Needs and Future Perspectives of Modelling Skin Permeability and Metabolism Mark Cronin School of Pharmacy and Chemistry Liverpool John Moores University England E-mail: m.t.cronin@ljmu.ac.uk
Reasons for Studying Skin Penetration Topical delivery of drugs Risk assessment of chemicals Deliberate Occupational Accidental
Risk Assessment Risk is a function of hazard and exposure Identified from toxicity testing Rinse off, leave on, occupational etc
Identifying Hazard: Repeat Dose Toxicity Testing Aims to derive a concentration that causes no effect 100% Effect(s) 50% LOAEL NOEL NOEL = No Observed Effect Level 0% Dose
Other Dermal Risks following Exposure Skin sensitisation Skin irritation and corrosion Skin cancer Lethality
The Cosmetics Industry > 5 billion personal hygiene products sold per year in the EU Approx. 1.7 million people employed directly and indirectly in the EU cosmetic industry Skin care products have the biggest market share (27%) Europeans use 450 million personal hygiene products daily The average consumer spends the same amount of money on cosmetics as they do on bread EU Cosmetic Industry was Worth Approximately 70 Billion in 2011
Ensuring the Safety of Cosmetics and Global Regulation of Cosmetics Cosmetics are a vanity product. European Union Cosmetics Regulation No testing on products No further testing on ingredients US FDA colours and food substances How are we going to ensure safety of existing and new cosmetics ingredients?
The Project Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety www.cosmostox.eu 6.7 Mio EUR 15 Partners EC FP7 Cosmetics Europe COSMOS is an EU project developing methods for determining the safety of cosmetic ingredients for humans, without the use of animals, using computational models.
Aims of COSMOS Collate, curate and quality control new sources of toxicological data Establish thresholds of toxicological concern Develop innovative strategies to use existing and novel in silico approaches to predict toxicity Establish kinetic and PBPK models for in vitro, in silico and other relevant data to predict target organ concentrations and long term toxicity to humans Integrate open source and open access modelling approaches into adaptable and flexible in silico workflows
Project Threshold of Toxicological Concern (TTC) New Toxicological Databases PBPK and In Vitro In Vivo Extrapolation In Silico Models
What's in A Name? that which we call a rose, by any other name would smell as sweet Aqua Paraffinum Liquidum Benzyl alcohol Methylchoroisothiazolinone CI 15510
COSMOS Inventory of Cosmetics Ingredients A reference list of chemicals or substances that may have been used in cosmetics formulations (or may now be banned) COSING 19,000+ INCI names 3,500+ INCI names PCPC 3000+ INCI name: International Nomenclature for Cosmetics Ingredients Cosing database: Cosmetics ingredients database PCPC: Personal Care Products Council, US trade organization Thanks to
Over 4,400 Substances in the COSMOS Cosmetics Ingredients Inventory: Use Classes Thanks to
Cosmetics Ingredients: Types of Chemistry Thanks to
How to Assure Safety with no Data Risk is a function of hazard and exposure If you are not exposed at a high enough level to a hazard, there is minimal or no risk Can we define a generic threshold for chemicals below which we assume exposure has minimal risk Many caveats and exceptions to be noted
Define a Threshold With Minimal Risk No Effect Concentration Levels (NOELs) available for many compounds 5 th Percentile Munro IC et al (1996) Fd. Chem. Toxicol. 34: 829
the TTC approach, in general, [is] scientifically acceptable for human health risk assessment of systemic toxic effects caused by chemicals present at very low levels, as based on sound exposure information.
Are the Current TTC Values Acceptable for Cosmetics? There are enormous implications Safety of consumers Regulation and Industry NGOs Scientific credibility of this approach must be assured Moving on from the Cramer Classes Models are required to assess the dermal route of exposure
TTC is Derived from Oral NOEL Values: Is the Oral Route Protective of Dermal Exposure? Scenario 1 Oral absorption high Dermal absorption high Scenario 3 Oral absorption low Dermal absorption high Scenario 2 Oral absorption high Dermal absorption low Scenario 4 Oral absorption low Dermal absorption low absorption/permeability via dermal and oral routes metabolism differences between skin and liver
Predicting Dermal Absorption We have reasonably robust models Potts and Guy Bunge ten Berge Kasting Dancik Magnusson. and many others Are the models fit for purpose? The purpose being to identify compounds with significant dermal penetration
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
Example of the 42 Entries for 2-Ethoxyethanol: Data for in Vitro Human (Flow-through) Membrane Area (cm2) Vehicle Exposure (Hour) % Absorbed Flux Dermatomed 0.64 Methanol 24 7.5 5.487 0.000059 Dermatomed 0.64 Neat 24 8.29 68.82 0.000074 Dermatomed 0.64 Water 24 5.09 12.89 Dermatomed 0.64 Water 24 14.12 25.78 Dermatomed 0.64 Water 24 17.8 97.69 Dermatomed 0.64 Water 24 11.26 25.32 Dermatomed 0.64 Water 24 13.37 30.82 Dermatomed 0.64 Water 24 11.56 24.33 Dermatomed 0.64 Water 24 12.06 26.82 Dermatomed 0.64 Neat 24 584.07 Dermatomed 0.64 Neat 24 1131.64 Full thickness 0.64 Water 24 5.09 12.89 Full thickness 3.14 Neat 4 820.2 0.000882 Full thickness 3.14 Acetone 4 7.4 832.8 0.00298 Dermatomed 0.64 Neat 640 0.00069 Dermatomed 0.64 Water 1360 0.00163 Dermatomed 0.64 Water 1870 0.00268 kp
Molecular Size vs Hydrophobicity: Experimental Skin Permeability vs COSMOS Inventory MW log P
Very Initial Analysis of Potts and Guy (K p ) and Magnusson (J max ) Models Difficulty in identifying data for modelling or validation Depth of detail captured Intrinsic experimental variation Encouraging fit despite variability of data Few / no highly absorbed compounds are poorly predicted
Skin Metabolism In ADME, metabolism important factor for toxicokinetic bioavailability Sites of metabolism: Liver, GI tract, kidneys, lungs Cosmetic ingredients: Skin Reactive metabolites cause toxic effects
Skin Metabolism Difficulties in terminology Clearance amount systemically available Detoxifying or toxifying effect new compounds For the four box risk assessment approach we need to be able to predict Toxic metabolites from the skin that may not be produced by the liver Compounds rapidly cleared by the liver but not by the skin (although they will proceed from the skin to the liver in systemic circulation )
Predicting Metabolites in the Skin Prediction of potential metabolites is feasible Existing rule-based software can be used http://wwwucc.ch.cam.ac.uk/projects/ prediction-metabolismmetaprint2d
Metabolism Rules Being Developed Transparent set of metabolism rules Xenobiotic > Oxidoreductase > Carbonyl reductase CBR3 > Quinone Metabolism type (xenobiotic, lipid, protein,...) Classification (oxidation, hydrolysis,...) Enzyme activity type (skin: incl. enzyme isoform) Compound class Liver metabolism profiler Skin metabolism profiler Human Thanks to
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
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) All parameters related to human metabolism / enzymes Direct integration into KNIME nodes Thanks to
More Future Needs Understanding formulations Vehicles Other effects of mixtures Predicting permeability of nanoparticles
A Vision for the Future Rapid risk assessment from chemical structure Decision tree related to TTC (Profs Guy and Williams and an ILSI Expert Group) Identification of high risk chemicals through prediction of High dermal absorption Relevant metabolites Clearance Clear appreciation of effects of formulation
Conclusions The Threshold of Toxicological Concern (TTC) provides a risk assessment approach Needs to be updated for dermal exposure Current predictive models for skin absorption are rough and ready Probably acceptable to identify highly absorbed compounds Predictive models for metabolism are being developed
Acknowledgements The European Community s Seventh Framework Program (FP7/2007-2013) COSMOS Project under grant agreement n 266835 and Cosmetics Europe Co-workers in Liverpool, EU, USA