Physicochemical determinants of linear alkylbenzene sulfonate (LAS) disposition in skin exposed to aqueous cutting fluid mixtures

NDELA and nickel modulation of triazine disposition in skin

Cutting fluids can become contaminated with metals (e.g., nickel, Ni) and nitrosamines (e.g., N-nitrosodiethanolamine, NDELA) and there is concern that these classes of contaminants can modulate dermal disposition and ultimately the toxicity of cutting fluid additives, such as irritant biocides (e.g., triazine). Biocides are added to these formulations to prevent bacterial degradation of commercial cutting fluids. The purpose of this study was to assess the dermal absorption and skin deposition of 14C-triazine when topically applied to porcine skin in an in vitro flow-through diffusion cell system as aqueous soluble oil (mineral oil, MO) or aqueous synthetic (polyethylene glycol, PEG) mixtures. 14C-Triazine mixtures were formulated with NDELA and/or Ni, or with a combination of three additional cutting fluid additives; namely, 5% linear alkylbenzene sulfonate (LAS), 5% triethanolamine (TEA) and 5% sulfurized ricinoleic acid. Neither Ni nor NDELA was absorbed during these 8-h studies. However, 14C-triazine absorption ranged from 2.72 to 3.29% dose in MO and 2.29-2.88% dose in PEG with significantly greater triazine absorption in MO than PEG when all additives and contaminates were present. The difference between these two diluents was most pronounced when NDELA and/or Ni were present in cutting fluids. These contaminants also enhanced triazine deposition on the skin surface and skin tissues especially with PEG-based mixtures. In essence, the dermal disposition of irritant biocides could be dependent on whether the worker is exposed to a soluble oil or synthetic fluid when these contaminants are present. Workers should therefore not only be concerned about dermatotoxicity of these contaminants, but also the modulated dermal disposition of cutting fluid additives when these contaminants are present in cutting fluid formulations.

Skin absorption of six performance amines used in metalworking fluids

Every year, 10 million workers are exposed to metalworking fluids (MWFs) that may be toxic. There are four types of MWFs: neat oils and three water-based MWFs (soluble oil, semisynthetic and synthetic), which are diluted with water and whose composition varies according to the mineral oils ratio. MWFs also contain various additives. To determine the absorption of six amines used as corrosion inhibitors and biocides in MWFs, porcine skin flow-through diffusion cell experiments were conducted with hydrophilic ethanolamines (mono-, di- and triethanolamine, MEA, DEA and TEA respectively) and a mixture of lipophilic amines (dibutylethanolamine, dicyclohexylamine and diphenylamine). The six amines were dosed in four vehicles (water and three generic water-based MWF formulations) and analyzed using a scintillation counter or gas chromatography/mass spectrometry. These 24 h studies showed that dermal absorption significantly (P < 0.05) increased from water for the six amines (e.g. 1.15 ± 0.29% dose; DEA in water) compared to other formulations (e.g. 0.13 ± 0.01% dose; DEA in semisynthetic MWF) and absorption was greatest for dibutylethanolamine in all the formulations. The soluble oil formulation tended to increase the dermal absorption of the hydrophilic amines. The permeability coefficient was significantly higher (P < 0.05) with TEA relative to the other hydrophilic amines (e.g. 4.22 × 10(-4) ± 0.53 × 10(-4) cm h(-1) [TEA in synthetic MWF] vs. 1.23 × 10(-4) ± 0.10 × 10(-4) cm h(-1) [MEA in synthetic MWF]), except for MEA in soluble oil formulation. Future research will confirm these findings in an in vivo pig model along with dermatotoxicity studies. These results should help MWF industries choose safer additives for their formulations to protect the health of metalworkers.

In silico models to predict dermal absorption from complex agrochemical formulations

Dermal absorption is a critical part in the risk assessment of complex mixtures such as agrochemical formulations. To reduce the number of in vivo or in vitro absorption experiments, the present study aimed to develop an in silico prediction model that considers mixture-related effects. Therefore, an experimental 'real-world' dataset derived from regulatory in vitro studies with human and rat skin was processed. Overall, 56 test substances applied in more than 150 mixtures were used. Descriptors for the substances as well as the mixtures were generated and used for multiple linear regression analysis. Considering the heterogeneity of the underlying data set, the final model provides a good fit (r² = 0.75) and is able to estimate the influence of a newly composed formulation on dermal absorption of a well-known substance (predictivity Q²Ext = 0.73). Application of this model would reduce animal and non-animal testings when used for the optimization of formulations in early developmental stages, or would simplify the registration process, if accepted for read-across.

Phosphorylation of histone H2AX generated by linear alkylbenzene sulfonates and its suppression by UVB exposure

We previously demonstrated that the nonionic surfactants, nonylphenol polyethoxylates (NPEOs) induced the phosphorylation of histone H2AX (γ-H2AX), accompanied by DNA double-strand breaks (DSBs), and that exposure to ultraviolet (UV) degraded NPEOs, which sometimes enhanced their DNA-damaging ability. In this study, we showed that linear alkylbenzene sulfonates (LAS), general anion surfactants, also generated DSBs with γ-H2AX, and this ability was attenuated by UVB exposure. In the human breast adenocarcinoma cell line, MCF-7, γ-H2AX was generated in a dose-dependent manner immediately after cells were treated with LAS, and this was attributed to the formation of DSBs and was independent of cell cycle phases. The ability to generate γ-H2AX was markedly reduced in LAS exposed to UVB. HPLC analysis revealed that LAS were a mixture of various alkyl chain lengths, the peaks of which were detected at individual retention times. UVB evenly decreased all peaks of LAS, without migration of peaks to other retention times, which indicated that UVB may degrade the benzene ring of LAS, but did not shorten the alkyl chains. UVB is an important environmental factor in the degradation of LAS exhibiting the ability to induce DSBs, the most serious type of DNA damage.

Predicting skin permeability from complex chemical mixtures: incorporation of an expanded QSAR model

Quantitative structure-activity relationship (QSAR) models have been widely used to study the permeability of chemicals or solutes through skin. Among the various QSAR models, Abraham's linear free-energy relationship (LFER) model is often employed. However, when the experimental conditions are complex, it is not always appropriate to use Abraham's LFER model with a single set of regression coefficients. In this paper, we propose an expanded model in which one set of partial slopes is defined for each experimental condition, where conditions are defined according to solvent: water, synthetic oil, semi-synthetic oil, or soluble oil. This model not only accounts for experimental conditions but also improves the ability to conduct rigorous hypothesis testing. To more adequately evaluate the predictive power of the QSAR model, we modified the usual leave-one-out internal validation strategy to employ a leave-one-solute-out strategy and accordingly adjust the Q(2) LOO statistic. Skin permeability was shown to have the rank order: water > synthetic > semi-synthetic > soluble oil. In addition, fitted relationships between permeability and solute characteristics differ according to solvents. We demonstrated that the expanded model (r(2) = 0.70) improved both the model fit and the predictive power when compared with the simple model (r(2) = 0.21).

Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design

Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

New evidence on the health hazards and control of metalworking fluids since completion of the OSHA advisory committee report

BACKGROUND

Metalworking fluids (MWF) are used in the manufacture of engines, transmissions, chassis parts and other products. In 2003, OSHA denied a union petition to promulgate a standard for MWF. The 3rd Circuit Court of Appeals rejected a union lawsuit to compel OSHA to regulate MWF. OSHA relied exclusively on the 1999 Metal Working Fluids Standards Advisory Committee report, therefore, only evidence available before 1999 was quoted supporting the denial. This review was conducted to identify studies published since 1998.

METHODS

Electronic reference sources were queried for the terms for metalworking fluids, machining fluids, cutting fluids, cutting oils, coolants, machining, and machinist. All items returned were reviewed for relevance to MWF regulation.

RESULTS

The review noted 227 reports in the peer reviewed literature directly relevant to regulation of MWF exposures. Of these, 26 addressed cancer; 58 respiratory effects; 32 skin effects or absorption; 45 microbial contaminants; and 76 exposure measurements and controls. Three major studies identified excess cancer including lung, liver, pancreatic, laryngeal, and leukemia associated with MWF exposures. Reports strengthened associations of asthma and hypersensitivity pneumonitis with recent exposure to MWF.

CONCLUSIONS

Material new evidence demonstrates significant risks to material impairment of health at prevailing exposure levels and feasibility of lower exposure limits.

Dermatotoxicity of Cutting Fluid Mixtures:In VitroandIn VivoStudies

Cutting fluids are widely used in the metal-machining industry to lubricate and reduce heat generation when metals are cut by a metal-cutting tool. These cutting fluids have caused occupational irritant contact dermatitis (OICD), and many of the additives used in these cutting fluid mixtures are thought to be responsible for OICD in workers. The purpose of this study was to assess single or various combinations of these additives in initiating the OICD response following an acute 8-hour exposure in porcine skin in vivo and in vitro using the isolated perfused porcine skin flap (IPPSF) and human epidermal keratinocytes (HEK). Pigs (n = 4) were exposed to 5% mineral oil (MO) or 5% polyethylene glycol (PEG) aqueous mixtures containing various combinations of 2% triazine (TRI), 5% triethanolamine (TEA), 5% linear alkylbenzene sulfonate (LAS), or 5% sulfurized ricinoleic acid (SRA). Erythema and edema were evaluated and skin biopsies for histopathology were obtained at 4 and 8 hours. IPPSFs (n = 4) were exposed to control MO or PEG mixtures and complete MO or PEG mixtures, and perfusate samples were collected hourly to determine interleukin- (IL-) 8 release. The only significant (p < 0.05) mixture effects observed in IPPSFs were with SRA + MO that caused an increase in IL-8 release after 1 or 2 hours' exposure. In vivo exposure to TRI alone appeared to increase erythema, edema, and dermal inflammation compared to the other additives, while SRA alone was least likely to initiate a dermal inflammatory response. In 2-component mixture exposures, the presence of TRI appeared to increase the dermal inflammatory response at 4 and 8 hours especially with the PEG mixtures. In the 3- and 4-component mixtures, MO mixtures are more likely to incite an inflammatory response than PEG mixtures. TRI exhibited the highest toxicity toward HEK, which correlates well to the in vivo irritation and morphology results. In summary, these preliminary studies suggest that the biocide, TRI, is the more potent of the 4 performance additives in causing dermal irritation, and this may vary depending on whether the worker is exposed to a synthetic (PEG)- or MO-based fluid. These findings will however require further clinical studies to validate these acute dermal effects as well as human cumulative irritation following exposure to similar cutting fluid formulations in the workplace.

Predicting skin permeability from complex chemical mixtures

Occupational and environmental exposure to topical chemicals is usually in the form of complex chemical mixtures, yet risk assessment is based on experimentally derived data from individual chemical exposures from a single, usually aqueous vehicle, or from computed physiochemical properties. We present an approach using hybrid quantitative structure permeation relationships (QSPeR) models where absorption through porcine skin flow-through diffusion cells is well predicted using a QSPeR model describing the individual penetrants, coupled with a mixture factor (MF) that accounts for physicochemical properties of the vehicle/mixture components. The baseline equation is log k(p) = c + mMF + a sigma alpha2(H) + b sigma beta2(H) + s pi2(H) + rR2 + vV(x) where sigma alpha2(H) is the hydrogen-bond donor acidity, sigma beta2(H) is the hydrogen-bond acceptor basicity, pi2(H) is the dipolarity/polarizability, R2 represents the excess molar refractivity, and V(x) is the McGowan volume of the penetrants of interest; c, m, a, b, s, r, and v are strength coefficients coupling these descriptors to skin permeability (k(p)) of 12 penetrants (atrazine, chlorpyrifos, ethylparathion, fenthion, methylparathion, nonylphenol, rho-nitrophenol, pentachlorophenol, phenol, propazine, simazine, and triazine) in 24 mixtures. Mixtures consisted of full factorial combinations of vehicles (water, ethanol, propylene glycol) and additives (sodium lauryl sulfate, methyl nicotinate). An additional set of 4 penetrants (DEET, SDS, permethrin, ricinoleic acid) in different mixtures were included to assess applicability of this approach. This resulted in a dataset of 16 compounds administered in 344 treatment combinations. Across all exposures with no MF, R2 for absorption was 0.62. With the MF, correlations increased up to 0.78. Parameters correlated to the MF include refractive index, polarizability and log (1/Henry's Law Constant) of the mixture components. These factors should not be considered final as the focus of these studies was solely to determine if knowledge of the physical properties of a mixture would improve predicting skin permeability. Inclusion of multiple mixture factors should further improve predictability. The importance of these findings is that there is an approach whereby the effects of a mixture on dermal absorption of a penetrant of interest can be quantitated in a standard QSPeR model if physicochemical properties of the mixture are also incorporated.