title: Methods for assigning confidence to toxicity data with multiple values--Identifying experimental outliers.
abstract: The assessment of data quality is a crucial element in many disciplines such as predictive toxicology and risk assessment. Currently, the reliability of toxicity data is assessed on the basis of testing information alone (adherence to Good Laboratory Practice (GLP), detailed testing protocols, etc.). Common practice is to take one toxicity data point per compound - usually the one with the apparently highest reliability. All other toxicity data points (for the same experiment and compound) from other sources are neglected. To show the benefits of incorporating the "less reliable" data, a simple, independent, statistical approach to assess data quality and reliability on a mathematical basis was developed. A large data set of toxicity values to Aliivibrio fischeri was assessed. The data set contained 1813 data points for 1227 different compounds, including 203 identified as non-polar narcotic. Log KOW values were calculated and non-polar narcosis quantitative structure-activity relationship (QSAR) models were built. A statistical approach to data quality assessment, which is based on data outlier omission and confidence scoring, improved the linear QSARs. The results indicate that a beneficial method for using large data sets containing multiple data values per compound and highly variable study data has been developed. Furthermore this statistical approach can help to develop novel QSARs and support risk assessment by obtaining more reliable values for biological endpoints.
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England, United Kingdom.
authors.forename: Fabian P
authors.initials: FP
authors.lastname: Steinmetz
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England, United Kingdom.
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Enoch
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England, United Kingdom.
authors.forename: Judith C
authors.initials: JC
authors.lastname: Madden
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England, United Kingdom.
authors.forename: Mark D
authors.initials: MD
authors.lastname: Nelms
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England, United Kingdom.
authors.forename: Neus
authors.initials: N
authors.lastname: Rodriguez-Sanchez
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England, United Kingdom.
authors.forename: Phil H
authors.initials: PH
authors.lastname: Rowe
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England, United Kingdom; School of Environmental Sciences, Northeast Normal University, Changchun, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Wen
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England, United Kingdom. Electronic address: M.T.Cronin@ljmu.ac.uk.
authors.forename: Mark T D
authors.initials: MT
authors.lastname: Cronin
mesh_terms: D016001:Confidence Intervals; D015386:Hazardous Substances / Q000633:toxicity*; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D013223:Statistics as Topic; D018675:Toxicity Tests / Q000379:methods*
chemical_list: D015386:Hazardous Substances
keywords: Confidence; Conflicting data; Data quality; Microtox; Predictive toxicology; QSAR