of empirically derived databases of well-defined and quantified toxicity endpoints, which are based on a strategic evaluation of diverse sets of chemical structures, modes of toxic action, and species. This review provides a brief overview of four databases created for the purpose of developing QSARs for estimating toxicity of chemicals to aquatic organisms. The evolution of QSARs based initially on general chemical classification schemes, to models founded on modes of toxic action that range from nonspecific partitioning into hydrophobic cellular membranes to receptor-mediated mechanisms is summarized. Finally, an overview of expert systems that integrate chemical-specific mode of action classification and associated QSAR selection for estimating potential toxicological effects of organic chemicals is presented.

Animals↗

PubMed2003

Guidelines for developing and using quantitative structure-activity relationships.

Numerous quantitative structure-activity relationships (QSARs) have been developed to predict properties, fate, and effects of mostly discrete organic chemicals. As the demand for different types of regulatory testing increases and the cost of experimental testing escalates, there is a need to evaluate the use of QSARs and provide some guidance to avoid their misuse, especially as QSARs are being considered for regulatory purposes. This paper provides some guidelines that will promote the proper development and use of QSARs. While this paper uses examples of QSARs to predict toxicity, the proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects.

Animals↗

PubMed2003

Essential and desirable characteristics of ecotoxicity quantitative structure-activity relationships.

Quantitative structure-activity relationships (QSAR) developed and applied in the prediction of ecotoxic potencies far out number those in other areas, such as health effects. There are yet to be any formal guidelines for the development of ecotoxicological QSARs. Despite this, the depth and breadth of our knowledge of QSARs as they apply to ecotoxicology, especially short-term aquatic toxicity, allow for the formulation of characteristics that appear to be essential and/or desirable for high-quality QSARs. The three components of a QSAR are the biological activity, the property/structural descriptors, and the statistical methodology. Problems may arise from all three components and may be compounded by interactions between them. In an effort to minimize any tribulations associated with development and application of ecotoxic QSARs, a number of essential or desirable characteristics have been identified. Ecotoxicological data used in formulating the QSAR must be reliable, of high quality, and reflect a well-defined and continuous endpoint; this dataset should be diverse both in terms of potency and chemical structure (i.e., property). Descriptors used in formulating the QSAR should be of high quality, reproducible, of a number and type consistent with the endpoint being modeled, and when possible allow for a mechanistic interpretation of the QSAR. The statistical process used in formulating a QSAR should be as rigorous as possible, appropriate for the endpoint being modeled, and allow for the development of as easily interpretable (i.e., transparent) QSARs as possible. The resultant QSAR should be validated, only used within the descriptor space and chemical domain of the model, and relied on in relation to the total weight of evidence; precision of the QSAR and expectations from its application need to be related to the error in the original ecotoxicological and descriptor measurements. Finally, development of QSARs should be through the interaction of a group of multidisciplinary experts.

Endpoint Determination↗

PubMed2002

In vitro estrogen receptor binding of PCBs: measured activity and detection of hydroxylated metabolites in a recombinant yeast assay.

The estrogenic activities of 17beta-estradiol, biphenyl, chlorinated biphenyls, and Aroclor mixtures 1221, 1242, and 1248 were measured with a modified recombinant yeast estrogen assay (i.e., a Saccharomyces cerevisiae-based lac-Z (beta-galactosidase) reporter assay). Modifications of the assay included the use of glass vials instead of plastic microtiter plates and the addition of the medium and yeast before the test substrate. 14C-labeled compounds were used to follow improvements in the assay procedures. 14C-17beta-estradiol recovery from plastic microtiter plates and glass vials using the standard or the modified procedure was approximately 89%. However, 14C-4-CB (4-chlorobiphenyl) recovery was considerably less, ranging from 3% in plastic microtiter plates using the standard procedure to 26% in vials using the modified procedure. These results suggest that the toxicity of strongly hydrophobic chemicals may be underestimated. Using the modified yeast estrogen assay, full agonist activity was observed for 4-CB, 2,4,6-CB, and 2,5-CB while each of the Aroclor mixtures were only partial agonists. The equivalent EC50 values in ppm were in environmentally relevant concentrations for biphenyl (19 ppm), 4-CB (4.5 ppm), 2,5-CB (21 ppm), 2,4,6-CB (0.8 ppm), Aroclor 1221 (2.9 ppm), Aroclor 1242 (0.65 ppm), and Aroclor 1248 (2.3 ppm). Estrogen receptor binding for the individual PCB congeners was 25- to 650-fold less than the reported estrogen binding for the corresponding hydroxylated PCB metabolite. Gas chromatographic/mass spectrometric analysis of yeast extracts indicated that S. cerevisiae hydroxylated the individual PCB congeners in the ppb range. With the exception of biphenyl, the concentration of hydroxylated metabolites obtained from incubation of S. cerevisiae with PCB congeners was consistent with the concentration necessary to elicit a positive estrogen receptor-binding response. This work provides evidence that S. cerevisiae are capable of metabolic transformation of PCBs and that estrogen receptor binding of PCBs is mediated through the hydroxylated metabolite rather than through the direct interaction of the PCB congeners with the estrogen receptor.

Hydroxylation↗

PubMed2002

Identifying the mechanism of aquatic toxicity of selected compounds by hydrophobicity and electrophilicity descriptors.

The most successful quantitative structure-activity relationships (QSARs) have been developed by separating toxicants by their mechanisms of action (MOAs). However, since the activity of a chemical compound on an organism is dependent upon several physical, chemical and biological factors, among which interactions may also exist, the MOA of a compound is not easily determined. In this study, the use of discriminant analysis and logistic regression in distinguishing between narcotic and reactive compounds was investigated. The discriminating variables included hydrophobicity (log(K(ow))) and electrophilicity descriptors (S(av)(N), E(HOMO), and E(LUMO)). Classification results showed that logistic regression gave a smaller total error rate compared to discriminant analysis. Since the value of the descriptors can be calculated, the classification methods can be used in predictive toxicology.

Hydrophobic and Hydrophilic Interactions↗

PubMed2002

Structure-activity relationships for gene activation oestrogenicity: evaluation of a diverse set of aromatic chemicals.

Structure-activity relationships for oestrogenicity were developed based on 120 aromatic chemicals evaluated in the Saccharomyces cerevisiae-based Lac-Z reporter assay. Relative gene activation was compared to 17 beta-estradiol and varied over eight orders of magnitude. Analysis of the data compared to 17 beta-estradiol identified three structural criteria that were related to xenoestrogen activity and potency: (1) the hydrogen-bonding ability of the phenolic ring mimicking the A-ring, (2) a hydrophobic centre similar in size and shape to the B- and C-rings, and (3) a hydrogen-bond donor mimicking the 17 beta-hydroxyl moiety of the D-ring, especially with an oxygen-to-oxygen distance similar to that between the 3- and 17 beta-hydroxyl groups of 17 beta-estradiol. Binding data were segregated into activity clusters including strong, moderate, weak, and detectable gene expression, and those compounds that were inactive. The hydrogen-bonding ability of hydroxy group in the 3-position on 17 beta-estradiol was observed to be essential for gene activation. Compounds with a 4-hydroxyl substituted benzene ring and a hydrophobic moiety of size and shape equivalent to the B-ring of 17 beta-estradiol were generally observed to be weakly active compounds. Moderately active compounds have a 4-hydroxyl substituted benzene ring with a hydrophobic moiety equivalent in size and shape to the B- and C-ring of 17 beta-estradiol, or have a high hydrogen-bond donor capacity owing to the presence of halogens on a nonphenolic ring. Strongly active compounds, similar to 4,4'-diethylethylene bisphenol (DES), possess the same hydrophobic ring structure as described for moderately active compounds and an additional hydroxyl group with an oxygen-to-oxygen distance close to that exhibited by the 3- and 17-hydroxyl groups of 17 beta-estradiol.

Benzophenones↗

PubMed2002

Regression comparisons of Tetrahymena pyriformis and Poecilia reticulata toxicity.

The toxicity data of chemicals common to both the Poecilia reticulata mortality assay and the Tetrahymena pyriformis growth impairment assay were evaluated. Two chemicals were not toxic at saturation in the T. pyriformis assay. In addition, due to abiotic transformation, a third chemical was removed from further consideration. Each chemical was a priori assigned a mode of toxic action: neutral non-covalent, polar non-covalent, or electrophilic covalent toxicity. To further investigate comparisons between endpoints, polar and electrophilic chemicals were separated into class-based groups. The polar non-covalent chemicals were separated into phenols and anilines, while the electrophilic chemicals were separated into those reacting via Schiff-base formation (i.e., aldehydes) and those reacting via bimolecular substitution to a nucleophile (i.e., selected nitroaromatics). A comparison of toxic potency as a collective set was statistically described by the relationship; log(LC50(-1)) = 1.05(log(IGC50(-1))) + 0.56, n = 124; r2 = 0.85; s = 0.42; F = 682; Pr > F = 0.0001. The relationship between endpoints was inversely proportional to reactivity associated with the mode of action. While the comparative toxicity for neutral narcotics exhibited an excellent fit (r2 = 0.94), the fits for polar narcotics and electrophiles were poorer, r2 = 0.69 and 0.62, respectively. Investigations into class-based groupings indicated fit of toxic potency data for aldehydes (r2 = 0.85) and phenols (r2 = 0.81) were quite good. However, fits for anilines (r2 = 0.43) and nitroaromatics (r2 = 0.68) revealed that toxicity was not as well related between endpoints for these chemicals.

Aldehydes↗

PubMed2002

Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis.

Quantitative structure-activity relationships (QSARs) for the toxicity of 200 phenols to the ciliated protozoan Tetrahymena pyriformis, and the validation of the QSARs using a test set of a further 50 compounds, are reported. The phenols are structurally heterogeneous and represent a variety of mechanisms of toxic action including polar narcosis, weak acid respiratory uncoupling, electrophilicity, and those compounds capable of being metabolised or oxidised to quinones. For each compound, a total of 108 physico-chemical descriptors have been calculated. A variety of methods were utilised to develop QSARs and are compared. The response-surface, or two parameter, approach was found to be successful, but only following the removal of compounds known to form quinones. Stepwise regression produced a seven parameter QSAR with good statistical fit, but was less interpretable and transparent than the response-surface. Partial least squares produced a good model for phenolic toxicity following supervised selection of parameters, this, however, was the least transparent of all approaches attempted. In all approaches, a large number of outliers were observed, typically these were compounds capable of being metabolised to quinones. The strengths and weaknesses of each of the approaches to predict the toxicity of the validation (test) set of phenols to T. pyriformis are discussed.

Animals↗

PubMed2002

Structure-toxicity relationships for aliphatic chemicals evaluated with Tetrahymena pyriformis.

Quantitative structure-activity relationships were developed for the toxicity data of 500 aliphatic chemicals tested in the two-day Tetrahymena pyriformis population growth impairment assay. These chemicals represented a number of structural classes spanning a variety of mechanisms of toxic action including narcoses and electrophilic mechanisms. A series of quantitative structure-toxicity models correlating toxic potency [log(IGC(50)(-1))] with a limited number of mechanistically interpretable descriptors were developed for toxicological domains within the data set. The descriptors included the 1-octanol/water partition coefficient (log K(ow)) (for hydrophobicity) and the energy of the lowest unoccupied molecular orbital (E(lumo)) to quantify electrophilic reactivity. Neutral (nonpolar) narcosis was well modeled by the equation [log(IGC(50)(-1)) = 0.723(0.140) (log K(ow)) - 1.79(0.031); n = 215, r(2) (adj.) = 0.926, s = 0.274, r(2) (pred.) = 0.925]. Chemical classes fitting this domain included saturated alcohols, ketones, nitriles, esters, and sulfur-containing compounds. When the neutral narcotic chemicals were combined with diester narcotics, carboxylic sodium salts, Schiff-based forming aldehydes, electrophilic compounds capable of acting by a S(N)2 mechanism, and proelectrophiles, the model [log(IGC(50)(-1)) = 0.45(0.014) (log K(ow)) - 0.342 (0.035) (E(lumo)) - 1.11(0.05); n = 353, r(2) (adj.) = 0.859, s = 0.353, r(2) (pred.) = 0.857] provided a good fit to the data. The model [log(IGC(50)(-1)) = 0.273(0.018) (log K(ow)) - 0.116(0.056) (E(lumo)) - 0.558(0.054); n = 35, r(2) (adj.) = 0.873, s = 0.141, r(2) (pred.) = 0.838] provided an excellent fit of the data for compounds containing a carboxyl [RC(=O)O] group. The toxicity of aliphatic amines [RCN] was modeled by the equation [log(IGC(50)(-1)) = 0.676(0.048) (log K(ow)) - 1.23(0.08) n = 30, r(2) (adj.) = 0.873, s = 0. 336, r(2) (pred.) = 0.848]. The potency of saturated aliphatic isothiocyanates was a constant (0.0202 mM). Aliphatic chemicals that did not model well by equations involving log K(ow) and E(lumo) included amino alcohols and alpha-haloactivated compounds.

Animals↗

PubMed2002

Xenoestrogenic gene expression: structural features of active polycyclic aromatic hydrocarbons.

Estrogenicity was assessed using the Saccharomyces cerevisiae-based Lac-Z reporter assay and was reported as the logarithm of the inverse of the 50% molar beta-galactosidase activity (log[EC50(-1)]). In an effort to quantify the relationship between molecular structure of polycyclic aromatic hydrocarbons (PAHs) and estrogenic gene expression, a series of PAHs were evaluated. With noted exceptions, the results of these studies indicate that the initial two-dimensional structural warning for estrogenicity, the superpositioning of a hydroxylated aromatic system on the phenolic A-ring of 17-beta-estradiol, can be extended to the PAHs. This two-dimensional-alignment criterion correctly identified estrogenicity of 22 of the 29 PAHs evaluated. Moreover, the estrogenic potency of these compounds was directly related to the size of the hydrophobic backbone. The seven compounds classified incorrectly by this structural feature were either dihydroxylated naphthalenes or aromatic nitrogen-heterocyclic compounds; all such compounds were false positives. Results with dihydroxylated naphthalenes reveal derivatives that were nonestrogenic when superimposed on the phenolic A-ring of 17-beta-estradiol had the second hydroxyl group in the position of the C-ring or were catechol-like in structure. Structural alerts for nitrogen-heterocyclic compounds must take into account the position of the hydroxyl group and the in-ring nitrogen atom; compounds with the hydroxyl group and nitrogen atom involved with the same ring were observed to be nonactive.

Estrogens↗

PubMedDate not supplied

Structure-based classification of antibacterial activity.

The aim of this study was to develop a simple quantitative structure-activity relationship (QSAR) for the classification and prediction of antibacterial activity, so as to enable in silico screening. To this end a database of 661 compounds, classified according to whether they had antibacterial activity, and for which a total of 167 physicochemical and structural descriptors were calculated, was analyzed. To identify descriptors that allowed separation of the two classes (i.e. those compounds with and without antibacterial activity), analysis of variance was utilized and models were developed using linear discriminant and binary logistic regression analyses. Model predictivity was assessed and validated by the random removal of 30% of the compounds to form a test set, for which predictions were made from the model. The results of the analyses indicated that six descriptors, accounting for hydrophobicity and inter- and intramolecular hydrogen bonding, provided excellent separation of the data. Logistic regression analysis was shown to model the data slightly more accurately than discriminant analysis.

Anti-Bacterial Agents↗

PubMedDate not supplied

Molecular quantum similarity analysis of estrogenic activity.

The main objective of this study was to evaluate the capability of 120 aromatic chemicals to bind to the human alpha estrogen receptor (hER alpha) by the use of quantum similarity methods. The experimental data were segregated into two categories, i.e., those compounds with and without estrogenicity activity (active and inactive). To identify potential ligands, semiquantitative structure-activity relationships were developed for the complete set correlating the presence or lack of binding affinity to the estrogen receptor with structural features of the molecules. The structure-activity relationships were based upon molecular similarity indices, which implicitly contain information related to changes in the electron distributions of the molecules, along with indicator variables, accounting for several structural features. In addition, the whole set was split into several chemical classes for modeling purposes. Models were validated by dividing the complete set into several training and test sets to allow for external predictions to be made.

Estrogens, Non-Steroidal↗