TY - JOUR
T1 - Evaluation of a Bayesian Network for Strengthening the Weight of Evidence to Predict Acute Fish Toxicity from Fish Embryo Toxicity Data
AU - Lillicrap, Adam
AU - Moe, S. Jannicke
AU - Wolf, Raoul
AU - Connors, Kristin A.
AU - Rawlings, Jane M.
AU - Landis, Wayne G.
AU - Madsen, Anders
AU - Belanger, Scott E.
N1 - Funding Information:
The authors declare no conflicts of interest. The authors acknowledge financial support from the NIVA internal strategic funding initiative (DIGISIS), grant/contract number 160016 for NIVA's basic funding (GB/SIS) from the Norwegian Research Council. No other individuals or organizations were involved with this manuscript.
Publisher Copyright:
© 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC)
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - The use of fish embryo toxicity (FET) data for hazard assessments of chemicals, in place of acute fish toxicity (AFT) data, has long been the goal for many environmental scientists. The FET test was first proposed as a replacement to the standardized AFT test nearly 15 y ago, but as of now, it has still not been accepted as a standalone replacement by regulatory authorities such as the European Chemicals Agency (ECHA). However, the ECHA has indicated that FET data can be used in a weight of evidence (WoE) approach, if enough information is available to support the conclusions related to the hazard assessment. To determine how such a WoE approach could be applied in practice has been challenging. To provide a conclusive WoE for FET data, we have developed a Bayesian network (BN) to incorporate multiple lines of evidence to predict AFT. There are 4 different lines of evidence in this BN model: 1) physicochemical properties, 2) AFT data from chemicals in a similar class or category, 3) ecotoxicity data from other trophic levels of organisms (e.g., daphnids and algae), and 4) measured FET data. The BN model was constructed from data obtained from a curated database and conditional probabilities assigned for the outcomes of each line of evidence. To evaluate the model, 20 data-rich chemicals, containing a minimum of 3 AFT and FET test data points, were selected to ensure a suitable comparison could be performed. The results of the AFT predictions indicated that the BN model could accurately predict the toxicity interval for 80% of the chemicals evaluated. For the remaining chemicals (20%), either daphnids or algae were the most sensitive test species, and for those chemicals, the daphnid or algal hazard data would have driven the environmental classification. Integr Environ Assess Manag 2020;16:452–460.
AB - The use of fish embryo toxicity (FET) data for hazard assessments of chemicals, in place of acute fish toxicity (AFT) data, has long been the goal for many environmental scientists. The FET test was first proposed as a replacement to the standardized AFT test nearly 15 y ago, but as of now, it has still not been accepted as a standalone replacement by regulatory authorities such as the European Chemicals Agency (ECHA). However, the ECHA has indicated that FET data can be used in a weight of evidence (WoE) approach, if enough information is available to support the conclusions related to the hazard assessment. To determine how such a WoE approach could be applied in practice has been challenging. To provide a conclusive WoE for FET data, we have developed a Bayesian network (BN) to incorporate multiple lines of evidence to predict AFT. There are 4 different lines of evidence in this BN model: 1) physicochemical properties, 2) AFT data from chemicals in a similar class or category, 3) ecotoxicity data from other trophic levels of organisms (e.g., daphnids and algae), and 4) measured FET data. The BN model was constructed from data obtained from a curated database and conditional probabilities assigned for the outcomes of each line of evidence. To evaluate the model, 20 data-rich chemicals, containing a minimum of 3 AFT and FET test data points, were selected to ensure a suitable comparison could be performed. The results of the AFT predictions indicated that the BN model could accurately predict the toxicity interval for 80% of the chemicals evaluated. For the remaining chemicals (20%), either daphnids or algae were the most sensitive test species, and for those chemicals, the daphnid or algal hazard data would have driven the environmental classification. Integr Environ Assess Manag 2020;16:452–460.
KW - Acute fish toxicity
KW - Bayesian network
KW - Fish embryo toxicity
KW - Hazard assessment
KW - Weight of evidence
UR - http://www.scopus.com/inward/record.url?scp=85079895575&partnerID=8YFLogxK
U2 - 10.1002/ieam.4258
DO - 10.1002/ieam.4258
M3 - Journal article
C2 - 32125082
AN - SCOPUS:85079895575
SN - 1551-3793
VL - 16
SP - 452
EP - 460
JO - Integrated Environmental Assessment and Management
JF - Integrated Environmental Assessment and Management
IS - 4
ER -