Pragmatic Approaches to Using Computational Methods To Predict Xenobiotic Metabolism

Piechota, Przemyslaw; Cronin, Mark T.D.; Hewitt, Mark; Madden, Judith C.

doi:10.1021/ci400050v

Cited by 27 publications

(21 citation statements)

References 15 publications

(28 reference statements)

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“…Clearly this is even more vital for liver than most other organs. Whilst considerable progress has been made in the development of metabolic simulators (67), there has still been no true assessment of their reliability to, for example, predict reactive metabolites. Due to the long-term effort in the development of systems for prediction of metabolism, it is likely that they are comprehensive.…”

Section: An Example Of In Silico Modelling: Development Of Structuralmentioning

confidence: 99%

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

et al. 2017

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In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

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Section: An Example Of In Silico Modelling: Development Of Structuralmentioning

confidence: 99%

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

et al. 2017

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“…For example, the influence of external exposure conditions can be modeled using chemical fate and speciation models (Mackay and Webster, 2003;Fu et al, 2009;Rendal et al, 2011;Arnot et al, 2012;Hendren et al, 2013). A wide variety of chemical structure-based in silico prediction models are available that allow evaluating certain ADME characteristics (Kulkarni et al, 2005;Gleeson, 2008;Yang et al, 2012;Piechota et al, 2013) or a chemical's ability to interact with a particular MIE-associated target (Ellison et al, 2011;Enoch and Cronin, 2012;Rana et al, 2012;Vedani et al, 2012;Vinken, 2013;Wu et al, 2013). Dedicated web-based computational platforms are being continuously developed further (Jeliazkova, 2012).…”

Section: Integration With Computational Modelsmentioning

confidence: 99%

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

et al. 2015

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To elucidate the effects of chemicals on populations of different species in the environment, efficient testing and modeling approaches are needed that consider multiple stressors and allow reliable extrapolation of responses across species. An adverse outcome pathway (AOP) is a concept that provides a framework for organizing knowledge about the progression of toxicity events across scales of biological organization that lead to adverse outcomes relevant for risk assessment. In this paper, we focus on exploring how the AOP concept can be used to guide research aimed at improving both our understanding of chronic toxicity, including delayed toxicity as well as epigenetic and transgenerational effects of chemicals, and our ability to predict adverse outcomes. A better understanding of the influence of subtle toxicity on individual and population fitness would support a broader integration of sublethal endpoints into risk assessment frameworks. Detailed mechanistic knowledge would facilitate the development of alternative testing methods as well as help prioritize higher tier toxicity testing. We argue that targeted development of AOPs supports both of these aspects by promoting the elucidation of molecular mechanisms and their contribution to relevant toxicity outcomes across biological scales. We further discuss information requirements and challenges in application of AOPs for chemical- and site-specific risk assessment and for extrapolation across species. We provide recommendations for potential extension of the AOP framework to incorporate information on exposure, toxicokinetics and situation-specific ecological contexts, and discuss common interfaces that can be employed to couple AOPs with computational modeling approaches and with evolutionary life history theory. The extended AOP framework can serve as a venue for integration of knowledge derived from various sources, including empirical data as well as molecular, quantitative and evolutionary-based models describing species responses to toxicants. This will allow a more efficient application of AOP knowledge for quantitative chemical- and site-specific risk assessment as well as for extrapolation across species in the future.

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“…Meteor and other in silico metabolism programs are known to over predict the number of possible metabolites if less restrictive constraints are used 35,49 . However, having a certain number of false positives (i.e.…”

Section: Resultsmentioning

confidence: 99%

In Silico Enzymatic Synthesis of a 400 000 Compound Biochemical Database for Nontargeted Metabolomics

Menikarachchi

Hill

Hamdalla

et al. 2013

J. Chem. Inf. Model.

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Current methods of structure identification in mass spectrometry based non-targeted metabolomics rely on matching experimentally determined features of an unknown compound to those of candidate compounds contained in biochemical databases. A major limitation of this approach is the relatively small number of compounds currently included in these databases. If the correct structure is not present in a database it cannot be identified, and if it cannot be identified it cannot be included in a database. Thus, there is an urgent need to augment metabolomics databases with rationally designed biochemical structures using alternative means. In this study, we present a database of in silico enzymatically synthesized metabolites (IIMDB) to partially address this problem. The database, which is available from http://metabolomics.pharm.uconn.edu/iimdb/, includes ~23,000 known compounds (mammalian metabolites, drugs, secondary plant metabolites and glycerophospholipids) collected from existing biochemical databases plus more than 400,000 computationally generated human phase I and phase II metabolites of these known compounds. The IIMDB database features a user-friendly web interface and a programmer-friendly RESTful web service. Ninety-five percent of the computationally generated metabolites in IIMDB were not found in any existing database. However, 21,640 were identical to compounds already listed in PubChem, HMDB, KEGG or HumanCyc. Furthermore, a vast majority of these in silico metabolites were scored as biological using BioSM, a software program that identifies biochemical structures in chemical structure space. These results suggest that in silico biochemical synthesis represents a viable approach for significantly augmenting biochemical databases for non-targeted metabolomics applications.

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Pragmatic Approaches to Using Computational Methods To Predict Xenobiotic Metabolism

Cited by 27 publications

References 15 publications

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

In Silico Enzymatic Synthesis of a 400 000 Compound Biochemical Database for Nontargeted Metabolomics

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