TY - JOUR
T1 - An alternative QSAR-based approach for predicting the bioconcentration factor for regulatory purposes
AU - Gissi, Andrea
AU - Gadaleta, Domenico
AU - Floris, Matteo
AU - Olla, Stefania
AU - Carotti, Angelo
AU - Novellino, Ettore
AU - Benfenati, Emilio
AU - Nicolotti, Orazio
PY - 2014
Y1 - 2014
N2 - The REACH (Registration, Evaluation, Authorization and restriction of Chemicals) and BPR (Biocidal Product Regulation) regulations strongly promote the use of non-animal testing techniques to evaluate chemical risk. This has renewed the interest towards alternative methods such as QSAR in the regulatory context. The assessment of bioconcentration factor (BCF) required by these regulations is expensive, in terms of costs, time, and laboratory animal sacrifices. Herein, we present QSAR models based on the ANTARES dataset, which is a large collection of known and verified experimental BCF data. Among the models developed, the best results were obtained from a nine-descriptor highly predictive model. This model was derived from a training set of 608 chemicals and challenged against a validation and blind set containing 152 and 76 chemicals, respectively. The model's robustness was further controlled through several validation strategies and the implementation of a multi-step approach for the applicability domain. Suitable safety margins were used to increase sensitivity. The easy interpretability of the model is ensured by the use of meaningful biokinetics descriptors. The satisfactory predictive power for external compounds suggests that the new models could represent a reliable alternative to the in vivo assay, helping the registrants to fulfill regulatory requirements in compliance with the ethical and economic necessity to reduce animal testing.
AB - The REACH (Registration, Evaluation, Authorization and restriction of Chemicals) and BPR (Biocidal Product Regulation) regulations strongly promote the use of non-animal testing techniques to evaluate chemical risk. This has renewed the interest towards alternative methods such as QSAR in the regulatory context. The assessment of bioconcentration factor (BCF) required by these regulations is expensive, in terms of costs, time, and laboratory animal sacrifices. Herein, we present QSAR models based on the ANTARES dataset, which is a large collection of known and verified experimental BCF data. Among the models developed, the best results were obtained from a nine-descriptor highly predictive model. This model was derived from a training set of 608 chemicals and challenged against a validation and blind set containing 152 and 76 chemicals, respectively. The model's robustness was further controlled through several validation strategies and the implementation of a multi-step approach for the applicability domain. Suitable safety margins were used to increase sensitivity. The easy interpretability of the model is ensured by the use of meaningful biokinetics descriptors. The satisfactory predictive power for external compounds suggests that the new models could represent a reliable alternative to the in vivo assay, helping the registrants to fulfill regulatory requirements in compliance with the ethical and economic necessity to reduce animal testing.
KW - Applicability domain
KW - Bioconcentration factor
KW - Biokinetics descriptors
KW - BPR
KW - QSAR
KW - REACH
UR - http://www.scopus.com/inward/record.url?scp=84893069416&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893069416&partnerID=8YFLogxK
U2 - 10.14573/altex.1305221
DO - 10.14573/altex.1305221
M3 - Article
C2 - 24247988
AN - SCOPUS:84893069416
VL - 31
SP - 23
EP - 36
JO - ALTEX : Alternativen zu Tierexperimenten
JF - ALTEX : Alternativen zu Tierexperimenten
SN - 1868-596X
IS - 1
ER -