New insight into isobolographic analysis for combinations of a full and partial agonist: Curved isoboles

Cooperative induction of CXCL chemokines by inflammatory cytokines and oxidized phospholipids

Inflammation is initiated and driven by a mixture of mediators, which modify effects of each other. This study analysed in vitro pro-inflammatory activity of inflammatory cytokines (TNFα and IL-1β) in a combination with a lipid DAMP molecule, oxidized palmitoyl-arachidonoyl-phosphatidylcholine (OxPAPC). The study was performed on endothelial and monocytic cell lines. The cells were treated with different concentrations of TNFα or IL-1β, OxPAPC and their combinations, either in the presence or absence of drugs regulating inflammation. Pro-inflammatory effects of TNFα/IL-1β and OxPAPC were estimated by analysis of chemokines CXCL8, CXCL2 and CXCL3 by ELISA and RT-PCR. Toxicity was determined by analysis of metabolic activity. Statistical significance was estimated by ANOVA and Dunnett's test. OxPAPC was a much weaker chemokine inducer as compared to TNFα or IL-1β. However, OxPAPC and TNFα/IL-1β together induced effects that were significantly stronger than the arithmetical sum of individual effects. This cooperative action of OxPAPC and TNFα was reversed by inhibitors of p38 MAPK. We hypothesise that the boosting of TNFα and IL-1β effects by OxPAPC may be more pathologically important than the action of the lipid alone. Inhibitors of p38 MAPK may become a tool for analysis of pathological role of oxidized phospholipids.

Interactive effects of cyanobacterial metabolites aeruginosin-98B, anabaenopeptin-B and cylindrospermopsin on physiological parameters and novel in vivo fluorescent indicators in Chironomus aprilinus larvae

We aimed to determine the effects of single cyanobacterial metabolites aeruginosin-B (AER-B), anabaenopeptin-B (ANA-B), cylindrospermopsin (CYL), their binary and ternary mixtures on biomarkers of Chironomus aprilinus larvae: oxygen consumption, fat body structure and two novel fluorescent indicators: imaging of nuclei in cells of body integument, and the catecholamine level. The obtained results showed that oxygen consumption was inhibited by single tested cyanobacterial metabolites except for ANA-B at the lowest concentration (250 μg/L). Although the mixtures of the metabolites inhibited oxygen consumption with antagonistic interactions between the components stimulation was noted in the group exposed to the lowest concentrations of AER-B + CYL (125 μg/L + 125 μg/L, respectively) and the ternary mixture of AER-B + ANA-B + CYL (83.3 μg/L + 83.3 μg/L + 83.3 μg/L, respectively). In vivo fluorescent staining with Hoechst 34580 showed that single AER-B had lower cytotoxic potential on body integument cells than ANA-B and CYL and most binary mixtures except for AER-B + CYL induced synergistic toxicity. Catecholamine level was decreased in animals exposed to single metabolites, their binary and ternary mixtures; however, the interactions between the components in the ternary mixture were antagonistic. Fat body was found to be disrupted in the larvae exposed to single metabolites and their combinations. Antagonistic toxic interactions between the oligopeptide components were found in most binary and the ternary mixtures; however, synergistic effect was noted in the binary mixture of AER-B + CYL. The results suggest that in natural conditions Chironomus larvae and possibly other benthic invertebrates may be affected by cyanobacterial metabolites, however various components and in mixtures and their concentrations may determine varied physiological effects and diverse interactions.

How to use statistics to claim antagonism and synergism from binary mixture experiments

We review statistical approaches applicable for the analysis of data from binary mixture experiments, which are commonly used in pesticide science for evaluating antagonistic or synergistic effects. Specifically, two different situations are reviewed, one where every pesticide is only available at a single dose level and a mixture simply combines these doses, and one where the pesticides and their mixture are used at increasing doses. The former corresponds to using factorial designs whereas the latter corresponds to fixed-ratio designs. We consider dose addition and independent action as references for lack of antagonistic and synergistic effects. Data from factorial designs should be analyzed using two-way analysis of variance models whereas data from fixed-ratio designs should be analyzed using non-linear dose-response analysis. In most cases, independent action seems the more natural choice for factorial designs. In contrast, dose addition is more appropriate for fixed-ratio designs although dose addition is not equally compatible with all types of dose-response data. Fixed-ratio designs should be preferred as they allow validation of the assumed dose-response relationship and, consequently, provide much stronger claims about antagonistic and synergistic effects than factorial designs. Finally, it should be noted that, in any case, simple ways of summarizing pesticide mixture effects may come at the price of more or less restrictive modeling assumptions. © 2021 Society of Chemical Industry.

An eight-compound mixture but not corresponding concentrations of individual chemicals induces triglyceride accumulation in human liver cells

In real life, organisms are exposed to complex mixtures of chemicals at low concentration levels, whereas research on toxicological effects is mostly focused on single compounds at comparably high doses. Mixture effects deviating from the assumption of additivity, especially synergistic effects, are of concern. In an adverse outcome pathway (AOP)-guided manner, we analyzed the accumulation of triglycerides in human HepaRG liver cells by a mixture of eight steatotic chemicals (amiodarone, benzoic acid, cyproconazole, flusilazole, imazalil, prochloraz, propiconazole and tebuconazole), each present below its individual effect concentration at 1-3 μM. Pronounced and significantly enhanced triglyceride accumulation was observed with the mixture, and similar effects were seen at the level of pregnane-X-receptor activation, a molecular initiating event leading to hepatic steatosis. Transcript pattern analysis indicated subtle pro-steatotic changes at low compound concentrations, which did not exert measurable effects on cellular triglycerides. Mathematical modeling of mixture effects indicated potentially more than additive behavior using a model for compounds with similar modes of action. The present data underline the usefulness of AOP-guided in vitro testing for the identification of mixture effects and highlight the need for further research on chemical mixtures and harmonization of data interpretation of mixture effects.

Combinations of a full and partial agonist: Experimental evidence of curved isoboles

Concentration addition as a classic null model for toxicology and pharmacology is based on Loewe's mathematical formulation and the linearity of the isoboles. Novel mathematical models, however, propose curved isoboles in certain conditions. This article aims to test the hypothesis of the curvature of isoboles in experimental measurements. With the assumption of linear isoboles, a partial agonist acts as an antagonist above its maximal effect level. The isoboles automatically convert to a positive slope. For curved isoboles, a partial agonist acts as an antagonist at higher effect levels than its maximal effect alone. The discrepancies between effect levels were studied with an estrogen receptor binding assay (BMAEREluc/ERα) using a mixture of 17β-estradiol and fulvestrant as a partial agonist. A mixture of 17β-estradiol and fulvestrant acts as a partial agonist and causes the diminishing of the effect level of 17β-estradiol at a significantly higher level than the maximal effect of their partial-agonistic dose-response curve. Measured, elevated effect levels were well predicted by the mathematical model. Nonlinear isoboles may change our understanding and definition of synergism or antagonism and prompt further attention in receptor theory.

Estimation of competitive antagonist affinity by the Schild method and from functional inhibition curves using a novel form of the Gaddum equation

The equilibrium dissociation constant of competitive antagonists represents the affinity of the receptor-ligand interaction, and it is a key characteristic of many therapeutic drugs or toxic compounds. Two commonly used methods by which the affinity of the antagonist can be estimated are Schild analysis and the Cheng-Prusoff method. However, both methods yield different results when applied to systems with slopes not equal to one. The Gaddum equation, which is fundamental for both methods, should be extended to incorporate the slope parameter of the dose-response curves and this extension should diminish the differences between the Schild and Cheng-Prusoff methods. In this study, we derived a novel form of the Gaddum equation with a slope parameter (Hill coefficient) of agonist dose-response curve. We also derived the subsequent equations for Schild and Cheng-Prusoff analysis and we validated the proposed model by the measurement of several known estrogen receptor competitive antagonists. Standardized in vitro yeast reporter gene assay (BMAEREluc/ERα) has been used for the measurements and the range of used antagonist concentrations was 1.37-46.03 μM. By applying our mathematical model, both Schild and Cheng-Prusoff methods provide more similar values of antagonist affinity than the original mathematical approach. The correctness of the model has also been demonstrated by the measurement of a partial agonist with a known receptor affinity. The presented mathematical model significantly reduces the differences in values calculated by the Cheng-Prusoff and Schild methods and yields more accurate estimations of antagonist affinity.