Download Quantitative structure-activity relationships for nasal pungency

47, 93–98 (1999)
Copyright © 1999 by the Society of Toxicology
TOXICOLOGICAL SCIENCES
Quantitative Structure-Activity Relationships for Nasal Pungency
Thresholds of Volatile Organic Compounds
K. M. Hau,* ,1 D. W. Connell,† and B. J. Richardson‡
*Health and Safety Office, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; †Faculty of Environmental Sciences,
Griffith University, Nathan, Queensland 4111, Australia; and ‡Department of Biology and Chemistry, City University of Hong Kong,
83 Tat Chee Avenue, Kowloon, Hong Kong
Received November 15, 1997; accepted August 20, 1998
teristics that provide information on the potency of chemicals
in causing nasal irritation. Nasal irritation data in terms of RD 50
and RD 0 have been obtained on animals (Alarie et al., 1995;
Hansen and Nielsen, 1994a, b; Nielsen and Alarie, 1982;
Nielsen et al., 1988; Nielsen and Kristiansen, 1988; Nielsen
and Vinggaard, 1988; Nielsen and Yamagiwa, 1989; Silver et
al., 1986), and an extensive database has been compiled
(Schaper, 1993). It is, however, still uncertain as to how well
these animal data correlate with human data. Nasal pungency
thresholds have been collected in recent years on humans.
Cometto-Muniz and Cain (1990, 1991, 1993, 1994a, b) have
investigated the relationships between the human data and a
number of physicochemical factors for a range of VOCs. More
recently, Abraham et al. (1996) have developed a quantitative
structure-activity relationship (QSAR) for the human data using a solvation equation.
The levels of VOCs commonly encountered in the nonindustrial work environment (Brown et al., 1994) are often
well below the known nasal irritation thresholds of most
VOCs. Individual VOCs at typical indoor levels, which may
not cause nasal irritation separately, but together can have an
additive or even synergistic effect in causing nasal irritation. In
order to understand the relationship between VOCs found in
the indoor environment and nasal irritation, the combined
effect of individual VOCs on nasal irritation needs to be
elucidated. The development of QSARs for nasal irritation
caused by VOCs is a useful tool in understanding the mechanism involved and thus the possible combined effect of VOCs.
Also, QSARs allow the prediction of nasal irritation by compounds for which this characteristic has not been measured.
The aim of the present paper is to develop a QSAR for human
nasal pungency thresholds, utilizing a model developed originally for odor thresholds (Hau and Connell, 1998).
A model was developed for describing the triggering of nasal
pungency in humans, based on the partition of volatile organic
compounds (VOCs) between the air phase and the biophase. Two
partition parameters are used in the model: the water-air partition
coefficient and the octanol-water partition coefficient. The model
was validated using data from the literature, principally on alcohols, acetates and ketones. The model suggests that all test compounds, regardless of their chemical functional groups, bind to a
common receptor site within the hydrophobic interior of the bilayer membrane of the trigeminal nerve endings. There is probably
only a slight, non-specific interaction between the VOC molecule
and the receptor molecule, whereas this type of non-specific interaction for the detection of odor is much stronger. In practical
terms, the suggestion that all VOCs share a common irritation
receptor site implies that nasal-pungency thresholds of individual
VOCs may be additive. Quantitative structure-activity relationships (QSARs) for nasal-pungency thresholds were also developed
from the model, which can be used to predict nasal-pungency
thresholds of common VOCs. Although the present model does not
offer additional precision over that of M. H. Abraham et al., 1996,
Fundam. Appl. Toxicol. 31, 71–76, it requires fewer descriptors and
offers a physiological basis to the QSAR. Another advantage of the
present model is that it also provides a basis for comparison
between the olfactory process and nasal pungency.
Key Words: human nasal pungency thresholds; volatile organic
compounds (VOCs); quantitative structure-activity relationships
(QSARs); VOC receptor sites.
Common volatile organic compounds (VOCs) in the indoor
environment can be a major cause of nasal irritation among
building occupants. It is believed by many researchers (Cain
and Cometto-Muniz, 1995; Cometto-Muniz and Cain, 1992;
Molhave, 1992; WHO, 1989) that nasal irritation caused by
VOCs may contribute to the loosely defined sick-building
syndrome (SBS). Nasal irritation is mediated through the trigeminal nerve, which inervates the nasal cavity and the surrounding facial area (Nielsen and Hansen, 1993; Silver, 1992).
Nasal pungency thresholds of chemicals are useful charac1
METHODS
Model development. The model developed was based on the behavior of
common indoor VOCs that are relatively non-reactive compounds. These
substances tend to interact with the biological system through reversible
processes without inflicting permanent damage on biological tissues. The
To whom correspondence should be addressed.
93
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HAU, CONNELL, AND RICHARDSON
model can be seen as involving a partition process of the airborne VOC
molecules between the air phase, the mucus covering the nasal epithelium, and
the cell membrane of the trigeminal nerve endings scattered throughout the
epithelium. Once within the biophase (the bilayer cell membrane), the molecule combines with a receptor site that is commonly believed to be part of a
protein molecule within the biophase (Nielsen, 1991).
The partition process can be represented by the following:
Kma
Kbm
@S#air 43 @S#mucus 43 @S#bio,
where [S] air is the concentration of the VOC in the air, [S] mucus is the concentration of the VOC in the mucus, [S] bio is the concentration of the VOC in the
biophase, K ma is the partition coefficient between the mucus and the air phase,
and K bm is the partition coefficient between the biophase and the mucus.
Once the VOC molecule is in the biophase, it can reversibly bind to a
relevant chemoreceptor site, thus activating the receptor molecule. This process can be represented as follows:
may vary from one type of compound-receptor complex to another. At the
pungency threshold of a compound, Equation 1 becomes
p5
@S#bio 1 @R# O
¢
¡ @S2R#,
where [R] is the concentration of unactivated receptor sites in the biophase
relevant to the VOC, [S 2 R] is the concentration of the VOC-receptor
complex in the biophase, and K A is the equilibrium constant of the reversible
reaction.
It is assumed that all partition processes and reversible reactions with the
chemoreceptors reach equilibrium so rapidly that the non-equilibrium state
need not be considered. Thus all concentration terms used below are equilibrium concentrations. This means that the following relationships occur:
where p is the minimum proportion of the activated receptor sites necessary for
the detection of pungency, and [NPT] is the nasal pungency threshold of the
compound. It is further hypothesized that VOCs with a common chemical
functional group bind to the same receptor site to give a response. Thus for a
homologous series, p is a constant for all the members in that series.
To make the relationship in Equation 2 useful, the two terms K A and K ba
need to be expressed in terms of readily available physicochemical properties
of a compound. By definition,
Kba 5 Kbm p Kma.
Kma 5 Kwa
Kbm 5 a K mow
Kbm 5 @S#bio/@S#mucus
Kba 5 @S#bio/@S#air 5 $@S#bio/@S#mucus% p $@S#mucus/@S#air% 5 Kbm p Kma,
where K ba is the partition coefficient between the biophase and the air phase.
Therefore K A 5 [S 2 R]/{[S] bio p [R]}.
This model is similar to that used in Molhave and Nielsen (1992) and
Hansen and Nielsen (1994a), which is based on the classical receptor theory.
An important principle of that theory is that the sensory response is related to
the proportion of the activated receptor sites. This principle is also used in the
present model:
KA 5 b K now
KA@S#bio@R#
@S#bio
5
.
@R# 1 KA@S#bio@R# @S#bio 1 1/KA
Previously, it was shown that [S] bio equals [S] airK ba, thus:
@S#airKba
@S#air
@S#bio
5
5
.
@S#bio 1 1/KA @S#airKba 1 1/KA @S#air 1 $1/KAKba%
(5)
(6)
where b and n are empirical constants for a homologous series.
Combining Equations 2 to 6 gives
p5
By substituting for [S 2 R] derived from the equation for K A above, then
(4)
where K ow is the partition coefficient between octanol and water, and a and m
are empirical constants for a given receptor environment.
The term K A may change from one homologous series to another, and with
the lipophilicity of the members within a homologous series. Within a homologous series K A can be expressed as follows:
@S 2 R#
Proportion of the activated receptors 5
.
@R# 1 @S 2 R%
5
(3)
where K wa is the partition coefficient between water and air.
It is also expected that the chemoreceptors are certain protein molecules
inside the lipid bilayer membrane of the trigeminal nerve endings. As the
biophase is lipophilic, the term K bm can then be expressed as a function of K ow
(Hansch and Leo, 1979; Lyman et al., 1990). A general expression is
Kma 5 @S#mucus/@S#air
@S 2 R#
(2)
As the layer of mucus covering the nasal cavity is chemically similar to saline
water at about pH 7.2 (Davies and Taylor, 1959), the partition of organic
compounds in the mucus layer would be similar to that of water, and thus:
KA
@R# 1 @S 2 R#
@NPT#
,
@NPT# 1 $1/KAKba%
@NPT#
.
@NPT# 1 $1/ ab KwaK m1n
ow %
m1n
Rearranging, then, [NPT]abK waK ow
5 p/(1 2 p) and [NPT]K wa 5 {p/ab
2(m1n)
2c
(1 2 p)} K ow . Thus [NPT]K wa 5 gK ow
, where g 5 {p/ab(1 2 p)}, and c 5
m 1 n.
Taking the logarithms of both sides:
log$@NPT#Kwa% 5 G 2 c log Kow
(7)
log@NPT# 5 G 2 log Kwa 2 c log Kow
(8)
or
(1)
It is hypothesized that a minimum proportion of the available receptor sites
need to be occupied by the VOC molecules before pungency can be detected.
This minimum proportion corresponds to the nasal pungency threshold, and it
where G 5 log {p/ab(1 2 p)}, both G and c being constants for a homologous
series.
The model, as summarized in Equation 7, predicts that the plot of the
logarithm of the product [NPT]K wa against the logarithm of K ow should give a
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QSARs FOR NASAL PUNGENCY THRESHOLDS
linear relationship for compounds with a common functional group binding to
a common receptor, and thus at least for a homologous series.
Model validation. The model represented by Equation 7 was validated by
fitting the Equation with available data on nasal pungency thresholds, together
with the other two physicochemical properties K wa and K ow, and evaluating the
linear relationship between log {[NPT]K wa} and log K ow. NPT data published
in Abraham et al. (1996) were used as this set is the only systematically
obtained human data available. Validation was first carried out with three
individual homologous series, aliphatic alcohols, acetates, and ketones. Results
from validating the homologous series were then further investigated to see if
the different series are related to each other. Values in units of part per million
(ppm) by volume were used in this study.
Data on partition coefficients. Two partition coefficients, K wa and K ow, are
needed to validate the model. Values of K wa were either obtained directly from
Amoore and Hautala (1983) or derived from the water solubilities, vapor
pressures, or Henry’s Law constants in Howard (1989) or Verschueren (1996).
Values of K ow were obtained from Howard (1989), Hansch and Leo (1979),
BioByte (1997), or Lyman et al. (1990).
Treatment of data. Data used for both K wa and K ow are dimensionless
ratios based on concentration units of mass per unit volume (or mol/m 3)
usually calculated for or determined at 25°C. It is therefore essential that the
nasal pungency threshold value in Equation 7 is also based on units of mass per
unit volume in order to make Equation 7 physically meaningful. The data of
[NPT] from Abraham et al. (1996) are expressed in units of ppm (volume by
volume). The ppm unit in the gas phase can be easily converted to mol/m 3 by
dividing it by a constant factor under standard temperature and pressure
without involving the use of individual molecular weights. It is therefore
possible to directly use the concentration unit of ppm (v/v) for all the [NPT]
values in validating Equation 7, and the conversion factor is incorporated in the
constant term G of the equation. To validate the model expressed in Equations
7 or 8, linear regression analysis was carried out on the logarithm of
[NPT]pK wa against the logarithm of K ow for the three homologous series.
Equation 7 was used as the basis of the validation, as this equation allows ready
evaluation of the linear relationship predicted by the model, and also estimation of the empirical constants G and c. NPT data for other VOCs published in
Abraham et al. (1996) were then fitted into the resulting regression equations
for further examination.
RESULTS
The data for the test compounds used for this study are
summarized in Table 1. The regression lines for the three
homologous series (alcohols, acetates and ketones) are shown
in Figure 1. The corresponding linear regression equations are:
Alcohols: log{[NPT]Kwa} 5 7.63 2 0.86 log Kow
r 5 20.971, n 5 11
TABLE 1
Nasal Pungency Thresholds and Partition Coefficients
of Test Compounds
log NPT a
Alcohols
Methanol
Ethanol
Propanol
Propan-2-ol
Butan-1-ol
Butan-2-ol
t-Butanol
Pentan-1-ol
Hexan-1-ol
Heptan-1-ol
Octan-1-ol
Acetates
Methyl acetate
Ethyl acetate
n-propyl acetate
n-butyl acetate
s-butyl acetate
t-butyl acetate
n-pentyl acetate
n-hexyl acetate
n-heptyl acetate
n-octyl acetate
n-decyl acetate
n-dodecyl acetate
Ketones
Acetone
Pentan-2-one
Heptan-2-one
Nonan-2-one
Others
pyridine
toluene
ethylbenzene
n-propylbenzene
chlorobenzene
octyne
4.53
3.908
3.494
4.259
3.199
3.975
4.516
3.205
2.621
2.322
1.988
Kwa b
5600
4800
3600
3000
2800
2400
2000
2060
1520
876
960
5.051
4.828
4.245
3.562
3.597
3.976
3.217
2.803
2.492
1.954
0.699
0.097
210
180
110
91
63
73
25
18.7
12.1
7.2
2.5
0.9
5.116
3.472
2.906
2.53
620
380
180
89
3.105
4.471
4.004
3.172
4.023
4.486
log Kow c
1700
3.7
2.9
3.6
6.4
0.302
20.77
20.31
0.25
0.05
0.88
0.61
0.35
1.56
2.03
2.72
2.97
0.18
0.73
1.23
1.82
1.51
1.38
2.26
2.83
3.36
3.88
4.94
6
20.24
0.91
1.98
3.14
0.65
2.73
3.15
3.57
2.84
3.6
a
Log (Nasal pungency thresholds in ppm) obtained from Abraham et al.
(1996).
b
Dimensionless partition coefficients between water and air.
c
Log (dimensionless partition coefficients between octanol and water).
(9)
Acetates: log{[NPT]Kwa} 5 7.72 2 1.29 log Kow
r 5 20.996, n 5 12 (10)
Ketones: log{[NPT]Kwa} 5 7.34 2 1.00 log Kow
r 5 20.972, n 5 4. (11)
As the slopes and intercepts of Equations 9 –11 are close to one
another, the three sets of data were pooled and a linear regression analysis performed on the combined data set yielding the
following linear regression equation:
Combined set: log{[NPT]Kwa} 5 7.72 2 1.21 log Kow
r 5 20.977, n 5 27.
(12)
As the individual data sets can be pooled without significantly lowering the correlation coefficient, the rest of the data
published in Abraham et al. (1996) were also incorporated into
the regression analysis. It was found that all data, with the
exception of heptan-4-ol for which a reliable value of K wa is not
available, published in Abraham et al. (1996), can be fitted to
a linear regression line with a high correlation coefficient as
reflected in Equation 13.
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HAU, CONNELL, AND RICHARDSON
FIG. 1. Plot of Log {[NPT]K wa} vs Log K ow and the regression lines for
the series of alcohols, acetates and ketones in Abraham et al. (1996). NPT
refers to the nasal pungency thresholds (in ppm). K wa is the partition coefficient
between water and air, while K ow is the partition coefficient between octanol
and water.
Combined set plus others:
log{[NPT]Kwa} 5 7.69 2 1.16 log Kow
r 5 20.971, n 5 33.
(13)
Figure 2 shows the plot of all test data and the regression lines
represented by Equations 12 and 13.
DISCUSSION
Evaluation of the Model and Mechanisms of Action
Equations 9 to 11 and their regression lines in Figure 1 show
that the logarithm of the product of [NPT] and K wa is linearly
correlated with the logarithm of K ow for the three homologous
series with high-correlation coefficients. These linear relationships are as predicted by the present model and are well
described by Equation 7 which is based on a partition process
of the VOC molecules between the air phase and the biophase.
The linear relationship for each test series suggests the hypothesis (i.e., that different molecules with a common functional
group have a similar partition behavior, bind to the same
receptor site, and need to occupy the same minimum proportion (p) of the sites to trigger a response) appears to be valid.
While the high correlation coefficients indicate the model
described by Equation 7 is reasonable, the slopes of the regression lines give other important information. Franke (1984)
described a general physicochemical model for binding of drug
molecules to biological receptors, which provides insight into
the property of the environment of binding sites. The model,
when applied to sensory irritation (Nielsen et al., 1990), suggests that the slope of the regression line should be approximately unity, if the binding site is totally within a hydrophobic
environment, or the slope should be approximately 0.5, if the
binding site is covered with water so that part of the molecule
is bound to the protein while the other portion is surrounded by
water. The theory developed by Franke (1984) does not account for a slope greater than unity. The model in the present
study, however, predicts that a slope actually greater than unity
can occur if K ow influences the partition process as well as the
binding equilibrium constant K A. The dependence of K A on
K ow is a result of enhanced binding of the VOC molecule to the
receptor site because of the interaction between the hydrophobic portion of the VOC molecule and the receptor protein. The
slope of the regression line is thus a measure of the hydrophobicity of the environment surrounding the receptor site plus the
hydrophobic interaction between the VOC molecule and the
receptor molecule.
The slopes for the three series are close to unity, with that for
acetates slightly above unity. The near-unity values can be
explained by one of the following two possibilities. The first
one is that the relevant receptor sites for the test series are close
to the interface between the lipid bi-layer membrane and the
mucus layer, so that the biophase in which binding occurs is
more polar than octanol, and thus the empirical constant m in
Equation 5 is significantly smaller than 1. However, the hydrophobic interaction between the VOC molecule and the
receptor molecule is such that K A in Equation 6 is positively
related to K ow. The two terms m and n combine to give a slope
which could be close to or slightly above unity. The second
possibility is that the receptor site is inside the hydrophobic
environment so that m is near unity, but the hydrophobic
interaction between the receptor molecule and the VOC molecule is weak, so that K A is not strongly related to K ow.
The three regression lines in Figure 1 are close and almost
parallel to each other. When all the data from the three homologous series are pooled, the linear relationship is still ex-
FIG. 2. Plot of Log {[NPT]K wa} vs Log K ow and the regression lines for
combined data of alcohols, acetates and ketones, and for the entire set of
compounds (combined set plus others) in Abraham et al. (1996). NPT refers to
the nasal pungency thresholds (in ppm). K wa is the partition coefficient between
water and air, while K ow is the partition coefficient between octanol and water.
QSARs FOR NASAL PUNGENCY THRESHOLDS
tremely good with a high correlation coefficient as reflected in
Equation 12. This suggests that VOCs belonging to the 3 series
may bind to the same receptor site or closely related receptor
sites. Further incorporation of other unrelated VOCs in Abraham et al. (1996) into the regression analysis did not substantially change the outcome as represented in Equation 13. The
very good linear relationship obtained suggests that all test
compounds bind to either the same receptor site or closely
related receptor sites regardless of their functionality. An overall slope of 1.16 for the regression line of the combined set,
plus others having different functional groups, indicates that
the common receptor site is probably within the hydrophobic
interior of the lipid bilayer membrane of the nerve ending, and
that there is a slight, non-specific hydrophobic interaction
between the VOC molecule and the receptor molecule so that
the combined influence of K ow is greater than a single order.
Development of QSARs
The model in the present study provides some understanding
of the partition process and the receptor environment involved
in triggering nasal irritation, and it also allows the development
of quantitative structure-activity relationships (QSARs) for
nasal irritation caused by VOCs. While the QSAR developed
by Abraham et al. (1996) is based on a solvation equation
utilizing four descriptors, much simpler QSARs based on the
present model can be developed by rearranging Equations 9 to
11, and 13 as follows:
Alcohols: log[NPT] 5 7.63 2 log Kwa 2 0.86 log Kow
(14)
Acetates: log[NPT] 5 7.72 2 log Kwa 2 1.29 log Kow
(15)
Ketones: log[NPT] 5 7.34 2 log Kwa 2 1.00 log Kow
(16)
All functional groups: log[NPT] 5 7.69 2 log Kwa
2 1.16 log Kow.
(17)
In order to examine how well the QSARs in Equations
14 –17 predict the nasal pungency thresholds of the test compounds, the predicted NPTs for the test compounds were
worked out using the QSARs and compared with their corresponding observed values. Good linear relationships were obtained between the predicted and the observed values. The
correlation coefficients for the linear relationships are 0.96,
0.98, 0.95, and 0.92, respectively, for the four QSARs represented by Equations 14 –17. For VOCs belonging to the alcohols, acetates and ketones families, the nasal pungency thresholds can, therefore, be accurately predicted using QSARs
represented by Equations 14 –16. For other VOCs, the NPT can
also be predicted as a first approximation using the generic
QSAR represented by Equation 17. However, the applicability
of the QSAR represented by Equation 17 to a wide range of
VOCs requires further investigation.
Comparison can also be made between the QSARs in the
97
present study and those for odor thresholds (Hau and Connell,
1998). The slopes of the regression lines for odor thresholds of
alcohols, acetates and ketones are 1.65, 1.65, and 1.88 respectively. The degree of dependence on K ow is significantly higher
for odor thresholds when compared to the current work on
pungency. This higher degree of dependence is either related to
the more hydrophobic receptor environment or due to the
higher level of hydrophobic interaction between the VOC
molecule and the olfactory receptor protein. This dependence
also means that the sensitivity of humans to odor increases
sharply as the size of the VOC molecule increases. The phenomenon can be seen as an adaptive feature in humans to
compensate for the much lower volatility of larger molecules,
as there is still a practical need for the human nose to detect
odors of some relatively non-volatile compounds. On the other
hand, there does not appear to be a need for humans to
maintain a high sensitivity to irritation by large molecules. As
the molecular size of a VOC increases, so odor becomes the
dominant chemical sense, while nasal pungency diminishes in
relative importance.
Responses to Mixtures
The suggestion that all test compounds (and possibly other
relatively non-reactive VOCs) may bind to a common receptor
site has an important implication in the understanding of nasal
pungency thresholds for mixtures. With a common receptor
site, nasal pungency thresholds for the individual components
in a mixture may be additive, i.e., sub-threshold concentrations
of individual compounds may become irritating in a mixture.
Since sick-building syndrome (SBS) is believed to be associated with the sensory perception of mixtures of VOCs, various
approaches to setting standards for VOCs have been proposed
(Molhave and Nielsen, 1992) including the use of the concept
of total volatile organic compounds (TVOC). Molhave and
Nielsen (1992) have discussed the prospect of setting a nasal
irritation based standard for VOC mixtures. This approach
appears to be logical, as hundreds of VOCs may be present in
the indoor environment which, though all may be below their
respective nasal pungency thresholds, can add up to exert an
irritation effect. However, this suggestion suffers the limitation
that the concentrations of common VOCs in the indoor environment are usually in ppb levels, even in the so-called “Sick
Buildings,” which are at least three orders of magnitude below
their nasal pungency thresholds. It is therefore doubtful if the
total concentrations of VOCs would be high enough to exert a
combined irritation effect in those “Sick Buildings.” Instead,
odor thresholds of common VOCs are closer to the usual
concentrations in the indoor environment. It is therefore suggested that an odor-based VOC standard (Hau and Connell,
1998) would be more appropriate than an irritation-based standard.
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HAU, CONNELL, AND RICHARDSON
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