Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes

Dang, Na Le; Hughes, Tyler B.; Miller, Grover P.; Swamidass, S. Joshua

doi:10.1021/acs.chemrestox.6b00336

Cited by 35 publications

(56 citation statements)

References 110 publications

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“…2016), involving the physicochemical and reactivity properties of the chemicals [read-across and quantitative structure–activity relationships (QSARs)]. Both are important for the interactions with specific biological targets and pathways, and therefore allow the prediction of toxic effects (Dang et al. 2017; Zang et al.…”

Section: Discussionmentioning

confidence: 99%

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

Carvaillo

Barouki

Coumoul

et al. 2019

Environ Health Perspect

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Background:Available toxicity data can be optimally interpreted if they are integrated using computational approaches such as systems biology modeling. Such approaches are particularly warranted in cases where regulatory decisions have to be made rapidly.Objectives:The study aims at developing and applying a new integrative computational strategy to identify associations between bisphenol S (BPS), a substitute for bisphenol A (BPA), and components of adverse outcome pathways (AOPs).Methods:The proposed approach combines a text mining (TM) procedure and integrative systems biology to comprehensively analyze the scientific literature to enrich AOPs related to environmental stressors. First, to identify relevant associations between BPS and different AOP components, a list of abstracts was screened using the developed text-mining tool AOP-helpFinder, which calculates scores based on the graph theory to prioritize the findings. Then, to fill gaps between BPS, biological events, and adverse outcomes (AOs), a systems biology approach was used to integrate information from the AOP-Wiki and ToxCast databases, followed by manual curation of the relevant publications.Results:Links between BPS and 48 AOP key events (KEs) were identified and scored via 31 references. The main outcomes were related to reproductive health, endocrine disruption, impairments of metabolism, and obesity. We then explicitly analyzed co-mention of the terms BPS and obesity by data integration and manual curation of the full text of the publications. Several molecular and cellular pathways were identified, which allowed the proposal of a biological explanation for the association between BPS and obesity.Conclusions:By analyzing dispersed information from the literature and databases, our novel approach can identify links between stressors and AOP KEs. The findings associating BPS and obesity illustrate the use of computational tools in predictive toxicology and highlight the relevance of the approach to decision makers assessing substituents to toxic chemicals. https://doi.org/10.1289/EHP4200

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Section: Discussionmentioning

confidence: 99%

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

Carvaillo

Barouki

Coumoul

et al. 2019

Environ Health Perspect

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“…13,14 Likewise, alert structures are commonly used to identify liabilities in molecule structure, 15–17 and we previously demonstrated that metabolism models can improve the specificity of alerts. 18 The hope is that modeling of metabolism and reactivity will improve substantially on purely statistical approaches to understanding toxicity, as are commonly relied upon within industry and regulatory agencies. 19 Metabolism modeling with machine learning does not produce detailed enzyme mechanics, but they do provide finer grain information that is currently used, and for this reason are a significant step forward.…”

Section: Introductionmentioning

confidence: 99%

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation

Dang

Hughes

Miller

et al. 2018

Chem. Res. Toxicol.

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Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we modeled N-dealkylations that produce both amine and aldehyde metabolites and then predicted the reactivity of the aldehyde. This model used a deep learning approach previously developed by our group to predict other types of drug metabolism. In this study, we trained the model to predict N-dealkylation by human liver microsomes (HLM), finding that including isozyme-specific metabolism data alongside HLM data significantly improved results. The final HLM model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve). Next, we combined the metabolism, metabolite structure prediction, and previously published reactivity models into a bioactivation model. This combined model predicted the structure of the most likely reactive metabolite of a small validation set of drug-like molecules known to be bioactivated by N-dealkylation. Applying this model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. Our results suggest that N-dealkylation may be an under-appreciated bioactivation pathway, especially in clinical contexts where aldehyde detoxification pathways are inhibited. Moreover, this is the first report of a bioactivation model constructed by combining a metabolism and reactivity model. These results raise hope that more comprehensive models of bioactivation are possible. The model developed in this study is available at http://swami.wustl.edu/xenosite/ .

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“…2 ; Table 1 ; Supplementary Table 1 ). However, it is also reported that the occurrence of the drug-induced toxicities depends on many factors such as dosage, metabolites reactivity, structural alert metabolism, competition for detoxification pathways, and individual differences between patients [ 3 , 22 – 24 ]. The results of the analyses have also shown that the majority of the drugs (72%) were potentially reactive, unstable or toxic, and 70% of them were also potentially toxic at normal treatment concentrations, based on their doses or LD-50 values evaluation (Supplementary Table 1 , Fig.…”

Section: Discussionmentioning

confidence: 99%

“…This shows that structural alerts do not predict metabolism and toxicity adequately. Therefore, some medicines may be designated as safe or unsafe when actually it is not true [ 3 , 22 ]. The drug-induced toxicities caused by structural alerts and reactive metabolites may be caused by either covalent interactions or noncovalent interactions with cellular macromolecules such as DNA, proteins and lipids [ 27 ], but in many cases their exact mechanism is not known [ 3 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities

Chikowe

Phiri

Mbewe

et al. 2021

BMC Pharmacol Toxicol

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Background Drug-induced toxicity is one of the problems that have negatively impacted on the well-being of populations throughout the world, including Malawi. It results in unnecessary hospitalizations, retarding the development of the country. This study assessed the Malawi Essential Medicines List (MEML) for structural alerts and reactive metabolites with the potential for drug-induced toxicities. Methods This in-silico screening study used StopTox, ToxAlerts and LD-50 values toxicity models to assess the MEML drugs. A total of 296 drugs qualified for the analysis (those that had defined chemical structures) and were screened in each software programme. Each model had its own toxicity endpoints and the models were compared for consensus of their results. Results In the StopTox model, 86% of the drugs had potential to cause at least one toxicity including 55% that had the potential of causing eye irritation and corrosion. In ToxAlerts, 90% of the drugs had the potential of causing at least one toxicity and 72% were found to be potentially reactive, unstable and toxic. In LD-50, 70% of the drugs were potentially toxic. Model consensus evaluation results showed that the highest consensus was observed between ToxAlerts and StopTox (80%). The overall consensus amongst the three models was 57% and statistically significant (p < 0.05). Conclusions A large number of drugs had the potential to cause various systemic toxicities. But the results need to be interpreted cautiously since the clinical translation of QSAR-based predictions depends on many factors. In addition, inconsistencies have been reported between screening results amongst different models.

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Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes

Cited by 35 publications

References 110 publications

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation

In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities

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