TY - JOUR

T1 - Using conditional inference trees and random forests to predict the bioaccumulation potential of organic chemicals

AU - Strempel, Sebastian

AU - Nendza, Monika

AU - Scheringer, Martin

AU - Hungerbühler, Konrad

N1 - Copyright © 2013 SETAC.

PY - 2013/5

Y1 - 2013/5

N2 - The present study presents a data-oriented, tiered approach to assessing the bioaccumulation potential of chemicals according to the European chemicals regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). The authors compiled data for eight physicochemical descriptors (partition coefficients, degradation half-lives, polarity, and so forth) for a set of 713 organic chemicals for which experimental values of the bioconcentration factor (BCF) are available. The authors employed supervised machine learning methods (conditional inference trees and random forests) to derive relationships between the physicochemical descriptors and the BCF values. In a first tier, the authors established rules for classifying a chemical as bioaccumulative (B) or nonbioaccumulative (non-B). In a second tier, the authors developed a new tool for estimating numerical BCF values. For both cases the optimal set of relevant descriptors was determined; these are biotransformation half-life and octanol-water distribution coefficient (log D) for the classification rules and log D, biotransformation half-life, and topological polar surface area for the BCF estimation tool. The uncertainty of the BCF estimates obtained with the new estimation tool was quantified by comparing the estimated and experimental BCF values of the 713 chemicals. Comparison with existing BCF estimation methods indicates that the performance of this new BCF estimation tool is at least as high as that of existing methods. The authors recommend the present study's classification rules and BCF estimation tool for a consensus application in combination with existing BCF estimation methods.

AB - The present study presents a data-oriented, tiered approach to assessing the bioaccumulation potential of chemicals according to the European chemicals regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). The authors compiled data for eight physicochemical descriptors (partition coefficients, degradation half-lives, polarity, and so forth) for a set of 713 organic chemicals for which experimental values of the bioconcentration factor (BCF) are available. The authors employed supervised machine learning methods (conditional inference trees and random forests) to derive relationships between the physicochemical descriptors and the BCF values. In a first tier, the authors established rules for classifying a chemical as bioaccumulative (B) or nonbioaccumulative (non-B). In a second tier, the authors developed a new tool for estimating numerical BCF values. For both cases the optimal set of relevant descriptors was determined; these are biotransformation half-life and octanol-water distribution coefficient (log D) for the classification rules and log D, biotransformation half-life, and topological polar surface area for the BCF estimation tool. The uncertainty of the BCF estimates obtained with the new estimation tool was quantified by comparing the estimated and experimental BCF values of the 713 chemicals. Comparison with existing BCF estimation methods indicates that the performance of this new BCF estimation tool is at least as high as that of existing methods. The authors recommend the present study's classification rules and BCF estimation tool for a consensus application in combination with existing BCF estimation methods.

KW - Chemistry

KW - Biotransformation

KW - Environmental Monitoring

KW - Environmental Pollutants

KW - Environmental Pollution

KW - Half-Life

KW - Organic Chemicals

KW - Trees

UR - http://www.scopus.com/inward/record.url?scp=84876417882&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/8a2d8b7d-fd00-39e5-b703-9cb2adca3cd6/

U2 - 10.1002/etc.2150

DO - 10.1002/etc.2150

M3 - Journal articles

C2 - 23382013

VL - 32

SP - 1187

EP - 1195

JO - Environmental Toxicology and Chemistry

JF - Environmental Toxicology and Chemistry

SN - 0730-7268

IS - 5

ER -