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Computational Chemistry
Studies on a New Class of Peptide Antidepressants* Joseph J. Hlavka, PhD Innapharma, Inc. 1 Maynard Drive, Suite 205 Park Ridge, NJ 07656 *This study was sponsored by Innapharma,
Inc.
KEY WORDS: computational chemistry, antidepressant peptides, regression
coefficients and quantitative structure activity relationships ABSTRACT Computational chemistry studies were performed
to determine the quantitative structure activity relationships (QSAR)
of a new series of small peptides that have demonstrated potent antidepressant
activity in animal models for depression and in Phase II clinical
trials. When comparing graphically the calculated activity value (Z*)
with the observed activity value (Z), we observe an excellent correlation
between the two, resulting in a good predicator of activity. INTRODUCTION Heterogeneous unipolar depression is a
common psychiatric disorder that is mediated by neurochemical changes
in the central nervous system. Administration of antidepressant drugs
for the treatment of unipolar depression has gained wide acceptance
in the medical community over the past decades. Endogenous depression,
which is a genetically determined biochemical disorder, is classified
as unipolar depression. We have synthesized a series of new peptides1
that have demonstrated potent antidepressant activity in animal models
for depression2,3 and in Phase II clinical trials.4,5 MATERIALS AND METHODS Computational chemistry studies^{a} were performed
to determine the quantitative structure activity relationships (QSAR)
of peptides in this series. A threedimensional structure was computed
for a neutral species in the gas phase for each of eleven peptides
in our series. An example for our lead compound, nemifitide, is given
in Figure 1. The structures were then used to determine twelve
physiochemical parameters, called descriptors, for the eleven peptides.
Some of the descriptors have marked interdependencies. For example,
molar refraction is highly correlated with molecular volume, so one
may not want to use both. After removal of these dependencies, the
number of descriptors was easily reduced to the following seven: heat
of formation, ionization energy, dipole moment, polarizability_{b} (second
derivative term), molecular volume, octanol/water partition coefficient
(log P), and acid dissociation constant in the physiological range
(pK_{a}). Biological data in
terms of the experimental Z in Table 1 are represented by stressinduced
depression testing2 measurements of Z score and percentage of responders.^{b} We reduced the physicochemical
units to a common scale (i.e., the deviation from the group mean divided
by the standard deviation) to enable comparison of descriptors with
disparate units, and then performed three types of correlational analyses:
multiple linear regression analysis (MLR), principal component analysis
(PCA), and partial least squares analysis (PLS). The MLR technique gave a good fit, but
the coefficients were poorly determined (almost all were nonsignificant
by t‑test). After standardization of the descriptors, three
principal components were computed and antidepressant activity was
best correlated with the third component. Table 2 lists the regression
coefficients derived after translation from PCA analysis back to the
original variables. Finally, data were subjected to PLS analysis;
one dimension was sufficient. The PLS coefficients are also shown
in Table 2. For ease of comparison among and between the methods,
the regression coefficients for all descriptors for both methods of
analysis are expressed in arbitrary units, adjusted to set the coefficient
of log P equal to 2.0. RESULTS AND DISCUSSION Results of both PCA and PLS analysis agree
that the octanol/water partition (log P) is the most important factor
in predicting antidepressant activity. This is not unexpected since
compounds with higher log P more readily cross biological membranes
in general and the bloodbrain barrier in particular. In addition, both methods predict that
nemifitide should be the most active and that Innapharma compound
(IC) 00955 and IC 00924 have comparable activity. This predicted
profile of activity was observed in our animal models for depression.2 The calculated (predictive fuction) Z,
when expressed in standardized terms (i.e., in units of standard deviation),
is as follows: Z 5 O.65 Heat  0.96
Ionization  0.015 Dipole moment 1 0.77 Polarz'nB 1.89 Volume 1 1.76 LogP  0.46 pKa When comparing graphically the calculated
Z* with the observed Z, we observe an excellent correlation between
the two, resulting in a good predicator of activity (Figure 2). We
next plan to do the computational chemistry calculations on peptides
that have not yet been synthesized. Those molecules that show a good
calculated Z* will be synthesized and tested in order to confirm the
reliability of this potentially predicative method. REFERENCES 1. Hlavka JJ, Abajian H, Noble J:
Pharm Res 13(Suppl 9), 143, 1996. 2. Hlavka JJ, Nicolau G, Abajian H,
Noble J: Drugs of the Future 22(12):13141318, 1997. 3. Hlavka JJ, Abajian H, Nicolau G,
Feighner JP: XVIth Int Symp Med Chem [Abstracts]. Bologna, Italy,
September 1822, 2000, p 422. 4. Feighner JP, Ehrensing RH, Kastin
AJ, et al: J Affec Disord 61:119126, 2000. 5. Feighner JP, Noble JF, Sverdlov
L, et al: Am J Psych, in press.
^{a}The computational
chemistry studies were done using the QSAR properties in HyperChem
and the Pallas system from CompuDrug. ^{b}The author thanks Dr. Lev Sverdlov, Statistical Department,
Innapharma, Inc., Park Ridge, NJ, for these determinations.
Table 2. Regression coefficients of physiochemical descriptors and
antidepressant activity Descriptor Measure of Antidepressant Activity Z score Percentage
of Responders Method of Analysis
PCA PLS PCA PLS Heat of formation
0.54 0.74 0.54 0.74 Ionization potential
0.86 1.09
0.79 1.09 Dipole moment 0.35 0.02 0.02 0.02 Polarizability_{b}_{ } 0.73 0.87 0.49 0.87_{} (second derivative term) Molecular volume 0.70 1.01 0.62 1.01 Octanol/water partition coefficient 2.00 2.00 2.00 2.00 (log P) PK_{a} dissociation constant
0.35 0.53
0.21 0.53 (within the physiological
range) Regression coefficients
of correlation are expressed in arbitrary units, adjusted to set that
of log P equal to 2.0. PCA = Principal component
analysis. PLS = Partial least squares
analysis.
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