A quantitative structure-permeability relationship model for split-thickness skin absorption, reasoning for the choice of the database.

The skin is the largest organ in the human body, protecting the body from xenobiotic invasion (1). Local and systemic drugs may also be administered through the skin, therefore the need to measure the permeability of the skin to chemicals has long been apparent. The use of in vivo or in vitro techniques is time-consuming, since it is not only necessary to conduct a permeation study, but also to optimize experimental conditions and build analytical methods for each chemical. Moreover, it is not possible to assess the permeability of compounds not yet synthesised. An alternative option can be the development of Quantitative Structure-Permeability Relationships (QSPRs). These in silico models aim to form a relationship between the absorption of chemicals through the skin and their physico-chemical and/or structural properties (2). Knowing that permeability can be affected by different experimental conditions, the aim of this study is to build a QSPR based on uniform and consistent experimental conditions, but with a significant database size.
Two different databases were compared: the first one was obtained only from Zhang et al (3), the second one was created from multiple literature sources, fulfilling the following conditions:
- Data (log Kp values) were obtained by an in vitro diffusion system;
- The membrane was human stratum corneum and viable epidermis;
- The donor solvent was an aqueous solution;
- No permeation enhancement technologies were used;
- No association with other chemicals were considered.
The geometrical structures of all chemicals were optimized with MM2 forcefield. Molecular descriptors and fingerprints were generated where possible. For each database, a wide range of Multi Linear Regression models were built using QSARins (4, 5) through a stepwise forward regression process. The models have been validated according to Golbraikh and Tropsha (6) criteria and the best ones have been selected according to the Multi-Criteria Decision Making (7).
The model calculated from the data obtained from a single source shows better correlation, robustness, and predictivity, revealing a grade of uncertainty coming from an inter laboratory variability of the different sources used to build the database.

REFERENCES
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