Benjamin Deacon
About
My research project
Computational modelling of skin absorption under complex experimental conditionsComputational models, along side experiments, have been used to assess skin permeation of chemicals. Models often have one use scenario where they are applicable. We aim to look at novel applications for mathematical models, generating results using conditions similar to published experiment and real life conditions.
Supervisors
Computational models, along side experiments, have been used to assess skin permeation of chemicals. Models often have one use scenario where they are applicable. We aim to look at novel applications for mathematical models, generating results using conditions similar to published experiment and real life conditions.
My qualifications
ResearchResearch interests
Currently researching skin absorption of chemicals. Learning to use python and matlab.
Previously have completed research for a range of topics including:
- Ab initio simulations of the degradation of biodegradable batteries
- Structure and properties of stacking faults in high temperature superconductors for fusion energy
- Polymers for Electronic Energy Storage
Research interests
Currently researching skin absorption of chemicals. Learning to use python and matlab.
Previously have completed research for a range of topics including:
- Ab initio simulations of the degradation of biodegradable batteries
- Structure and properties of stacking faults in high temperature superconductors for fusion energy
- Polymers for Electronic Energy Storage
Publications
Permeability and partition coefficients of the skin barrier are important for assessing dermal absorption, bioavailability, and safety of cosmetics and medicine. We use the Potts and Guy equation to analyse the dependence of skin permeability on the hydrophobicity of permeants and highlight the significant differences in published datasets. Correlations of solute partition to skin are examined to understand the likely causes of the differences in the skin permeability datasets. Recently published permeability datasets show weak correlation and low dependence on hydrophobicity. As expected, early datasets show good correlation with hydrophobicity due to the related derivation. The weaker correlation of later datasets cannot be explained by the partition to skin lipids. All the datasets of solute partition to skin lipid showed a similar correlation to hydrophobicity where the log-linear correlation coefficient of partition is almost the same of the log-linear coefficient of Potts and Guy equation. Weak correlation and dependence of the late permeability datasets with SC lipid/water partition and that they are significantly under predicted by the Potts and Guy equation suggests either additional non-lipid pathway at play or a weaker skin barrier property.
In silico methods to estimate and/or quantify skin absorption of chemicals as a function of chemistry are needed to realistically predict pharmacological, occupational, and environmental exposures. The Potts-Guy equation is a well- established approach, using multi-linear regression analysis describing skin permeability (Kp) in terms of the octanol/water partition coefficient (logP) and molecular weight (MW). In this work, we obtained regression equations for different human datasets relevant to environmental and cosmetic chemicals. Since the Potts-Guy equation was published in 1992, we explored recent datasets that include different skin layers, such as dermatomed (including dermis to a defined thickness) and full-skin. Our work was consistent with others who have observed that fits to the Potts-Guy equation are stronger for experiments focused on the epidermis. Permeability estimates for dermatomed skin and full skin resulted in low regression coefficients when compared to epidermis datasets. An updated regression equation uses a combination of fitted permeability values obtained with a published 2D compartmental model previously evaluated. The resulting regression equation was: logKp = = -2.55 +0.65*logP – 0.0085*MW, R2=0.91 (applicability domain for all datasets: MW ranges from 18->584 g/mol and -4 ->5 for logP) . This approach demonstrates the advantage of combining mechanistic with structural activity relationships in a single modeling approach. This combination approach results in an improved regression fit when compared to permeability estimates obtained using the Potts-Guy approach alone. The analysis presented in this work assumes a one-compartment skin absorption route, future modeling work will consider adding multiple compartments.