Conditional Toxicity Value (CTV) Predictor: An
            <i>In Silico</i>
            Approach for Generating Quantitative Risk Estimates for Chemicals

Wignall, Jessica A.; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z.; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A.

doi:10.1289/ehp2998

Cited by 59 publications

(91 citation statements)

References 41 publications

(51 reference statements)

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“…The cumulative noncarcinogenic risk for multiple pollutants was calculated as

, where

stands for the number of assessed pollutants. The

values were either retrieved from various databases/documents (Excel Table S8) or estimated by the CTV software ( http://toxvalue.org ; Wignall et al. 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…QSAR models in the CTV software ( http://toxvalue.org ; Wignall et al. 2018 ) were also adopted to predict the

values (

) for pollutants with unavailable NOAEL and LOAEL data.…”

Section: Methodsmentioning

confidence: 99%

“…2018 ) were also adopted to predict the

values (

) for pollutants with unavailable NOAEL and LOAEL data. The QSAR model for

prediction had a mean error of units and its applicability domain covers

of environmental chemicals ( Wignall et al. 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…2018 ). The QSAR model for

prediction has a mean prediction error of 0.92

units ( Wignall et al. 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…Wignall et al. ( 2018 ) developed conditional toxicity value (CTV) software based on quantitative structure–activity relationship (QSAR) models for predicting toxicity threshold values such as

, which can be employed to fill the data gap encountered in HRAs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas

Tian

Shang

et al. 2020

Environ Health Perspect

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Background: Groundwater is a main drinking-water source for Chinese rural residents. The overall pollution status of organic micropollutants (OMPs) and metals in the groundwater and corresponding health risks are unknown. Objectives: Our objective was to comprehensively screen for and assess the health risks of OMPs and metals in groundwater of rural areas in China where groundwater is used for drinking so as to provide a benchmark for monitoring and improving groundwater quality in future developments. Methods: One hundred sixty-six groundwater samples were collected in the rural areas of China, and 1,300 OMPs and 25 metals were screened by GC-MS, LC-QTOF/MS, and ICP-MS analysis. To assess the noncarcinogenic and carcinogenic risks of the detected pollutants, missing toxicity threshold values were extrapolated from existing databases or predicted by quantitative structure–activity relationship (QSAR) models. Monte Carlo simulation was performed to account for uncertainties in the exposure parameters and toxicity thresholds. Results: Two hundred thirty-three OMPs and 25 metals were detected from the 166 samples. The concentration summation for the detected OMPs ranged from 2.9 to among the different sampling sites. Cumulative noncarcinogenic risks for the OMPs were estimated to be negligible. However, high metal risks were calculated in 23% of the sites. Forty-two carcinogens (including 38 OMPs) were identified and the cumulative carcinogenic risks in 34% of the sites were calculated to be (i.e., one excess cancer case in a population of 10 thousand people). The carcinogenic risks were estimated to be mainly associated with exposures to the metals, which were calculated to contribute 79% (0–100%) of the cumulative carcinogenic risks. Discussion: The overall status of OMPs and metals pollution in the groundwater and the corresponding health risks were determined preliminarily, which may provide a benchmark for future efforts in China to ensure the safety of drinking water for the local residents in rural areas. The joint application of QSARs and Monte Carlo simulation provided a feasible way to comprehensively assess the health risks of the large and ever-increasing number of pollutants detected in the aquatic environment. https://doi.org/10.1289/EHP6483

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“…The cumulative noncarcinogenic risk for multiple pollutants was calculated as

, where

stands for the number of assessed pollutants. The

values were either retrieved from various databases/documents (Excel Table S8) or estimated by the CTV software ( http://toxvalue.org ; Wignall et al. 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…QSAR models in the CTV software ( http://toxvalue.org ; Wignall et al. 2018 ) were also adopted to predict the

values (

) for pollutants with unavailable NOAEL and LOAEL data.…”

Section: Methodsmentioning

confidence: 99%

“…2018 ) were also adopted to predict the

values (

) for pollutants with unavailable NOAEL and LOAEL data. The QSAR model for

prediction had a mean error of units and its applicability domain covers

of environmental chemicals ( Wignall et al. 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…2018 ). The QSAR model for

prediction has a mean prediction error of 0.92

units ( Wignall et al. 2018 ).…”

Section: Methodsmentioning

confidence: 99%

, which can be employed to fill the data gap encountered in HRAs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas

Tian

Shang

et al. 2020

Environ Health Perspect

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Toward a Universal Acute Fish Threshold of Toxicological Concern

Plugge

Das

Kostal

2021

Enviro Toxic and Chemistry

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Threshold of toxicological concern (TTC) is a concept that has been around for decades in human health sciences. Ecotoxicology recently adopted a variant of this concept as eco-TTC. Adoption of the concept of TTC considerably reduces the amount of animal testing required for regulatory purposes. We provide an application of a universal TTC for the entirety of acute fish toxicity data (i.e., establishment of an exposure level below which there would be minimal probability of acute fish toxicity for any chemical, without consideration of mechanism of action). We calculated TTC values for a number of subgroups using various approaches. These approaches were evaluated using data from a cohort of 69 999 acute fish toxicological assays. This database was normalized/curated for units, exposure duration, quality assurance/control, and duplicates, which reduced it to 47 694 assays. Data were not normally but log-normally distributed, making geometric means the most appropriate statistical parameter. Thus, we developed descriptive statistics using geometric means with 95, 99, and 99.9% confidence intervals. Various assessment factors (akin to predicted-no-effect concentration derivation) were applied to the geometric means to derive TTCs. Other approaches employed were the calculation of y = 0 intercepts as well as development of 95 and 99.75% cutoffs of cumulative data as well as modular uncertainty scoring tool (MUST) analysis. All of the methodologies derived highly congruent TTCs ranging from to 2 to 8 μg/L except for the 99.75th percentile cutoff of 0.3 μg/L. The data would be most useful in making a binary testing/no testing required decision. For acute fish toxicity, a TTC value of 2 μg/L was most appropriate, based on the 95th percentile of data distribution without any assessment factor.

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A comparison between 2D and 3D descriptors in QSAR modeling based on bio‐active conformations

Senderowitz

Bahia

Kaspi

et al. 2023

Molecular Informatics

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QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

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Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals

Cited by 59 publications

References 41 publications

Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas

Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas

Toward a Universal Acute Fish Threshold of Toxicological Concern

A comparison between 2D and 3D descriptors in QSAR modeling based on bio‐active conformations

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