Case studies of the application of molecular shape analysis to elucidate drug action

Journal of Molecular Structure (Theochem), 134 (1986) 317-323 Elsevier Science Publishers B.V., Amsterdam - Printed in The Netherlands

CASE STUDIES OF THE APPLICATION OF MOLECULAR SHAPE ANALYSIS TO ELUCIDATE DRUG ACTION

D. ERIC WALTERS*

and A. J. HOPFINGER**

Department of Medicinal Chemistry, Searle Research and Development, Parkway, Skokie, IL 60077 (U.S.A.)

4901 Searle

(Received 7 June 1985)

ABSTRACT Molecular Shape Analysis (MSA) is an effective means of quantitatively describing the steric and/or electrostatic shape of molecules. These quantitative shape descriptors make it possible to include molecular shape in quantitative structure-activity relationship (QSAR) analysis. This paper describes the application of MSA to a series of carbamate inhibitors of acetylcholinesterase. A QSAR equation for cholinesterase inhibition has been constructed using one of the MSA descriptors derived from common overlap steric volume. INTRODUCTION

The biological activity and specificity of bioactive compounds is expected to depend on their physical, electronic, and steric (shape) properties. Traditional QSAR methods deal effectively with thermodynamic and electronic properties using such descriptors as partition coefficients, molar refractivity, sigma constants, and pK, values. Steric properties, however, have proven more difficult to treat on a quantitative basis, since they involve both the size and shape of molecules or parts of molecules. Descriptors such as van der Waals volume and surface area can reflect the size of substituents, but they contain very little information about shape. The Taft steric parameter, E, [ 11, has found some application in QSAR, but E, values cannot be determined for many substituents, and some workers have claimed that the model reaction from which E, values are derived may be influenced by hyperconjugative effects [2]. Another approach to the problem is the STERIMOL parameter set devised by Verloop [3], in which each substituent is represented by a length descriptor and four perpendicular width descriptors. While this approach more adequately describes the shape of a substituent, a much larger set of compounds is required to statistically accommodate this many descriptors. Also, the STERIMOL parameters do not provide information on the orientations and distances between substituents of a molecule in space. *Present address: Nutra Sweet Group, G. D. Searle & Co., Skokie, IL 60077, U.S.A. **Present address: Department of Medicinal Chemistry, University of Illinois at Chicago, Box 6998, Chicago, IL 60680, U.S.A. 0166-1280/86/$03.50

0 1986 Elsevier Science Publishers B.V.

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More recently, Hopfinger and coworkers have described Molecular Shape Analysis (MSA) [4-121, in which quantitative steric and/or electrostatic shape descriptors can be derived for sets of compounds and included in QSAR equations. In MSA, shape similarities and differences can be described quantitatively in terms of common-overlap steric volume between pairs of molecules (I’,,), representing atoms as spheres of standard van der Waals radii. Molecules are superimposed in such a way as to maximize their shape similarities and/or on the basis of superposition of similar functional groups and fragments. In practice, one compound of the set is selected as the reference compound, and the MSA descriptor for each compound is based on common overlap steric volume with this reference compound. This shape descriptor can be incorporated directly into QSAR equations along with more traditional descriptors such as partition coefficients, molar refractivity, and sigma constants. Table 1 is a compilation of several studies carried out using MSA. For some sets of compounds, MSA has made possible improvements in QSAR TABLE 1 Summary of QSAR studies using MSA descriptors Compounds 1. Phenyl-triazines

2. Phenyl-triazines 3. Phenyl-triazines 4. Quinazolines 5. Benzylpyrimidines 6. Benzylpyrimidines 7. Phenyl-dialkyltriazenes 8. Phenyl-dialkyltriazenes 9. Polycyclic aromatic hydrocarbons 10. GABA agonists

Ref.

Activity

Descriptor+

n

R

S

DHFR-inhibition (Walker 256 tumor or L1210 leukemia) DHFR-inhibition (bovine) DHFR-inhibition (rat) DHFR-inhibition (rat) DHFR-inhibition (bovine) DHFR-inhibition (bovine) Ames mutagenicity

S,, x:n, D,

27

0.953

0.44

4

S,, xn, D,

31

0.926

0.25

5

S,, xn, D,

20

0.947

0.23

5

S,, En, A9

35

0.965

0.36

6

V,, ET

23

0.931

0.14

7

F, in

22

0.961

0.11

8

s,, log P, u+

18

0.981

0.25

9

Antitumor

S,, log P, (I+

24

0.901

0.15

9

Ames mutagenicity

S,, log P, AE

30

0.904

0.31

10

GABA-A receptor

F, log P, Q,

27

-

-

11

“V,: common overlap steric volume; S,: (V,) “‘; X:nsum of octanol-water fragment constants for substituents; D, and D,: length of a substituent in the 3- or I-position; AB: torsion angle of 4-NH2 relative to ring; F: electrostatic potential field MSA descriptor; log P: log octanol-water partition coefficient; AE: difference between HOMO and LUMO energies; Q,: net charge on a specific atom x; a+: Hammett sigma constant for a special ring system. bThis study resulted in a discriminant analysis equation with correlation coefficient = 0.823, F = 16.1, significance
319

equations reported in the literature. For example, Blaney et al. [13] developed a QSAR equation for benzylpyrimidine inhibitors of dihydrofolate reductase (DHFR) using n and u descriptors. For a set of 23 compounds, they obtained a correlation equation with r = 0.931 and s = 0.146. Using MSA, Hopfinger [8] generated a QSAR equation with r = 0.961 and s = 0.11. In other instances, MSA has permitted inclusion of compounds which were discarded as outliers in other studies. For the DHFR inhibitors, three classes of compounds (triazines, quinazolines, and benzylpyrimidines) consistently showed a dependence on lipophilicity and MSA descriptors [4-71. It was even demonstrated that a triazine could serve as the MSA reference compound in generating a QSAR equation for the quinazolines [6]. In several cases, MSA has been used to identify the active conformation of compounds for which more than one low-energy conformation may exist. This was done for several classes of DHFR inhibitors [4-71, mutagenic antitumor triazenes [ 91, and gamma-aminobutyric acid (GABA) [ 111. MSA ANALYSIS OF CARBAMATE INHIBITORS OF ACETYLCHOLINESTBRASE

This report focuses upon the development of a QSAR equation for a set of 20 structurally diverse carbamate inhibitors of acetylcholinesterase (l-20), using MSA. In this study, MSA provided both a significant QSAR for the system studied, and some information about the steric and electrostatic requirements of the enzyme active site. Methods The biological activity measured was in vitro inhibition of eel acetylcholinesterase. The concentration needed for 60% inhibition (C,,) was determined, and activity was expressed as log (l/C,,). Structures were generated initially from the X-ray crystallographic study of aldicarb [14]. The carbamate analogs were constructed using standard bond lengths and angles, and conformational energy was minimized with respect to bond lengths, bond angles, and torsion angles using a molecular mechanics force field [15]. In the case of conformationally flexible compounds, all low-energy conformations were considered in developing the MSA descriptors. Pairs of molecules were superimposed on the basis of overlap of the carbamate functional group. The MSA descriptor used was So, defined as S,, = (V,,)’ ‘3, where V, is the common overlap steric volume [4] between a given compound and the reference compound. It was found that the most significant correlation between activity and S,, was obtained when compound 17 was used as the shape reference compound for the set of analogs.

320

RI,

c’~,.o-,

.NHCH2

R2’ 1:

w3)3C, ,C’eN.-O.,/NHCH3 CH3SCH2 8 i

C%\ ,C;N--0.C/NHCH3 CH3S a 1.

/ &

COOCH3

d? /

C*eN’0,C-NHCH3

,c**N’0.Ce-NHCH3 S u LI 0

S’

1: r,o

‘1

KH3j3C, KH313C,

,C*kN-OyNHCH3

,CiN’0.C~NHCH3

SCH;

8

CH3

::

‘3

CH,SCH,

(CH3j3C, C’rN/0.C-NHCH3

NCSCH2

a

u

(1

,C*-N’0.~‘NHCH3

CHfHfiHz, ,~;,~O.,.NHCH3

a

CH3SCH2

c-s

a I! (CH313C,

(CH313C, ,C’-=N’O-C’NHCH3 CH,OCH,

w02cH2

,C’~N--0.c~NHCH3

8 I!

bl ?!

Fig. 1. The twenty carbamate inhibitors used to construct the MSA-based QSAR.

Results and Discussion Several molecular descriptors were considered as potential activity correlates in the QSAR analysis of this set of compounds: (1) calculated partition coefficients for the fragments RI and Rz (nal + a2). See Fig. 1 for the definition of RI and Rz;

321

(2) calculated aqueous and octanol solvation free energies (Fnzo and F,,) for RI and R? [ 161; (3) residual charge densities (Q) on the atoms as calculated by the CNDO/B molecular orbital method [ 171; (4) total molecular volume and surface area of each compound; (5) common overlap steric volume (V,Jand the derived parameter So described above. Multidimensional linear regression analysis was used to establish the QSAR between the biological activity and the molecular descriptors. Table 2 lists for each of the 20 compounds, the values of the descriptors found to be critical in explaining the biological activity. These include the shape descriptor, So, the fragment partition coefficient AaI + a2, and the calculated charge on the carbon atom labeled C* on each molecule, Qc*. The table also shows TABLE 2 Biological activities and descriptor values for the acetylcholinesterase this study Compound

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Soa

53.52 62.29 66.16 66.85 69.51 50.36 66.70 66.70 72.71 70.72 63.03 69.52 54.26 53.15 51.35 49.16 73.40 49.64 54.43 53.92

Q,yb 0.1514 0.1137 0.1131 0.0976 0.1965 0.1205 0.1105 0.1105 0.0744 0.0716 0.1495 0.0755 0.1534 0.1544 0.2091 0.1683 0.0736 0.1583 0.1543 0.1277

nR1 + R2’

3.68 1.78 2.09 2.71 0.59 0.80 2.41 2.41 1.09 1.83 -0.63 2.91 1.47 5.09 3.78 2.68 1.01 3.00 1.98 2.32

inhibitors used in

1% WC50)

Observedd

Calculated

5.88 7.39 9.70 8.82 9.30 5.94 8.62 9.30 5.58 5.59 9.31 6.68 7.80 6.34 4.22 4.98 5.28 4.58 6.06 6.62

6.71 8.45 8.82 7.94 8.95 4.89 8.70 8.70 6.03 5.90 9.70 6.22 7.20 6.44 4.06 4.65 5.87 4.98 7.22 6.59

= (Vo)“3, where V,, is the common overlap steric volume of each compound with the shape reference standard (compound 17) based upon spatial superposition of the C*NO NHCH, fragments. 8 bQ~* is the charge density on the C* atom as calculated by CNDO/B. ‘nRI+m is the calculated log (octanol-water partition coefficient) for the substituent(s) on C*. dC50 is the concentration necessary for 50% enzyme inhibition. %

322

the observed and calculated activities. The correlation equation used to compute log (l/C,,) is log (l/C,,) = 1.887 [S,] - 0.0138 - 0.0865

[S,]” + 175.26

[Qc*] - 567.49

[Qc*12

[7ra1+ aa] - 67.90

n = 20, R = 0.929, s = 0.631 This equation explains about 87% of the variance in activity over a range of 5.5 log units for a set of compounds which is structurally very heterogeneous. The shape descriptors are critical to the success of this equation; these terms have the largest linear correlation coefficients among the descriptors examined. This QSAR emphasizes the importance of the molecular shape to active site binding. The requirement for the Qc* term suggests that this site may represent a significant point of electrostatic interaction with the enzyme. Such an interpretation is consistent with the binding of acetylcholine to the enzyme. Figure 2 shows a series of stereo views of some of the compounds examined, all oriented in the same way. Figure 2a shows the most active compound, 3. Figures 2b, 2c and 2d show compounds 14,17, and 18, respectively; these are compounds of relatively low activity. For these compounds, the figures highlight the regions of these compounds which extend beyond the space occupied by compound 3, which may represent regions which are steritally unfavorable for inhibitor occupation at the binding site. On the other hand, Fig. 2e shows compound 11, which extends beyond the space occupied by compound 3 in another direction. Since this compound retains high activity, it may be inferred that this region is sterically accessible at the

(a)

(b)

Fig. 2. Stereo views of acetylcholinesterase inhibitors. (a) Compound 3; carbamyl @oup is shaded. (b) Compound 14; the shaded area extends beyond space occupied by compound 3. (c) Compound 17; theshaded area extends beyond space occupied by compound 3. (d) Compound 18; the shaded area extends beyond space occupied by compound 3. (e) Compound 11; the shaded area extends beyond space occupied by compound 3.

323

binding site. It is interesting to note that compound 17 is the optimum reference compound for the MSA-based QSAR, yet it is relatively inactive. This demonstrates that the overall shape of a molecule, as measured by the other compounds in the data base, can override bioactivity in providing information in a regression fit involving molecular shape. CONCLUSION

Molecular Shape Analysis has been used to show that for a set of 20 carbamate inhibitors of acetylcholinesterase, the shape of the molecule is an important determinant of biological activity. Further, the shapes of these molecules have been treated quantitatively to derive a QSAR equation for the activities of these compounds. Based on these results, inferences can be made regarding the shape of the active site of the enzyme. The QSAR also indicates that the net charge on a specific atom is correlated with activity; this atom may, therefore, represent an important binding site for the inhibitors. These findings may prove useful in the design of further inhibitors. In general, MSA is a useful means of quantitating molecular shape. MSA makes it possible to quantitatively relate shape, thermodynamic, and electronic information to biological activity. ACKNOWLEDGEMENTS

We acknowledge the contributions of Dr. Robert Battershell and his colleagues at Diamond Shamrock Corporation (now SDS Biotech) in the MSA study of the carbamates. REFERENCES 1 R. W. Taft, in M. S. Newman (Ed.), Steric Effects in Organic Chemistry, Wiley, New York, 1956. 2 C. K. Hancock, E. A. Meyers and B. J. Yager, J. Am. Chem. Sot., 83 (1961) 4211. 3 A. Verloop, in E. J. Ariens (Ed.), Drug Design, Vol. 6, Academic Press, New York, 1976. 4 A. J. Hopfinger, J. Am. Chem. Sot., 102 (1980) 7196. 5 A. J. Hopfinger, Arch. Biochem. Biophys., 206 (1981) 153. 6 C. Batter-shell, D. Maihotra and A. J. Hopfinger, J. Med. Chem., 24 (1981) 812. 7 A. J. Hopfinger, J. Med. Chem., 24 (1981) 818. 8 A. J. Hopfinger, J. Med. Chem., 26 (1983) 990. 9 A. J. Hopfinger and R. Potenzone, Jr., Mol. Pharmacol., 21 (1982) 187. 10 S. N. Mohammed, A. J. Hopfinger and D. R. Bickers, J. Theor. Biol., 102 (1983) 323. 11 D. E. Walters and A. J. Hopfinger, in M. Kuchar (Ed.), QSAR in Design of Bioactive Compounds, J. R. Prous, Barcelona, 1984. 12 M. Mabilia, R. A. Pearlstein and A. J. Hopfinger, in G. C. K. Roberts (Ed.), Molecular Graphics and Drug Design, in press. 13 J. M. Blaney, S. W. Dietrich, M. A. Reynolds and C. Hansch, J. Med. Chem., 22 (1979) 614. 14 F. Takusagawa and R. A. Jacobson, J. Agric. Food Chem., 25 (1977) 333. 15 A. J. Hopfinger, Conformational Properties of Macromolecules, Academic Press, New York, 1973. 16 A. J. Hopfinger and R. B. Battershell, J. Med. Chem., 19 (1976) 569. 17 J. A. Pople and G. A. Segal, J. Chem. Phys., 44 (1966) 3289.