(T1530-01-06) Use of In Silico Models to Predict In Vitro Skin Flux and Comparison with Experimental Results for Transdermal Drug Delivery

Purpose: The purpose of the present research work was to use various mathematical and in-silico models to predict solubility, permeability coefficient and skin flux of a small molecule API (Axitinib) and compare the predicted results with experimental data. Furthermore, the purpose of the research was to evaluate effect of different permeation enhancers and excipients on the skin flux.

Methods: The in-vitro skin flux was predicted using various mathematical and in-silico models such as Finite Dose Skin Permeation Calculator², Lien & Gao model, Potts & Guy model, and Barratt model⁴. Additionally, various permeation enhancers were selected based on Hansen solubility parameters⁵, and the effect of the selected permeation enhancers on the in-vitro skin flux was evaluated experimentally using a Franz cell diffusion apparatus⁽⁶⁾ and a HPLC-UV method. Flux of Axitinib in various excipients such as N-Methyl-2-pyrrolidone (NMP), Transcutol P®, Capmul PG-12®, Glyceryl monooleate (GMO) and lauryl lactate was evaluated. Franz diffusion cell of 9mm orifice diameter along with 5mL of 0.05 M Phosphate Buffer pH 5.5 w/ 3% HCPD as a receptor media was used for the flux testing.

Results: It can be observed that the solubility data of Axitinib in selected permeation enhancers/solvents correlate with the corresponding Hansen solubility parameter values. The HSP distance (Ra) between the Axitinib and the selected solvents/permeation enhancers correlate with the observed experimental data where the solvents with lower values of Ra have higher experimental solubility. The skin flux of Axitinib was predicted using the 'Finite Dose Skin Permeation Calculator’⁷. While the observed experimental flux is higher than the predicted flux, the predicted time to maximum flux (≈12hr) is similar to the experimental flux observed in Axitinib-NMP control. The model used for predicting the flux only considers the Axitinib by itself as an input parameter and the higher experimental flux value can be attributed to the solvents that were used to dissolve the API for the liquid flux testing. This model can be further used to predict time and skin depth concentration profile to evaluate the potential of Axitinib to accumulate in various layers of the skin and further indirectly evaluate the potential for skin irritation and sensitization. The experimental flux of Axitinib in Transcutol P® as a control/vehicle solvent is higher as compared to the Axitinib flux in N-Methyl-2-pyrrolidone (NMP) as a control/vehicle solvent. This indicates that Transcutol P® not only acts as solubilizing agent but also acts as an effective permeation enhancer. The flux of Axitinib with Transcutol P® as a vehicle solvent can be further enhanced by addition of excipients such as Capmul PG-12®, Glyceryl monooleate (GMO) and lauryl lactate. Glyceryl monooleate along with Transcutol P® had the highest flux amongst the tested permeation enhancers and excipients.

Conclusion: GMO along with Transcutol P® had the highest flux amongst the tested permeation enhancers and excipients and these suggest a possibility of a synergistic effect between the two excipients to increase the flux of Axitinib. The Hansen solubility parameter related to the dispersive forces (δD) seems to be the driver for solubilization of Axitinib and the increase in flux could be attributed to the differences in the polarity (δP) of the excipients. It can be concluded that various in-silico approaches and mathematical models can be successfully used as a pre-formulation tool to predict key attributes such as solubility, permeability, and skin flux and aid in Transdermal drug product development. These preformulation approaches can be further used to develop transdermal patch formulations that can achieve the skin flux to meet the required therapeutic dose.

References: (1) Ramadon, D., McCrudden, M. T. C., Courtenay, A. J., & Donnelly, R. F. (2022). Enhancement strategies for transdermal drug delivery systems: current trends and applications. Drug delivery and translational research, 12(4), 758–791. https://doi.org/10.1007/s13346-021-00909-6
(2) Wang, T.-F., G. B. Kasting, et al. (2007). “A multi phase microscopic model for stratum corneum permeability. II. Estimation of physicochemical parameters and application to a large permeability database.” Journal of Pharmaceutical Sciences 96(11): 3024-3051.
(3) Miller, M. A. and G. B. Kasting (2010). “Toward a Better Understanding of Pesticide Dermal Absorption: Diffusion Model Analysis of Parathion Absorption in Vitro and in Vivo.” Journal of Toxicology and Environmental Health 73(4): 284 – 300.
(4) Lian, G., Chen, L., & Han, L. (2008). An evaluation of mathematical models for predicting skin permeability. Journal of pharmaceutical sciences, 97(1), 584–598. https://doi.org/10.1002/jps.21074
(5) HSPiP 6th Edition 6.0.04
(6) PermeGear. Franz Cell – The Original. https://permegear.com/. [Online] [Cited: 05 01, 2024.] https://permegear.com/franz-cells/.
(7) Fedorowicz, Adam, et al. Finite Dose Skin Permeation Calculator. https://www.cdc.gov. [Online] [Cited: 05 01, 2024.] https://www.cdc.gov/niosh/topics/skin/finiteskinpermcalc.html.


Predicted Hansen Solubility Parameters and the experimental solubility results of Axitinib in the selected excipients


Predicted Skin Permeability Coefficients using mathematical models and skin flux using the Finite Dose Skin Permeation Calculator.


Experimental skin flux data of Axitinib and effect of various permeation enhancers