Finding the Data to Develop and Evaluate (Q)SARs and Populate Categories for Toxicity Prediction

Abstract

This chapter describes the sources of data for in silico modelling. It is assumed that the modeller will not normally have the facilities to experimentally determine toxicological data, thus they must rely on existing data. Data can be obtained from in-house sources (e.g. for industry) or from publicly available databases and the scientific literature. For the publicly available data, the sources of toxicologically information and the relevant advantages and disadvantages are defined. The sources include “well-established” datasets and the use of literature searching, through to the use of databases and more global (meta) data portals which call on a number of databases. To use the data collected efficiently, the modeller must define the required endpoint, allow the nature of the data to drive the modelling approach and control the quality of the data and implications for that on in silico models.

In silico toxicology: computational methods for the prediction of chemical toxicity

“…While large datasets may not be readily available, it is possible to gather data from various public resources: prepared datasets suitable for QSAR modeling such as DSSTox (http://www.epa.gov/comptox/dsstox/), databases that contain toxicity data such as ECOTOX (http://cfpub.epa.gov/ecotox/), global search engines that retrieve data from several databases such as ToxNet (http://toxnet.nlm.nih.gov/), and scientific literature. A discussion of advantages and limitations of these resources in addition to case studies on obtaining toxicity data are available in Ref . Examples and descriptions of many toxicity databases are provided in Refs .…”

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties

“…solubility limitations for compounds studies, use of solvents, alternative routes of exposure, etc.) [101]. Weight of evidence approaches may assist in cases where highly variable or contradictory data are recorded, resulting in higher confidence being assigned to a particular value or identifying data that should be excluded.…”

In silicomodels for drug-induced liver injury – current status

“…Some of the main databases are listed and described elsewhere [78]. Of these, the following may be of particular interest: the WOMBAT-PK 2010 database contains 9450 clinical pharmacokinetic measurements for 1260 entries [79]; the Registry of Toxic Effects of Chemical Substances (RTECS) database is a compilation of basic toxicity information (acute toxicity, tumorigencity, mutagenicity, skin and eye irritation, reproductive effects and multiple dose effects) for 169,000 compounds [80]; the BioPrint database contains information for 2450 compounds and is composed of three main data sets: i) chemical descriptors, ii) in-house-generated in vitro pharmacology and ADME data and iii) collected and curated in vivo effects of drugs [81]; the Leadscope database comprises more than 160,000 chemical structures with toxicity data, such as acute, multiple dose studies including subchronic liver, carcinogenicity, genetic toxicity, reproductive and irritation [82]; Lhasa Ltd's Vitic Nexus (VX) database holds records about carcinogenicity, mutagenicity, genetic toxicity, hERG, hepatotoxicity, skin sensitisation and irritation for 12,461 structures [83]; and the ChEMBL database of bioactive drug-like small molecules, which contains 2D structures, calculated properties and abstracted bioactivities [84].…”