Mathematical models for dermal drug absorption

Selzer, Dominik; Neumann, Dirk; Schaefer, Ulrich F.

doi:10.1517/17425255.2015.1063615

Cited by 16 publications

(12 citation statements)

References 113 publications

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“…Other in silico methods have been developed with a theoretical approach, producing equations able to predict permeability and dermal absorption 199 , mainly from compartmental models. The multicompartmental spreadsheet-based model developed by Dancik et al 200 considers the characteristics of skin layers and attaches to each one a corresponding equation, obtained as combination of diffusion principles and experimental coefficients, evaluating several exposure scenarios.…”

Section: Equation-based Modelsmentioning

confidence: 99%

Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies

Pecoraro

Tutone

Hoffman

et al. 2019

J. Chem. Inf. Model.

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The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, dataset building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues arised and to address the challenges to develop in future research.

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Section: Equation-based Modelsmentioning

confidence: 99%

Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies

Pecoraro

Tutone

Hoffman

et al. 2019

J. Chem. Inf. Model.

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Section: Featured Applicationmentioning

confidence: 99%

“…Linear methods mainly include multiple regression methods (MLR), partial least squares (PLS), and principal component regression (PCR) [25]. Nonlinear methods include support vector regression (SVR) and artificial neural network (ANN) methods [26][27][28][29][30].However, the QSPR study based on various artificial intelligence algorithms also has some shortcomings, such as high computational cost [31]. Therefore, it is necessary to develop a QSPR model with high accuracy, high efficiency, and good stability.The pKa value is a key parameter of some compounds, but its determination experiments are cumbersome.…”

mentioning

confidence: 99%

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Zhang

Liu

et al. 2018

Applied Sciences

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Featured Application:The proposed hybrid intelligent model can be applied in engineering design, material performance prediction, numerical calculation, and the prediction of physical and chemical properties.Abstract: A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.The establishment of the QSPR model mainly involves the following steps: acquisition of experimental data, construction and optimization of the molecular structure, calculation and screening of molecular descriptors, establishment and verification of the model, etc. First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15]. The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors. Many methods have been developed to screen molecular descriptors and can be mainly divided into two categories [16][17][18]. The first category includes the common methods, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), and forward/backward/bi-directional stepwise multiple linear regression (MLR). The second includes the modern search algorithms, such as genetic algorithm (GA), simulated annealing algorithm (SA), ant colony algorithm (AC), particle swarm optimization (PSO), and other swarm intelligence algorithms [7,11,[19][20][21]. The common methods are the most simple and efficient, but their overall performances are low in complex nonlinear problems. The modern search algorithms based on the optimization strategy have obvious advantages and can search for optimal variables and deal with complex large data points. The model establishment is important in the QSPR study and commonly used QSPR model...

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“…General mathematical approaches for compartmental models have been discussed in detail in the literature [16][17][18] and used for modelling drugs transport in skin [19,20]. The compartmentalization of biological systems can aid in the elimination of space dependence in the mathematical formalism of the diffusion equation, which is generally a parabolic partial differential equation (PDE) [21]. As a result, a set of ordinary differential equations (ODEs) can be used to simulate transport processes in the transdermal drug delivery system instead of the PDE approach.…”

Section: Introductionmentioning

confidence: 99%

Compartmental modeling of skin transport

Amarah

Petlin

Grice

et al. 2018

European Journal of Pharmaceutics and Biopharmaceutics

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The primary objective of this study is to introduce a simple and flexible mathematical approach which models transport processes in skin using compartments. The main feature of the presented approach is that the rate constants for exchange between compartments are derived from physiologically relevant diffusional transport parameters. This allows for better physical interpretation of the rate constants, and limits the number of parameters for the compartmental model. The resulting compartmental solution is in good agreement with previously published solutions for the diffusion model of skin when ten or more compartments are used. It was found that the new compartmental model with three compartments provided a better fit of the previously publish water penetration data than the diffusion model. Two special cases for which it is difficult to implement the diffusion model were considered using our compartmental approach. In both cases the compartmental model predictions agreed well with the diffusion model.

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Mathematical models for dermal drug absorption

Cited by 16 publications

References 113 publications

Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies

Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Compartmental modeling of skin transport

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