T.W. Schultz

An in silico skin absorption model for fragrance materials.

Fragrance materials are widely used in cosmetics and other consumer products. The Research Institute for Fragrance Materials (RIFM) evaluates the safety of these ingredients and skin absorption is an important parameter in refining systemic exposure. Currently, RIFMs safety assessment process assumes 100% skin absorption when experimental data are lacking. This 100% absorption default is not supportable and alternate default values were proposed. This study aims to develop and validate a practical skin absorption model (SAM) specific for fragrance material. It estimates skin absorption based on the methodology proposed by Kroes et al. SAM uses three default absorption values based on the maximum flux (J(max)) - namely, 10%, 40%, and 80%. J(max) may be calculated by using QSAR models that determine octanol/water partition coefficient (K(ow)), water solubility (S) and permeability coefficient (K(p)). Each of these QSAR models was refined and a semi-quantitative mechanistic model workflow is presented. SAM was validated with a large fragrance-focused data set containing 131 materials. All resulted in predicted values fitting the three-tiered absorption scenario based on Jmax ranges. This conservative SAM may be applied when fragrance material lack skin absorption data.

Relationships of quantitative structure-activity to comparative toxicity of selected phenols in the Pimephales promelas and Tetrahymena pyriformis test systems

The relative toxic response of 27 selected phenols in the 96-hr acute flowthrough Pimephales promelas (fathead minnow) and the 48- to 60-hr chronic static Tetrahymena pyriformis (ciliate protozoan) test systems was evaluated. Log Kow-dependent linear regression analyses revealed that the data from each test system consisted of two linear equations. The less toxic chemicals form a relationship which models polar narcosis; these chemicals are slightly more active than the baseline toxicity of nonionic narcotic chemicals. The more toxic chemicals form a relationship which models uncoupling of oxidative phosphorylation. Regression analysis of fathead minnow toxicity (log LC50 (mol/liter] vs Tetrahymena toxicity (log BR (mmol/liter] showed good correlation between the two systems. An exception appears to be 4-nitrophenol, which is more active in the Tetrahymena system than in the fathead minnow and lies outside the 95% confidence interval. Reanalysis following deletion of 4-nitrophenol results in the equation log LC50 = -0.9192 (log BR) -3.5035; n = 26, r2 = 0.887.

Structure-toxicity relationships for phenols to Tetrahymena pyriformis

Quantitative structure-activity relationships are developed for the toxicity of 166 varied phenol derivatives to the ciliate Tetrahymena pyriformis. A variety of physico-chemical descriptors were calculated but no significant relationship could be obtained for all 166 compounds. When certain chemical groups were omitted from the correlation however, notably the carboxyl-, amino-, nitro, nitroso and acetamide- substituted phenols, an excellent correlation was obtained between toxicity and two parameters. These two parameters (log P and energy of the lowest unoccupied molecular orbital) are explained mechanistically in that they model transport and electrophilicity. The resultant QSAR gave accurate prediction of the toxicity of alkyl, halogenated, alkoxy and aldehyde substituted phenols.

Measurement and Estimation of Electrophilic Reactivity for Predictive Toxicology

Measurement and Estimation of Electrophilic Reactivity for Predictive Toxicology Johannes A. H. Schw€obel, Yana K. Koleva, Steven J. Enoch, Fania Bajot,MarkHewitt, Judith C.Madden, David W. Roberts, Terry W. Schultz, and Mark T. D. Cronin* School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England College of Veterinary Medicine, Department of Comparative Medicine, The University of Tennessee, 2407 River Drive, Knoxville, Tennessee 37996-4543, United States

A Strategy for Structuring and Reporting a Read-Across Prediction of Toxicity

Category formation, grouping and read across methods are broadly applicable in toxicological assessments and may be used to fill data gaps for chemical safety assessment and regulatory decisions. In order to facilitate a transparent and systematic approach to aid regulatory acceptance, a strategy to evaluate chemical category membership, to support the use of read-across predictions that may be used to fill data gaps for regulatory decisions is proposed. There are two major aspects of any read-across exercise, namely assessing similarity and uncertainty. While there can be an over-arching rationale for grouping organic substances based on molecular structure and chemical properties, these similarities alone are generally not sufficient to justify a read-across prediction. Further scientific justification is normally required to justify the chemical grouping, typically including considerations of bioavailability, metabolism and biological/mechanistic plausibility. Sources of uncertainty include a variety of elements which are typically divided into two main issues: the uncertainty associated firstly with the similarity justification and secondly the completeness of the read-across argument. This article focuses on chronic toxicity, whilst acknowledging the approaches are applicable to all endpoints. Templates, developed from work to prepare for the application of new toxicological data to read-across assessment, are presented. These templates act as proposals to assist in assessing similarity in the context of chemistry, toxicokinetics and toxicodynamics as well as to guide the systematic characterisation of uncertainty both in the context of the similarity rationale, the read across data and overall approach and conclusion. Lastly, a workflow for reporting a read-across prediction is suggested.

A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity

Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of organic chemical reactions and the association of these reactions to cellular targets of importance in toxicology. The identification of plausible “molecular initiating events” based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chemical reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chemical is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; whats more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.

Structure–activity relationships for reactivity of carbonyl-containing compounds with glutathione

For toxicological-based structure–activity relationships to advance, will require a better understanding of molecular reactivity. A rapid and inexpensive spectrophotometric assay for determining the reactive to glutathione (GSH) was developed and used to determine GSH reactivity (reactGSH) data for 21 aliphatic derivatives of esters, ketones and aldehydes. From these data, a series of structure–activity relationships were evaluated. The structure feature associated with reactGSH was an acetylenic or olefinic moiety conjugated to a carbonyl group (i.e. polarized α,β-unsaturation). This structure conveys the capacity to undergo a covalent interaction with the thiol group of cysteine (i.e. Michael- addition). Quantitatively reactGSH of the α,β-unsaturated carbonyl compounds is reliant upon the specific molecular structure with several tendencies observed. Specifically, it was noted that for α,β-unsaturated carbonyl compounds: (1) the acetylenic-substituted derivatives were more reactive than the corresponding olefinic-substituted ones; (2) terminal vinyl-substituted derivatives was more reactive than the internal vinylene-substituted ones; (3) methyl substitution on the vinyl carbon atoms diminishes reactivity and methyl-substitution on the carbon atom farthest from the carbonyl group causes a larger reduction; (4) derivatives with carbon–carbon double bond on the end of the molecule (i.e. vinyl ketone) were more reactive than one with the carbon–oxygen bond at the end of the molecule (i.e. aldehyde) and (5) the ester with an additional unsaturated vinyl groups were more reactive than the derivative having an unsaturated ethyl group.

Multivariate Discrimination between Modes of Toxic Action of Phenols

A set of 221 phenols, for which toxicity data to the ciliate Tetrahymena pyriformis were available, was subjected to stepwise linear discriminant analysis (LDA) in order to classify their toxic mechanisms of action. The compounds were a priori grouped into the following four mechanisms according to structural rules: polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Hydrophobicity with and without correction for ionisation (log K o w , log D o w u), acidity constant (pK a ), frontier orbital energies (E L U M O , E H O M O ) and hydrogenbond donor and acceptor counts were used as molecular descriptors. LDA models employing 3-6 variables achieved 86-89% overall correct classification of the four mechanisms, with more varied performance for respiratory uncouplers and pro-electrophiles. For the latter, a separate model was developed that discriminated compounds undergoing metabolic activation from compounds with different mechanisms very accurately. Model validation was performed by evaluating the simulated external prediction through LDA models built from complementary subsets.

A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity.

Several pieces of legislation have led to an increased interest in the use of in silico methods, specifically the formation of chemical categories for the assessment of toxicological endpoints. For a number of endpoints, this requires a detailed knowledge of the electrophilic reaction chemistry that governs the ability of an exogenous chemical to form a covalent adduct. Historically, this chemistry has been defined as compilations of structural alerts without documenting the associated electrophilic chemistry mechanisms. To address this, this article has reviewed the literature defining the structural alerts associated with covalent protein binding and detailed the associated electrophilic reaction chemistry. This information is useful to both toxicologists and regulators when using the chemical category approach to fill data gaps for endpoints involving covalent protein binding. The structural alerts and associated electrophilic reaction chemistry outlined in this review have been incorporated into the OECD (Q)SAR Toolbox, a freely available software tool designed to fill data gaps in a regulatory environment without the need for further animal testing.

Complex dynamics in a model microbial system

The forced double-Monod model (for a chemostat with a predator, a prey and periodically forced inflowing substrate) displays quasiperiodicity, phase locking, period doubling and chaotic dynamics. Stroboscopic sections reveal circle maps for the quasiperiodic regimes and noninvertible maps of the interval for the chaotic regimes. Criticality in the circle maps sets the stage for chaos in the model. This criticality may arise with an increase in the period or amplitude of forcing.