Computational Toxicology Methods in Public Health Practice

Demchuk, Eugene; Ruíz, Patricia; Wilson, J. Deanna; Scinicariello, Franco; Pohl, Hana R.; Fay, Mike; Mumtaz, Moiz; Hansen, Henrik; Rosa, Christopher T. De

doi:10.1080/15376510701857148

Cited by 23 publications

(5 citation statements)

References 68 publications

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“…In their harmonized approach for risk assessment, the EFSA warned of the impact of metabolism polymorphisms on cancer susceptibility, which can lead to a more than 10‐fold difference in the internal dose of the substance. Acetaldehyde is often provided as an exemplary substance for genetic aspects in toxicology [101] and, as detailed above, there is an excellently characterized functional polymorphism in the major route for the elimination of acetaldehyde. However, no established safety factor or genetic epidemiological proof for the risk associated with acetaldehyde outside ethanol metabolism is available in the literature.…”

Section: Discussionmentioning

confidence: 99%

Carcinogenicity of acetaldehyde in alcoholic beverages: risk assessment outside ethanol metabolism

2009

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The life-time cancer risks for acetaldehyde from alcoholic beverages greatly exceed the usual limits for cancer risks from the environment set between 1 : 10,000 and 1 : 1,000,000. Alcohol consumption has thus been identified as a direct source of acetaldehyde exposure, which in conjunction with other sources (food flavourings, tobacco) results in a magnitude of risk requiring intervention. An initial public health measure could be to reduce the acetaldehyde content in alcoholic beverages as low as technologically possible, and to restrict its use as a food flavour additive.

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Section: Discussionmentioning

confidence: 99%

Carcinogenicity of acetaldehyde in alcoholic beverages: risk assessment outside ethanol metabolism

2009

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“…How much the HWS chemicals are representative of chemicals in trade and manufacturing remains to be seen. Perhaps, they represent 2%–5% of the total amount [51]. Caution is advised when generalizing from the HSW numbers to ambient environment.…”

Section: Resultsmentioning

confidence: 99%

Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes

et al. 2012

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An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals-drugs, pesticides, and environmental pollutants-interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. OPEN ACCESSMolecules 2012, 17 3384In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D 13 C and 1D 15 N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D 13 C-NMR and 15 N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold 2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

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“…Structure activity relationship (SAR), benchmark dose (BMD), and physiologically based pharmacokinetic (PBPK) models are some of the tools used in this approach (El-Masri et al, 2002;Demchuk et al, 2008). This field relies heavily on biomedical judgment and generalizable knowledge gained from decades of experimental and health risk assessment research.…”

Section: Computational Toxicological Methodsmentioning

confidence: 99%