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SEAC SAFETY & ENVIRONMENTAL ASSURANCE CENTRE Mechanistic model-based approach to Skin Sensitisation Risk Assessment Gavin Maxwell, Richard Cubberley, Seraya Dhadra, Nichola Gellatly, John Paul Gosling*, Ruth Pendlington, Juliette Pickles, Joe Reynolds, Richard Stark, Dawei Tang and Cameron MacKay SEAC, Unilever, Colworth Science Park, Sharnbrook, Bedford, MK44 1LQ, UK; *School of Mathematics, University of Leeds, Leeds, LS2 9JT, UK Abstract No. 2646 - Poster Board No. P144 1. Model Scope 2. Model Schematic 3. Model Assumptions 4. Prior parameter uncertainty by Expert Knowledge elicitation Despite our understanding of the key events that drive skin sensitisation our ability to combine non-animal hazard data with exposure information to establish a safe level of human exposure for a sensitising chemical remains a key gap. Our aim is to apply mechanistic understanding of skin sensitisation to improve our ability to make risk assessment decisions. Central to our approach is a toxicokinetic- toxicodynamic (TKTD), ordinary differential equation (ODE) model covering several of the key events captured in the skin sensitisation adverse outcome pathway (AOP) that outputs naïve CD8 + T cell activation as a surrogate measure for sensitisation induction in humans. Chemical-specific model parameters are derived from bespoke in vitro experiments designed to measure reactivity rate and skin bioavailability, whilst biological parameters are taken from the immunological literature. The model has been used to simulate a study published previously by Friedmann et al. in which 165 healthy volunteers were exposed to one of five doses of the contact allergen 2,4-dinitrochlorobenzene (DNCB). As a significant proportion of each dose cohort were sensitised to DNCB within this study, comparison of model simulation results to these clinical data have provided an opportunity to explore the relationship between naïve CD8 + T cell activation and clinical sensitisation. To do so, the model was parameterised for DNCB and prediction made for extent of naïve CD8 + T cell activation occurring across dose. Reverse dosimetry analysis was then performed to calculate the dose-threshold for sensitisation to DNCB and the probability of the average individual acquiring contact allergy at any given dose. Importantly, uncertainty due to both parameter uncertainty (limitations in our knowledge of parameter values) and model uncertainty (limitations due to validity of modelling assumptions) has been characterised. The uncertainty in model predictions for the DNCB case study is significant, therefore additional historical case studies are underway to address this finding. 5. Posterior parameters by Bayesian inference 6. Predict probability of skin sensitisation to DNCB 7. Model evaluation and next steps Acknowledgements All uncertainty analysis was performed in collaboration with John Paul Gosling (University of Leeds) who was funded by UK NC3Rs PANEL A = TOXICOKINETIC MODEL (Reynolds, 2016): (1) diffusion and partitioning into the stratum corneum and skin; (2) sensitiser clearance by dermal capillaries; (3) covalent modification of protein nucleophiles by hapten. PANEL B = TOXICODYNAMIC MODEL (MacKay, 2016): (4) proteasome processing of protein nucleophiles to form small peptides and transport to the endoplasmic reticulum (ER); (5) binding of peptides and hapten-peptide complexes to Class I MHC and transport to plasma membrane; (6) binding of pMHC and hapten-pMHC to CD8 + T cell receptors and (7) activation of naïve specific CD8 + T cells. Objective: TKTD model scope should be simplest representation of the chemistry and biology capable of reproducing the induction of contact allergy to DNCB to enable prediction of a safe level of skin exposure Model is based upon the following major assumptions: 1. Extent of naïve CD8+ T-cell receptor (TcR) triggering is the key determinant of human allergic status 2. Existence of at least one T-cell specific to the ‘antigen’ 3. Required T-cell co-stimulatory signals are sufficient 4. Accompanying CD4+ T-cell response is optimal 5. DC migration from exposure site is sufficient Uncertainty analysis was considered for all model assumptions (major and minor), and evidence for and against each assumption was systematically documented. Table documenting literature evidence and expert uncertainty Preliminary data analysis Final elicited judgement Posterior parameter estimates for diffusion and partitioning rates Expert knowledge elicitation is a process for characterising experts’ uncertainty when data/information is lacking (Cooke, 1991; O’Hagan, 2006; Rowe and Wright, 1999). EKE was used to obtain prior probability distributions for all TK/TD model parameters. Example of hapten-protein degradation rate is shown to illustrate this three-step process: 1. experts capture evidence related to the parameter of interest, including any sources of uncertainty; 2. experts are asked to make judgements on possible or potential parameter; 3. a probability distribution is used to represent this uncertainty. A simple compartmental model (Davies, 2011) was applied to skin penetration data (OECD TG 428 modified to include additional time points and free/bound measurements - Pendlington, 2008, Reynolds, 2016) to update prior estimates for diffusion and partitioning rates using Bayesian analysis. A similar approach was applied to protein reactivity data to obtain estimates of DNCB reaction rate and protein binding kinetics within human skin (data and data analysis not shown). Prior Distribution Posterior Samples Fitted Distribution Table documenting model assumptions, evidence and uncertainty To predict the likelihood of an allergic immune response to DNCB the following sequence of events were explicitly modelled: 1. haptenation of nucleophilic amino acids in the skin; 2. protein degradation by proteasome; 3. presentation of proteasome-derived peptides by Class I MHC; 4. recognition of peptide-MHC by T cell receptor (TcR) A threshold was defined (using literature data) to enable the model output (average CD8 + TcR triggering rate over time) to be converted into a probability that an individual would become allergic as a result of DNCB exposure. A reverse dosimetry approach (Clewell, 2008) was used to back-calculate the doses of DNCB that would cross the TcR signal threshold and cause allergy. The model prediction was benchmarked against historical clinical data from Friedmann and colleagues – ED 50 (dose sensitising 50% cohort) for a single exposure to DNCB, which was found to be 16.5 μg/cm 2 . Sensitivity analysis (Oakley, 2004) was performed to determine the parameters contributing the most uncertainty to simulated sensitising dose. Next Steps: 1. A framework for evaluating a TKTD model-based approach to skin sensitisation risk assessment is currently under development. 2. Model development is ongoing to enable the T cell memory response to be simulated in collaboration with University of Leeds, University of Manchester, Salford Royal NHS Foundation Trust & University of Southampton SAFETY SCIENCE IN THE 21ST CENTURY For more information visit www.tt21c.org Clewell, H.J. et al. 2008. Quantitative interpretation of human biomonitoring data. Toxicol. Appl. Pharmacol. 231, 122–33. Cooke, R.M. 1991. Experts in Uncertainty: Opinion and Subjective Probability in Science. Oxford University Press, New York. Davies M. et al. 2011. Determining epidermal disposition kinetics for use in an integrated non-animal approach to skin sensitisation risk assessment. Toxicol. Sci. 119. 308-318. Friedmann P.S. et al. 1983. Quantitative relationships between sensitizing dose of DNCB and reactivity in normal subjects. Clin. Exp. Immunol. 53, 709-15 MacKay C. et al. 2016. Toxicokinetic/Toxicodynamic (TK/TD) modelling of skin sensitisation. Part II: Toxicodynamics and hazard characterisation. Tox. Sci. Submitted OECD. 2012. The Adverse Outcome Pathway for Skin Sensitisation initiated by covalent binding to proteins. OECD Environ. Heal. Saf. Publ. Ser. Test. Assess. 168. 1-46 Oakley J.E. and O’Hagan A. 2004. Probabilistic sensitivity analysis of complex models: a Bayesian approach. J.R. Stat. Soc. Ser. B. 66. 751-69 O’Hagan A. et al. 2006. Uncertain Judgements: Eliciting Experts’ Probabilities. John Wiley & Sons, Chichester Pendlington R. U. et al. 2008. Development of a modified in vitro skin absorption method to study the epidermal/dermal disposition of a contact allergen in human skin. Cutan. Ocul. Toxicol. 27. 283-94. Reynolds J. et al. 2016. Toxicokinetic/Toxicodynamic (TK/TD) modelling of skin sensitisation. Part I: Toxicokinetics. Tox. Sci. Submitted Rowe G. and Wright G. 1999. The Delphi technique as a forecasting tool: issues and analysis. Int. J. Forecast. 15. 353-375. Sensitivity analysis identified the following model parameters as most sensitive: ƙ α * = TcR trigger rate threshold; A c = average area of contact between DC and T cell; β = rate of haptenated- protein turnover; m T = density of peptide-MHC in DC and T cell contact zone. Abstract Modified from OECD, ‘Adverse Outcome Pathway for Skin Sensitisation initiated by covalent binding to proteins’ report Epidermis Epidermis Lymph Node Induction Elicitation Clin. Exp. Immunol. (1983) 53, 709-715. Quantitative relationships between sensitizing dose of DNCB and reactivity in normal subjects P. S. Friedmann, C. Moss, S. Schuster & J. M. Simpson - 165 healthy human volunteers divided into five dose groups - Single exposure to between 62.5 - 1000 μg DNCB applied to 7.1cm 2 volar forearm in 100μL acetone vehicle - Sensitisation assessed four weeks later by DNCB challenge - Sensitisation varied between 8 - 100% across dose groups
