Chemoinformatics in Modern Regulatory Science

Yang, Chihae; Rathman, James F.; Tarkhov, Aleksey; Sacher, Oliver; Kleinoeder, Thomas; Liu, Jie; Magdziarz, Thomas; Mostraq, Aleksandra; Marusczyk, Joerg; Mehta, Darshan; Schwab, Christof H.; Bienfait, Bruno

doi:10.1002/9783527806539.ch8

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Molecular weight was considered as an additional descriptor, as calculated directly from formula by EPA's Distributed Structure-Searchable Toxicity (DSSTox) Database 35 . Finally, 729 binary ToxPrint descriptors were used to identify the presence (1) or absence (0) of specific chemical substructures within each structure 36 . ToxPrint descriptors are open-source (freely available) descriptors that are "designed to provide excellent coverage of environmental, regulatory, and commercial-use chemical space" 36 , as opposed to proprietary and/or pharmaceutical-centered chemical descriptors 37 .…”

Section: Mm3: Pathway Prediction Modelsmentioning

confidence: 99%

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Ring

Arnot

Bennett

et al. 2018

Environ. Sci. Technol.

116

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Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study, chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources-consumer (near-field), dietary, far-field industrial, and far-field pesticide-to the general population. The balanced accuracies of these source-based *

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Section: Mm3: Pathway Prediction Modelsmentioning

confidence: 99%

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Ring

Arnot

Bennett

et al. 2018

Environ. Sci. Technol.

116

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show abstract

“…In this situation many approaches to the prediction of toxicity, bioaccumulation, and degradation in the environment have been and still are developed. [76,77] Expert systems for the prediction of toxicity have been around for quite a while but the availability of new high-quality data has allowed the building of new models on toxicity prediction of much higher quality and predictivity. Not surprisingly, methods of machine learning and artificial intelligence have been applied to toxicity prediction.…”

Section: Regulatory Sciencementioning

confidence: 99%

Chemistry in Times of Artificial Intelligence

Gasteiger

2020

ChemPhysChem

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Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.

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“…The chemicals used in this study were characterized using two structure-based fingerprints PubChem 53 and ToxPrint chemotypes 54 ; two physicochemical descriptors (acid dissociation constant, acidic and basic pKa and logarithm of water-octanol partition coefficient, logP) computed using the OPERA software 55 ; 12 molecular descriptors calculated using the Chemistry Development Kit (CDK) 56,57 implemented in KNIME the KNIME analytics platform 58 (version 2.11.3); and 1875 descriptors (1444 1D, 2D and 431 3D descriptors) calculated using PaDEL software 59 . PubChem fingerprints were generated in the KNIME analytics platform 58 (version 2.11.3).…”

Section: Molecular Descriptorsmentioning

confidence: 99%

Using chemical structure information to develop predictive models for in vitro toxicokinetic parameters to inform high-throughput risk-assessment

Pradeep

Patlewicz

Pearce

et al. 2020

Computational Toxicology

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The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitrobased risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure-activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Cl int (classification and regression) using a dataset of 1487 chemicals; utilization of predicted TK parameters to estimate uncertainty in steady-state plasma concentration (C ss ); and subsequent in vitro-in vivo extrapolation analyses to derive bioactivity-exposure ratio (BER) plot to compare human oral equivalent doses and exposure predictions using androgen and estrogen receptor activity data for 233 chemicals as an example dataset. The results demonstrate that fub is structurally more predictable than Cl int . The model with the highest observed performance for fub had an external test set RMSE/σ=0.62 and R 2 =0.61, for Cl int classification had an external test set accuracy = 65.9%, and for intrinsic clearance regression had an external test set RMSE/σ=0.90 and R 2 =0.20. This relatively low performance is in part due to the large uncertainty in the underlying Cl int data. We show that C ss is relatively insensitive to uncertainty in Cl int . The models were benchmarked against the ADMET Predictor software. Finally, the BER analysis allowed identification of 14 out of 136 chemicals for further risk assessment demonstrating the utility of these models in aiding risk-based chemical prioritization.

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Chemoinformatics in Modern Regulatory Science

Cited by 4 publications

References 23 publications

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Chemistry in Times of Artificial Intelligence

Using chemical structure information to develop predictive models for in vitro toxicokinetic parameters to inform high-throughput risk-assessment

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