Presentation of the SEURAT-1 COSMOS Project: Prediction of Systemic Toxicity Following

Dermal Exposure

Mark Cronin1, Elena Fioravanzo2, Judith Madden1, Andrea Richarz1, Lothar Terfloth3, Fabian Steinmetz1, Faith Williams4, Chihae Yang5

1Liverpool John Moores University, England 2Soluzioni Informatiche srl, Italy

3Molecular Networks GmbH – Computerchemie, Germany 4Newcastle University, England

5Altamira LLC, USA

In Silico Models

Project:

Development of Computational Models

New Toxicological Databases

Cosmetics Inventory COSMOS TTC v1.0 Munro

Threshold of Toxicological Concern (TTC)

PBPK and In Vitro – In Vivo Extrapolation

Role of Metabolism Prediction in the

Prediction of Chronic Toxicity

• Prediction of detoxification

• Identification of toxic metabolites

• Prediction of clearance

• Supporting in vitro-in vivo extrapolation

• PBPK modelling for route-to-route extrapolation

TTC is Derived from Oral NOEL Values:

Is the Oral Route Protective of Dermal Exposure?

Scenario 1 Oral bioavailability high Dermal bioavailability high

Scenario 2 Oral bioavailability high Dermal bioavailability low

Scenario 3 Oral bioavailability low Dermal bioavailability high

Scenario 4 Oral bioavailability low Dermal bioavailability low

absorption/permeability via dermal and oral routes

metabolism differences between skin and liver

COSMOS Dermal Absorption Database

• Data for 380+ compounds • 2400+ in vitro studies (rat, mouse, pig, human) • 1000+ in vivo studies (rat, mouse, pig, human, monkey)

Thanks

to

COSMOS Dermal Absorption Database

• Data for 380+ compounds • 2400+ in vitro studies (rat, mouse, pig, human) • 1000+ in vivo studies (rat, mouse, pig, human, monkey)

Thanks

to

Very few or no data for

metabolism e.g. kinetics

Very little systematic information

on metabolites

Prediction of Metabolites

Existing Software for Metabolism Prediction [Summarised from Kirchmair J et al (2012) J. Chem. Inf. Model. 52: 617]

Sites of Metabolism

Prediction of Kinetics Prediction of CYP Binding, Affinity, Induction and Inhibition

MetaPrint2D

OASIS TIMES

Virtual ToxLab

Molecular Networks

Prediction of Metabolites

Existing Software for Metabolism Prediction [Summarised from Kirchmair J et al (2012) J. Chem. Inf. Model. 52: 617]

Sites of Metabolism

Prediction of Kinetics Prediction of CYP Binding, Affinity, Induction and Inhibition

MetaPrint2D

OASIS TIMES

Virtual ToxLab

Molecular Networks

Software Optimised for Skin

Metabolism Prediction

Skin Metabolism Rules - Details

Metabolism type Classification (EC enzyme nomenclature)

Enzyme (skin) Enzyme activity type (liver)

Specification

• Xenobiotic • Lipid (essential ingredient in cosmetics)

• Steroid • Protein • Carbohydrate

Phase I • Oxidoreductase • Hydrolase • Isomerase • Ligase • Lyase Phase II • Transferase

Enzyme activity type • Alcohol dehydrogenase • Aldo-keto reductase • Monoamine oxidase • Carboxylesterase • ...

Enzyme + isoform • Skin metabolism • ADH1B = Short chain • ADH5 = Long chain alcohols

Compound class • Primary amine • Aliph. alcohol • Ketone in ring • ...

Thanks

to

Skin Metabolism Parameters

• Rule set enhanced by metabolism parameters

– Metabolism probability

– Kinetics (Michaelis-Menten constants for enzymatic reactions)

– Skin/liver ratio (low ratio ↔ low probability of transformation in skin)

– Detection rate of enzyme in skin (population variance)

Thanks

to

Metabolism Rules

Compounds

Property Profile i.e.

Probability of

Metabolite Formation

Thanks

to

KNIME Nodes for Human Skin Metabolism Prediction

• Dataset of 670 compounds reported by Obach et al.

• Based on molecular descriptors including probability of metabolism

• Reasonably good model performance

• Similar study for metabolic clearance (ongoing) using ToxCast Data

QSARs for Metabolic Clearance:

Global Models

Thanks

to

• Local (QSAR) models usually provide more reliable prediction

– but are very restricted

• Limited “read-across” may be possible given:

– Reliable data for a (small) number of compounds – An understanding of effects of physico-chemical properties on clearance

QSARs for Metabolic Clearance:

Are Local Models a Better Approach?

Conclusions:

Future Needs for Skin Metabolism Prediction

• Better and more systematic data collation

• Development of improved models with greater coverage

• Possibility of adapting current liver metabolism prediction software

– Develop using knowledge of the differences between liver and skin metabolism

• Identification of most probable stable metabolite(s)

• Better prediction of rate and extent of clearance

Acknowledgements

• The European Community’s Seventh Framework Program (FP7/2007-2013) COSMOS Project under grant agreement n° 266835 and Cosmetics Europe

• The contributions of a number of experts through ILSI-EU Expert Groups

http://www.cosmostox.eu