A model to analyze effects of complex mixtures on survival

In ecotoxicology there is a growing interest in effects of mixtures. The aim of this research was to develop a biology-based model that describes effects of mixtures on survival in time. The model works from the individual compounds in the mixture. Such an approach requires parameters for each individual compound in the mixture. For narcotic compounds we underpinned theoretical relations between the toxic parameters and the logK(ow) with experimental data by analyzing almost 300 datasets from the open literature, allowing a vast reduction in effort in the assessment of effects of mixtures. To illustrate the use of the model we simulated the effect of a mixture of 14 PAHs on the survival of Pimephales promelas. The simulation showed that due to the combined effect of the compounds in the mixture effects can be seen at very low concentrations.     

Assessment of covariate effects in Aalen's additive hazard model

We study the properties of test statistics for a covariate effect in Aalen's additive hazard model and propose several new test statistics. The proposed statistics are derived by using the weights from linear rank statistics for comparing two survival curves. We compare these statistics with the two statistics proposed by Aalen using Monte Carlo simulations. Several different survival configurations are considered in the simulation study: proportional hazards; crossing hazards; hazard differences early in time, and hazard differences for large survival times. Of the proposed test statistics, one is superior for detecting hazard differences for large survival times and another is superior for detecting early hazard differences and crossing hazards. © 1998 John Wiley & Sons, Ltd.     

A stratified generalized additive model and permutation test for temporal heterogeneity of smoothed bivariate spatial effects

Generalized additive models (GAMs) with bivariate smoothers are frequently used to map geographic disease risks in epidemiology studies. A challenge in identifying health disparities has been the lack of intuitive and computationally feasible methods to assess whether the pattern of spatial effects varies over time. In this research, we accommodate time-stratified smoothers into the GAM framework to estimate time-specific spatial risk patterns while borrowing information from confounding effects across time. A backfitting algorithm for model estimation is proposed along with a permutation testing framework for assessing temporal heterogeneity of geospatial risk patterns across two or more time points. Simulation studies show that our proposed permuted mean squared difference (PMSD) test performs well with respect to type I error and power in various settings when compared with existing methods. The proposed model and PMSD test are used geospatial risk patterns of patent ductus arteriosus (PDA) in the state of Massachusetts over 2003-2009. We show that there is variation over time in spatial patterns of PDA risk, adjusting for other known risk factors, suggesting the presence of potential time-varying and space-related risk factors other than the adjusted ones.     

A partially linear additive model for clustered proportion data

Proportion data with support lying in the interval [0,1] are a commonplace in various domains of medicine and public health. When these data are available as clusters, it is important to correctly incorporate the within‐cluster correlation to improve the estimation efficiency while conducting regression‐based risk evaluation. Furthermore, covariates may exhibit a nonlinear relationship with the (proportion) responses while quantifying disease status. As an alternative to various existing classical methods for modeling proportion data (such as augmented Beta regression) that uses maximum likelihood, or generalized estimating equations, we develop a partially linear additive model based on the quadratic inference function. Relying on quasi‐likelihood estimation techniques and polynomial spline approximation for unknown nonparametric functions, we obtain the estimators for both parametric part and nonparametric part of our model and study their large‐sample theoretical properties. We illustrate the advantages and usefulness of our proposition over other alternatives via extensive simulation studies, and application to a real dataset from a clinical periodontal study.     

Penetration of industrial chemicals across the skin: A predictive model

The recently reported dermal absorption and toxicity potential of industrial chemicals is reconsidered using an alternative physicochemically based model of skin penetration. In this model, the outermost, and least permeable, component of the skin [namely, the stratum corneum (SC)] is considered to provide only a lipoidal transport pathway into the body for chemicals that come into contact with the skin. The predictive algorithm of the model is biophysically compatible with known SC properties, and is based on experimental determinations of permeability coefficients through human skin in vitro for nearly 100 compounds of widely divergent physicochemical properties. This simpler prediction results in significantly lower estimates of maximum percutaneous penetration fluxes. © 1993 Wiley-Liss, Inc.     

Water quality objectives for mixtures of toxic chemicals: problems and perspectives

The need to develop water quality objectives not only for single substances but also for mixtures of chemicals seems evident. For that purpose, the conceptual basis could be the use of the two existing biometric models: concentration addition (CA) and independent action (IA), which is also called response addition. Both may allow calculation of the toxicity of mixtures of chemicals with similar modes of action (CA) or dissimilar modes of action (IA), respectively. The joint research project Prediction and Assessment of the Aquatic Toxicity of Mixtures of Chemicals (PREDICT) within the framework of the IVth Environment and Climate Programme of the European Commission, provided the opportunity to address (a) chemometric and QSAR criteria to classify substances as supposedly similarly or dissimilarly acting; (b) the predictive values of both models for the toxicity of mixtures at low, statistically nonsignificant effect concentrations of the individual components; and (c) the predictability of mixture toxicity at higher levels of biological complexity. In this article, the general outline, methodological approach, and some preliminary findings of PREDICT are presented. A procedure for classifying chemicals in relation to their structural and toxicological similarities has been developed. The predictive capabilities of CA and IA models have been demonstrated for single species and, to some extent, for multispecies testing. The role of very low effect concentrations in multiple mixtures has been evaluated. Problems and perspectives concerning the development of water quality objectives for mixtures are discussed.     

Toxic effects of chemical mixtures

Exposures to chemical mixtures have reportedly produced unexpected effects. Examination of new case studies, as well as those previously reported, shows that when the human body is exposed to mixtures of chemicals that include lipophilic and hydrophilic species, the lipophiles facilitate the absorption of the hydrophiles at enhanced levels and produce effects that are not expected from an individual chemical. These effects include enhanced acute and chronic responses, low-level concentration response, and unexpected target organ attack. Octanol:water partition coefficients are predictive of relative lipophilicity and hydrophilicity. The findings have implications for safe drinking water standards, air quality standards, safe industrial and environmental exposure levels, product formulation, product labeling, and protocols for toxicity testing of chemical products.     

Atomic charges of individual reactive chemicals in binary mixtures determine their joint effects: an example of cyanogenic toxicants and aldehydes

Environmental contaminants are usually encountered as mixtures, and many of these mixtures yield synergistic or antagonistic effects attributable to an intracellular chemical reaction that pose a potential threat on ecological systems. However, how atomic charges of individual chemicals determine their intracellular chemical reactions, and then determine the joint effects for mixtures containing reactive toxicants, is not well understood. To address this issue, the joint effects between cyanogenic toxicants and aldehydes on Photobacterium phosphoreum were observed in the present study. Their toxicological joint effects differed from one another. This difference is inherently related to the two atomic charges of the individual chemicals: the oxygen charge of -CHO (O(aldehyde toxicant)) in aldehyde toxicants and the carbon-atom charge of a carbon chain in the cyanogenic toxicant (C(cyanogenic toxicant)). Based on these two atomic charges, the following QSAR (quantitative structure-activity relationship) model was proposed: When (O(aldehyde toxicant) -C(cyanogenic toxicant) )> -0.125, the joint effect of equitoxic binary mixtures at median inhibition (TU, the sum of toxic units) can be calculated as TU = 1.00 ± 0.20; when (O(aldehyde toxicant) -C(cyanogenic toxicant) ) ≤ -0.125, the joint effect can be calculated using TU = - 27.6 x O (aldehyde toxicant) - 5.22 x C (cyanogenic toxicant) - 6.97 (n = 40, r = 0.887, SE = 0.195, F = 140, p < 0.001, q(2) (Loo) = 0.748; SE is the standard error of the regression, F is the F test statistic). The result provides insight into the relationship between the atomic charges and the joint effects for mixtures containing cyanogenic toxicants and aldehydes. This demonstrates that the essence of the joint effects resulting from intracellular chemical reactions depends on the atomic charges of individual chemicals. The present study provides a possible approach for the development of a QSAR model for mixtures containing reactive toxicants based on the atomic charges.     

A food web bioaccumulation model for organic chemicals in aquatic ecosystems

The present study examines a new bioaccumulation model for hydrophobic organic chemicals in aquatic food webs. The purpose of the model is to provide site-specific estimates of chemical concentrations and associated bioconcentration factors, bioaccumulation factors, and biota-sediment accumulation factors in organisms of aquatic food webs using a limited number of chemical, organism, and site-specific data inputs. The model is a modification of a previous model and incorporates new insights regarding the mechanism of bioaccumulation derived from laboratory experiments and field studies as well as improvements in model parameterization. The new elements of the model include: A model for the partitioning of chemicals into organisms; kinetic models for predicting chemical concentrations in algae, phytoplankton, and zooplankton; new allometric relationships for predicting gill ventilation rates in a wide range of aquatic species; and a mechanistic model for predicting gastrointestinal magnification of organic chemicals in a range of species. Model performance is evaluated using empirical data from three different freshwater ecosystems involving 1,019 observations for 35 species and 64 chemicals. The effects of each modification on the model's performance are illustrated. The new model is able to provide better estimates of bioaccumulation factors in comparison to the previous food web bioaccumulation model while the model input requirements remain largely unchanged.     

Protection of human health from mixtures of radionuclides and chemicals in drinking water

This study was undertaken to develop a common scale for evaluating health risks from contaminated drinking water. For different agents, many unrealistic models of risk have been used. By intent, regulatory toxicology depends on “data-sparse, model-intensive” analogies from exotic animal genetics and novel exposures (NCRP 1989). The question is, does a risk evaluation so derived have any predictive validity? Absence of data prevents answer because regulatory toxicology rationalizes in step-by-step logic, which we callabsolute (i.e., predicts cases of disease in a population). Absolute models ensure safety, but do so at the cost of realism. In contrast, we makerelative comparisons in the manner of horsepower or RBE from radiation biology. All pollutants are assumed to contribute to toxic injury. Next, relative potencies are linked to the most credible standards. Thus, experience is transferred from well-studied chemicals to the new chemical by “data-intensive, model-sparse” methods. This logos provides much relative precision. Then, pollutants are compared with: (1) common foodstuffs, (2) ambient radiation background, or (3) utility-pure drinking water. Finally, an assessment is made for a waste disposal area.     

Initial effects of the 1998 revised narcotic law in substitute drug treatment of narcotic dependent patients--results of a physician survey

To study the consequences of the last revision of the German narcotics act (1998, which forbids prescribing codeines as substitutes), a survey was carried out several months after implementation with 639 physicians, who gave substitution treatment. It could be determined how far codeine patients were ready to change their substitute, how content physicians said their patients were after changing the substitute and which were the attitudes of physicians to the revision in question. RESULTS: The majority of patients (70%) who were formerly substituted by means of codeine accepted methadone as a substitute. After the method of substitution had been changed, 51% of patients had been classified as "very content" and 35% as "reasonably content". Especially those physicians who prescribed codeines in the past, unlike physicians who used methadone for substitution treatment only, criticised the revision in question and feared that many patients should be without medical care as a consequence of the new situation.     

An amphibian model to test the effects of xenobiotic chemicals on development of the hematopoietic system

A number of manmade chemicals have deleterious effects on the developing immune system. Very few assay systems are available to study the effects of xenobiotics on hematopoietic stem cells. In rodent models, assays require exposure of pregnant females and analysis of the hematopoietic potential of stem cells from the offspring. These models are less relevant to lower vertebrates such as fish or amphibians where exposure of embryos is direct. To overcome this problem, an amphibian model was developed. Diploid (2N) embryos (16-20 h of age) of the South African clawed frog, Xenopus laevis, were exposed to 10 microg/ml diazinon or 10(-6) M lead acetate for 2 h. After 2 h, the ventral blood island (VBI) was transplanted from a chemically treated or untreated control embryo to an untreated triploid (3N) host embryo. After 55 d, the contribution of the donor VBI-derived stem cells to populations in the blood, thymus, and spleen was assessed by flow cytometry. Diazinon, but not lead acetate, interfered with the ability of transplanted stem cells to contribute to hematopoiesis. Because amphibian embryos are very sensitive indicators of the toxic effects of chemicals, this VBI assay could be employed to test any toxic chemical that is suspected of having a negative effect on development of the hematopoietic system.     

Growth of Daphnia magna exposed to mixtures of chemicals with diverse modes of action

Concentrations causing inhibition of growth of Daphnia magna after 16 days of exposure were determined for nine chemicals that presumably act through different modes of action. The joint toxic effect of a mixture of these chemicals is found to be nonadditive.     

Cognitive effects of endocrine-disrupting chemicals in animals.

A large number of chemical pollutants including phthalates, alkylphenolic compounds, polychlorinated biphenyls and polychlorinated dibenzodioxins, organochlorine pesticides, bisphenol A, and metals including lead, mercury, and cadmium have the ability to disrupt endocrine function in animals. Some of these same chemicals have been shown to alter cognitive function in animals and humans. Because hormonally mediated events play a central role in central nervous system development and function, a number of researchers have speculated that the changes in cognitive function are mediated by the endocrine-like actions of these chemicals. In this paper we review the evidence that cognitive effects of chemicals classified as environmental endocrine disruptors are mediated by changes in hormonal function. We begin by briefly reviewing the role of gonadal steroids, thyroid hormones, and glucocorticoids in brain development and brain function. We then review the endocrine changes and cognitive effects that have been reported for selected endocrine-disrupting chemicals, discuss the evidence for causal relationships between endocrine disruption and cognitive effects, and suggest directions for future research.     

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.