Molecular Modeling Study of the Penetration Kinetic of Diverse Compounds through the Human Skin by Three-Dimensional Quantitative Structure Activity Relationship

The control of permeation is essential for the topical application of lotions, creams, and ointments, and the toxicological and risk assessment of materials from environmental and occupational hazards. This study developed a three-dimensional quantitative structure-activity relationship (3DQSAR) model to predict permeation of a variety of 210 compounds through human skin. Molecular descriptors were computed using a GRid Independent Descriptors (GRIND) approach. After variable selection via the genetic algorithm method, the 118 selected descriptors were correlated with skin permeability constants by PLS regression and support vector machine (SVM). Partial least squares regression (PLSR) and support vector regression (SVR) are two popular Chemometrics models that are being subjected to a comparative study in the presented work. Kennard-Stone algorithm was employed to split data set to a training set of 150 molecules and a test set of 60 molecules. Genetic algorithm (GA), as an influential linear tool, was used to certain the best and interpretative subset of variables for the predictive model structure. The best results were obtained by PLS regression with the correlation coefficient of R2=0.77 and SVM regression with the correlation R2=0.79. This strategy led to a final 3DQSAR model that presented Q2=0.61 and R2 pred=0.73. The obtained results revealed that the hydrogen bonding donor and hydrogen bonding acceptor of investigated compounds dramatically influences their ability to penetrate through human skin. Furthermore, it was found that permeability was enhanced by increasing hydrophobicity and lowered with increasing molecular weight. Citation: Rezaei S, Behnejad H, Ghasemi JB (2019) Molecular Modeling Study of the Penetration Kinetic of Diverse Compounds through the Human Skin by ThreeDimensional Quantitative Structure Activity Relationship. Med Chem (Los Angeles) 9: 065-073. Med Chem (Los Angeles), an open access journal ISSN: 2161-0444 Volume 9(6): 065-073 (2019) 66 coefficient (K), diffusion coefficient in the skin (D), and thickness of the skin (L). The permeability coefficient (kp) quantifies the percutaneous absorption of chemicals through the skin defined as follows [8]: . ss p d J K D k L C = = (1) Where, Jss is the steady state flux of the solute. Early QSARs to predict skin permeation of chemicals revealed that hydrophobicity was correlated linearly with increasing permeability [9,10]. Using a larger dataset, Flynn presented a QSAR approach for 95 chemicals, reporting algorithms for compounds with low and high molecular weights. Also, they demonstrated that skin permeability (kp) was a function of partitioning between aqueous and non-aqueous layers, as described by the octanol-water partition coefficient. Subsequently, many QSARs for skin permeation have been published utilizing the Flynn dataset, where the data have either been used as a whole or subsets taken into account for different types of compounds [11]. Potts and Guy were among the first to demonstrate the use of the logarithm of the octanol-water partition coefficient (log kow) in combination with molecular size descriptors, i.e., molecular weight (MW) or molecular volume (MV) to model skin permeability [12,13]. They obtained the following relationship as follow: log kp=0.711 log kow 0.0061 MW 6.3 (2) Other QSAR analyses have confirmed hydrophobicity and molecular size to be the influential descriptors of skin permeability for chemicals. It has also been demonstrated that the hydrogen bonding character of a compound might also influence greatly its ability to penetrate through the human skin [14-21]. Hydrogen bonding has been parameterized using a number of approaches including the number of hydrogen bonds that may be formed by a compound (Hb) hydrogen bond donor (Hd) and acceptor (Ha) ability of a compound, and the HYBOT plus series of descriptors introduced by Raevsky et al., as well as many others [20,22,23]. To achieve this goal, a dataset of 210 compounds, which had not previously been utilized for QSAR development, was analyzed. Furthermore, to explore the fundamental physicochemical meaning of skin permeability, a whole variety of physicochemical properties, hypothesized to be of significance, were calculated. These included calculated descriptors unique to this study to investigate the role of molecular size and hydrophobicity in skin permeability. Results indicate that GRIND-based 3D-QSAR approach can reliably predict continuation of our data in different areas in chemistry and related fields using the QSAR approach, is to develop, for the first time to our knowledge, a valid and predictive 3D-QSAR model, able to correlate and predict skin permeability with the applicability of GRID independent descriptors (GRIND) [24,25]. Materials and Methods