Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.

Recent technological advances in the field of big data have increased our capabilities to query large databases and combine information from different domains and disciplines. In the area of preclinical studies, initiatives like SEND (Standard for Exchange of Nonclinical Data) will also contribute t...

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Autor principal:	Jordi Munoz-Muriedas
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1ea09e5ad16046efa06e994e66ecb1b4
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spelling	oai:doaj.org-article:1ea09e5ad16046efa06e994e66ecb1b42021-12-02T20:07:12ZLarge scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.1932-620310.1371/journal.pone.0252533https://doaj.org/article/1ea09e5ad16046efa06e994e66ecb1b42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252533https://doaj.org/toc/1932-6203Recent technological advances in the field of big data have increased our capabilities to query large databases and combine information from different domains and disciplines. In the area of preclinical studies, initiatives like SEND (Standard for Exchange of Nonclinical Data) will also contribute to collect and present nonclinical data in a consistent manner and increase analytical possibilities. With facilitated access to preclinical data and improvements in analytical algorithms there will surely be an expectation for organisations to ensure all the historical data available to them is leveraged to build new hypotheses. These kinds of analyses may soon become as important as the animal studies themselves, in addition to being critical components to achieve objectives aligned with 3Rs. This article proposes the application of meta-analyses at large scale in corporate databases as a tool to exploit data from both preclinical studies and in vitro pharmacological activity assays to identify associations between targets and tissues that can be used as seeds for the development of causal hypotheses to characterise of targets. A total of 833 in-house preclinical toxicity studies relating to 416 compounds reported to be active (pXC50 ≥ 5.5) against a panel of 96 selected targets of interest for potential off-target non desired effects were meta-analysed, aggregating the data in tissue-target pairs. The primary outcome was the odds ratio (OR) of the number of animals with observed events (any morphology, any severity) in treated and control groups in the tissue analysed. This led to a total of 2139 meta-analyses producing a total of 364 statistically significant associations (random effects model), 121 after adjusting by multiple comparison bias. The results show the utility of the proposed approach to leverage historical corporate data and may offer a vehicle for researchers to share, aggregate and analyse their preclinical toxicological data in precompetitive environments.Jordi Munoz-MuriedasPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252533 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jordi Munoz-Muriedas Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.
description	Recent technological advances in the field of big data have increased our capabilities to query large databases and combine information from different domains and disciplines. In the area of preclinical studies, initiatives like SEND (Standard for Exchange of Nonclinical Data) will also contribute to collect and present nonclinical data in a consistent manner and increase analytical possibilities. With facilitated access to preclinical data and improvements in analytical algorithms there will surely be an expectation for organisations to ensure all the historical data available to them is leveraged to build new hypotheses. These kinds of analyses may soon become as important as the animal studies themselves, in addition to being critical components to achieve objectives aligned with 3Rs. This article proposes the application of meta-analyses at large scale in corporate databases as a tool to exploit data from both preclinical studies and in vitro pharmacological activity assays to identify associations between targets and tissues that can be used as seeds for the development of causal hypotheses to characterise of targets. A total of 833 in-house preclinical toxicity studies relating to 416 compounds reported to be active (pXC50 ≥ 5.5) against a panel of 96 selected targets of interest for potential off-target non desired effects were meta-analysed, aggregating the data in tissue-target pairs. The primary outcome was the odds ratio (OR) of the number of animals with observed events (any morphology, any severity) in treated and control groups in the tissue analysed. This led to a total of 2139 meta-analyses producing a total of 364 statistically significant associations (random effects model), 121 after adjusting by multiple comparison bias. The results show the utility of the proposed approach to leverage historical corporate data and may offer a vehicle for researchers to share, aggregate and analyse their preclinical toxicological data in precompetitive environments.
format	article
author	Jordi Munoz-Muriedas
author_facet	Jordi Munoz-Muriedas
author_sort	Jordi Munoz-Muriedas
title	Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.
title_short	Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.
title_full	Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.
title_fullStr	Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.
title_full_unstemmed	Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.
title_sort	large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1ea09e5ad16046efa06e994e66ecb1b4
work_keys_str_mv	AT jordimunozmuriedas largescalemetaanalysisofpreclinicaltoxicitydatafortargetcharacterisationandhypothesesgeneration
_version_	1718375289396920320

Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.

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