Generalized Concentration Addition Model Research Articles

Reflected generalized concentration addition and Bayesian hierarchical models to improve chemical mixture prediction.

Environmental toxicants overwhelmingly occur together as mixtures. The variety of possible chemical interactions makes it difficult to predict the danger of the mixture. In this work, we propose the novel Reflected Generalized Concentration Addition (RGCA), a piece-wise, geometric technique for sigmoidal dose-responsed inverse functions that extends the use of generalized concentration addition (GCA) for 3+ parameter models. Since experimental tests of all relevant mixtures is costly and intractable, we rely only on the individual chemical dose responses. Additionally, RGCA enhances the classical two-step model for the cumulative effects of mixtures, which assumes a combination of GCA and independent action (IA). We explore how various clustering methods can dramatically improve predictions. We compare our technique to the IA, CA, and GCA models and show in a simulation study that the two-step approach performs well under a variety of true models. We then apply our method to a challenging data set of individual chemical and mixture responses where the target is an androgen receptor (Tox21 AR-luc). Our results show significantly improved predictions for larger mixtures. Our work complements ongoing efforts to predict environmental exposure to various chemicals and offers a starting point for combining different exposure predictions to quantify a total risk to health.

Individual and combined ecotoxic effects of water-soluble polymers.

Water-soluble polymers (WSPs) are a class of high-molecular-weight compounds which are widely used in several applications, including water treatment, food processing, and pharmaceuticals. Therefore, they pose a potential threat for water resources and aquatic ecosystems. We assessed the ecotoxicity of four WSPs-non-ionic polyacrylamide (PAM) and polyethylene glycol (PEG-200), anionic homopolymer of acrylic acid (P-AA), and cationic polyquaternium-6 (PQ-6)-as single compounds and in mixture. For this purpose in vitro and in vivo assays were used to record baseline toxicity, mutagenic potential, endocrine effects, and growth inhibition in the freshwater alga Raphidocelis subcapitata. Furthermore, the mixture toxicity of the two polymers P-AA and PQ-6 which showed effects in the algae tests was evaluated with the concentration addition (CA), independent action (IA), and generalized concentration addition (GCA) model and compared with experimental data. No toxic effects were observed among the polymers and their mixtures in the in vitro assays. On the contrary, in the growth inhibition test with R. subcapitata the cationic PQ-6 caused high inhibition while the anionic P-AA and its mixture with the cationic polymer caused low inhibition. The non-ionic polymers PEG-200 and PAM showed no effect in R. subcapitata in the tested concentration range up to 100 mg/L. The IA model represented the mixture effect of the combination experiment better than the CA and GCA models. The results indicate (1) that the toxic effects of anionic and cationic polymers are most likely due to interactions of the polymers with the surfaces of organisms or with nutrients in the water and (2) that the polymers elicit their effects through different mechanisms of action that do not interact with each other.

Predicting joint toxicity of chemicals by incorporating a weighted descriptor into a mixture model: Cases for binary antibiotics and binary nanoparticles

A prediction method that integrated a mixture descriptor with an established mixture toxicology method was proposed for the joint toxicity of chemical pollutants. A weighted descriptor derived from the single descriptor of each component was employed to calculate a mixture descriptor, which was successfully embedded into the generalized concentration addition (GCA) model named the extended GCA (XGCA) model. To develop and validate the proposed approach, binary antibiotic mixtures (ciprofloxacin and oxytetracycline) and metal-oxide (copper oxide and zinc oxide) nanoparticle mixtures were selected to study their toxicity to freshwater green algae. The results showed that concentration-response curve (CRC) derived from the XGCA model was closer to the observed CRC than those from the GCA, Concentration Addition (CA), and Independent Action (IA) models. The difference between effect concentrations predicted by the XGCA model and observed did not exceed a factor of 1.6. The XGCA model was relatively more accurate at predicting joint toxicity (in terms of effect concentrations and effect errors) than the reference models, independent of component types and mixture ratios. The XGCA model predicts the joint toxicity through molecular structural or nanostructural characters, thus modes of toxic action are not preconditions for predicting the toxicity of the mixtures. This result demonstrates the practicability of using the XGCA method in toxicity assessments of mixture pollutants with unknown modes of action.

Application of generalized concentration addition to predict mixture effects of glucocorticoid receptor ligands

Environmental exposures often occur in complex mixtures and at low concentrations. Generalized concentration addition (GCA) is a method used to estimate the joint effect of receptor ligands that vary in efficacy. GCA models have been successfully applied to mixtures of aryl hydrocarbon receptor (AhR) and peroxisome proliferator-activated receptor gamma (PPARγ) ligands, each of which can be modeled as a receptor with a single binding site. Here, we evaluated whether GCA could be applied to homodimer nuclear receptors, which have two binding sites, to predict the combined effect of full glucocorticoid receptor (GR) agonists with partial agonists. We measured transcriptional activation of GR using a cell-based bioassay. Individual concentration-response curves for dexamethasone (full agonist), prednisolone (full agonist), and medroxyprogesterone 17-acetate (partial agonist) were generated and applied in three additivity models, GCA, effect summation (ES), and relative potency factor (RPF), to generate response surfaces. GCA and RPF yielded adequate predictions of the experimental data for two full agonists. However, GCA fit experimental data significantly better than ES and RPF for all other binary mixtures. This work extends the application of GCA to homodimer nuclear receptors and improves prediction accuracy of mixture effects of GR agonists.

A mixture of persistent organic pollutants relevant for human exposure inhibits the transactivation activity of the aryl hydrocarbon receptor in vitro

While humans are exposed to mixtures of persistent organic pollutants (POPs), their risk assessment is usually based on a chemical-by-chemical approach. To assess the health effects associated with mixed exposures, knowledge on mixture toxicity is required. Several POPs are potential ligands of the Aryl hydrocarbon receptor (AhR), which involves in xenobiotic metabolism and controls many biological pathways. This study assesses AhR agonistic and antagonistic activities of 29 POPs individually and in mixtures by using Chemical-Activated LUciferase gene eXpression bioassays with 3 transgenic cell lines (rat hepatoma DR-H4IIE, human hepatoma DR-Hep G2 and human mammary gland carcinoma DR-T47-D). Among the 29 POPs, which were selected based on their abundance in Scandinavian human blood, only 4 exerted AhR agonistic activities, while 16 were AhR antagonists in DR-H4IIE, 5 in DR-Hep G2 and 7 in DR-T47-D when tested individually. The total POP mixture revealed to be AhR antagonistic. It antagonized EC50 TCDD inducing AhR transactivation at a concentration of 125 and 250 and 500 fold blood levels in DR-H4IIE, DR-T47-D and DR-Hep G2, respectively, although each compound was present at these concentrations lower than their LOEC values. Such values could occur in real-life in food contamination incidents or in exposed populations. In DR-H4IIE, the antagonism of the total POP mixture was due to chlorinated compounds and, in particular, to PCB-118 and PCB-138 which caused 90% of the antagonistic activity in the POP mixture. The 16 active AhR antagonists acted additively. Their mixed effect was predicted successfully by concentration addition or generalized concentration addition models, rather than independent action, with only two-fold IC50 underestimation. We also attained good predictions for the full dose-response curve of the antagonistic activity of the total POP mixture.

Acute toxicity of copper hydroxide and glyphosate mixture in Clarias gariepinus: interaction and prediction using mixture assessment models.

The study aimed to assess the single and joint lethal toxicity, type of interaction and the extent to which simple mathematical model of concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) could predict the joint toxicity of copper hydroxide and glyphosate mixture in Clarias gariepinus. Static bioassay were setup to determine the individual and combined (based on ratio 1:2) lethal concentrations (LCx) of the pesticides. Data from the static bioassays were then fitted into the synergistic ratio (SR), concentration-addition (toxicity unit; TU) and isobologram model to determine the type of interaction between the different classes of pesticides, while the CA, IA and GCA models were used to predicted the observed mixture effects. The estimated 24 h, 48 h, 72 h and 96 h LC50 for copper hydroxide were 198.66 mg/L, 167.51 mg/L, 138.64 mg/L, and 104.82 mg/L; glyphosate were 162.92 mg/L, 103.88 mg/L, 61.95 mg/L, and 52.6l mg/L; while the mixtures were 63.18 mg/L, 59.06 mg/L, 56.42 mg/L, and 50.67 mg/L, respectively. Glyphosate was 2 times more toxic than copper hydroxide to C. gariepinus when acting singly. The SR and RTU was <1 indicate that the interaction between the pesticides was synergistic. Synergism was also corroborated by the isobologram model. The interaction of the mixture of copper hydroxide and glyphosate followed the IA model while the CA and GCA model underestimated the observed mixture effects. The study showed that copper hydroxide was practically non-toxic, while glyphosate and the mixture were slightly toxic to C. gariepinus

Generalized Concentration Addition Model Predicts Glucocorticoid Activity Bioassay Responses to Environmentally Detected Receptor-Ligand Mixtures.

Many glucocorticoid receptor (GR) agonists have been detected in waste and surface waters domestically and around the world, but the way a mixture of these environmental compounds may elicit a total glucocorticoid activity response in water samples remains unknown. Therefore, we characterized 19 GR ligands using a CV1 cell line transcriptional activation assay applicable to water quality monitoring. Cells were treated with individual GR ligands, a fixed ratio mixture of full or partial agonists, or a nonequipotent mixture with full and partial agonists. Efficacy varied (48.09%-102.5%) and potency ranged over several orders of magnitude (1.278 × 10-10 to 3.93 × 10-8 M). Concentration addition (CA) and response addition (RA) mixtures models accurately predicted equipotent mixture responses of full agonists (r2 = 0.992 and 0.987, respectively). However, CA and RA models assume mixture compounds produce full agonist-like responses, and therefore they overestimated observed maximal efficacies for mixtures containing partial agonists. The generalized concentration addition (GCA) model mathematically permits < 100% maximal responses, and fell within the 95% confidence interval bands of mixture responses containing partial agonists. The GCA, but not CA and RA, model predictions of nonequipotent mixtures containing both full and partial agonists fell within the same statistical distribution as the observed values, reinforcing the practicality of the GCA model as the best overall model for predicting GR activation. Elucidating the mechanistic basis of GR activation by mixtures of previously detected environmental GR ligands will benefit the interpretation of environmental sample contents in future water quality monitoring studies.

Generalized concentration addition accurately predicts estrogenic potentials of mixtures and environmental samples containing partial agonists

Cell-based bioanalytical tools are considered one alternative to overcome limitations of sensitivities of instrumental, analytical chemistry for monitoring estrogenic chemicals in the environment. Because these tools also reflect non-additive interactions of chemicals in mixtures, their outcomes often deviate from outcomes of chemical analytical approaches that assume additivity, e.g. the concentration addition (CA) model. Often this is because CA is unable to adequately represent effects of partial agonists, i.e. estrogens with lesser efficacies compared to 17β-estradiol. A generalized concentration addition (GCA) model has been proposed to address this shortcoming. In the present study, we investigated effects of mixtures of isomers of nonylphenol as partial model agonists in a cell-based estrogenicity assay. Whether the GCA model was able to more accurately predict the outcomes of these and previously published mixture experiments was evaluated, as well as the potency of a set of comprehensively characterized sewage effluent samples, compared to CA. If samples contained partial agonists, the GCA model consistently predicted potencies of mixtures and extracts of environmental samples more accurately than did the CA model. These findings enable more accurate estimations of potencies of estrogenicity explained by concentrations of agonists and partial agonists, thus significantly improving the ability to identify causative chemicals.

Fixed Bed Modeling of Nonsteroidal Anti-Inflammatory Drug Removal by Ion-Exchange in Synthetic Urine: Mass Removal or Toxicity Reduction?

Ion-exchange removal of nonsteroidal anti-inflammatory drugs (NSAIDs) in synthetic urine can selectively remove pharmaceuticals with minimal coremoval of nutrients to enhance nutrient recovery efforts. However, the effect of endogenous metabolites in urine on ion-exchange removal, and the corresponding reduction in ecotoxicity potential of pharmaceuticals in treated urine entering the environment, is unknown. To assess treatment efficacy, this work paired predicted breakthrough curves determined by the homogeneous surface diffusion model to an in vitro bioassay to evaluate COX-1 inhibition. The presence of endogenous metabolites in urine significantly impacted pharmaceutical removal, by competing for ion-exchange sites on the resin and reducing the resin capacity for pharmaceuticals. This indicates ion-exchange would be ineffective at removing NSAIDs and other negatively charged compounds in urine. Due to hydrolysis of pharmaceutical metabolites back to the parent compound, treatment systems should be designed based on the ultimate pharmaceutical concentration in ureolyzed urine. Mass removal and COX-1 inhibition followed a nonlinear correlation and mixture toxicity followed the generalized concentration addition model. This work demonstrates the importance of evaluating removal of contaminants of emerging concern, such as pharmaceuticals, using a risk-based approach to ecotoxicity end points in conjunction with mass removal.

Generalized concentration addition approach for predicting mixture toxicity.

A new mathematical model for analyzing data and predicting the effect of mixtures of toxic substances is presented as a generalized form of the concentration addition model. The proposed method, the generalized concentration addition (GCA) model, can be applied to mixtures with arbitrary strengths of interactions (synergistic or antagonistic). It requires mixture effect data for least 1 exposure concentration of the mixture in which fractions of all components and concentration-response functions for each component are known. The GCA model evaluates the interaction between components by introducing a novel response function, which is independent of the response functions for each individual components, to describe the effect of addition between different components. The GCA method was applied to published mixture toxicity data, and it was found to fit the mixture effect better than both the concentration addition model and the independent action model, the implication being that the proposed approach is widely applicable. Environ Toxicol Chem 2017;36:265-275. © 2016 SETAC.

Concentration Addition, Independent Action and Generalized Concentration Addition Models for Mixture Effect Prediction of Sex Hormone Synthesis In Vitro

Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be accounted for by single chemicals.

Generalized Concentration Addition Predicts Joint Effects of Aryl Hydrocarbon Receptor Agonists with Partial Agonists and Competitive Antagonists

BackgroundPredicting the expected outcome of a combination exposure is critical to risk assessment. The toxic equivalency factor (TEF) approach used for analyzing joint effects of dioxin-like chemicals is a special case of the method of concentration addition. However, the TEF method assumes that individual agents are full aryl hydrocarbon receptor (AhR) agonists with parallel dose–response curves, whereas many mixtures include partial agonists.ObjectivesWe assessed the ability of generalized concentration addition (GCA) to predict effects of combinations of full AhR agonists with partial agonists or competitive antagonists.MethodsWe measured activation of AhR-dependent gene expression in H1G1.1c3 cells after application of binary combinations of AhR ligands. A full agonist (2,3,7,8-tetrachlorodibenzo-p-dioxin or 2,3,7,8-tetrachlorodibenzofuran) was combined with either a full agonist (3,3′,4,4′,5-pentachlorobiphenyl), a partial agonist (2,3,3′,4,4′-pentachlorobiphenyl or galangin), or an antagonist (3,3′-diindolylmethane). Combination effects were modeled by the TEF and GCA approaches, and goodness of fit of the modeled response surface to the experimental data was assessed using a nonparametric statistical test.ResultsThe GCA and TEF models fit the experimental data equally well for a mixture of two full agonists. In all other cases, GCA fit the experimental data significantly better than the TEF model.ConclusionsThe TEF model overpredicts effects of AhR ligands at the highest concentration combinations. At lower concentrations, the difference between GCA and TEF approaches depends on the efficacy of the partial agonist. GCA represents a more accurate definition of additivity for mixtures that include partial agonist or competitive antagonist ligands.

Differential Diagnosis of Pulmonary Tuberculosis and Lung Cancer—Are Cytologic Studies of Blood and Bone Marrow of Value?

Cell-based bioanalytical tools are considered one alternative to overcome limitations of sensitivities of instrumental, analytical chemistry for monitoring estrogenic chemicals in the environment. Because these tools also reflect non-additive interactions of chemicals in mixtures, their outcomes often deviate from outcomes of chemical analytical approaches that assume additivity, e.g. the concentration addition (CA) model. Often this is because CA is unable to adequately represent effects of partial agonists, i.e. estrogens with lesser efficacies compared to 17β-estradiol. A generalized concentration addition (GCA) model has been proposed to address this shortcoming. In the present study, we investigated effects of mixtures of isomers of nonylphenol as partial model agonists in a cell-based estrogenicity assay. Whether the GCA model was able to more accurately predict the outcomes of these and previously published mixture experiments was evaluated, as well as the potency of a set of comprehensively characterized sewage effluent samples, compared to CA. If samples contained partial agonists, the GCA model consistently predicted potencies of mixtures and extracts of environmental samples more accurately than did the CA model. These findings enable more accurate estimations of potencies of estrogenicity explained by concentrations of agonists and partial agonists, thus significantly improving the ability to identify causative chemicals.