Structure-Toxicity Relationships for Aliphatic Compounds Encompassing a Variety of Mechanisms of Toxic Action to Vibrio fischeri

Abstract

QSARs based upon the logarithm of the octanol-water partition coefficient, logP, and energy of the lowest unoccupied molecular orbital, E_LUMO were developed to model the toxicity of aliphatic compounds to the marine bacterium Vibrio fischeri. Statistically robust, hydrophobic-dependent QSARs were found for chloroalcohols and haloacetonitriles. Modelling of the toxicity of the haloesters and the diones required the use of terms to describe both hydrophobicity and electrophilicity. The differences in intercepts, slopes, and fit of these models suggest different electrophilic mechanisms occur between classes, as well as within the diones and haloesters. In order to model globally the toxicity of aliphatic compounds to V. fischeri, all the data determined in this study were combined with those determined previously for alkanones, alkanals, and alkenals. A highly predictive two-parameter QSAR [pT₁₅ = 0.760(log P) ?0.625(E _LUMO) ?0.466; n = 63, s = 0.462, r ² = 0.846, F = 171, Pr > F = 0.0001] was developed for the combined data that models across classes and is independent of mechanisms of action. The toxicity of these compounds to V. fischeri compares well to the toxicity (50% population growth inhibition) to the ciliate Tetrahymena pyriformis (r ² = 0.850).     

Quantitative structure-toxicity relationships for chlorophenols to bioluminescent lux-marked bacteria using atom-based semi-empirical molecular-orbital descriptors

Literature data on the toxicity of chlorophenols for three luminescent bacteria (Vibrio fischeri, and the lux-marked Pseudomonas fluorescens 10586s pUCD607 and Burkholderia spp. RASC c2 (Tn4431)) have been analyzed in relation to a set of computed molecular physico-chemical properties. The quantitative structure-toxicity relationships of the compounds in each species showed marked differences when based upon semi-empirical molecular-orbital molecular and atom based properties. For mono-, di- and tri-chlorophenols multiple linear regression analysis of V. fischeri toxicity showed a good correlation with the solvent accessible surface area and the charge on the oxygen atom. This correlation successfully predicted the toxicity of the heavily chlorinated phenols, suggesting in V. fischeri only one overall mechanism is present for all chlorophenols. Good correlations were also found for RASC c2 with molecular properties, such as the surface area and the nucleophilic super-delocalizability of the oxygen. In contrast the best QSTR for P. fluorescens contained the 2nd order connectivity index and ELUMO suggesting a different, more reactive mechanism. Cross-species correlations were examined, and between V. fischeri and RASC c2 the inclusion of the minimum value of the nucleophilic susceptibility on the ring carbons produced good results. Poorer correlations were found with P. fluorescens highlighting the relative similarity of V. fischeri and RASC c2, in contrast to that of P. fluorescens.     

Response-surface analyses for toxicity to Tetrahymena pyriformis: reactive carbonyl-containing aliphatic chemicals

A response-plane has been developed with Tetrahymena pyriformis population growth impairment toxicity data [log 1/50% growth inhibitory concentration (IGC50)], the 1-octanol/water partition coefficient (log Kow), and the energy of the lowest unoccupied molecular orbital (Elumo). A statistically robust plane [log 1/IGC50 = 0.530 (log Kow) -0.890 (Elumo) -0.271, n = 50, s = 0.295, r2 = 0.855, F = 145] was found for reactive carbonyl-containing aliphatic chemicals. These compounds had a variety of electrophilic mechanisms of action and included aldehydes acting as Schiff-base formers, alpha,beta-unsaturated aldehydes and alpha,beta-unsaturated ketones acting as Michael-type acceptors, and selected alpha-diones acting as selective binders to arganine residues; gamma-diones acting as selective binders to tubulin; and beta-diones with unknown mechanisms of action. Outliers to this model broadly fell into two groups: small reactive molecules (e.g., acrolein) that were more toxic than predicted and molecules in which the reactive center was sterically hindered by an alkyl group (e.g., 2,4-dimethyl-2,6-heptadienal) that were less toxic than predicted.     

Systematic search for surrogate chromatographic models of biopartitioning processes

A systematic approach for identifing surrogate chromatographic models for biopartitioning processes is described. The method is based on a comparison of the system constant ratios of the solvation parameter model for biopartitioning processes and a database of system constant ratios for reversed-phase liquid chromatographic and micellar electrokinetic chromatographic systems compiled from literature sources. An acceptance filter of < or = 0.2 is applied for each difference in system constant ratio for the compared systems to provide a reasonable probability of success without outputting too many systems with limited predictive properties. Surrogate chromatographic models identified for the non-specific toxicity of neutral organic compounds to the fathead minnow and the soil-water distribution constant are tested by construction of a correlation model for the characteristic property of the biological process and the chromatographic retention factors for a structurally varied group of compounds. Although these models are not the best that could be obtained based on ranking of the differences in system constant ratios the predictive ability of the correlation models is suitable for typical applications and similar to the accepted uncertainty in the measurements of the biological property. Retention factors on the immobilized artificial membrane column (IAM PC DD 2) with 10% (v/v) methanol-water as mobile phase are able to estimate non-specific toxicity to the fathead minnow with a standard error (SE) of 0.22 log units and coefficient of determination (r2) of 0.97 for 31 compounds. Retention factors on a Bakerbond DIOL column with 20% (v/v) acetonitrile-water as mobile phase are able to estimate the soil-water distribution constant with an SE of 0.38 log units and r2 = 0.88 for 59 compounds. Other potential surrogate chromatographic models are identified for non-specific toxicity to the guppy, tadpole, Vibrio fischeri and Terahymena pyriformis as well as the plant cuticle matrix-water distribution constant. On the other hand reversed-phase chromatographic systems seem poorly suited for estimating intestinal absorption and the blood-brain distribution constant.     

Discriminating toxicant classes by mode of action: 3. Substructure indicators

Decision support for selecting suitable QSARs for predictive purposes is suggested by a stepwise procedure: The first tier pre-filters the compounds based on substructure indicators for baseline versus excess toxicity. This step, if sufficiently conservative, discriminates chemicals, whose toxicity can be reliably estimated from their log KOW from those, that require further classification by biological and chemical domain. A test set of 115 chemicals from 9 different MOA classes was used to compare the discriminatory power of several classification schemes based on substructure indicators. Performance, evaluated by contingency table statistics, is varied and no single scheme provides sufficient applicability and reliability for pre-filtering chemical inventories. Major improvements are feasible with combined use of three classification schemes: assignments of baseline toxicants are protective, recognition of excess toxicants is acceptable and applicability range increases favourably.     

Structure-activity relationships for abiotic thiol reactivity and aquatic toxicity of halo-substituted carbonyl compounds

Using abiotic thiol reactivity (EC50) and Tetrahymena pyriformis toxicity (IGC50) data for a group of halo-substituted ketones, esters and amides (i.e. SN2 electrophiles) and related compounds a series of structure-activity relationships are illustrated. Only the alpha-halo-carbonyl-containing compounds are observed to be thiol reactive with the order I > Br > Cl > F. Further comparisons disclose alpha-halo-carbonyl compounds to be more reactive than non-alpha-halo-carbonyl compounds; in addition, the reactivity is reduced when the number of C atoms between the carbonyl and halogen is greater than one. Comparing reactivity among alpha-halo-carbonyl-containing compounds with different beta-alkyl groups shows the greater the size of the beta-alkyl group the lesser the reactivity. A comparison of reactivity data for 2-bromoacetyl-containing compounds of differing dimensions reveals little difference in reactivity. Regression analysis demonstrates a linear relationship between toxicity and thiol reactivity: log (IGC(50)(-1)) = 0.848 log (EC(50)(-1)) + 1.40; n=19, s=0.250, r2=0.926, r2(pred)=0.905, F=199, Pr > F=0.0001.     

Discriminating toxicant classes by mode of action: 3. Substructure indicators§

Decision support for selecting suitable QSARs for predictive purposes is suggested by a stepwise procedure: The first tier pre-filters the compounds based on substructure indicators for baseline versus excess toxicity. This step, if sufficiently conservative, discriminates chemicals, whose toxicity can be reliably estimated from their log?K _OW from those, that require further classification by biological and chemical domain. A test set of 115 chemicals from 9 different MOA classes was used to compare the discriminatory power of several classification schemes based on substructure indicators. Performance, evaluated by contingency table statistics, is varied and no single scheme provides sufficient applicability and reliability for pre-filtering chemical inventories. Major improvements are feasible with combined use of three classification schemes: assignments of baseline toxicants are protective, recognition of excess toxicants is acceptable and applicability range increases favourably.