April Journal-2009.p65 - Association of Biotechnology and Pharmacy

Current Trends in Biotechnology and Pharmacy
Vol. 3 (2) 149-154, April 2009. ISSN 0973-8916
(CI0), First-order connectivity index (CI1),
Second-order connectivity index (CI2), dipole
moment (DM), total energy at its current
geometry after optimization of structure (TE), heat
of formation at its current geometry after
optimization of structure (HF), ionization potential
(IP), electron affinity (EA), octanol-water partition
coefficient(logP), molar refractivity(MR), shape
index order 1 (SI1), shape index order 2 (SI2),
shape index order 3 (SI3), Zero-order valance
connectivity index (VCI0), First-order valance
connectivity index (VCI1), Second-order valance
connectivity index (VCI2). (Physicochemical
parameters data will be provided on request).
Results and Discussion
The QSAR studies of the thiomethane
series resulted in several QSAR equations. Intercorrelation
between the descriptors involved in
the QSAR model is ≤ 0.57. The best equation
when we considered only one parameter is Eq.
1.
pIC 50
= 4.336 (± 0.596) - 1.004 (± 0.151) logP
(1)
n =13, R= 0.895, R 2 = 0.802, R 2 = 0.783, SEE =
adj
0.264, F = 44.42, P < 0.001, q 2 = 0.726,
S PRESS
= 0.309, SDEP = 0.297.
The above equation is statistically
significant one. The R 2 and internal predictivity
of the model is good. When we have considered
the best equation containing two parameters is
Eq. 2.
pIC 50
= -5.993 (± 4.613) - 0.831 (± 0.165) logP +
1.075 (± 0.581) IP (2)
n =13, R= 0.923, R 2 = 0.852, R 2 = 0.823, SEE =
adj
0.238, F = 28.80, P < 0.001, q 2 = 0.743,
S PRESS
= 0.299, SDEP = 0.287.
When we considered three parameters
for developing model, there was no significant
improvement in R 2 and q 2 . Eq. 2 was selected as
the best model on the basis of high q 2 values and
152
R 2 value. The values given in the parentheses
are 95% confidence intervals of the regression
coefficients. Eq. 2 could explain 85.2% and predict
74.3% of the variance of the HIV-1 protease
inhibitory activity data. The calculated HIV-1
protease inhibitory activity values by Eq.2 are given
in table1. This model showed good correlation
coefficient (R) of 0.923 between descriptors [logP
and IP] and HIV-1 protease inhibitory activity.
This model also indicates statistical significance
> 99.9% with F value F (2,10)
=28.80. The residual
of the observed and calculated activities are
shown in fig. 1. The predictive ability of the
selected model was also confirmed by external
r 2 method. According to Tropsha et al., the
CVext
proposed QSAR model is predictive as it satisfies
the conditions r 2 > 0.5 and CVext R2 > 0.6 Pred (r2 CVext
= 0.885, R2 = 0.991) (22). The robustness of
Pred
this model was checked by Y–randomization test.
The low R 2 and q 2 values indicate that the good
results in our original model are not due to a
chance correlation or structural dependency of
the training set.
Residual value
0.6
0.4
0.2
-0.2
-0.6
1 4 5 6 7 8 9 10 11 12 13 14 15 16
Compound number
The QSAR shows a linear relationship
of HIV-1 protease inhibitory activity with the logP.
Its negative sign indicates that highly hydrophobic
groups are not good for improving the activity of
the series. The positive coefficient of IP showed
that the presence of electron donating groups is
favor for activity. Thus we conclude that the
biological activity will be increased if substituents
that bring about changes in the molecule as
mentioned above are attached to it.
Ravichandran et al