Extract
Note: Please read the complete
full text with Figures and Tables at
Introduction
Skin permeability is of relevance to a number of
applications including the design of skin cream products, risk
assessment of hazardous chemicals, and in particular,
transdermal delivery of drugs. There has been a continuous
interest in predicting skin permeability since
Scheuplein[1,2] and Scheuplein and
Blank[3,4] first introduced
anatomically-based physicochemical models describing percutaneous
absorption. This is partly due to ethical difficulties with
respect to human and animal experiments and partly due to
economic considerations and increasing legislation in the
risk assessment of industrial chemicals, for example, the
newly-proposed European chemicals strategy: Registration,
Evaluation, Authorization and Restriction of Chemicals
(REACH)[5].
Previous studies on predicting skin permeability can be
categorized into mechanistic and empirical
models[3,6,7]. The advantage of many mechanistic models is that they can
provide insights into the mechanisms of skin permeation.
However, most mechanistic models are complex in nature
and several challenges remain for practical use. A main
challenge is that mechanistic models involve parameters that are
not easily measurable and attainable. Assumptions also have
to be made. Some assumptions may be oversimplified and
not necessary apply to real situation. In the domain of skin
permeability prediction, empirical models have been also
frequently reported[8]. Many empirical models employ the
so-called quantitative structure-activity relationship (QSAR)
methods which attempt to relate statistically the biological
activity of a series of compounds to their physicochemical
and/or structural properties[9]. QSAR methods, stretching
back over a century, had been applied in many
fields[10,11]. In the last 30 years, QSAR methods had been developed to
relate percutaneous penetration properties of a range of
chemical compounds to their physicochemical parameters.
Compared with the mechanism model, the QSAR model for
skin permeability does not consider the dynamic process of
skin permeation. Most QSAR models for skin permeability
work well within the range of the experimental data, but often
can not be extrapolated. From the point of view of practical
application, the approach is simple and can provide
acceptable predictions once validated. Various QSAR models for
skin permeability have been reported. Moss et
al[8] recently gave a comprehensive review on different QSAR models for
skin permeability. An early study by Potts and
Guy[12] analyzed the data of
Flynn[13] and related the skin permeability
of the analyzed compounds to their octanol-water partition
property and molecular weight. They proposed the
empirical equation of skin permeability as log
Kp=0.71log Kow
-0.0061MW-6.3, where log Kp is skin permeability is given in
cm/s, log Kow is solute partition coefficient in octanol/water,
MW is molecular weight. Different empirical equations were
also proposed by other researchers using other structural
molecular parameters such as the number of hydrogen bonds
and molecular volume[14,15]. Most QSAR models for skin
permeability employed the multiple linear regression (MLR)
method. The method provides an efficient way to determine
the most relevant physicochemical descriptors. The main
drawback of the MLR approach is that it assumes linearity
between the descriptors and skin permeability. The artificial
neural network (ANN) is similar to the MLR approach, but is
more suitable for extracting both linear and nonlinear effects
of chosen descriptors on skin permeability. Recently, Degim
and colleagues[16,17] used the ANN model to predict skin
permeability. The dataset was limited to 40 chemical
compounds in the range of
-0.77£logKow£4.57 and 32
Da£MW£ 389 Da. The descriptors used include the partial
charge, log Kow and MW of each compound. Abraham
et al[18] argued that log
Kow was an empirical colligation variable and did not
give the actual structural features of the chemical compounds
that influence skin permeability. Therefore, some
researchers have related skin permeability to Abraham descriptors
which they believe can better describe the actual features of
molecules and improve the precision of the
model[19].
In this paper, we report that the ANN model predicts skin
permeability of chemicals using Abraham descriptors. A large
database of skin permeability containing 215 data points was
compiled from literature. The correlation between the skin
permeability coefficient and the Abraham descriptors were
obtained from the trained neural network. In addition, the
predictability of the neural network model was compared to
the MLR model. The ANN model was shown to give better
prediction results which indicate non-linearity and
complexity of correlation between Abraham descriptors and skin
permeability. Some insight into the degree of nonlinear
behavior of every Abraham descriptors has also been assessed
with a functional dependence to understand relationships.
Materials and methods
Data set A structurally-diverse set of compounds was
selected to construct the MLR and ANN models
(n=215, -2.11≤log Kow£7.6, 18
Da£MW≤518 Da, -0.85≤log Kp
(cm/h)≤-5.22). Skin permeability data expressed
in log Kp (cm/h), were obtained from literature and regulatory
sources[13,20,21]. Abraham descriptors of
R2 (excess molar refraction),
p2H (the dipolarity/polarizability),
Σα2H,
Σβ2H (the overall or effective
hydrogen-bond acidity and basicity), and
Vx (the McGowan characteristic volume) were obtained using Abraham
Solvation Parameters (ABSOLV) program (Pharma Algorithms
Software, Toronto, Canada). The program was written to
read molecular structures as Simplified Molecular Input Line
Entry System (SMILES) strings which were obtained from
the PubChem net database[22]. The dataset is listed in Table 1.
Prediction models The chemical compounds were listed
alphabetically according to their chemical names and divided
into 5 subsets. Details about the division of the sub-datasets
are also shown in Table 1. Subsets 1, 3, and 5 were used as
the training dataset, subset 4 as the validation dataset, and
subset 2 as the testing dataset. For comparison, a MLR
model had also been generated using subsets 1, 3, 4, and 5.
The Neural Network Toolbox of MATLAB 7.0.2 was used to
construct the ANN model. Back-propagation networks had
been employed in this study. There are some empirical rules
about constructing artificial neural networks, for example, 2
hidden layers are sufficient for
generality[23]. The number of units of the hidden layer normally is not more than twice the
input [24]. In our example, there were 5 inputs corresponding
to the 5 Abraham descriptors and 1 output for skin
permeability. To determine the ANN architecture, all
network structures with the maximum allowed numbers of
hidden layers and hidden units limited to 2 and 10, respectively,
had been investigated. Each network was trained by
3-folding cross validation using the 3 subsets 1, 3, and 5. For the
training of each network, the average mean square errors
(MSE) between the model prediction and experimental data
were obtained. The ANN architecture with the smallest
average value of MSE was chosen. The MSE is defined as
follows:
 (1)
where log Kp,obs is the experimental value, log
Kp,cal is the predicted value, and
N is the number of data points in the training dataset.
After determining the ANN architecture, the 3 subsets of
1, 3, and 5 were combined with subset 4 to further tune the
ANN parameters to obtain the final ANN model. Finally,
subset 2 that had not been used for training the network was
used to test the predictability of the model. The same subset
2 was also used to test the MLR model. The early-stopping
method was used for the training and validation.
Results
Comparison between the MLR and ANN models The
best ANN structure with minimal MSE contained 7 neurons
at the first hidden layer and 1 neuron at the second hidden
layer. The model was described as ANN-5711, which
corresponded to a minimal MSE of 0.10511. Figure 1 shows the
relationship between predicted values and experimental
results. The statistical test results for the ANN model are
shown in Table 2.
Many previous QSAR models for skin permeability were
based on the MLR method. Here, for comparison, subsets 1,
3, 4, and 5 were also used to build a MLR model using the 5
Abraham descriptors. The best-fitted equation was as
follows:
 (2)
When using the above equation, the MSE for the
predicted log KP was found to be 0.25 for datasets 1, 3, 4, and 5,
and 0.21 for the testing dataset 2. Figure 2 shows the
relationship between the experimental results and predicted
values using the MLR model of equation 2. The statistical test
results for the MLR model are shown in Table 3.
For the training dataset, the
R2 and MSE of the MLR model was 0.70 and 0.25, respectively. However, the ANN
model on the same training dataset produced much improved
results with R2=0.841 and MSE=0.133, respectively. For the
testing dataset, the ANN model improved the
R2 of the MLR model by 12% and the MSE value by 30%. For the whole
dataset, the MLR model produced a
R2 value of 0.699. This coefficient was improved to 0.832 with the ANN model. The
MSE value of the MLR model was 0.243 which compares
with a much-reduced value of 0.136 for the ANN model.
Clearly, the ANN model can better predict skin permeability
from Abraham descriptors (Table 4).
Dependence of skin permeability on the
descriptors The ANN model was used to analyze the influence of each
Abraham descriptor. Skin permeability was calculated using
the ANN model by varying 1 Abraham descriptor each time
while keeping the rest of the descriptors constant mean value
of each range. The results are shown in Figure 3.
Discussion
Generally, log Kp has inversely depended on
p2H, the solute polarity. The statum corneum (SC) lipid phase is mainly
composed of hydrocarbon substance. It is well known that
solute partition into the lipid phase decreases in the
hydrocarbon solvents with increasing solute polarity. The
relationship between skin permeability and the partition
coefficient can be expressed by the following equation:
 (3)
where Km is the skin-water partition coefficient of the solute,
D is its diffusivity through the skin, and
d is the diffusion path length.
Σα2H and
Σβ2H reflect solute hydrogen bonding activity.
Many researchers have discussed the relationship between
hydrogen bonding and skin
permeability[15,19], and have suggested an inverse relationship between them. This was also
observed in this study. The reason is similar as that of
p2H; the increasing solute hydrogen bond acceptor and donor
activity resulted in decreased partitioning into the organic
phase due to the free energy cost associated with the
disruption of hydrogen bonds[19]. It is interesting to note that
in the MLR model there was a positive relationship between
Σα2H and log
Kp. This suggests that although the MLR model
has reasonable precision, it may not be necessary to provide
correct relationships between skin permeability and some of
the Abraham descriptors.
Predicted skin permeability increased with the McGowan
characteristic volume descriptor. This was not expected. A
similar, unexpected relationship was observed before by
Potts and Guy[19]. They explained that the molecular volume
represented a combination of physical phenomena: the
impact of molecular size on partition and diffusion. On the one
hand, increasing molecular size increased the hydrophobic
surface area and this increased the partitioning into a lipid
phase. On the other hand, larger molecules diffused more
slowly, leading to reduced permeability. The positive
relationship between Kp and
Vx tends to suggest that
partitioning effects dominate.
The effect of R2 on skin permeability is rather complex. In
previous studies, Potts and Guy have suggested that the
effect of R2 was not
significant [19].
An artificial neural network model for predicting skin
permeability was developed and compared with a multiple
linear regression model. The model was based on a
comprehensive set of experimental data from various sources in the
open literature. Both the MLR and ANN (ANN-5711)
models are related to Abraham descriptors. Compared to the
MLR model, the ANN model was shown to give better
prediction results which indicate non-linearity and complexity
of correlation between Abraham descriptors and skin
permeability. Some insight into the degree of nonlinear
behavior of every Abraham descriptors has also been assessed
with a functional dependence to understand relationships.
References
1 Scheuplein RJ. Mechanism of percutaneous adsorption. I. Routes
of penetration and the influence of solubility. J Invest Dermatol
1965; 45: 334_46.
2 Scheuplein RJ. Mechanism of percutaneous absorption. II.
Transient diffusion and the relative importance of various routes of
skin penetration. J Invest Dermatol 1967; 48: 79_88.
3 Scheuplein RJ, Blank IH. Permeability of the skin. Physiol Rev
1971; 51: 702_47.
4 Scheuplein RJ, Blank IH. Mechanism of percutaneous absorption.
IV. penetration of nonelectrolytes (alcohols) from aqueous
solutions and from pure liquids. J Invest Dermatol 1973; 60: 286_326.
5 CEFIC. CEFIC Workshop on methods to determine dermal
permeation for human risk assessment. Utrecht: Institute of
Occupational Medicine; 13_15 June 2004. Report No.: TM/04/07.
6 Guy RH, Hadgraft J. A theoretical description relating skin
penetration to the thickness of the applied medicament. Int J Pharm
1980; 6: 321_32.
7 Wang TF. Microscopic models for the structure and
permeability of the stratum corneum barrier layer of skin [PhD thesis],
Buffalo: University at Buffalo, State University of New York,
2003.
8 Moss GP, Dearden JC, Patel H. Quantitative
structure-permeability relationships (QSPRs) for percutaneous absorption.
Toxicology In vitro 2002; 16: 299_317.
9 Martin YC. Quantitative drug design. New York: Marcel Dekker;
1978.
10 Ma XL, Chen C, Yang J. Predictive model of blood-brain barrier
penetration of organic compounds. Acta Pharmacol Sin 2005;
26: 500_12.
11 Chen C, Yang J. MI-QSAR models for prediction of corneal
permeability of organic compounds. Acta Pharmacol Sin 2006;
27: 193_204.
12 Potts RO, Guy RH. Predicting skin permeability. Pharm Res
1992; 9: 663_9.
13 Flynn GL. Physiochemical determinants of skin absorption. In:
Gerity TR, Henry CJ, editors. Principles of route-to-route
extrapolation for risk assessment. Amsterdam: Elsevier Science
Publishing Co Inc; 1990. p 93_127.
14 Lien EJ, Gao H. QSAR analysis of skin permeability of various
drugs in man as compared to in vivo and in
vitro studies in rodents. Pharm Res 1995; 4: 583_7.
15 Pugh WJ, Degim T, Hadgraft J. Epidermal
permeability-penetrant structure relationships: 4, QSAR of permeant diffusion
across human stratum corneum in terms of molecular weight,
H-bonding and electronic charge. Int J Pharm 2000; 197: 203_11.
16 Degim T, Hadgraft J, Ilbasmis S, Ozkan Y. Prediction of skin
penetration using artificial neural network (ANN) modeling. J
Pharm Sci 2003; 92: 656_64.
17 Degim T. Understanding skin penetration: computer aided
modelling and data interpretation. Current Computer-Aided Drug
design 2005; 1: 11_9.
18 Abraham MH, Martins F, Mitchell RC. Algorithms for skin
permeability using hydrogen bond descriptors: the problem of
steroids. J Pharm Pharmacol 1997; 49: 858_65.
19 Potts RO, Guy RH. A predictive algorithm for skin
permeability-the effects of molecular-size and hydrogen-bond activity. Pharm
Res 1995; 12: 1628_33.
20 Johnson ME, Blankschtein D, Langer R. Evaluation of solute
permeation through the stratum corneum: Lateral bilayer
diffusion as the primary transport mechanism. J Pharm Sci 1997; 86:
1162_72.
21 Patel H, Berge WT, Cronin MTD. Quantitative
structure-activity relationships (QSARs) for the prediction of skin permeation
of exogenous chemicals, Chemosphere 2002; 48: 603_13.
22 PubChem Compound Browser. Bethesda (MD): National Library
of Medicine (US); Available from URL:
http://pubchem.ncbi.nlm.nih.gov.
23 Sontag ED. Feedback stabilization using two-hidden-layer nets.
IEEE Transactions on Neural Networks 1992; 3: 981_90.
24 Boger Z, Guterman H. Knowledge extraction from artificial
neural network models. IEEE Systems, Man, and Cybernetics
Conference; 12_15 October 1997; Orlando, FL.
|
