•A strategy to evaluate chemical category membership is presented.•Templates to assess similarity and characterise uncertainty are developed.•A strategy to apply new toxicological data to strengthen ...read-across predictions.•A workflow for reporting a read-across prediction is described.•Read-across prediction to aid in regulatory decisions.
Category formation, grouping and read across methods are broadly applicable in toxicological assessments and may be used to fill data gaps for chemical safety assessment and regulatory decisions. In order to facilitate a transparent and systematic approach to aid regulatory acceptance, a strategy to evaluate chemical category membership, to support the use of read-across predictions that may be used to fill data gaps for regulatory decisions is proposed. There are two major aspects of any read-across exercise, namely assessing similarity and uncertainty. While there can be an over-arching rationale for grouping organic substances based on molecular structure and chemical properties, these similarities alone are generally not sufficient to justify a read-across prediction. Further scientific justification is normally required to justify the chemical grouping, typically including considerations of bioavailability, metabolism and biological/mechanistic plausibility. Sources of uncertainty include a variety of elements which are typically divided into two main issues: the uncertainty associated firstly with the similarity justification and secondly the completeness of the read-across argument. This article focuses on chronic toxicity, whilst acknowledging the approaches are applicable to all endpoints. Templates, developed from work to prepare for the application of new toxicological data to read-across assessment, are presented. These templates act as proposals to assist in assessing similarity in the context of chemistry, toxicokinetics and toxicodynamics as well as to guide the systematic characterisation of uncertainty both in the context of the similarity rationale, the read across data and overall approach and conclusion. Lastly, a workflow for reporting a read-across prediction is suggested.
Metals, Toxicity and Oxidative Stress VALKO, M; MORRIS, H; CRONIN, M. T. D
Current medicinal chemistry,
01/2005, Letnik:
12, Številka:
10
Journal Article
Recenzirano
Metal-induced toxicity and carcinogenicity, with an emphasis on the generation and role of reactive oxygen and nitrogen species, is reviewed. Metal-mediated formation of free radicals causes various ...modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). Whilst iron (Fe), copper (Cu), chromium (Cr), vanadium (V) and cobalt (Co) undergo redox-cycling reactions, for a second group of metals, mercury (Hg), cadmium (Cd) and nickel (Ni), the primary route for their toxicity is depletion of glutathione and bonding to sulfhydryl groups of proteins. Arsenic (As) is thought to bind directly to critical thiols, however, other mechanisms, involving formation of hydrogen peroxide under physiological conditions, have been proposed. The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Common mechanisms involving the Fenton reaction, generation of the superoxide radical and the hydroxyl radical appear to be involved for iron, copper, chromium, vanadium and cobalt primarily associated with mitochondria, microsomes and peroxisomes. However, a recent discovery that the upper limit of "free pools" of copper is far less than a single atom per cell casts serious doubt on the in vivo role of copper in Fenton-like generation of free radicals. Nitric oxide (NO) seems to be involved in arsenite-induced DNA damage and pyrimidine excision inhibition. Various studies have confirmed that metals activate signalling pathways and the carcinogenic effect of metals has been related to activation of mainly redoxsensitive transcription factors, involving NF-kappaB, AP-1 and p53. Antioxidants (both enzymatic and nonenzymatic) provide protection against deleterious metal-mediated free radical attacks. Vitamin E and melatonin can prevent the majority of metal-mediated (iron, copper, cadmium) damage both in vitro systems and in metalloaded animals. Toxicity studies involving chromium have shown that the protective effect of vitamin E against lipid peroxidation may be associated rather with the level of non-enzymatic antioxidants than the activity of enzymatic antioxidants. However, a very recent epidemiological study has shown that a daily intake of vitamin E of more than 400 IU increases the risk of death and should be avoided. While previous studies have proposed a deleterious pro-oxidant effect of vitamin C (ascorbate) in the presence of iron (or copper), recent results have shown that even in the presence of redox-active iron (or copper) and hydrogen peroxide, ascorbate acts as an antioxidant that prevents lipid peroxidation and does not promote protein oxidation in humans in vitro. Experimental results have also shown a link between vanadium and oxidative stress in the etiology of diabetes. The impact of zinc (Zn) on the immune system, the ability of zinc to act as an antioxidant in order to reduce oxidative stress and the neuroprotective and neurodegenerative role of zinc (and copper) in the etiology of Alzheimers disease is also discussed. This review summarizes recent findings in the metal-induced formation of free radicals and the role of oxidative stress in the carcinogenicity and toxicity of metals.
This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and ...environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.
Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate animals required for the assessment of the potential of compounds to cause harm to the aquatic ...environment. A key philosophy in the development of alternatives is a greater understanding of the relevant adverse outcome pathway (AOP). One alternative method is the fish embryo toxicity (FET) assay. Although the trends in potency have been shown to be equivalent in embryo and adult assays, a detailed mechanistic analysis of the toxicity data has yet to be performed; such analysis is vital for a full understanding of the AOP. The research presented herein used an updated implementation of the Verhaar scheme to categorize compounds into AOP-informed categories. These were then used in mechanistic (quantitative) structure–activity relationship ((Q)SAR) analysis to show that the descriptors governing the distinct mechanisms of acute fish toxicity are capable of modeling data from the FET assay. The results show that compounds do appear to exhibit the same mechanisms of toxicity across life stages. Thus, this mechanistic analysis supports the argument that the FET assay is a suitable alternative testing strategy for the specified mechanisms and that understanding the AOPs is useful for toxicity prediction across test systems.
Adverse outcome pathways (AOPs) were introduced in modern toxicology to provide evidence-based representations of the events and processes involved in the progression of toxicological effects across ...varying levels of the biological organisation to better facilitate the safety assessment of chemicals. AOPs offer an opportunity to address knowledge gaps and help to identify novel therapeutic targets. They also aid in the selection and development of existing and new in vitro and in silico test methods for hazard identification and risk assessment of chemical compounds. However, many toxicological processes are too intricate to be captured in a single, linear AOP. As a result, AOP networks have been developed to aid in the comprehension and placement of associated events underlying the emergence of related forms of toxicity—where complex exposure scenarios and interactions may influence the ultimate adverse outcome. This study utilised established criteria to develop an AOP network that connects thirteen individual AOPs associated with nephrotoxicity (as sourced from the AOP-Wiki) to identify several key events (KEs) linked to various adverse outcomes, including kidney failure and chronic kidney disease. Analysis of the modelled AOP network and its topological features determined mitochondrial dysfunction, oxidative stress, and tubular necrosis to be the most connected and central KEs. These KEs can provide a logical foundation for guiding the selection and creation of in vitro assays and in silico tools to substitute for animal-based in vivo experiments in the prediction and assessment of chemical-induced nephrotoxicity in human health.
The need to assess the ability of a chemical to act as a mutagen or a genotoxic carcinogen (collectively termed genotoxicity) is one of the primary requirements in regulatory toxicology. Several ...pieces of legislation have led to an increased interest in the use of in silico methods, specifically the formation of chemical categories for the assessment of toxicological endpoints. A key step in the development of chemical categories for genotoxicity is defining the organic chemistry associated with the formation of a covalent bond between DNA and an exogenous chemical. This organic chemistry is typically defined as structural alerts. To this end, this article has reviewed the literature defining the structural alerts associated with covalent DNA binding. Importantly, this review article also details the mechanistic organic chemistry associated with each of the structural alerts. This information is extremely important in terms of meeting regulatory requirements for the acceptance of the chemical category approach. The structural alerts and associated mechanistic chemistry have been incorporated into the Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox.
Several pieces of legislation have led to an increased interest in the use of in silico methods, specifically the formation of chemical categories for the assessment of toxicological endpoints. For a ...number of endpoints, this requires a detailed knowledge of the electrophilic reaction chemistry that governs the ability of an exogenous chemical to form a covalent adduct. Historically, this chemistry has been defined as compilations of structural alerts without documenting the associated electrophilic chemistry mechanisms. To address this, this article has reviewed the literature defining the structural alerts associated with covalent protein binding and detailed the associated electrophilic reaction chemistry. This information is useful to both toxicologists and regulators when using the chemical category approach to fill data gaps for endpoints involving covalent protein binding. The structural alerts and associated electrophilic reaction chemistry outlined in this review have been incorporated into the OECD (Q)SAR Toolbox, a freely available software tool designed to fill data gaps in a regulatory environment without the need for further animal testing.
A new dataset of cosmetics-related chemicals for the Threshold of Toxicological Concern (TTC) approach has been compiled, comprising 552 chemicals with 219, 40, and 293 chemicals in Cramer Classes I, ...II, and III, respectively. Data were integrated and curated to create a database of No-/Lowest-Observed-Adverse-Effect Level (NOAEL/LOAEL) values, from which the final COSMOS TTC dataset was developed. Criteria for study inclusion and NOAEL decisions were defined, and rigorous quality control was performed for study details and assignment of Cramer classes. From the final COSMOS TTC dataset, human exposure thresholds of 42 and 7.9 μg/kg-bw/day were derived for Cramer Classes I and III, respectively. The size of Cramer Class II was insufficient for derivation of a TTC value. The COSMOS TTC dataset was then federated with the dataset of Munro and colleagues, previously published in 1996, after updating the latter using the quality control processes for this project. This federated dataset expands the chemical space and provides more robust thresholds. The 966 substances in the federated database comprise 245, 49 and 672 chemicals in Cramer Classes I, II and III, respectively. The corresponding TTC values of 46, 6.2 and 2.3 μg/kg-bw/day are broadly similar to those of the original Munro dataset.
Display omitted
•COSMOS TTC dataset is a new TTC dataset comprising 552 cosmetics-related chemicals.•It expands the coverage of chemical space for cosmetics-related chemicals.•Human thresholds of 42 for Class I and 7.9 μg/kg-bw/day for Class III are derived.•No human threshold is proposed for Cramer Class II due to insufficient data.•Combining COSMOS and Munro datasets gives similar thresholds to Munro TTC values.
This study outlines how mechanistic organic chemistry related to covalent bond formation can be used to rationalize the ability of low molecular weight chemicals to cause respiratory sensitization. ...The results of an analysis of 104 chemicals which have been reported to cause respiratory sensitization in humans showed that most of the sensitizing chemicals could be distinguished from 82 control chemicals for which no clinical reports of respiratory sensitization exist. This study resulted in the development of a set of mechanism-based structural alerts for chemicals with the potential to cause respiratory sensitization. Their potential for use in a predictive algorithm for this purpose alongside an externally validated quantitative structure–activity relationship model is discussed.
•Modified Verhaar scheme has improved performance; 35% fewer compounds misclassified.•Modified Verhaar scheme correctly classifies 49% of compounds in test datasets.•A KNIME workflow improves the ...scheme further; 63% of compounds correctly classified.•Mechanistic QSAR models have been built from compounds in the resultant categories.
Assessment of the potential of compounds to cause harm to the aquatic environment is an integral part of the REACH legislation. To reduce the number of vertebrate and invertebrate animals required for this analysis alternative approaches have been promoted. Category formation and read-across have been applied widely to predict toxicity. A key approach to grouping for environmental toxicity is the Verhaar scheme which uses rules to classify compounds into one of four mechanistic categories. These categories provide a mechanistic basis for grouping and any further predictive modelling. A computational implementation of the Verhaar scheme is available in Toxtree v2.6. The work presented herein demonstrates how modifications to the implementation of Verhaar between version 1.5 and 2.6 of Toxtree have improved performance by reducing the number of incorrectly classified compounds. However, for the datasets used in this analysis, version 2.6 classifies more compounds as outside of the domain of the model. Further amendments to the classification rules have been implemented here using a post-processing filter encoded as a KNIME workflow. This results in fewer compounds being classified as outside of the model domain, further improving the predictivity of the scheme. The utility of the modification described herein is demonstrated through building quality, mechanism-specific Quantitative Structure Activity Relationship (QSAR) models for the compounds within specific mechanistic categories.
PDF
