•Assignment of Cramer class plays a significant role in setting the TTC value.•The assignment of 1016 fragrance materials by 3 different approaches was compared.•Two in silico programs differed in ...16% of cases in the Cramer class assigned.•Strategies for assuring the robust assignment of a Cramer class are proposed.
The Threshold of Toxicological Concern (TTC) is a pragmatic approach in risk assessment. In the absence of data, it sets up levels of human exposure that are considered to have no appreciable risk to human health. The Cramer decision tree is used extensively to determine these exposure thresholds by categorizing non-carcinogenic chemicals into three different structural classes. Therefore, assigning an accurate Cramer class to a material is a crucial step to preserve the integrity of the risk assessment. In this study the Cramer class of over 1000 fragrance materials across diverse chemical classes were determined by using Toxtree (TT), the OECD QSAR Toolbox (TB), and expert judgment. Disconcordance was observed between TT and the TB. A total of 165 materials (16%) showed different results from the two programs. The overall concordance for Cramer classification between TT and expert judgment is 83%, while the concordance between the TB and expert judgment is 77%. Amines, lactones and heterocycles have the lowest percent agreement with expert judgment for TT and the TB. For amines, the expert judgment agreement is 45% for TT and 55% for the TB. For heterocycles, the expert judgment agreement is 55% for TT and the TB. For lactones, the expert judgment agreement is 56% for TT and 50% for the TB. Additional analyses were conducted to determine the concordance within various chemical classes. Critical checkpoints in the decision tree are identified. Strategies and guidance on determining the Cramer class for various chemical classes are discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
An array of industrial processing units generates many multimeric hazardous compounds, such as complex technical lignin and its toxic derivatives, thereby persist in expelled water bodies. The ...inclusion of some group of motifs in the complex technical lignin structure helps it resist degrade biologically, most often even recalcitrant. Relatively small concentrations of lignin are harmful to aquatic organisms and can trigger environmental hazards. Sadly, the entire biotransformation pathway and insightful information about these toxic derivatives are incomplete and missing in the literature. This is mainly because the current conventional treatments often fail to identify all transformed compounds and their environmental fate. Thus, a robust toolset is much needed to cover this literature gap. Inadequate performance of conventional remediation processes and biological degradation patterns can be maximally optimized with the aid of predictive toolset methods that could offer better degradability and complete transformed compound information. A predictive toolset-assisted biodegradation pattern determination is a multifaceted and reliable analytical technique that can help to overcome existing shortcomings by providing an entire transformation pathway. Considering the above critiques, this work reports on the degradation pattern, and toxicological endpoints of five hazardous compounds, i.e., 2-chlorosyringaldehyde, 5-chlorovanillin, catechol, guaiacyl 4-O-5 guaiacyl, and syringyl β-O-4 syringyl β-O-4 sinapyl alcohol, that persists in water matrices. The predictive transformation pattern was revealed notably less complex end-products of catechol as; succinate, and 2-Oxo-4-pentenoate. The gastrointestinal (GI) absorption rate was found high for all tested compounds, excluding trimer compound, i.e., syringyl β-O-4 syringyl β-O-4 sinapyl alcohol. The toxicity and persistence profile tested via Toxtree showed that the Cramer Rules, Verhaar Scheme, and Structural Alerts for Reactivity, (START) biodegradation ability as positive, and all five target compounds were found as class-II persistent compounds. Furthermore, the Ecological Structure-Activity Relationships (ECOSAR)assisted testing specifies that all tested derivatives have multiple aquatic toxic levels. In summary, the current findings endorse the hazardous compounds and undertake prescreening of the deprivation policy to protect the environment.
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•A complete chemical transformation of hazardous compounds is reported.•Transformed compounds found comparatively less complex than the parental ones.•Predictive analyses suggest aquatic toxicity as significant environmental hazard.•Hazardous compounds found to be class(II) persistent compounds.•Findings affirms predictive technologies assisted abetment of hazardous compounds.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of predictive models, ranging from short-term biological ...assays (e.g., mutagenicity tests) to theoretical models, has been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and refinement of animal tests. This chapter reports and describes some of the most noted (Q)SAR models based on human expert knowledge and statistical approaches, aiming at predicting the carcinogenicity of chemicals. Additionally, the performance of the selected models has been evaluated, and the results are interpreted in details by applying these predictive models to some pharmaceutical molecules.
Lead hybridization concept was used to design and synthesize twenty novel hybrid compounds by combining fungicidal leads viz. 6-flouro-1,3-benzothiazol-2-amine and 1,2,4-triazoles in a single ...molecule, with the aim of discovery of high potential novel fungicides. Antifungal evaluation of synthesized 6-flourobenzothiazol-2-yl-1,2,4-triazoles against various phytopathogenic fungi revealed synergistic effect of combination of leads with one another in all the test compounds. Some of the synthesized compounds showed excellent fungitoxicity comparable with the standard fungicides used. In silico toxicity of all the compounds was equivalent to the standard fungicides used. Docking studies and Lipinski filtration were performed in order to present the rationale of structure activity relation. Compounds 2, 8, 15 and 18 were screened to act as leads for further modification and use.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Evaluated the exclusion rules implemented within Toxtree using a large inventory of substances.•Proposed refinements to improve the manner in which specific exclusions had been implemented.•Assessed ...relevance and coverage of representatives from a medical device chemical inventory vs other inventories.•Proposed additional exclusion rules for chemicals extracted from medical devices.
The Threshold of Toxicological Concern (TTC) is a pragmatic approach used to establish safe thresholds below which there can be no appreciable risk to human health. Here, a large inventory of ∼45,000 substances (referred to as the LRI dataset) was profiled through the Kroes TTC decision module within Toxtree v3.1 to assign substances into their respective TTC categories. Four thousand and two substances were found to be not applicable for the TTC approach. However, closer examination of these substances uncovered several implementation issues: substances represented in their salt forms were automatically assigned as not appropriate for TTC when many of these contained essential metals as counter ions which would render them TTC applicable. High Potency Carcinogens and dioxin-like substances were not fully captured based on the rules currently implemented in the software. Phosphorus containing substances were considered exclusions when many of them would be appropriate for TTC. Refinements were proposed to address the limitations in the current software implementation. A second component of the study explored a set of substances representative of those released from medical devices and compared them to the LRI dataset as well as other toxicity datasets to investigate their structural similarity. A third component of the study sought to extend the exclusion rules to address application to substances released from medical devices that lack toxicity data. The refined rules were then applied to this dataset and the TTC assignments were compared. This case study demonstrated the importance of evaluating the software implementation of an established TTC workflow, identified certain limitations and explored potential refinements when applying these concepts to medical devices.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several ...in vitro tests based on the adverse outcome pathway have been developed for hazard identification, such as the direct peptide reactivity assay, KeratinoSens™ and the human cell line activation test. Here, we describe the development of an artificial neural network (ANN) prediction model for skin sensitization risk assessment based on the integrated testing strategy concept, using direct peptide reactivity assay, KeratinoSens™, human cell line activation test and an in silico or structure alert parameter. We first investigated the relationship between published murine local lymph node assay EC3 values, which represent skin sensitization potency, and in vitro test results using a panel of about 134 chemicals for which all the required data were available. Predictions based on ANN analysis using combinations of parameters from all three in vitro tests showed a good correlation with local lymph node assay EC3 values. However, when the ANN model was applied to a testing set of 28 chemicals that had not been included in the training set, predicted EC3s were overestimated for some chemicals. Incorporation of an additional in silico or structure alert descriptor (obtained with TIMES‐M or Toxtree software) in the ANN model improved the results. Our findings suggest that the ANN model based on the integrated testing strategy concept could be useful for evaluating the skin sensitization potential.
ANN model for skin sensitization risk using DPRA/KeratinoSens™/h‐CLAT/structure alerts.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Risk assessment for most human health effects is based on the threshold of a toxicological effect, usually derived from animal experiments. The Threshold of Toxicological Concern (TTC) is a concept ...that refers to the establishment of a level of exposure for all chemicals below which there would be no appreciable risk to human health. When carefully applied, the TTC concept can provide a means of waiving testing based on knowledge of exposure limits. Two main approaches exist; the first of these is a General Threshold of Toxicological Concern; the second approach is a TTC in relation to structural information and/or toxicological data of chemicals. The structural scheme most routinely used is that of Cramer and co-workers from 1978. Recently this scheme was encoded into a software program called Toxtree, specifically commissioned by the European Chemicals Bureau (ECB). Here we evaluate two published datasets using Toxtree to demonstrate its concordance and highlight potential software modifications. The results were promising with an overall good concordance between the reported classifications and those generated by Toxtree. Further evaluation of these results highlighted a number of inconsistencies which were examined in turn and rationalised as far as possible. Improvements for Toxtree were proposed where appropriate. Notable of these is a necessity to update the lists of common food components and normal body constituents as these accounted for the majority of false classifications observed. Overall Toxtree was found to be a useful tool in facilitating the systematic evaluation of compounds through the Cramer scheme.
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
•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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The Toxic Substances Control Act (TSCA) mandates the US EPA perform risk-based prioritisation of chemicals in commerce and then, for high-priority substances, develop risk evaluations that integrate ...toxicity data with exposure information. One approach being considered for data poor chemicals is the Threshold of Toxicological Concern (TTC). Here, TTC values derived using oral (sub)chronic No Observable (Adverse) Effect Level (NO(A)EL) data from the EPA's Toxicity Values database (ToxValDB) were compared with published TTC values from Munro et al. (1996). A total of 4554 chemicals with structures present in ToxValDB were assigned into their respective TTC categories using the Toxtree software tool, of which toxicity data was available for 1304 substances. The TTC values derived from ToxValDB were similar, but not identical to the Munro TTC values: Cramer I ((ToxValDB) 37.3 c. f. (Munro) 30 μg/kg-day), Cramer II (34.6 c. f. 9.1 μg/kg-day) and Cramer III (3.9 c. f. 1.5 μg/kg-day). Cramer III 5th percentile values were found to be statistically different. Chemical features of the two Cramer III datasets were evaluated to account for the differences. TTC values derived from this expanded dataset substantiated the original TTC values, reaffirming the utility of TTC as a promising tool in a risk-based prioritisation approach.
•Substances present in ToxValDB were assigned into their respective TTC categories.•Used ToxValDB toxicity values to derive new Cramer TTC values.•Evaluated whether the Cramer TTC values derived from the ToxValDB and Munro datasets were statistically equivalent.•Compared and contrasted the chemistry of the two datasets to rationalise any (dis)similarities in TTC values.•Study provides increased confidence in the existing TTC values based on the Munro dataset.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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