Biomedical subjects

Aynur O Aptula

Publications and source records attributed to Aynur O Aptula.

13 recordsLinked to original sources

PubMed2007

Electrophilic chemistry related to skin sensitization. Reaction mechanistic applicability domain classification for a published data set of 106 chemicals tested in the mouse local lymph node assay.

This article presents an overview of electrophilic reaction mechanisms relevant to skin sensitization, with reference to a published skin sensitization test data set for 106 chemicals. Where appropriate to aid the interpretation, additional data on a small number of further compounds are also discussed. It is shown that there is a close correspondence in the way differences and similarities in skin sensitization potency of chemicals relate to differences and similarities in their physical organic chemistry and electrophilic reaction mechanistic chemistry. The 106 chemicals are classified into their reaction mechanistic applicability domains, and reactivity-sensitization trends are analyzed for each domain: the Michael acceptor and pro-Michael acceptor electrophile domain; the SNAr electrophile domain; the SN2 electrophile domain; the Schiff base electrophile domain; the acyl transfer electrophile domain; and the non-electrophilic non-pro-electrophilic domain. The last of these domains should be populated mainly by non-sensitizers. Classification of 87 of the 106 compounds, using these domains, was straightforward. In most of the domains and subdomains where there are sufficient compounds, clear trends can be seen, in conformity with the Relative Alkylation Index (RAI) model, between sensitization potential and reactivity/hydrophobicity. Of the remaining 19 compounds, 7 are alpha-X-methyl-gamma-lactones that on the basis of published organic chemistry studies and guinea pig sensitization data can be classed as pro-Michael acceptors by elimination of HX but that are mostly negative in the LLNA, indicating a difference in bioactivation capabilities between mice and guinea pigs. The other 12 compounds, whose chemistry was not immediately obvious, were found after further analysis and literature research to fit into appropriate mechanistic domains that rationalize their skin sensitizing properties.

Animals↗

PubMed2007

Haptens, prohaptens and prehaptens, or electrophiles and proelectrophiles.

It is argued that the term 'hapten', and derived terms such as 'pro-hapten' and 'pre-hapten' are ambiguous and unnecessary. It is proposed that their use be abandoned. Instead, when considering the chemical basis of skin sensitization, it is preferable to classify compounds according to the chemical reaction mechanisms by which they can modify proteins.

Allergens↗

PubMed2006

Mechanistic applicability domains for nonanimal-based prediction of toxicological end points: general principles and application to reactive toxicity.

In light of new legislation (e.g., the REACH program in the European Union), several initiatives have recently emerged to increase acceptance of (quantitative) structure-activity relationships [(Q)SARs] to reduce reliance on animal (in vivo) testing. Among the principles for assessing the validity of (Q)SARs is the need for a defined domain of applicability, i.e., identification of the range of compounds for which the (Q)SAR can confidently be applied for purposes of toxicity prediction. Here, we attempt to develop a "natural" classification into applicability domains based on considering how a compound and the target organism between them "decide" on the nature and extent of the toxic effect. With particular emphasis on reactive toxicity, we present rules, based on organic reaction mechanistic principles, for classifying reactive toxicants into their appropriate mechanistic applicability domains.

Animal Testing Alternatives↗

PubMed2006

Mechanistic applicability domains for non-animal based prediction of toxicological endpoints. QSAR analysis of the schiff base applicability domain for skin sensitization.

Several recent (1999 onward) publications on skin sensitization to aldehydes and ketones, which can sensitize by covalent binding to skin protein via Schiff base formation, present QSARs based on the Taft sigma parameter to model reactivity and log P to model hydrophobicity. Here, all of the data are reanalyzed together in a stepwise self-consistent way using the parameters log P (octanol/water) and Sigmasigma, the latter being the sum of Taft sigma values for the two groups R and R' in RCOR'. A QSAR is derived: pEC3 = 1.12(+/-0.07) Sigmasigma + 0.42(+/-0.04) log P - 0.62(+/-0.13); n = 16 R(2) = 0.952 R(2)(adj) = 0.945 s = 0.12 F = 129.6, based on mouse local lymph node assay (LLNA) data for 11 aliphatic aldehydes, 1 alpha-ketoester and 4 alpha,beta-diketones. In developing this QSAR, an initial regression equation for a training set of 10 aldehydes was found to predict a test set consisting of the other 6 compounds. The QSAR is found to be well predictive for LLNA data on a series of alpha,gamma-diketones and also correctly predicts the nonsensitizing properties of simple dialkylketones. It is shown to meet all of the criteria of the OECD principles for applicability within regulatory practice. In view of the structural diversity within the sets of compounds considered here, the present findings confirm the view that within the mechanistic applicability domain the differences in sensitization potential are dependent solely on differences in chemical reactivity and partitioning.

Benzaldehydes↗

PubMed2005

Non-enzymatic glutathione reactivity and in vitro toxicity: a non-animal approach to skin sensitization.

The development of non-animal methods to predict the potential of chemicals to cause skin sensitization is of great importance. On the basis of many published studies into the underlying chemical mechanisms skin sensitization, the immunological priming which leads to the disease allergic contact dermatitis, is recognized as a reactive chemistry endpoint. Consequently, the combination of chemical assays with in vitro techniques may provide a useful surrogate to animal testing for skin sensitization. This study attempts to investigate the relationship between skin sensitization assessed in the local lymph node assay (LLNA) initially and a thiol reactivity index based on glutathione (GSH), pEC(50) thiol (EC(50) being defined as the concentration of the test substance which gives 50% depletion of free thiol under standard conditions) in combination with a measure of cytotoxicity (pIGC(50)) to Tetrahymena pyriformis (TETRATOX). The pEC(50) thiol values and the pIGC(50) values were determined for twenty-four compounds for which LLNA test data were available. Thiol reactivity was found to discriminate sensitizers from non-sensitizers according to the rule: pEC(50) thiol>-0.55 indicates that the compound will be a skin sensitizer. However, because of metabolic activation a pEC(50) thiol<-0.55 does not necessarily mean that the compound will be a non-sensitizer. Excess toxicity to T. pyriformis (i.e. the extent of toxic potency over that expected by non-polar narcosis) was determined in order to assess biological reactivity. The best discrimination based on excess toxicity in the TETRATOX assay was given by the "rule": excess toxicity>0.50 indicates that the compound will be a skin sensitizer. These approaches become more powerful when combined. When taken together, the thiol and TETRATOX assays predict the sensitization potential of 23 of the 24 compounds correctly. alpha-Hexylcinnamic aldehyde is incorrectly predicted to be a non-sensitizer, whereas LLNA results suggest it may be a weak sensitizer, this inaccuracy being rationalized in terms of its high hydrophobicity. Due to the selectivity of electro(nucleo)philic reactions some sensitizing compounds will not be identified using a single nucleophile such as thiol.

Allergens↗

PubMed2005

Chemistry-toxicity relationships for the effects of di- and trihydroxybenzenes to Tetrahymena pyriformis.

This paper presents a mechanistic analysis of aquatic toxicity data, quantified as pIGC(50) assessed in the 40 h Tetrahymena pyriformis population growth impairment assay, for 40 polyhydroxybenzene derivatives. The toxicity trends of these phenolic compounds have been shown to be consistent with mechanistic organic chemistry principles. Thus, it is shown that the compounds can be grouped into two chemical mechanism of action domains, according to whether they can be oxidized to electrophilic quinones or quinone methides. Compounds in which the hydroxy groups are oriented meta, but not ortho or para, to one another cannot be oxidized to electrophilic quinones or quinone methides and act as polar narcotics. Their toxicities are found to be well-correlated with hydrophobicity (modeled by log D): pIGC(50) = 0.83 (+/-0.04) log D - 1.27 (+/-0.09): n = 10, r(2) (adj) = 0.981, q(2) = 0.974, s = 0.15, and F = 460. Compounds with hydroxy groups oriented ortho or para to one another are more toxic than predicted by this equation, and the toxicity trends within this group of compounds are rationalized in terms of the electrophilic chemistry of their oxidation products. A quantitative correlation is demonstrated between toxicity and electrophilicity of the oxidation products, as modeled by the activation energy index (AEI), a new molecular orbital parameter derived from the computed highest occupied molecular orbital (HOMO) and HOMO-1 orbital energies of the electrophiles and the intermediates for Michael addition of n-butylamine: pIGC(50) (adj) = -0.49 (+/-0.06) AEI + 6.85 (+/-0.69): n = 18, r(2) (adj) = 0.810, q(2) = 0.774, s = 0.24, and F = 73. Outliers to these quantitative structure-activity relationships (QSARs) are easily rationalized in terms of their chemistry (tetrabromocatechol, 4,6-dinitro-1,2,3-trihydroxybenzene, and 2,3,4-trihydroxybenzophenone) or in a demonstrable deficiency in the descriptor (the methyl-substituted hydroquinones, for which the AEI parameter as defined here fails to model the electron donation effects of the methyl groups). The AEI parameter is a mechanism-based molecular orbital parameter new to QSAR and, on the basis of the present findings, it shows promise for further applications. However, some deficiencies have been identified with it, particularly with regard to modeling the electronic effects of methyl (and presumably other alkyl) groups, and there is scope to refine the concept so as to deal with these deficiencies.

Animals↗

PubMed2005

From experiment to theory: molecular orbital parameters to interpret the skin sensitization potential of 5-chloro-2-methylisothiazol-3-one and 2-methylisothiazol-3-one.

5-Chloro-2-methylisothiazol-3-one (MCI) and 2-methylisothiazol-3-one (MI) are the major constituents of the commercial biocide Kathon CG. These two compounds have both been shown to exhibit skin sensitization potential: MCI is classified as an extreme sensitizer while MI is classified as a moderate sensitizer. The purpose of the present investigation was to provide further insights into the chemistry underlying their skin sensitizing properties. First, a molecular modeling (in silico) study was carried out of the initial reaction pathways of MI and MCI with nucleophiles representative of those involved in the skin sensitization process, and we compared the findings with the reported chemical and allergenic properties of these compounds. These reaction pathways were assessed using molecular orbital calculations. A novel parameter, the activation energy (AE) index, is proposed and is calculated from a knowledge of the energy changes in the frontier molecular orbitals as the electrophile is converted to an anionic intermediate. The AE indices correspond to the reactivity of MCI and MI with nucleophiles and also their skin sensitization potential. Second, the previously unexplained formation of final reaction products from MCI and butylamine is discussed and a reaction mechanism is proposed. A key finding of this analysis is that the reaction produces "positive chlorine" in the form of N-chloro- and/or N,N-dichloro-butylamine, which could contribute to the skin sensitizing properties of MCI.

Animals↗

PubMed2005

Skin sensitization: reaction mechanistic applicability domains for structure-activity relationships.

The prediction of skin sensitization potential with minimum animal testing is currently of great importance in light of forthcoming legislation. A number of structure-activity relationships for skin sensitization have been published over the years, but their applicability has often been limited to structural classes. The concept of an applicability domain for a quantitative structure-activity relationship [(Q)SAR] is increasingly being viewed as key for the predictive application of (Q)SARs. This is particularly the case for skin sensitization if more widely applicable SARs are to be developed. In this paper, we analyze a recently published chemical data set for skin sensitization, apply reaction mechanistic criteria to domain classification, and evaluate the structure-activity trends observed within each of these mechanistic domains.

Acylation↗

PubMed2003

Stepwise discrimination between four modes of toxic action of phenols in the Tetrahymena pyriformis assay.

For a set of 220 phenols with literature data on their toxicity and associated mode of action (MOA) toward the ciliate Tetrahymena pyriformis, a stepwise classification scheme was developed that allows the identification of four MOAs from molecular hydrophobicity and AM1-based quantum chemical descriptors, employing linear discriminant analysis or binary logistic regression. Taking the AM1 lowest unoccupied molecular orbital energy as the only parameter, an initial separation of polar narcotics and proelectrophiles from oxidative uncouplers and soft electrophiles is correct to 97%, and for the subsequent discrimination between polar narcotics and proelectrophiles as well as between oxidative uncouplers and soft electrophiles, 99 and 98% correct classifications are achieved using three and two molecular descriptors, respectively. The results are discussed in terms of detailed contingency table statistics and with respect to relationships between molecular descriptors and mechanisms of toxicity. Statistical model evaluation includes simulated external validation employing complementary subset models.

Animals↗

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↗

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

Description of the electronic structure of organic chemicals using semiempirical and ab initio methods for development of toxicological QSARs.

The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K(ow)). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.

Animals↗