Dataset Information

Alarms about structural alerts.

ABSTRACT: Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.

SUBMITTER: Alves V

PROVIDER: S-EPMC5423727 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Alarms about structural alerts.

Alves Vinicius V Muratov Eugene E Capuzzi Stephen S Politi Regina R Low Yen Y Braga Rodolpho R Zakharov Alexey V AV Sedykh Alexander A Mokshyna Elena E Farag Sherif S Andrade Carolina C Kuz'min Victor V Fourches Denis D Tropsha Alexander A

Green chemistry : an international journal and green chemistry resource : GC 20160628 16

Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the t ...[more]

PMID: 28503093

Similar Datasets

Project description:Structural alerts are commonly used in drug discovery to identify molecules likely to form reactive metabolites and thereby become toxic. Unfortunately, as useful as structural alerts are, they do not effectively model if, when, and why metabolism renders safe molecules toxic. Toxicity due to a specific structural alert is highly conditional, depending on the metabolism of the alert, the reactivity of its metabolites, dosage, and competing detoxification pathways. A systems approach, which explicitly models these pathways, could more effectively assess the toxicity risk of drug candidates. In this study, we demonstrated that mathematical models of P450 metabolism can predict the context-specific probability that a structural alert will be bioactivated in a given molecule. This study focuses on the furan, phenol, nitroaromatic, and thiophene alerts. Each of these structural alerts can produce reactive metabolites through certain metabolic pathways but not always. We tested whether our metabolism modeling approach, XenoSite, can predict when a given molecule's alerts will be bioactivated. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. Metabolism models accurately predict whether alerts are bioactivated and thus serve as a practical approach to improve the interpretability and usefulness of structural alerts. We expect that this same computational approach can be extended to most other structural alerts and later integrated into toxicity risk models. This advance is one necessary step toward our long-term goal of building comprehensive metabolic models of bioactivation and detoxification to guide assessment and design of new therapeutic molecules.

Project description:The molecular initiating event for many mechanisms of toxicological action comprise the reactive, covalent binding between an exogenous electrophile and an endogenous nucleophile. The target sites for electrophiles are typically peptides, proteins, enzymes or DNA. Of these, the formation of covalent adducts with proteins and DNA are perhaps the most established as they are most closely associated with skin sensitisation and genotoxicity endpoints. As such, being able to identify electrophilic features within a chemical structure provides a starting point to characterise its reactivity profile. There are a number of software tools that have been developed to help identify structural features indicative of electrophilic reactive potential to address various purposes, including: 1) to facilitate category formation for read-across of toxicity effects such as skin sensitisation potential, as well as 2) to profile substances to identify potential confounding factors to rationalise their activity in high-throughput screening (HTS) assays. Here, three such schemes that have been published in the literature as collections of SMARTS patterns and their associated chemical-biological reaction domains have been compared. The goals are 1) to better understand their scope and coverage, and 2) to assess their performance relative to a published skin sensitisation dataset where manual annotations to assign likely mechanistic domains based on expert judgement were already available. The 3 schemes were then applied to the Tox21 library and the consensus outcome was reported to highlight the proportion of chemicals likely to exhibit a reactivity response, specific to a mechanistic reaction domain, but non-specific with respect to target-tissue based activity. ToxPrint fingerprints were computed and activity enrichments computed to compare the structural features identified for the skin sensitisation dataset and Tox21 chemicals for each 'consensus' reaction domain. Enriched ToxPrints were also used to identify ToxCast assays potentially informative for reactivity.

Dataset Information

Alarms about structural alerts.

Publications

Alarms about structural alerts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets