Review: Toxicometabolomics

Bouhifd, Mounir; Hartung, Thomas; Högberg, Helena T.; Kleensang, André; Zhao, Liang

doi:10.1002/jat.2874

Cited by 155 publications

(104 citation statements)

References 210 publications

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“…this demonstrated the limitations of relying on structural similarity alone in read-across prediction. A pioneering example is the use of metabolomics for grouping (Bouhifd et al, 2013). BASF has been developing a database (MetaMap ® tox) in which metabolomics and toxicity data are evaluated in combination (van Ravenzwaay, 2007).…”

Section: Negative Read-acrossmentioning

confidence: 99%

Read-across approaches - misconceptions, promises and challenges ahead

Patlewicz

2014

ALTEX

108

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Summary Read-across is a data gap filling technique used within category and analogue approaches. It has been utilized as an alternative approach to address information requirements under various past and present regulatory programs such as the OECD High Production Volume Programme as well as the EU's Registration, Evaluation, Authorisation and restriction of CHemicals (REACH) regulation. Although read-across raises a number of expectations, many misconceptions still remain around what it truly represents; how to address its associated justification in a robust and scientifically credible manner; what challenges/issues exist in terms of its application and acceptance; and what future efforts are needed to resolve them. In terms of future enhancements, read-across is likely to embrace more biologicallyorientated approaches consistent with the Toxicity in the 21 st Century vision (Tox-21c). This Food forThought article, which is notably not a consensus report, aims to discuss a number of these aspects and, in doing so, to raise awareness of the ongoing efforts and activities to enhance read-across. It also intends to set the agenda for a CAAT read-across initiative in 2014CAAT read-across initiative in -2015 to facilitate the proper use of this technique.

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Section: Negative Read-acrossmentioning

confidence: 99%

Read-across approaches - misconceptions, promises and challenges ahead

Patlewicz

2014

ALTEX

108

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“…Metabolism is involved directly or indirectly with cell function. Metabolomics technologies can be used to examine and identify metabolite changes in endogenous biochemical reactions and identify metabolic pathways and processes occurring within a living cell (Panopoulos et al, 2012;Bouhifd et al, 2013;Ramirez et al, 2013).…”

Section: Medium Conditionsmentioning

confidence: 99%

Good Cell Culture Practice for stem cells and stem-cell-derived models

Pamies

2017

Toxicology Letters

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SummaryThe first guidance on Good Cell Culture Practice (GCCP) dates back to 2005. This document expands this to include aspects of quality assurance for in vitro cell culture focusing on the increasingly diverse cell types and culture formats used in research, product development, testing and manufacture of biotechnology products and cell-based medicines. It provides a set of basic principles of best practice that can be used in training new personnel, reviewing and improving local procedures, and helping to assure standard practices and conditions for the comparison of data between laboratories and experimentation performed at different times. This includes recommendations for the documentation and reporting of culture conditions. It is intended as guidance to facilitate the generation of reliable data from cell culture systems, and is not intended to conflict with local or higher level legislation or regulatory requirements. It may not be possible to meet all recommendations in this guidance for practical, legal or other reasons. However, when it is necessary to divert from the principles of GCCP, the risk of decreasing the quality of work and the safety of laboratory staff should be addressed and any conclusions or alternative approaches justified. This workshop report is considered a first step toward a revised GCCP 2.0.

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“…Systems biology approaches have been developed to analyze large data sets provided by genomics, transcriptomics, proteomics, metabolomics, and protein-protein interaction (PPI) networks (Auerbach et al, 2015;Bouhifd et al, 2013;Church et al, 2014;Ivanov et al, 2007;Kumar et al, 2015;Mohsenizadeh et al, 2015;Spirin and Mirny, 2003;Wang et al, 2014). The challenge is in the integration and interpretation of these complex datasets.…”

Section: Systems Biology and Computational Modelingmentioning

confidence: 99%