April Journal-2009.p65 - Association of Biotechnology and Pharmacy
April Journal-2009.p65 - Association of Biotechnology and Pharmacy
April Journal-2009.p65 - Association of Biotechnology and Pharmacy
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Current Trends in <strong>Biotechnology</strong> <strong>and</strong> <strong>Pharmacy</strong><br />
Vol. 3 (2) 149-154, <strong>April</strong> 2009. ISSN 0973-8916<br />
protease inhibitors (4,5) <strong>and</strong> HIV-1 integrase<br />
inhibitors (6,7) <strong>and</strong> gp 120 envelope glycoprotein<br />
(8) were reported. Leonard et al. has developed<br />
a few QSAR models for anti-HIV activities <strong>of</strong><br />
different group <strong>of</strong> compounds (9,10). The present<br />
group <strong>of</strong> authors has developed a few quantitative<br />
structure-activity relationship models to predict<br />
anti-HIV activity <strong>of</strong> different group <strong>of</strong> compounds<br />
(11-20). In continuation <strong>of</strong> such efforts, in this<br />
article, we have performed QSAR analysis to<br />
explore the correlation between physicochemical<br />
<strong>and</strong> biological activity <strong>of</strong> thiomethane derivatives<br />
using modeling s<strong>of</strong>tware WIN CAChe 6.1<br />
(molecular modeling s<strong>of</strong>tware, a product <strong>of</strong> Fujitsu<br />
private limited, Japan) <strong>and</strong> statistical s<strong>of</strong>tware<br />
STATISTICA version 6 (StatS<strong>of</strong>t, Inc., Tulsa,<br />
USA).<br />
Materials <strong>and</strong> Methods<br />
In the present work we have taken 16<br />
thiomethane compounds (Table 1) <strong>and</strong> their HIV-<br />
1 protease inhibitory activity from the reported<br />
work (21). Many <strong>of</strong> these compounds inhibited<br />
wild type HIV-1 protease with IC 50<br />
values<br />
between 0.058 μM <strong>and</strong> 7.82 μM. There is high<br />
structural diversity <strong>and</strong> a sufficient range <strong>of</strong> the<br />
biological activity in the selected series <strong>of</strong><br />
thiomethane derivatives. It insists as to select these<br />
series <strong>of</strong> compounds for our QSAR studies. All<br />
the HIV-1 protease inhibitory activities used in<br />
the present study were expressed as pIC 50<br />
= -<br />
log 10<br />
IC 50<br />
. Where IC 50<br />
is the micro molar<br />
concentration <strong>of</strong> the compounds producing 50%<br />
reduction in the HIV-1 protease activity is stated<br />
as the means <strong>of</strong> at least two experiments. The<br />
compounds which did not show confirmed HIV-<br />
1 protease inhibitory activity <strong>and</strong> the compounds<br />
having particular functional groups at a particular<br />
position once in the above cited literature have<br />
not been taken for our study. We carried out<br />
QSAR analysis <strong>and</strong> established a QSAR model<br />
to guide further structural optimization <strong>and</strong> predict<br />
the potency <strong>and</strong> physiochemical properties <strong>of</strong><br />
clinical drug c<strong>and</strong>idates.<br />
150<br />
All the sixteen compounds (13<br />
compounds in training set <strong>and</strong> three in test set,<br />
training <strong>and</strong> test set selection has been done<br />
manually) were built on workspace <strong>of</strong> molecular<br />
modeling s<strong>of</strong>tware WIN CAChe 6.1, which is a<br />
product <strong>of</strong> Fujitsu private limited, Japan. The<br />
energy minimization was done by geometry<br />
optimization <strong>of</strong> molecules using MM2 (Molecular<br />
Mechanics) followed by semi empirical PM3<br />
method available in MOPAC module until the root<br />
mean square gradient value becomes smaller than<br />
0.001 kcal/mol Å. The physicochemical properties<br />
were calculated on project leader file <strong>of</strong> the<br />
s<strong>of</strong>tware. These properties were fed manually<br />
into statistical s<strong>of</strong>tware named STATISTICA<br />
version 6 (StatS<strong>of</strong>t, Inc., Tulsa, USA) <strong>and</strong> a<br />
correlation matrix was made to select the<br />
parameters having very less inter-correlation <strong>and</strong><br />
maximum correlation with activity. This was<br />
followed by multiple linear regression analysis to<br />
achieve best model.<br />
Internal validation was carried out by<br />
Leave one out (LOO) method using statistical<br />
s<strong>of</strong>tware STATISTICA. The cross-validated<br />
correlation coefficient, q 2 , was calculated using<br />
the following equation:<br />
q 2 = 1 – PRESS / ∑ (y i<br />
- y m<br />
) 2<br />
N<br />
N<br />
i=1<br />
PRESS = ∑ (y pred,i<br />
– y i<br />
) 2<br />
i=1<br />
Where y i<br />
is the activity for training set<br />
compounds, y m<br />
is the mean observed value,<br />
corresponding to the mean <strong>of</strong> the values for each<br />
cross-validation group, <strong>and</strong> y pred,i<br />
is the predicted<br />
activity for y i<br />
. The LOO predicted values are<br />
shown in table 1.<br />
In present study the calculated<br />
descriptors were conformational minimum<br />
energies (CME), Zero-order connectivity index<br />
Ravich<strong>and</strong>ran et al