Chapter 3
Modelling Simple Toxicity Endpoints:
Alerts, (Q)SARs and Beyond
Richard Williams, Martyn Chilton, Donna Macmillan, Alex Cayley,
Lilia Fisk and Mukesh Patel
Abstract The correlation of chemical structure with physicochemical and biological
data to assess a desired or undesired biological outcome now utilises both qualitative
and quantitative structure–activity relationships ((Q)SARs) and advanced computational methods. The adoption of in silico methodologies for predicting toxicity, as
decision support tools, is now a common practice in both developmental and regulatory contexts for certain toxicity endpoints. The relative success of these tools has
unveiled further challenges relating to interpreting and applying the results of models. These include the concept of what makes a negative prediction and exploring the
use of test data to make quantitative predictions. Due to several factors, including
the lack of understanding of mechanistic pathways in biological systems, modelling
complex endpoints such as organ toxicity brings new challenges. The use of the
adverse outcome pathway (AOP) framework as a construct to arrange models and
data, to tackle such challenges, is reviewed.
Keywords QSAR · Expert systems · Mutagenicity · Skin sensitisation · Negative
predictions · Defined approach · Hepatotoxicity · AOP · MIE
Abbreviations
(Q)SAR (Quantitative) structure–activity relationship
ADME Adsorption, distribution, metabolism, and excretion
AOP
Adverse outcome pathway
BSEP
Bile salt export pump
DA
Defined approach
DMSO Dimethyl sulphoxide
DNA
Deoxyribonucleic acid
DPRA Direct peptide reactivity assay
R. Williams (B) · M. Chilton · D. Macmillan · A. Cayley · L. Fisk · M. Patel
Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, UK
e-mail: Richard.Williams@lhasalimited.org
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_3
37
Modelling Simple Toxicity Endpoints:
Alerts, (Q)SARs and Beyond
Richard Williams, Martyn Chilton, Donna Macmillan, Alex Cayley,
Lilia Fisk and Mukesh Patel
Abstract The correlation of chemical structure with physicochemical and biological
data to assess a desired or undesired biological outcome now utilises both qualitative
and quantitative structure–activity relationships ((Q)SARs) and advanced computational methods. The adoption of in silico methodologies for predicting toxicity, as
decision support tools, is now a common practice in both developmental and regulatory contexts for certain toxicity endpoints. The relative success of these tools has
unveiled further challenges relating to interpreting and applying the results of models. These include the concept of what makes a negative prediction and exploring the
use of test data to make quantitative predictions. Due to several factors, including
the lack of understanding of mechanistic pathways in biological systems, modelling
complex endpoints such as organ toxicity brings new challenges. The use of the
adverse outcome pathway (AOP) framework as a construct to arrange models and
data, to tackle such challenges, is reviewed.
Keywords QSAR · Expert systems · Mutagenicity · Skin sensitisation · Negative
predictions · Defined approach · Hepatotoxicity · AOP · MIE
Abbreviations
(Q)SAR (Quantitative) structure–activity relationship
ADME Adsorption, distribution, metabolism, and excretion
AOP
Adverse outcome pathway
BSEP
Bile salt export pump
DA
Defined approach
DMSO Dimethyl sulphoxide
DNA
Deoxyribonucleic acid
DPRA Direct peptide reactivity assay
R. Williams (B) · M. Chilton · D. Macmillan · A. Cayley · L. Fisk · M. Patel
Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, UK
e-mail: Richard.Williams@lhasalimited.org
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_3
37