quantitative structure-activity relationships (qsar) and odour

ELSEVIER

Food Quality and &fnnu 5 (1994) 81-86 0 1994 Elsevier Science Limited

Printed in Great Britain. All rights reserved 095cK3293/94/$7.00

QUANTITATIVESTRUCTURE-ACTIVITYRELATIONSHIPS (QSAR)ANDODOUR

John C. Dearden

School of Pharmacy, Liverpool John Moores University, Byrom Street, Liverpool, UK, L3 3AF

(Paper presented at ‘UnderstandingFluvour Quality: Relating Sensq to Chemical and Physical Data ‘, 20-23 September 1992, Bristol, UK)

ABSTRACT

An introduction to QSAR is given, in which the three main classes of molecular descriptors-hydrophobic, elec- tronic and steric-are discussed. These can be applied to continuous biological data through the use of multiple regression analysis, or to discontinuous data by pattern recognition techniques.

A review is given of published @AR work in the$eld of olfaction. These cover studies of the character; thresh- old and intensity of odou?; and include both multiple re- gression analysis and patter-n recognition methods, as well as some molecular modelling.

A new @AR study is reported of the benzaldehyde- likeness of odour of 25 alkyl-substituted benzaldehydes and nitobenzenes, and benzonitm’le. An excellent cor-rela- tion was found with two molecular connectivity parame- ters, indicating that in these compounds odour similarity is governed primarily by stereochemical factors.

Keywords: Odour; QSAR; pattern recognition; musks; benzaldehydes; molecular modelling.

INTRODUCTION

Every property of a compound-be it physical, chemi- cal or biological-is a consequence of the distribution and polarisability of the electrons surrounding each molecule. In principle, therefore, it should be possible to describe any property in terms of such electronic be- haviour. However, molecular orbital theory is not yet sufficiently advanced to allow us to calculate any but the simplest properties of the simplest of molecules in this way. The next best thing is to attempt to describe complex properties, such as a biological activity, in terms of simpler physicochemical and structural prop- erties (known as parameters or descriptors) that might be considered to control the complex property.

In the case of biological activity, when a compound is

administered to an organism, it has to be transported from the site of administration to the site of action, and it then has to interact with a receptor in order to trig- ger the biological response. Transport of a xenobiotic within an organism is generally a partitioning process whereby the compound crosses lipid membranes. Its ability to do that depends on its hydrophobicity.

Interaction of a compound with a receptor depends primarily on two factors-the nature and strength of the interactive forces involved (electronic effects) and the complementarity of size and shape of compound and receptor (steric effects).

These three factors-hydrophobic, electronic and steric-may thus be regarded as controlling biological activity. Nevertheless, in our present state of knowledge a consideration of these factors does not permit us to make an absolute prediction of the biological activity of a compound. What can be done, however, is to exam- ine how the level of biological activity changes as the structure and physico-chemical properties change within a series of (usually) related compounds. The prime criterion for a valid QSAR is usually that all the compounds studied should act by the same mecha- nism, otherwise a poor correlation will be obtained.

Hydrophobicity is generally modelled by the loga- rithm of the octanol-water partition coefficient (p) , al- though other solvent pairs and other terms (e.g. HPLC capacity factors) can be used. Within a congeneric series of compounds, the hydrophobic substituent con- stant 7~ can be used ( QT = log Pderivative - log P,,,,,) .

Partition coefficients are difficult and tedious to measure, and several methods are now available for the calculation of log P. Probably the most successful are those of Hansch and Leo (1979) and Rekker and Mannhold (1992). They both involve the summation of hydrophobic contributions from molecular fragments, together with appropriate correction factors. Both methods have been computerised; the Hansch and Leo method is available from Daylight Information Systems of Irvine, California, and the Rekker method is avail- able from CompuDrug Chemistry of Budapest.

A wide variety of parameters is available to model electronic effects; they range from Hammett substituent constants, dipole moments and hydrogen bonding abil-

81

82 John. C. Deaden

ity to atomic charge and energies of highest occupied and lowest unoccupied molecular orbitals. These pa- rameters have been discussed in depth by Dearden (1990).

Steric effects fall into two main classes, size and shape. Size can readily be parametrised by molar vol- ume, molar refractivity and even molecular weight. Shape is more difficult to quantify; two successful para- meters are the Sterimol parameters (Verloop et al, 1976) and Kier’s kappa indices (Kier, 1985).

It is frequently observed that biological activity ini- tially increases with log P, but falls again at high log P values. The chief reason for this is that compounds of low log P value have difficulty entering lipid mem- branes, whilst compounds of high log P value have difficulty leaving such membranes. Consequently both exhibit low activity, due to low rate of arrival at the re- ceptor; compounds of intermediate hydrophobicity thus display greater activity.

Such biphasic behaviour usually approximates to a parabola, and hence the general form of a QSAR equa- tion (often called the Hansch equation, after the founder of modern QSAR, Corwin Hansch) is as fol- lows:

10g(1/C)=a10gP+b(10gP)*+&+dS+e (1)

where C = concentration to produce a defined biological effect E = one or more electronic terms S = one or more steric terms

a-e = constants determined by regression. For example, Fujita (1981) showed that the activity

of Rhloroacetyl-hrphenylglycine esters against the rice plant could be correlated thus:

log (l/C) =-0.33n-0.95$‘“” -0.62 rt 4.10 (2) (n = 28, 72 = 0.920, s = 0.261)

where C=

,fjortha = s

CT= n= T= S=

It is

molar concentration that reduces shoot elonga- tion by 50% in 6 days Taft steric constant for substituents in ortho posi- tion Hammett substituent constant number of compounds correlation coefficient standard error of estimate. now possible to generate hundreds of parame-

ters for each compound or substituent in the dataset, and this gives rise to several problems. The problem of choosing the best parameters is readily overcome by the use of step-wise or best subsets regression, but there is also a real risk of chance correlations arising with such large numbers of parameters, and some form of data reduction should be employed (Hyde, 1989). Elimination of highly collinear parameters also in- creases the stability of the statistical analysis. There are several methods available for data reduction (Hyde,

1989) and it is beyond the scope of this work to de- scribe them; they include principal components analy- sis, factor analysis and partial least squares analysis. Techniques such as partial least squares and canonical correlation analysis can handle more than one set of biological activities simultaneously (e.g., in the case of odour, two or more different odour characteristics).

The techniques described above can be applied only when the data are continuous (e.g., a set of concentra- tions at which odour can be detected). Noncontinuous data (e.g., presence or absence of a given odour charac- teristic) must be treated by one of the pattern recogni- tion techniques (Ham & Jurs, 1985), in which molecular parameters cluster in multidimensional hyperspace so that a hyperplane can be drawn between, say, active and non-active compounds; that is to say, molecular de- scriptors are selected that will allow one to discriminate between active and non-active compounds. Hence pre- dictions of activity or lack of it can be made through the use of such descriptors.

QSAR AND ODOUR

There are three main ways in which odour can be as- sessed, namely by character, detection threshold and intensity. Probably the first QSAR study of odour in- volved character. Amoore (19’71) assessed the ben- zaldehyde-likeness of the odour of 25 alkyl-substituted benzaldehydes and nitrobenzenes, and benzonitrile, and correlated this with molecular shape, which he as- sessed by a complicated method involving the produc- tion of three orthogonal silhouette photographs of models of each molecule. Although Amoore did not give a correlation equation, his data (Table 1) yield the following QSAR:

Odour similarity = 0.319 (Shape) - 30.3 (n = 26, ? = 0.834, s = 0.800)

(3)

The correlation is quite good, supporting Amoore’s contention that, in this series of compounds at least, the character of an odour is determined primarily by the molecular shape of the odorant.

Seeman et al. (1989) compared the odour character- istics of 2,6dialkyl-substituted benzenes and pyridines. They examined 146 odour characteristics, and grouped these by factor analysis into three groups-‘smoky’, ‘solvent’ and ‘green’. The differences between the two classes of compound were found to vary with the size of the substituents; for example, differences decreased steadily with the size for the ‘smoky’ factors, but varied biphasically with size when all factors were combined. The results support the hypothesis that odour charac- teristics are stereochemical in origin.

Kier et al. (19’7’7) obtained a Gaussian relationship between the odour similarity (OS) of a group of hetero- geneous floral-scented compounds and zero-order

Quantitive Structure-Activity Relationships (@AR) and Odour 83

TABLE 1. Data for Benzaldehydes, Nitrobenzenes and Benzonitrile

Compound Amoore’s shape parameter”

3 ” X 4 ” XF Observed BLb Predicted BL Difference between

observed and predicted BL

Benzaldehyde 120.6 O-936 0.150 7.17 6.41 0.76 2-Methylbenzaldehyde 109.0 1.315 0.464 3.79 4.03 -0.24 SMethylbenzaldehyde 106.8 1.178 0.323 4.35 4.82 -0.47 4Methylbenzaldehyde 108.7 1.213 0.343 3.62 4.58 -0.96 2-Ethylbenzaldehyde 103.1 1.563 0.468 2.98 2.03 0.95 3Ethylbenzaldehyde 101.6 1.495 0.388 1.34 2.40 -1.06 4Ethylbenzaldehyde 102.5 1.524 0.404 2.98 2.21 0.77 3Isopropylbenzaldehyde 101.0 1.713 0.772 1.54 1.49 0.05 4Isopropylbenzaldehyde 100.3 1.739 0.787 1.44 1.31 0.13 2-tButylbenzaldehyde 100.8 1.896 1.321 0.84 1.22 -0.38 3-&Butylbenzaldehyde 97.5 1.889 1.349 1.40 1.33 0.07 4dButylbenzaldehyde 97.1 1.913 1.363 1.25 1.17 0.08 Nitrobenzene 112.5 0.967 0.190 6.40 6.24 0.16 P-Methylnitrobenzene 108.1 1.312 0.463 3.87 4.05 -0.18 SMethylnitrobenzene 105.2 1.213 0.364 5.76 4.64 1.12 4Methylnitrobenzene 107.5 1.245 0.382 4.36 4.42 -0.06 2-Ethylnitrobenzene 103.3 1.569 0.479 1.69 2.01 -0.32 SEthylnitrobenzene 101.1 1.530 0.429 1.73 2.22 -0.49 4Ethylnitrobenzene 102.6 1.555 0.444 2.17 2.04 0.13 2-Isopropylnitrobenzene 101.5 1.756 0.816 1.20 1.24 -0.04 3Isopropylnitrobenzene 99.0 1.748 0.814 1.09 1.30 -0.21 4Isopropylnitrobenzene 100.5 1.771 0.827 1.59 1.14 0.45 2-OButylnitrobenzene 98.8 1.909 1.340 0.81 1.15 -0.34 SOButylnitrobenzene 99.7 1.923 1.390 1.31 1.15 0.16 4&Butylnitrobenzene 98.3 1.944 1.402 1.27 1.00 0.27 Benzonitrile 112.0 0.903 0.134 6.29 6.63 -0.34

“See Amoore (1971). Qenzaldehyde-likeness.

molecular connectivity, which is essentially a volume term:

(OS) = 3.12 exp [-1*66(‘X-9.51)‘] + 3.43 (4) (n=16,r2=0.917,s=0.225)

Dravnieks (19’77) attempted to correlate the odour intensity of 59 heterogeneous compounds with molecu- lar weight and substructural features (e.g. number of halogen atoms). His correlations can be regarded as mediocre, although he did not quote correlation coefficients.

Greenberg (1979), using only alkanes from Dravniek’s dataset, obtained a parabolic correlation with log p:

log (l/C) = 2.57 log P-O.24 (log p)* t 1.36 (5) (n = 7,7* = 0.94, s = 0.39)

Greenberg stated in his paper that he found steric parameters not to be significant; it is hard to under- stand this, since for alkanes there is perfect correlation between hydrophobicity and size.

Recently Edwards and Jurs (1989) reexamined Dravniek’s data, and obtained the following correlation for 55 compounds (the three carboxylic acids were out- liers, and 1-iodobutane could not be parametrised):

log C= -7.02 log MW t 0.57 3x:- 0.53 U + 3.93 S t 1.644 - 0.74 J

- 0.48 NC7 + 10.4 7,y;,, t 18.5 (6)

(n = 55, rz = 0.867, s = 0.630, F= 37.6)

where MW = molecular weight ‘,y: = third-order valence cluster molecular connectivity

U = a measure of unsaturation S = charge on most negative atom A = a polarity parameter J = average distance sum connectivity

NC7 = number of chains of length 7 7,y:h = seventh-order valence chain molecular connectivity.

Molecular connectiveties are topological parameters encoding shape and size, and so it is clear that eqn (6)) albeit containing rather a large number of descriptors, gives quite a good description of odour intensity in terms of electronic and steric effects (although A may be regarded as a hydrophobicity term).

Edwards et aL (1991) have also carried out a similar study on the odour thresholds of 53 aliphatic alcohols and 74 pyrazines. Using molecular connectivity param- eters, together with the sum of partial positively charged surface area, and the first principal moment of inertia, they obtained 1-2 = O-863 for the alcohols and r2

84 John. C. Dearden

= O-885 for the pyrazines, confirming that steric and electronic parameters also control odour threshold.

Mihara and Masuda (1988), on the other hand, found that they could correlate the odour threshold (7> of pyrazines with hydrophobic and electronic terms determined from gas chromatography:

where SIR =

AI =

log (l/7) = 0.04 (CUR- AAl) t 6.2 (n = 60; no statistics given)

(7)

difference in retention index between derivative and unsubstituted pyrazine, using a non-polar stationary phase difference in retention index of a compound using polar and non-polar stationary phases.

Kaliszan et al. (1982)) also using gas chromatography, obtained a reasonable correlation of the relative odour detection threshold (A) of phenols using a hydropho- bicity parameter corrected for ionisation:

log A= 2.23 (7r+ Cr,) -0.80 (7~+ &)‘t 0.33 (8) (n = 19, 72 = 0.83, s = 0.41)

In general, it appears that electronic and steric effects primarily control the character of an odour, whereas hydrophobicity may additionally play a part in controlling odour threshold and intensity. This is to be expected, since threshold and intensity must be affected by the ability of molecules to reach the appropriate ol- factory receptor(s).

criminate between the musks and non-musks, using up to 18 descriptors, which included molecular connectivi- ties, principal axes, number of heteroatoms, partial charges and molar refractivity, i.e. both electronic and steric parameters.

It was found that four methods (linear learning ma- chine, iterative least squares, simplex and adaptive least squares) all gave 100% correct classification. The worst result (c. 60% correct classification) was given by the K-nearest neighbour method.

Narvaez et al. (1986) extended this work by a study of 67 musk and 81 non-musk bicycle- and tricyclo-ben- zenoids, using the adaptive least squares technique. Again, 100% correct classification was obtained with 14 descriptors. The model was tested with 15 additional compounds, and correctly classified 14 of them.

The adaptive least squares method was also employed by Yoshii et al. (1991) in a study of 10 musks and 10 non- musks. They obtained 100% correct classification with three parameters, namely log P and two parameters representing interactomic distances. Validation by the leave-one-out technique gave two misclassifications.

Although there have been to date relatively few QSAR studies of odour character, a lack of consistency of results is apparent, with some workers finding hydro- phobicity to be an important determinant of odour character, whilst others have found that electronic and steric effects predominate.

MOLECULAR MODELLING

PATTERN RECOGNITION STUDIES OF ODOUR

When biological activities comprise merely classifi- cations (e.g. active and non-active) rather than contin- uous data, it is not normally possible to carry out QSAR analysis. However, the quantitative descriptors used in QSAR can still be applied, using pattern recognition techniques, to allow a discrimination between classes. It is therefore legitimate to consider such techniques as quantitative structure-activity methods.

Some early pattern recognition work by Doty et aZ. (1978) utilised molecular weight, vapour pressure, GC retention time and various substructural descriptors to distinguish between low, moderate and high intensities of trigeminal stimulation by 47 heterogeneous chemi- cals. They obtained up to 92.5% correct classification. As their intensities were obtained as continuous data, they also carried out classical QSAR analysis and ob- tained 72 = 0.77, s = 1.64 using nine descriptors.

Ham and Jurs (1985) investigated a series of 71 monocylic nitrobenzenes, of which 38 were musks and 33 were non-musks. They investigated the ability of seven different pattern recognition techniques to dis-

Little work appears to have been done to date on mod- elling of odoriferous compounds. Cookson et al. (1976) described the characteristic features of musk com- pounds as a benzene ring with a bulky, hydrophobic group, and containing at least one quaternary carbon on one side and a polar group on the other. Beets (1978) compared the differences in odours between enantiomers by superimposing their dipolar axes and measuring bulk difference between the enantiomers. Chastrette and Zakarya (1988) proposed an interaction model based on hydrogen handling and dispersion forces; for musks their model is very similar to that of Cookson et al. (1976). Models have also been developed for sandalwood (Chastrette et al, 1990) and anisic notes (Pierre, 1991). Yoshii et al. (1992) have developed a structure model for benzenoid musks based on a bulky hydrophobic group, the aromatic ring and an oxygen atom of the functional group. Using the model they were able correctly to discriminate 37 out of 40 ben- zenoids as odoriferous or odourless.

De Kidder and Schenk (1991) used molecular mod- elling to overlay a number of musk compounds and hence to propose a putative receptor; their proposals confirm the earlier work of Cookson et aL (1976) in defining the molecular characteristics required for

Quantitive Structure-Activity Relationships (@AR) and Odour 85

musk odour. Chastrette et al. (1992) have examined the role of chirality in structure-odour relationships, and obtained good predictions of odour likeness by super- position of optimised structures and consideration of angles between hydrogen bonds.

Clearly there is scope for much more research in the modelling of odoriferous compounds, and it is to be hoped that this will be forthcoming.

ODOUR CHARACTER OF BENZALDEHYDES AND NITROBENZENES The work of Amoore (1971) in correlating benzalde- hyde-likeness (BL) of odour was discussed earlier, when it was pointed out that Amoore had used a rather complicated method to quantify molecular shape. Kier et al. (1977) used molecular connectivity indices to cor- relate Amoore’s data, and obtained the following equa- tion, although they had to omit two compounds from the data set:

BL = -2.122 Ox’ + 2.424 3x; t 15.02 (9) (n= 24, rY = 0.878, s= 0.600)

The present author re-examined Amoore’s data, using a total of 38 parameters for the 26 compounds. They included log P and molar refractivity (calculated from MEDCHEM software, version 3.53)) heat of forma- tion, energies of highest occupied and lowest unoccu- pied molecular orbitals and dipole moment (all calculated from MOPAC 6) and molecular connectivities and kappa indicies (calculated from MOLCONN-X) .

Stepwise regression using the MINIlYAB statistical package gave:

BL=-5.52”~“+ 11.2 (10) (n = 26, 3 = 0.879, s= 0.683, F= 174.7)

01 I I I I I I I 1

0 1 2 3 4 5 6 7 8 Predicted Benzoldehyde-Likeness

FIG. 1. Observed benzaldehyde-likeness of odour of Amoore’s (1971) compounds versus values predicted from eqn (11).

TABLE 2. Predicted Benzaldehyde-likeness (BL) of Five Compounds Removed from Training Set

Compound Observed Predicted Difference BL BL

2-Ethylbenzaldehyde 2.98 1.84 1.14 4Isopropylnitrobenzene 1.44 1.15 0.29 2-Methylnitrobenzene 3.87 3.99 -0.12 4Ethylnitrobenzene 2.17 1.84 0.33 S&Butylnitrobenzene 1.31 1.12 0.19

BL=-8~083~‘+2~194~;,t 13.6 (II) (n = 26, r2 = 0.926, s = 0.545, F= 144.3)

Inclusion of further terms gave little improvement. The two parameters in eqn (11) are third-order valence molecular connectivity and fourth-order valence path- cluster molecular connectivity. Both of these terms reflect size and branching of a molecule; 4,$, for exam- ple, is a function of the substituent positions on the ring. The equation is a considerable improvement upon Amoore’s original correlation (eqn (3) ) , and also upon that of Kier et al. (eqn (9)).

Table 1 lists the relevant data, and Fig. 1 shows the good correlation between observed and predicted ben- zaldehyde-likeness of the compounds.

In order to validate eqn (11)) five compounds were removed at random from the dataset, and a new equa- tion was developed for the remaining compounds:

BL = -8.62 3x” t 2.59 '& t 14.1 (12) (n= 21, r’= 0.935, s= 0.561)

Equation (12) was then used to predict the benzalde- hyde-likeness of the odour of the five compounds that were removed. The results are given in Table 2. It can be seen that all predictions are good, the worst being that of P-ethylbenzaldehyde with a discrepancy of two standard errors; even that is quite acceptable, bearing in mind the range of odour similarity from C.81 to 7.17. All the other predictions are well within one stan- dard error.

It can be concluded that eqn (11) is robust, and gives an excellent quantitative description of the benzalde- hyde-likeness of the compounds examined. The corre- lation confirms Amoore’s claim that stereochemical factors control the odour similarity of these com- pounds.

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