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Modelling Simple Toxicity Endpoints: Alerts, (Q)SARs and Beyond

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Advances in Computational Toxicology

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 30))

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.

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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

EC3:

Effective concentration to cause a threefold increase in T-cell proliferation

GHS:

Globally harmonised system

GPMT:

Guinea pig maximisation test

h-CLAT:

Human cell line activation test

IATA:

Integrated approach to testing and assessment

KE:

Key event

kNN:

k-Nearest neighbours

LLNA:

Local lymph node assay

MIE:

Molecular initiating event

MW:

Molecular weight

OATP:

Organic anion transporting polypeptide

OECD:

Organisation for Economic Co-operation and Development

PPAR:

Peroxisome proliferator-activated receptor

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Authors and Affiliations

  1. Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK

    Richard Williams, Martyn Chilton, Donna Macmillan, Alex Cayley, Lilia Fisk & Mukesh Patel

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  1. Richard Williams
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  2. Martyn Chilton
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Correspondence to Richard Williams .

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  1. National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

    Huixiao Hong

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Williams, R., Chilton, M., Macmillan, D., Cayley, A., Fisk, L., Patel, M. (2019). Modelling Simple Toxicity Endpoints: Alerts, (Q)SARs and Beyond. In: Hong, H. (eds) Advances in Computational Toxicology. Challenges and Advances in Computational Chemistry and Physics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-16443-0_3

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