Design and in vitro antibiofilm activity of propolis diffusion-controlled biopolymers.

In this study, a novel pH-sensitive hydrogel beads that is based on gelatin/sodium alginate/chitosan (GEL/SA/CS) loaded with propolis ethanolic extracts (PE) were synthesized. The swelling behavior of GEL/SA/CS hydrogel beads was studied in different pH solutions and compared with unloaded CS (GEL/SA) hydrogel beads. The in vitro release studies have been revealed using four different pH (1.3, 5.0, 6.0, and 6.8), a saliva environment (pH 6.8), a simulated gastric fluid (SGF) (pH 1.3), and a simulated intestinal fluid (SIF) (pH 6.8) to simulate the physiological conditions in gastrointestinal (GI) tract. Propolis-loaded hydrogel beads were found to be stable at pH 1.3, 5.0, 6.0, simulated saliva, SGF, and SIF mediums, whereas the beads lose their stability at pH 6.8 buffer solution. Tested microorganisms displayed greater sensitivity to PE-loaded hydrogel beads compared with pure propolis. Contrary to antimicrobial activity results, antibiofilm activity results of PE-loaded GEL/SA and GEL/SA/CS hydrogel beads were found at low levels. According to the obtained results, the propolis-loaded GEL/SA/CS hydrogel beads synthesized within this study can be used in the treatment of GI tract diseases such as oral mucositis, gastric ulcer, ulcerative colitis, and GI cancer, as controlled releasing carriers of propolis.

Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology.

Mixtures risk assessment needs an efficient integration of in vivo, in vitro, and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry physiologically based pharmacokinetic (PBPK) toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, and xylenes. The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the second part of this work, we studied toluene, ethylbenzene, and xylene (TEX)-gene-disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short- and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short- and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures' toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.

Regular time-dependent cross-phenomena induced by hormesis: A case study of binary antibacterial mixtures to Aliivibrio fischeri.

Time-dependent cross-phenomenon in which the cross between the actual concentration-response curve (CRC) for mixture crosses the CRCs for reference model varies with time has been frequently reported in previous studies, expressed as a heterogeneous pattern of joint toxic action. However, the variation tendency of time-dependent cross-phenomenon is rarely addressed. In this study, the joint toxic actions of binary antibacterial mixtures (i.e., two quorum sensing inhibitors, tetracycline hydrochloride, erythromycin, and chloramphenicol with sulfonamides) were judged using independent action (IA) model to find the variation tendency of time-dependent cross-phenomenon. The results show that the time-dependent cross-phenomena of the test binary antibacterial mixtures follow a unified variation tendency and the corresponding joint toxic actions change regularly with an increase of both concentration and time. Through investigating the relationship between the stimulatory and inhibitory modes of action for the single agents and the time-dependent cross-phenomena of binary mixtures, the regular time-dependent cross-phenomena is speculated to be derived from the hormetic effects of the components in the mixtures. This study offers an advance for the variation tendency and mechanistic explanation of time-dependent cross-phenomenon, which will provide a support for the future development in the exploration of time-dependent cross-phenomenon and environmental risk assessment of pollutant mixtures.

Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.

Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of complex chemical mixtures in contaminated soils amended with compost or biochar. From the predicted bioavailability data, toxicity response for relevant ecological receptors was then forecasted to establish environmental risk implications and determine acceptable end-point remediation. The dataset corresponds to replicate samples collected over 180 days and analysed for total and bioavailable petroleum hydrocarbons and heavy metals/metalloids content. Further to this, a range of biological indicators including bacteria count, soil respiration, microbial community fingerprint, seeds germination, earthworm's lethality, and bioluminescent bacteria were evaluated to inform the environmental risk assessment. Parameters such as soil type, amendment (biochar and compost), initial concentration of individual compounds, and incubation time were used as inputs of the ML models. The relative importance of the input variables was also analysed to better understand the drivers of temporal changes in bioavailability and toxicity. It showed that toxicity changes can be driven by multiple factors (combined effects), which may not be accounted for in classical linear regression analysis (correlation). The use of ML models could improve our understanding of rate-limiting processes affecting the freely available fraction (bioavailable) of contaminants in soil, therefore contributing to mitigate potential risks and to inform appropriate response and recovery methods.

Chemical stabilization of polymers: Implications for dermal exposure to additives.

Technical benefits of additives in polymers stand in marked contrast to their associated health risks. Here, a multi-analyte method based on gas chromatography coupled to tandem mass spectrometry (GC-MS/MS) was developed to quantify polymer additives in complex matrices such as low-density polyethylene (LDPE) and isolated human skin layers after dermal exposure ex vivo. That way both technical aspects and dermal exposure were investigated. The effects of polymer additivation on the material were studied using the example of LDPE. To this end, a tailor-made polymer was applied in aging studies that had been furnished with two different mixtures of phenol- and diarylamine-based antioxidants, plasticizers and processing aids. Upon accelerated thermo-oxidative aging of the material, the formation of LDPE degradation products was monitored with attenuated total reflectance-Fourier transformed infrared (ATR-FTIR) spectroscopy. Compared to pure LDPE, a protective effect of added antioxidants could be observed on the integrity of the polymer. Further, thermo-oxidative degradation of the additives and its kinetics were investigated using LDPE or squalane as matrix. The half-lives of additives in both matrices revealed significant differences between the tested additives as well as between LDPE and squalane. For instance, 2-tert-butyl-6-[(3-tert-butyl-2-hydroxy-5-methylphenyl)methyl]-4-methylphenol (Antioxidant 2246) showed a half-life 12 times lower when incorporated in LDPE as compared to squalane. As a model for dermal exposure of consumers, human skin was brought into contact with the tailor-made LDPE containing additives ex vivo in static Franz diffusion cells. The skin was then analyzed for additives and decomposition products. This study proved 10 polymer additives of diverse pysicochemical properties and functionalities to migrate out of the polymer and eventually overcome the intact human skin barrier during contact. Moreover, their individual distribution within distinct skin layers was demonstrated. This is exemplified by the penetration of the procarcinogenic antioxidant N-phenylnaphthalen-2-amine (Neozon D) into the viable epidermis and the permeation through the skin of the neurotoxic plasticizer N-butylbenzenesulfonamide (NBBS). In addition, the analyses of additive degradation products in the isolated skin layers revealed the presence of 2-tert-butyl-4-methylphenol in all layers after contact to a polymer with substances of origin like Antioxidant 2246. Thus, attention needs to be paid to absorption of polymer additives together with their degradation products when it comes to dermal exposure assessment.

Heavy metals (Pb, Cd, As and MeHg) as risk factors for cognitive dysfunction: A general review of metal mixture mechanism in brain.

Human exposure to toxic heavy metals is a global challenge. Concurrent exposure of heavy metals, such as lead (Pb), cadmium (Cd), arsenic (As) and methylmercury (MeHg) are particularly important due to their long lasting effects on the brain. The exact toxicological mechanisms invoked by exposure to mixtures of the metals Pb, Cd, As and MeHg are still unclear, however they share many common pathways for causing cognitive dysfunction. The combination of metals may produce additive/synergetic effects due to their common binding affinity with NMDA receptor (Pb, As, MeHg), Na+ - K+ ATP-ase pump (Cd, MeHg), biological Ca+2 (Pb, Cd, MeHg), Glu neurotransmitter (Pb, MeHg), which can lead to imbalance between the pro-oxidant elements (ROS) and the antioxidants (reducing elements). In this process, ROS dominates the antioxidants factors such as GPx, GS, GSH, MT-III, Catalase, SOD, BDNF, and CERB, and finally leads to cognitive dysfunction. The present review illustrates an account of the current knowledge about the individual metal induced cognitive dysfunction mechanisms and analyse common Mode of Actions (MOAs) of quaternary metal mixture (Pb, Cd, As, MeHg). This review aims to help advancement in mixture toxicology and development of next generation predictive model (such as PBPK/PD) combining both kinetic and dynamic interactions of metals.

Monitoring simultaneous photocatalytic-ozonation of mixture of pharmaceuticals in the presence of immobilized TiO2 nanoparticles using MCR-ALS: Identification of intermediates and multi-response optimization approach.

The present study has focused on the degradation of a mixture of three pharmaceuticals, i.e. methyldopa (MDP), nalidixic acid (NAD) and famotidine (FAM) which were quantified simultaneously during photocatalytic-ozonation process. The experiments were conducted in a semi-batch reactor where TiO2 nanoparticles (crystallites mean size 8nm) were immobilized on ceramic plates irradiated by UV-A light in the proximity of oxygen and/or ozone. The surface morphology and roughness of the bare and TiO2-coated ceramic plates were analyzed using scanning electron microscopy (SEM) and atomic force microscopy (AFM). An analytical methodology was successfully developed based on both recording ultraviolet-visible (UV-Vis) spectra during the degradation process and a data analysis using multivariate curve resolution with alternating least squares (MCR-ALS). This methodology enabled the researchers to obtain the concentration and spectral profiles of the chemical compounds which were involved in the process. A central composite design was used to study the effect of several factors on multiple responses namely MDP removal (Y1), NAD removal (Y2) and FAM removal (Y3) in the simultaneous photocatalytic-ozonation of these pharmaceuticals. A multi-response optimization procedure based on global desirability of the factors was used to simultaneously maximize Y1, Y2 and Y3. The results of the global desirability revealed that 8mg/L MAD, 8mg/L NAD, 8mg/L FAM, 6L/h ozone flow rate and a 30min-reaction time were the best conditions under which the optimized values of various responses were Y1=95.03%, Y2=84.93% and Y3=99.15%. Also, the intermediate products of pharmaceuticals generated in the photocatalytic-ozonation process were identified by gas chromatography coupled to mass spectrometry.

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.

Genotoxicity but not the AhR-mediated activity of PAHs is inhibited by other components of complex mixtures of ambient air pollutants.

In this study, we compared the genotoxicity and aryl hydrocarbon receptor (AhR)-dependent transcriptional changes of selected target genes in human lung epithelial A549 cells incubated for 24 h, either with extractable organic matter (EOMs) from airborne particles <2.5 µm (PM2.5) collected at four localities from heavily polluted areas of the Czech Republic or two representative toxic polycyclic aromatic hydrocarbons (PAHs) present in EOMs, benzo[a]pyrene (B[a]P) and benzo[k]fluoranthene (B[k]F). Genotoxic effects were determined using DNA adduct analysis or analysis of expression of selected AhR-related genes involved in bioactivation of PAHs (CYP1A1, CYP1B1) and transcriptional repression (TIPARP). Sampled localities differing in the extent and source of air pollution did not exhibit substantially different genotoxicity. DNA adduct levels induced by three subtoxic EOM concentrations were relatively low (1-5 adducts/10(8) nucleotides), compared to levels induced by similar concentrations of B[a]P, while B[k]F gave very low DNA adduct levels. Here, we compared genotoxicity and gene deregulation induced by complex mixtures containing PAHs with the effects of the comparable concentrations of individual PAHs. Our results suggested inhibition of formation of B[a]P-induced DNA adducts compared to individual B[a]P, probably attributable to competitive inhibition by other non-genotoxic EOM components. In contrast, induction of AhR target genes appeared not to be antagonized by the components of complex mixtures, as induction of CYP1A1, CYP1B1 and TIPARP transcripts reached maximum levels induced by PAHs.

Multicomponent antibiotic substances produced by fermentation: implications for regulatory authorities, critically ill patients and generics.

Teicoplanin and polymyxin E (colistin) are antibiotics consisting of multiple, closely related subcomponents, produced by fermentation. The principal components comprise a complex mixture of chemically related, active substances (teicoplanin A(2-1)-A(2-5) and polymyxin E(1-2), respectively), which might be required to be present in specific ratios to ensure optimal antibacterial and clinical efficacy. These subcomponents differ in their fatty acid and amino acid composition and, as such, the lipophilic and protein binding characteristics differ between components. This has therapeutic implications for critically ill patients, as the volume of distribution of the teicoplanin A2 and polymyxin E analogues at the onset of an intravenous infusion may impact on expected pharmacokinetics and influence outcome.

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).

Application of data fusion in human health risk assessment for hydrocarbon mixtures on contaminated sites.

The exposure and toxicological data used in human health risk assessment are obtained from diverse and heterogeneous sources. Complex mixtures found on contaminated sites can pose a significant challenge to effectively assess the toxicity potential of the combined chemical exposure and to manage the associated risks. A data fusion framework has been proposed to integrate data from disparate sources to estimate potential risk for various public health issues. To demonstrate the effectiveness of the proposed data fusion framework, an illustrative example for a hydrocarbon mixture is presented. The Joint Directors of Laboratories Data Fusion architecture was selected as the data fusion architecture and Dempster-Shafer Theory (DST) was chosen as the technique for data fusion. For neurotoxicity response analysis, neurotoxic metabolites toxicological data were fused with predictive toxicological data and then probability-boxes (p-boxes) were developed to represent the toxicity of each compound. The neurotoxic response was given a rating of "low", "medium" or "high". These responses were then weighted by the percent composition in the illustrative F1 hydrocarbon mixture. The resulting p-boxes were fused according to DST's mixture rule of combination. The fused p-boxes were fused again with toxicity data for n-hexane. The case study for F1 hydrocarbons illustrates how data fusion can help in the assessment of the health effects for complex mixtures with limited available data.

Removal of a combination of endocrine disruptors from aqueous systems by seedlings of radish and ryegrass.

Endocrine disruptors (EDs) are widespread in the environment, especially aquatic systems, and cause dangerous effects on wildlife and humans. This work was aimed to assess the capacity of radish (Raphanus sativus L.) and ryegrass (Lolium perenne L.) seedlings to tolerate and remove two combinations of EDs containing bisphenol A (BPA), 17alpha-ethynilestradiol (EE2), and linuron from four aqueous media: distilled water, a solution of natural organic matter (NOM), a lake water and a river water. Seeds of the two species were germinated in each contaminated medium and, at the end of germination, the seedling growth was evaluated by biometric measurements and residual EDs were quantified by chromatographic analysis. Biometric measurements revealed that the phytotoxicity of the two combinations of EDs depended on the medium used. Radish showed a discrete tolerance in distilled water and lake water but was inhibited in the solution of NOM and river water. Ryegrass was negatively affected mainly in river water. The concentration of each ED appeared significantly reduced in all media in the presence of seedlings of both species, but not in the blanks without plants. In 5 days, radish removed up to 88% of BPA, 100% of EE2 and 42% of linuron, and in 6 days ryegrass removed up to 92% of BPA, 74% of EE2 and 16% of linuron. The considerable removal capacity of radish and ryegrass in all media tested encourages the use of phytoremediation to remove EDs from waters.

Bioavailability of cyanide and metal-cyanide mixtures to aquatic life.

Cyanide can be toxic to aquatic organisms, and the U.S. Environmental Protection Agency has developed ambient water-quality criteria to protect aquatic life. Recent work suggests that considering free, rather than total, cyanide provides a more accurate measure of the biological effects of cyanides and provides a basis for water-quality criteria. Aquatic organisms are sensitive to free cyanide, although certain metals can form stable complexes and reduce the amount of free cyanide. As a result, total cyanide is less toxic when complexing metals are present. Cyanide is often present in complex effluents, which requires understanding how other components within these complex effluents can affect cyanide speciation and bioavailability. The authors have developed a model to predict the aqueous speciation of cyanide and have shown that this model can predict the toxicity of metal-cyanide complexes in terms of free cyanide in solutions with varying water chemistry. Toxicity endpoints based on total cyanide ranged over several orders of magnitude for various metal-cyanide mixtures. However, predicted free cyanide concentrations among these same tests described the observed toxicity data to within a factor of 2. Aquatic toxicity can be well-described using free cyanide, and under certain conditions the toxicity was jointly described by free cyanide and elevated levels of bioavailable metals.

Measuring bioconcentration factors in fish using exposure to multiple chemicals and internal benchmarking to correct for growth dilution.

Modern chemical legislation requires measuring the bioconcentration factor (BCF) of large numbers of chemicals in fish. The BCF must be corrected for growth dilution, because fish growth rates vary between laboratories. Two hypotheses were tested: (1) that BCFs of multiple chemicals can be measured simultaneously in one experiment, and (2) that internal benchmarking using a conservative test substance in the chemical mixture can be used to correct for growth dilution. Bioconcentration experiments were conducted following major elements of the OECD 305 guideline. Fish were simultaneously exposed to 11 chemicals selected to cover a range of BCFs and susceptibility to biotransformation. A method was developed to calculate the growth-corrected elimination rate constant from the concentration ratio of the analyte and a benchmarking chemical for which growth dilution dominated other elimination mechanisms. This method was applied to the experimental data using hexachlorobenzene as the benchmarking chemical. The growth dilution correction lowered the apparent elimination rate constants by between 5% and a factor of four for eight chemicals, while for two chemicals the growth-corrected elimination rate constant was not significantly different from zero. The benchmarking method reduced the uncertainty in the elimination rate constant compared to the existing method for growth dilution correction. The BCFs from exposing fish to 10 chemicals at once were consistent with BCF values from single-chemical exposures from the literature, supporting hypothesis 1.

Integrated assessment of biomarker responses in caged shrimps (Litopenaeus vannamei) exposed to complex contaminants from the Maluan Bay of China.

The marine shrimp Litopenaeus vannamei were used as an active biomonitoring organism to assess the bioavailability and impact of metal contaminants in seven study sites along the Maluan Bay of China. Metal concentrations in the hepatopancreas of shrimps were determined in conjunction with four biomarkers responses after a 7 day in situ cage exposures. The results showed that contaminant tissue burdens at the deployment sites were greater than those of the reference site, and antioxidant enzyme activities were strongly inhibited compared to those of reference organisms. Variations in these biomarker responses were correlated significantly (p < 0.05 or p < 0.01) with the specific metal pollutants at the study sites, but no significant correlations existed between catalase activity responses and the metal contaminants. This suggests the presence of undetermined contaminants or other exposure routes that may be responsible for the decreased catalase activity. Multivariate analysis revealed a causal relationship between contaminants at each deployment site and the biochemical "response" of the caged shrimps at these sites and demonstrated the presence of two contaminant "hot" spots. This investigation suggested that the incorporation of chemical data on trace metal concentrations with the analysis of antioxidant enzymatic activities in caged shrimps can be a useful tool for the identification of causal toxic contaminants in complex mixtures.

Computational estimation of errors generated by lumping of physiologically-based pharmacokinetic (PBPK) interaction models of inhaled complex chemical mixtures.

Many cases of environmental contamination result in concurrent or sequential exposure to more than one chemical. However, limitations of available resources make it unlikely that experimental toxicology will provide health risk information about all the possible mixtures to which humans or other species may be exposed. As such, utilizing computational models in order to make toxicological predictions is a useful tool in complementing experimental efforts which examine mixtures in health risk assessment. This paper outlines a novel mathematical method which reduces the complexity of a mixtures model and increases computational efficiency via a biologically-based lumping methodology (BBLM). In contrast to previous chemical lumping methodologies, BBLM allows the computation of error as a measure of the difference between the lumped simulation based on BBLM and the full mathematical model. As a consequence, the modeler has the opportunity to find the optimal configuration in the tradeoff between simplification and accuracy in order to determine an acceptable number and composition of lumped chemicals. To demonstrate this method, lumped equations based on a typical inhalation physiologically-based pharmacokinetic (PBPK) model assuming a competitive inhibition interaction mechanism are developed for a mixture of arbitrary size. The novel methodology is further tested using literature data for a mixture of 10 volatile organic chemicals (VOCs). Through simulation of these chemicals, BBLM is shown to produce good approximations when compared to the unlumped simulation and experimental data.

Cocktail effects on biomarker responses in fish.

One of today's greatest challenges in environmental toxicology is to understand effects of mixture toxicity, commonly referred to as cocktail effects, in humans and in wildlife. Biomarker responses in fish are routinely used to assess exposure of anthropogenic chemicals in the aquatic environment. However, little is known about how cocktail effects affect these biomarker responses. For this reason, there is an obvious risk for misinterpretation of biomarker-data and this can have profound negative effects on stakeholder's decisions and actions, as well as on legislations and remediation-plans initiated in order to reduce exposure to certain chemicals. Besides, chemical safety-levels are traditionally based on experiences from lab-studies with single chemicals, which is unfortunate as a chemical can be more toxic when it is mixed with other chemicals, because of the cocktail effect. This review focuses on pharmacokinetic interactions between different classes of pollutants on detoxification mechanisms and how that affects two commonly used biomarkers in the aquatic environment: (1) induction of cytochrome P450 1A (CYP1A) that is mediated via activation of the arylhydrocarbon receptor (AhR), used to assess exposure to aromatic hydrocarbons; (2) induction of vitellogenin (VTG) that is mediated via activation of the estrogen receptor (ER), used to assess exposure to estrogenic chemicals. These responses can be either directly or indirectly affected by the presence of other classes of pollutants as a result of cocktail effects. For example, chemicals that inhibit the function of key metabolic enzymes and transporter pumps that are involved in elimination of AhR- and ER agonists, can result in bioaccumulation of aromatic hydrocarbons and estrogenic chemicals resulting in increased biomarker responses. This cocktail effect can lead to overestimation of the actual exposure pressure. On the contrary, induction of expression of key metabolic enzymes and transporter activities can result in increased elimination of AhR- and ER agonists that can lead to possible underestimation of the exposure. Another type of cocktail effect is inhibiting receptor cross-talk that may cause decreased biomarker responses that can also lead to underestimation of the actual exposure. To address the possible involvement of pharmacokinetic interactions including receptor cross-talks, we need to combine analyses on receptor signaling with studies on function of key biotransformation enzymes such as major catabolic CYP enzymes (e.g. CYP1-4) as well as efflux pumps (e.g. ATP-binding cassette transporter proteins). Besides, studies of inhibition of these enzymes and pumps activities pose a great potential to be used as future biomarkers as they are more clearly liked to adverse outcomes, compared to for example induction of CYP1A and VTG expression.

A mechanistic modeling framework for predicting metabolic interactions in complex mixtures.

BACKGROUND: Computational modeling of the absorption, distribution, metabolism, and excretion of chemicals is now theoretically able to describe metabolic interactions in realistic mixtures of tens to hundreds of substances. That framework awaits validation. OBJECTIVES: Our objectives were to a) evaluate the conditions of application of such a framework, b) confront the predictions of a physiologically integrated model of benzene, toluene, ethylbenzene, and m-xylene (BTEX) interactions with observed kinetics data on these substances in mixtures and, c) assess whether improving the mechanistic description has the potential to lead to better predictions of interactions. METHODS: We developed three joint models of BTEX toxicokinetics and metabolism and calibrated them using Markov chain Monte Carlo simulations and single-substance exposure data. We then checked their predictive capabilities for metabolic interactions by comparison with mixture kinetic data. RESULTS: The simplest joint model (BTEX interacting competitively for cytochrome P450 2E1 access) gives qualitatively correct and quantitatively acceptable predictions (with at most 50% deviations from the data). More complex models with two pathways or back-competition with metabolites have the potential to further improve predictions for BTEX mixtures. CONCLUSIONS: A systems biology approach to large-scale prediction of metabolic interactions is advantageous on several counts and technically feasible. However, ways to obtain the required parameters need to be further explored.

Predicting skin permeability from complex chemical mixtures: dependency of quantitative structure permeation relationships on biology of skin model used.

Dermal absorption of topically applied chemicals usually occurs from complex chemical mixtures; yet, most attempts to quantitate dermal permeability use data collected from single chemical exposure in aqueous solutions. The focus of this research was to develop quantitative structure permeation relationships (QSPR) for predicting chemical absorption from mixtures through skin using two levels of in vitro porcine skin biological systems. A total of 16 diverse chemicals were applied in 384 treatment mixture combinations in flow-through diffusion cells and 20 chemicals in 119 treatment combinations in isolated perfused porcine skin. Penetrating chemical flux into perfusate from diffusion cells was analyzed to estimate a normalized dermal absorptive flux, operationally an apparent permeability coefficient, and total perfusate area under the curve from perfused skin studies. These data were then fit to a modified dermal QSPR model of Abraham and Martin including a sixth term to account for mixture interactions based on physical chemical properties of the mixture components. Goodness of fit was assessed using correlation coefficients (r²), internal and external validation metrics (q²L00, q²L25%, q²EXT), and applicable chemical domain determinations. The best QSPR equations selected for each experimental biological system had r² values of 0.69-0.73, improving fits over the base equation without the mixture effects. Different mixture factors were needed for each model system. Significantly, the model of Abraham and Martin could also be reduced to four terms in each system; however, different terms could be deleted for each of the two biological systems. These findings suggest that a QSPR model for estimating percutaneous absorption as a function of chemical mixture composition is possible and that the nature of the QSPR model selected is dependent upon the biological level of the in vitro test system used, both findings having significant implications when dermal absorption data are used for in vivo risk assessments.