This is the inaugural session of ScitoVation’s 2022 Webinar Series. We have exciting speakers and topics planned for 2022. John Troutman and Abdullah Hamadeh presented a computational workflow for predicting in vivo dermal absorption by integrating a mechanistic model of skin penetration with in vitro permeation test (IVPT) measurements.
What you’ll learn:
- A Bayesian approach through which posterior distributions of uncertain model parameters are inferred.
- The likely range of in vivo dermal absorption based on the in vitro observations
- How the proposed workflow enables the reliable derivation of mechanistically determined upper bound estimates of dermal absorption in chemical risk assessment.
About our Speakers:
John Troutman earned a B.S. and a M.S. in Biology from Youngstown State University. Early in his career he worked at the University of Cincinnati, College of Medicine, on the development and use of transgenic mouse models in AIDS research. In 1999, he joined the Drug Safety Assessment organization at Procter & Gamble Pharmaceuticals. He served as study director for pre-clinical pharmacokinetic and metabolism studies in support of early discovery and late-stage drug development programs. In 2009, he joined P&G’s Global Product Stewardship Human Safety organization and is currently a Senior Scientist, specializing in the development and application of physiologically-based pharmacokinetic (PBPK) models to support the risk assessment of cosmetics and consumer products. His research focus is on the development of dermal pharmacokinetic models, the application of enzyme kinetics to study xenobiotic metabolism and the use of PBPK to facilitate extrapolations between different routes of exposure and populations in chemical risk assessment.
Abdullah Hamadeh received his PhD in control systems engineering from the University of Cambridge and conducted postdoctoral work in systems and synthetic biology at MIT. He is currently a Research Associate with the School of Pharmacy at the University of Waterloo. His main research interests include the development of optimization algorithms for complex systems, with applications in pharmacokinetic modeling, systems pharmacology as well as epidemiology. He is also an active contributor to the development of the Open Systems Pharmacology platform.
Estimation of the dermal absorption of chemical substances is a key component in the risk assessment of cosmetic, pharmaceutical, and industrial products to which skin may be exposed. However, in vitro and in vivo experimental studies are resource intensive and available data may not provide information that is directly relevant to address real time safety-related questions of a chemical’s dermal absorption for a given exposure scenario.
Computational workflows based on the integration of experimental data with mechanistic skin penetration models provide a platform for deriving such estimates in a way that is adaptable to specific contexts of relevance to the safety assessment. These contexts reflect variations in the demographics of the consumer population, skin conditions, the product formulation, and the environmental conditions of the exposure scenario.
We will present a computational workflow for predicting in vivo dermal absorption by integrating a mechanistic model of skin penetration with in vitro permeation test (IVPT) measurements. The workflow centers on an open source physiologically based pharmacokinetic model of dermal absorption developed using the Open Systems Pharmacology Suite. This model relates the skin penetration kinetics of permeants to their partitioning and diffusion across elementary sub-compartments of the skin. This mechanistic decomposition of skin permeation endows the model with a flexibility through which model-specific variables can be adjusted to distinguish between different exposure conditions while keeping fixed any model parameters that are context-invariant.
Given in vitro skin permeation data, we demonstrate a Bayesian approach through which posterior distributions of uncertain model parameters are inferred. By populating the model with samples of context-invariant parameters from this distribution and adjusting context-dependent parameters to suit the in vivo exposure assessment, simulations of the model yield estimates of the likely range of in vivo dermal absorption based on the in vitro observations. This workflow is applied to five compounds previously tested in vivo. In each case, the range of in vivo predictions encompassed the range observed experimentally. These studies demonstrate that the proposed workflow enables the reliable derivation of mechanistically determined upper bound estimates of dermal absorption in chemical risk assessment.