RepDose and FeDTex: Two databases focusing on systemic toxicity

Bitsch, Annette; Escher, Sylvia; Lewin, Geertje; Melber, Christine; Simetska, N.; Mangelsdorf, Inge

doi:10.1016/j.toxlet.2008.06.598

Cited by 1 publication

(1 citation statement)

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“…Other significant toxicological data resources include the Gene-Tox database from the National Institutes of Health (NIH) U.S. National Library of Medicine (NLM) which comprises mutagenicity data for more than 3,000 chemicals. The RepDose and FeDTex databases (Bitsch et al, 2008) are useful sources for No Observed (Adverse) Effect Level (NO(A)EL) or Lowest Observed (Adverse) Effect Level (LO(A)EL values from repeated dose studies for reproductive and developmental toxicity endpoints. RepDose consists of data on >400 chemicals investigated in 1,018 studies resulting in 6,002 specific effects.…”

Section: Resultsmentioning

confidence: 99%

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

et al. 2019

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A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo ,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.

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Section: Resultsmentioning

confidence: 99%