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Results for “Toxicity model”

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At least 19 recordsLinked to original sources

PubMed2002

The effect of precision of molecular orbital descriptors on toxicity modeling of selected pyridines.

The response-surface approach to QSARs attempts to model toxic potency of diverse groups of chemicals while avoiding problems associated with the identification of the mechanism of toxic action or specific chemical class often associated with other approaches. However, while hydrophobicity-dependent, simple regression QSARs derived for congeneric series of organic compounds typically have coefficients of determination greater than 0.90, more heterogeneous multiple regression QSARs exhibit typically 10-15% more unexplained variability. One difference between these approaches is the use of a quantum chemical (QC) descriptor, particularly molecular orbital (MO) energy values such as the energy of the lowest unoccupied molecular orbital (E(LUMO)). The reduced statistical fit exhibited by QSAR models, which include these QC-MO descriptors, could be a result of the variability inherent in the calculation of these descriptors. The present investigation with a structurally and mechanistically diverse set of pyridines revealed that variability is associated with the calculation of the MO descriptor E(LUMO) both between selected Hamiltonians and selected software packages. However, this variability in no way affects the statistical significance of QSARs for toxicity using these values. Specifically, the E(LUMO) values calculated with the PM3 and AM1 Hamiltonians in the two software packages were highly related. There was no relationship between molecular complexity or chemical reactivity and increased differences in individual ELUMO values as described by the standard errors of the mean. Although nine appeared to be the number of calculations, which best minimizes the standard error in energy values relative to computational costs; this minimization did not alter the statistics of the QSARs derived with single vs. mean E(LUMO) values. While the energy of the highest occupied molecular orbital (E(HOMO)) values were not used in the modeling of toxicity, a comparison of these values revealed greater variability between the Hamiltonians and software packages than observed for ELUMO values. Examination of the magnitudes of standard error of the E(HOMO) values in connection to structural features or reactivity likewise revealed no trends.

Forecasting↗

PubMed2006

Complexation and time-dependent accumulation of copper by larval fathead minnows (Pimephales promelas): implications for modeling toxicity.

Mechanistic models predicting copper (Cu) toxicity to aquatic biota in natural waters require organic and inorganic water chemistry, and quantified values for Cu binding by sensitive biological receptors. In bioaccumulation experiments using larval fathead minnows (FHM; Pimephales promelas), we investigated time to asymptotic accumulation of Cu and quantified the conditional stability constants (binding affinity; log K(Cu-FHM)) and binding-site densities of Cu-FHM complexation. Cu bioaccumulation increased rapidly, approaching an asymptote in exposures longer than 12 h, indicating that Cu loading at 24 h is an appropriate exposure duration for modeling Cu complexation by larval FHM. Results of Langmuir and Scatchard analyses of other bioaccumulation experiments produced log K(Cu-FHM) values of 6.52, and binding-site densities of 0.39 micromol g(-1)dry weight. These whole-body log K(Cu-FHM) values are approximately an order of magnitude lower than those reported for adult FHM gills. However, binding-site densities for larval and adult FHM are similar. Under similar test conditions, comparable concentrations of aqueous Cu cause 50% mortality in adult and larval FHM suggesting that binding site densities determine comparable metal-tissue loadings and have greater influence on Cu bioavailability than binding affinity.

Animals↗

PubMedDate not supplied

Tuning neural and fuzzy-neural networks for toxicity modeling.

The need for general reliable models for predicting toxicity has led to the use of artificial intelligence. We applied neural and fuzzy-neural networks with the QSAR approach. We underline how the networks have to be tuned on the data sets generally involved in modeling toxicity. This study was conducted on 562 organic compounds in order to establish models for predictive the acute toxicity in fish.

Animals↗

PubMed1979

Nutritional influences on metal toxicity: cadmium as a model toxic element.

The nutrient quality of the diet has been shown repeatedly to be a significant factor in modifying the response of man and animals to toxic element exposure. Deficiencies of several essential nutrients have been shown to exacerbate the effects of cadmium and supplements of such nutrients have been shown to ameliorate the toxicity. Thus the effects of exposure to a toxic element, such as cadmium, may vary, depending on interactions with other elements which are present in the diet in different concentrations.

Adolescent↗

PubMed1985

A general noninteractive multiple toxicity model including probit, logit, and Weibull transformations.

A multiple toxicity model for the quantal response of organisms is constructed based on an existing bivariate theory. The main assumption is that the tolerances follow a multivariate normal distribution function. However, any monotone tolerance distribution can be applied by mapping the integration region in the n-dimensional space of transforms on the n-dimensional space of normal equivalent deviates. General requirements to noninteractive bivariate tolerance distributions are discussed, and it is shown that bivariate logit and Weibull distributions, constructed according to the mapping procedure, meet these criteria. The univariate Weibull dose-response model is given a novel interpretation in terms of reactions between toxicant molecules and a hypothetical key receptor of the organism. The application of the multiple toxicity model is demonstrated using literature data for the action of gamma-benzene hexachloride and pyrethrins on flour beetles (Tribolium castaneum). Nonnormal tolerance distributions are needed when the mortality data include extreme response probabilities.

Analysis of Variance↗

PubMed2006

Predator learning favours mimicry of a less-toxic model in poison frogs.

Batesian mimicry--resemblance of a toxic model by an edible mimic--depends on deceiving predators. Mimetic advantage is considered to be dependent on frequency because an increase in mimic abundance leads to breakdown of the warning signal. Where multiple toxic species are available, batesian polymorphism is predicted--that is, mimics diversify to match sympatric models. Despite the prevalence of batesian mimicry in nature, batesian polymorphism is relatively rare. Here we explore a poison-frog mimicry complex comprising two parapatric models and a geographically dimorphic mimic that shows monomorphism where models co-occur. Contrary to classical predictions, our toxicity assays, field observations and spectral reflectances show that mimics resemble the less-toxic and less-abundant model. We examine "stimulus generalization" as a mechanism for this non-intuitive result with learning experiments using naive avian predators and live poison frogs. We find that predators differ in avoidance generalization depending on toxicity of the model, conferring greater protection to mimics resembling the less-toxic model owing to overlap of generalized avoidance curves. Our work supports a mechanism of toxicity-dependent stimulus generalization, revealing an additional solution for batesian mimicry where multiple models coexist.

Animals↗

PubMed1996

New research avenues in toxicology: 7-gas N-Gas Model, toxicant suppressants, and genetic toxicology.

Three research areas -- a 7-gas N-Gas Model, toxicant suppressants, and genetic toxicology -- are presented as new research approaches in toxicology. The current 6-gas N-Gas Model predicts the toxic potency of the combustion products of materials based on the toxicological interactions of the fire gases carbon monoxide (CO), carbon dioxide (CO2), low oxygen (O2) concentrations, hydrogen cyanide (HCN), hydrogen chloride, and hydrogen bromide. The present research includes nitrogen dioxide (NO2) in a new 7-gas model which incorporates the synergistic effects of NO2 and CO2, the antagonistic effects of NO2 and HCN, and the additive effects of NO2 with CO and low O2. The area of toxicant suppressants concerns chemicals, which when added to a material, will inhibit or reduce the concentration of a specific toxic gas normally generated during thermal decomposition of that material. The effectiveness of this approach was demonstrated at the US National Institute of Standards and Technology when HCN generation was reduced by 90% and the resultant toxicity of the combustion products was lowered by 50% when a flexible polyurethane (FPU) foam was treated with 0.1% (by weight) cuprous oxide (Cu2O). Although melamine-treated FPU foams are being promoted as more fire safe than standard foams, a melamine-treated foam generated 10 times more HCN than a foam without melamine. The addition of Cu2O to this melamine foam also reduced the HCN generation by 90%. The genetic toxicology research entails the examination of DNA damage that results from the exposure of human cells to various environmental toxicants and gases.

Animals↗

PubMed2001

Acute toxicity modeling of rainbow trout and silver sea bream exposed to waterborne metals.

Of three proposed acute toxicity models, the uptake-depuration (UD) model, the time-integrated concentration (TIC) model, and the concentration-time (CT) model are derived and verified with acute toxicity data to estimate the internal residues of waterborne metals in fish as a function of a few constants and variables. The main factors are the exposure time, the external exposure concentration, the bioconcentration factor (BCF), and the depuration rate constant (k2). The UD model is based on the concept of residue levels at the cell membrane well correlating with the whole-body concentrations, whereas the TIC and the CT models are based on the idea of irreversible inhibition of the enzyme acetylcholinesterase (AChE) governing the metal acute toxicity in that metals in the entire fish or in the aqueous phase can be described by the critical area under the time-concentration curve that is associated with a critical TIC of toxicant in the target tissue. A highly significant correlation (r2 > 0.9) was found between predictions and LC50(t) data for both the TIC and the CT models, indicating successfully describe 4- to 18-d LC50(t) data of arsenic (As), cobalt (Co), copper (Cu), and Co/Cu mixture in rainbow trout (Oncorhyuchus mykiss) and of Cu in fingerlings and subadults of silver sea bream (Sparus sarba). The time-dependent lethal internal concentration at the site of action that causes 50% mortality is also predicted for a given compound and species. It concludes that the TIC and the CT models can be applied to regulate the acute toxicity and to estimate incipient LC50 values and internal residues of waterborne metals in fish.

Animals↗

PubMed2003

Evaluation of fish early life-stage toxicity models of chronic embryonic exposures to complex polycyclic aromatic hydrocarbon mixtures.

Polycyclic aromatic hydrocarbons (PAHs) can cause a variety of effects in early life-stages of fish that have been chronically exposed as embryos, including mortality, deformities, and edemas. Mechanistic models of the chronic toxicity of complex mixtures of PAHs in fish have not been reported, with the exception of a previously untested model based on the lipids of fish as the site of action and toxicity caused through a narcosis mechanism. Four mechanism-based models of the chronic toxicity of embryonic exposures to complex mixtures of petrogenic PAHs in two species of fish, Pacific herring and pink salmon, were evaluated using a toxic-units approach: narcosis, aryl hydrocarbon receptor (AhR) agonism, alkyl phenanthrene toxicity, and combined toxicity. Alkyl phenanthrenes were the predominant PAH constituent determining early life-stage toxicity in both herring and salmon. The alkyl phenanthrene model had 67 to 80% accuracy in predicting the absence or presence of significant early life-stage toxicity, compared with a 40 to 50% accuracy and general underprediction of toxicity with the narcosis model. PAHs with high relative AhR affinity did not appear to contribute substantially to the observed early life-stage toxicity because of low concentrations of the most potent AhR agonists. Narcosis appeared to primarily contribute to embryo mortality and to be predominantly controlled by the concentration of naphthalenes. Except for the highest PAH exposure to herring, the primary toxic unit contribution to the combined toxicity model was alkyl phenanthrene toxicity to both herring and salmon. We recommend the continued use of total PAHs as a metric of exposure until mechanistic models have been further evaluated.

Animals↗

PubMed2006

Gene expression patterns in rainbow trout, Oncorhynchus mykiss, exposed to a suite of model toxicants.

The increased availability and use of DNA microarrays has allowed the characterization of gene expression patterns associated with exposure to different toxicants. An important question is whether toxicant induced changes in gene expression in fish are sufficiently diverse to allow for identification of specific modes of action and/or specific contaminants. In theory, each class of toxicant may generate a gene expression profile unique to its mode of toxic action. In this study, isogenic (cloned) rainbow trout Oncorhynchus mykiss were exposed to sublethal levels of a series of model toxicants with varying modes of action, including ethynylestradiol (xeno-estrogen), 2,2,4,4'-tetrabromodiphenyl ether (BDE-47, thyroid active), diquat (oxidant stressor), chromium VI, and benzo[a]pyrene (BaP) for a period of 1-3 weeks. An additional experiment measured trenbolone (anabolic steroid; model androgen) induced gene expression changes in sexually mature female trout. Following exposure, fish were euthanized, livers removed and RNA extracted. Fluorescently labeled cDNA were generated and hybridized against a commercially available Atlantic Salmon/Trout array (GRASP project, University of Victoria) spotted with 16,000 cDNA's. The slides were scanned to measure abundance of a given transcript in each sample relative to controls. Data were analyzed via Genespring (Silicon Genetics) to identify a list of up- and downregulated genes, as well as to determine gene clustering patterns that can be used as "expression signatures". The results indicate each toxicant exposure caused between 64 and 222 genes to be significantly altered in expression. Most genes exhibiting altered expression responded to only one of the toxicants and relatively few were co-expressed in multiple treatments. For example, BaP and Diquat, both of which exert toxicity via oxidative stress, upregulated 28 of the same genes, of over 100 genes altered by either treatment. Other genes associated with steroidogenesis, p450 and estrogen responsive genes appear to be useful for selectively identifying toxicant mode of action in fish, suggesting a link between gene expression profile and mode of toxicity. Our array results showed good agreement with quantitative real time polymerase chain reaction (qRT PCR), which demonstrates that the arrays are an accurate measure of gene expression. The specificity of the gene expression profile in response to a model toxicant, the link between genes with altered expression and mode of toxic action, and the consistency between array and qRT PCR results all suggest that cDNA microarrays have the potential to screen environmental contaminants for biomarkers and mode of toxic action.

Animals↗

PubMed2003

Development and field validation of a predictive copper toxicity model for the green alga Pseudokirchneriella subcapitata.

In this study, the combined effects of pH, water hardness, and dissolved organic carbon (DOC) concentration and type on the chronic (72-h) effect of copper on growth inhibition of the green alga Pseudokirchneriella subcapitata were investigated. Natural dissolved organic matter (DOM) was collected at three sites in Belgium and The Netherlands using reverse osmosis. A full central composite test design was used for one DOM and a subset of the full design for the two other DOMs. For a total number of 35 toxicity tests performed, 72-h effect concentration resulting in 10% growth inhibition (EbC10s) ranged from 14.2 to 175.9 micrograms Cu/L (factor 12) and 72-h EbC50s from 26.9 to 506.8 micrograms Cu/L (factor 20). Statistical analysis demonstrated that DOC concentration, DOM type, and pH had a significant effect on copper toxicity; hardness did not affect toxicity at the levels tested. In general, an increase in pH resulted in increased toxicity, whereas an increase of the DOC concentration resulted in decreased copper toxicity. When expressed as dissolved copper, significant differences of toxicity reduction capacity were noted across the three DOM types tested (up to factor 2.5). When expressed as Cu2+ activity, effect levels were only significantly affected by pH; linear relationships were observed between pH and the logarithm of the effect concentrations expressed as free copper ion activity, that is, log(EbC50Cu2+) and log(EbC10Cu2+): (1) log(EbC50Cu2+)= - 1.431 pH + 2.050 (r2 = 0.95), and (2) log(EbC10cu2+) = -1.140 pH -0.812 (r2 = 0.91). A copper toxicity model was developed by linking these equations to the WHAM V geochemical speciation model. This model predicted 97% of the EbC50dissolved and EbC10dissolved values within a factor of two of the observed values. Further validation using toxicity test results that were obtained previously with copper-spiked European surface waters demonstrated that for 81% of tested waters, effect concentrations were predicted within a factor of two of the observed. The developed model is considered to be an important step forward in accounting for copper bioavailability in natural systems.

Biological Availability↗

PubMed2005

Platelets can neutralize hydrogen peroxide in an acute toxicity model with cells involved in granulation tissue formation.

Platelets play a key role in the replacement of the blood clot with granulation tissue during the early steps of bone regeneration. We hypothesized that activated platelets can neutralize locally produced reactive oxygen species, thereby protecting cells involved in granulation tissue formation. The potential of platelet-released supernatant (PRS) to neutralize hydrogen peroxide (H(2)O(2)) was tested in an acute toxicity model with osteogenic, inflammatory, and endothelial cells. In the human fetal osteoblastic cell line 1.19 (hFOB), considerable morphological changes, cell shedding, and dysfunction of the respiratory chain were observed when cells were exposed to 3 mM H(2)O(2). Caspase-3 and poly-(ADP-ribose)-polymerase were not activated, suggesting that cell death occurred by necrosis. Preincubation of osteogenic cells, leukocytes, or endothelial cells with PRS decreased the acute toxicity of H(2)O(2). The capacity of platelets to release H(2)O(2)-detoxifying activity was retained for up to 72 h. Aminotriazole, an inhibitor of catalase, decreased the cytoprotective activity of PRS, whereas blocking of glutathione peroxidase by mercaptosuccinate had no effect. These results suggest that platelet-released catalase can rapidly neutralize cytotoxic amounts of H(2)O(2), a process that may play a role during the early stages of bone regeneration.

Blood Platelets↗

PubMed1993

Bronchospasmolytic activity and toxicity modelling of theophylline derivatives by a microcomputer based method.

The OASIS (Optimized Approach based on Structural Index Sets) microcomputer system was applied to model the bronchospasmolytic activity and toxicity of theophylline derivatives. The geometric and electronic factors responsible for biological activity of these compounds were determined. The molecular topology rather than compound metrics is the factor conditioning the theophylline activity. The opposite influence of topology on bronchospasmolytic activity and toxicity was established. Although the acceptor properties (acceptor superdelocalizability indices) determine both the activity and toxicity of the studied compounds the different positions of these effects is of decisive importance in both cases.

Bronchodilator Agents↗

PubMed2005

A Bayesian approach to jointly modeling toxicity and biomarker expression in a phase I/II dose-finding trial.

In this article, we propose a Bayesian approach to phase I/II dose-finding oncology trials by jointly modeling a binary toxicity outcome and a continuous biomarker expression outcome. We apply our method to a clinical trial of a new gene therapy for bladder cancer patients. In this trial, the biomarker expression indicates biological activity of the new therapy. For ethical reasons, the trial is conducted sequentially, with the dose for each successive patient chosen using both toxicity and activity data from patients previously treated in the trial. The modeling framework that we use naturally incorporates correlation between the binary toxicity and continuous activity outcome via a latent Gaussian variable. The dose-escalation/de-escalation decision rules are based on the posterior distributions of both toxicity and activity. A flexible state-space model is used to relate the activity outcome and dose. Extensive simulation studies show that the design reliably chooses the preferred dose using both toxicity and expression outcomes under various clinical scenarios.

Algorithms↗

PubMed2003

Modelling toxicity and mode of action of chemicals to analyse growth and emergence tests with the midge Chironomus riparius.

We present a new growth test data analysis for toxicity tests with the midge Chironomus riparius. The analysis is based on mathematical models which proved to be able to predict growth and emergence of non-exposed organisms for diverse feeding levels or densities. Here, we adapt these models to account for toxicity. We distinguish between two modes of action of the compounds: decrease of feeding or increase of growth energy costs. The models are used to analyse growth data with organisms exposed to copper spiked artificial sediments. Both models provide a good fitting of the data in the case of feeding ad libitum, but only the growth costs model can account for effects of copper in the case of food limitation. We also show that the threshold of effects do not depend on the age (the no effect concentrations (NOEC) are 6, 7 and 9 mg/kg, respectively, for second, third and fourth instars larvae), but that, as soon as this threshold is exceeded, fourth instar larvae are less affected by copper than earlier larvae. Our models constitute a step towards a more biologically relevant analysis of standardized tests, which should facilitate both the understanding of the mechanisms of toxicity and the change of scale from the individual to the population.

Age Factors↗

PubMed1985

Toxicity modeling and prediction with pattern recognition.

Empirical models can be constructed relating the change in toxicity to the change in chemical structure for series of similar compounds or mixtures. The first step is to translate the variation in structure to quantitative numbers. This gives a data table, a data matrix denoted by X, which then is analyzed. The same type of the models can be used to relate the variation of in vivo data to the variation of a battery of in vitro tests. A single data analytical model cannot be applied to a set of compounds of diverse chemical structure. For such data sets, separate models must be developed for each subgroup of compounds. The data analytical problem then partly is one of classification, pattern recognition (PARC). The assumption of structural and biological similarity within each subset of modeled compounds is then essential for empirical models to apply. PARC is often used to classify compounds as active (toxic) or inactive. The data structure is then often asymmetric which puts special demands on the data analysis, making the traditional PARC methods inapplicable. Depending on the desired information from the data analysis and on the type of available data, four levels of PARC can be distinguished: (I) the data X are used to develop rules for classifying future compounds into one of the classes represented in X; (II) same as I, but the possibility of future compounds belonging to "unknown" classes not represented in X is taken into account; (III) same as II, plus the quantitative prediction of one activity variable (here toxicity) in some classes; (IV) same as III, but several quantitative activity (toxicity) variables are predicted.

Mathematics↗

PubMed1987

Inhibition of galactosamine cytotoxicity in an in vivo/in vitro hepatocellular toxicity model.

A combined in vivo/in vitro model of galactosamine hepatotoxicity was employed to test whether previously reported cytoprotective actions of cystamine administration on galactosamine-induced hepatic injury in vivo could be attributed to a direct action of cystamine on toxicant-challenged hepatocytes. In this model, male Sprague-Dawley rats received a 400 mg/kg galactosamine challenge via intraperitoneal injection 1 hr prior to portal vein cannulation for hepatocyte isolation. Isolated cells are established in monolayer culture and galactosamine-induced cellular injury is then expressed over the ensuing 24-48 hr in culture. Consistent with the biochemical basis of galactosamine-induced hepatocellular injury in vivo, cytotoxicity could be prevented by in vitro uridine treatments within 3 hr of the in vivo galactosamine challenge, but not when added 12 hr later. Cystamine, in contrast, exhibited a cytoprotective effect even when added to cultures 12 hr after the in vivo toxicant challenge. Post-toxicant cytoprotection by cystamine in vitro was concentration dependent and did not produce an alteration of hepatocyte nonprotein sulfhydryl content. Post-toxicant cytoprotection by uridine and cystamine in this in vivo/in vitro model of toxicity were fully consistent with in vivo protection from galactosamine-induced necrosis by these agents. This model eliminates potential "extrahepatic" mechanisms for cystamine's hepatoprotective effect and demonstrates a direct cytoprotective action on galactosamine-challenged hepatocytes.

Animals↗

PubMed2002

Structure-toxicity modeling of pesticides to honey bees.

A quantitative structure-activity relationship (QSAR) model was derived for estimating the acute toxicity of pesticides on the honey bee. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H), molar refractivity (MR) and the H-bonding acceptor ability (HBA) of the pesticides. A three-layer feedforward neural network trained by the back-propagation algorithm was used as statistical engine for deriving a powerful QSAR model. The root mean square residual (RMSR) values for the training and testing sets were 0.430 and 0.386, respectively. The practical interest of this original model was discussed.

Animals↗