Masashi Kamo

An application of the biotic ligand model to predict the toxic effects of metal mixtures

The rapidly developing biotic ligand model (BLM) allows us to predict the toxicity of heavy metals in water of various chemistries; however, the current BLM predicts the toxicity of a single metal and not the toxic effects of metal mixtures. The toxic mechanisms of heavy metals are not yet completely understood, but hypocalcemia is suggested to be the most likely toxic mechanism for some metals. The BLM, which predicts the toxicity of metals by the amount of metals binding to ligand, is modified to predict the toxicity by the proportion of nonmetal binding ligand that is available for calcium uptake under the assumption that the organisms die because of hypocalcemia when so few ligands are available for calcium uptake. Because the proportion can be computed when multiple metals are present, the toxic effects of metal mixtures can be predicted. Zinc, copper, and cadmium toxicity to rainbow trout (Oncorhynchus mykiss) are considered. All data are collected from the literature, and a meta-analysis using the modified version of the BLM is conducted. The present study found that the proportion of nonmetal binding ligand is a constant value for any test condition. The proportion is not influenced by water chemistry or by metal species. Using the nature of constant proportion, toxicities of metals are well estimated. In addition, the toxic effects of metal mixtures are the simple sum of the toxicities of each metal (additive effect) corresponding to the bioavailable form of the metals. In terms of total concentration of metals in water, however, nonadditive effects, such as antagonism and synergism, are possible.

Metal Mixture Modeling Evaluation project: 2. Comparison of four modeling approaches

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.

Population-level ecological effect assessment: estimating the effect of toxic chemicals on density-dependent populations

We examined the relationship between individual-level and population-level effects of toxic chemicals, employing the equilibrium population size as an index of population-level effects. We first analyzed two-stage matrix models considering four life-history types and four density-dependent models, and then we analyzed ecotoxicological and life-history data of the fathead minnow (Pimephales promelas) and brook trout (Salvelinus fontinalis) as real examples. Our elasticity analysis showed that toxic impacts on density-dependent populations depended largely on the differences in density-dependence and in life histories of the organisms. In particular, the importance of adult survivability was considerably increased in iteroparous organisms with density-dependent juvenile survivability or fertility. Our results also suggested that population-level effects, as indicated by the percentage reduction in equilibrium population size, were often greater than the percentage reductions in vital rates of individuals. Our analysis indicates that assessing population-level risk and developing a risk-reduction strategy without considering density-dependence can be risky.

Testing an application of a biotic ligand model to predict acute toxicity of metal mixtures to rainbow trout

The authors tested the applicability of a previously developed biotic ligand model (BLM) to predict acute toxicity of single metals and metal mixtures (cadmium, lead, and zinc) to rainbow trout fry (Oncorhynchus mykiss) from a single available dataset. The BLM used in the present study hypothesizes that metals inhibit an essential cation (calcium) and organisms die as a result of its deficiency, leading to an assumption that the proportion of metal-binding ligand (f) is responsible for the toxic effects of metals on the survival of rainbow trout. The f value is a function of free-ion concentrations of metals computed by a chemical speciation model, and the function has affinity constants as model parameters. First, the survival effects of single metals were statistically modeled separately (i.e., f-survival relationship) by using the generalized linear mixed model with binomial distribution. The modeled responses of survival rates to f overlapped reasonably irrespective of metals tested, supporting the theoretical prediction from the BLM that f-survival relationships are comparable regardless of metal species. The authors thus developed the generalized linear mixed model based on all data pooled across the single-metal tests. The best-fitted model well predicted the survival responses observed in mixture tests (r = 0.97), providing support for the applicability of the BLM to predict effects of metal mixtures.

A Novel Approach to Determining a Population-Level Threshold in Ecological Risk Assessment: A Case Study of Zinc

ABSTRACT A novel approach to population-level assessment was applied in order to demonstrate its utility in estimating and managing the risk of zinc in a water environment. Much attention has been paid to population-level risk assessment, but there have been no attempts to determine a “safe” population-level concentration as an environmental criterion. Based on the published results of toxicity tests for various species, we first theoretically derived a threshold concentration at which a population size is unchanged due to the adverse effects of zinc exposure. To derive a zinc concentration that will protect populations in natural environments, we adopted the concept of species sensitivity distribution. Assuming the threshold concentrations of a set of species are log-normally distributed, we calculated the 95% protection level of zinc (PHC5 :population-level hazardous concentration of 5% of species), which is 107 μg/L. Meanwhile, the 95% protection criterion (HC5) based on conventional individual-level chronic toxicity, was calculated to be 14.6 μg/L. The environmentally “safe” concentration for a population-level endpoint is about 7 times greater than that for an individual-level endpoint. The proposed method provides guidance for a pragmatic approach to population-level ecological risk assessment and the management of chemicals.

Comparison of population-level effects of heavy metals on fathead minnow (Pimephales promelas).

To evaluate the population-level effects of heavy metals (copper, zinc, cadmium, hexavalent chromium, nickel) on fathead minnow, Pimephales promelas, we first estimated the concentration-effect relationships between the metal concentrations and individual traits (juvenile survivability, hatchability, fertility) by using toxicity data collected from the literature. A Leslie matrix model of fathead minnow was used to calculate population growth rates from these relationships. The population threshold concentrations (PTCs) leading to zero net growth of the fish population were as follows: Cu, 27.4; Cd, 33.2; Zn (soft water), 81.5; Zn (hard water), 85.8; Ni, 504.8; Cr, 3251.6 (microg L(-1)). By comparing the PTCs with no observed effect concentrations (NOECs), we found that some PTCs were equivalent to or even lower than the corresponding NOECs. This result suggests that current ecological risk assessments based on the NOECs can be inadequate for protecting aquatic populations and more efforts on the population-level studies are needed.

Assessing ecological risk of zinc in Japan using organism- and population-level species sensitivity distributions.

In Japan, the Environmental Quality Standard for zinc, established in 2003, was the first standard for the protection of aquatic species. To achieve this environmental criterion, the National Effluent Standard was lowered from 5 mgL(-1) to 2 mgL(-1) in 2006. However, some industries were permitted to apply a provisional effluent standard until 2011, when the provisional standard will revert to the national standard. Therefore, discussion about the environmental management of and countermeasures for the risk of zinc continues in Japan. The aim of this paper is to present the current status of the risk of zinc. Using long-term monitoring data for zinc from more than 3000 monitoring sites in Japan, both freshwater and marine, we found that the geometric mean concentration of zinc at freshwater sites was about 10.8 microgL(-1) and that the annual mean concentrations have been generally decreasing. We identified sites where zinc concentrations were high, and we also identified the most likely sources of zinc responsible for the high concentrations. The ecological risk of zinc was estimated at the conventional individual level and at the population level. Individual-level risk was detected at about 20% of freshwater sites, and population-level risk at about 2%. The risks were lower in more recent years; however, they remain at unacceptable levels. Our results show the necessity of risk reduction strategies. We propose a new approach for risk management and countermeasures that consider both individual- and population-level risks.

Explanation of non-additive effects in mixtures of similar mode of action chemicals.

Many models have been developed to predict the combined effect of drugs and chemicals. Most models are classified into two additive models: independent action (IA) and concentration addition (CA). It is generally considered if the modes of action of chemicals are similar then the combined effect obeys CA; however, many empirical studies report nonlinear effects deviating from the predictions by CA. Such deviations are termed synergism and antagonism. Synergism, which leads to a stronger toxicity, requires more careful management, and hence it is important to understand how and which combinations of chemicals lead to synergism. In this paper, three types of chemical reactions are mathematically modeled and the cause of the nonlinear effects among chemicals with similar modes of action was investigated. Our results show that combined effects obey CA only when the modes of action are exactly the same. Contrary to existing knowledge, combined effects are generally nonlinear even if the modes of action of the chemicals are similar. Our results further show that the nonlinear effects vanish out when the chemical concentrations are low, suggesting that the current management procedure of assuming CA is rarely inappropriate because environmental concentrations of chemicals are generally low.

Potential effects of life‐history evolution on ecological risk assessment

We investigated theoretically how the sensitivity of organisms to the toxicity of chemicals varies depending on their life-history traits, which are subject to evolution. We used a resource-allocation model in which organisms allocate their resources to reproduction, maintenance of life (reduction of death), and reduction of the toxicities of chemicals. First we investigated the optimal allocation rates in the absence of chemicals. We found that when evolution occurred in low-density populations, the allocation rate for reproduction was larger than that for maintenance of life, and hence an r-strategy evolved. The r-strategists had lower sensitivity (higher resistance) against the toxicity than K-strategists, which was the optimal strategy in high-density populations. Second, we examined the optimal allocation rates in the presence of chemicals. The allocation rate for the reduction of toxicity varied depending on the shape of functions for the reduction of toxicity. When the efficiency for the reduction was low, organisms did not allocate resources to reduce toxicity, and they remained sensitive to chemicals (sensitive type). When the toxicity was efficiently reduced, the organisms allocated resources to reduce the toxicity and became insensitive to the chemicals (resistant type). When the function for the reduction had a sigmoidal shape, evolutionary bistability appeared, and the organisms eventually evolved either to allocate resources for chemical reduction or not to do so depending on the initial conditions of evolution. This result explains the large variation in the sensitivities to chemicals in organisms collected from polluted areas. We also found that the toxicity required to reduce the population growth rate by 10% (EC10) was higher for the resistant type than for the sensitive type in general; however, when the toxicity tests were conducted under a resource-poor condition, EC10 was even smaller in the resistant type than in the sensitive type (i.e., resistant organisms are more sensitive than sensitive organisms). This counterintuitive result occurred because the allocation of resources for toxicity reduction was larger than needed, and was thus an overinvestment under the resource-poor condition. Together with the results, we conclude that lacking an understanding of the evolutionary aspect may lead to insufficient risk assessment and management.

Criteria for deviation from predictions by the concentration addition model

Loewes additivity (concentration addition) is a well-known model for predicting the toxic effects of chemical mixtures under the additivity assumption of toxicity. However, from the perspective of chemical risk assessment and/or management, it is important to identify chemicals whose toxicities are additive when present concurrently, that is, it should be established whether there are chemical mixtures to which the concentration addition predictive model can be applied. The objective of the present study was to develop criteria for judging test results that deviated from the predictions by the concentration addition chemical mixture model. These criteria were based on the confidence interval of the concentration addition models prediction and on estimation of errors of the predicted concentration-effect curves by toxicity tests after exposure to single chemicals. A log-logit model with 2 parameters was assumed for the concentration-effect curve of each individual chemical. These parameters were determined by the maximum-likelihood method, and the criteria were defined using the variances and the covariance of the parameters. In addition, the criteria were applied to a toxicity test of a binary mixture of p-n-nonylphenol and p-n-octylphenol using the Japanese killifish, medaka (Oryzias latipes). Consequently, the concentration addition model using confidence interval was capable of predicting the test results at any level, and no reason for rejecting the concentration addition was found. Environ Toxicol Chem 2016;35:1806-1814.