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In Silico Drug Design of Biofilm Inhibitors of Staphylococcus epidermidis

by Al-mulla, Aymen Faraoun, MS


Page 111

Log
cfu/ml

Isolate No. 58 growth curve results are estimated from the following
figures:

7

6.5

6

5.5

5

4.5

4

3.5

0 5 10 15 20 25 30
Time (hr)

Figure 4.26 Growth curve for isolate No.58 by using Viable Count

Figure 4.27 Growth curve for isolate No.58 by using OD


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Figure 4.28 Slime production for isolate No.58

Figure 4.29 Linear regression for Viable Count and OD for isolate No.58

For isolate No.10 (non producer) the growth curves were as follows:


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Log
cfu/ml
7.5

7

6.5

6

5.5

5

4.5

4

0 5 10 15 20 25 30 35
Time (hr)

Figure 4.30 Growth curve for isolate No.10 by using Viable Count

Figure 4.31 Growth curve for isolate No.10 by using OD


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Figure 4.32 Slime production for isolate No.10

Figure 4.33 Linear regression for Viable Count and OD for isolate No.10


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The results of linear regression and correlation coefficient for OD and
viable count (VC) curves are demonstrated in the following table:

Table 4.5 Linear regression and correlation coefficient for Viable Count and OD
curves of the three isolates

Isolate
No.
R2
VC OD VC OD
56 0.795 0.972 0.892 0.986
58 0.517 0.978 0.719 0.989
10 0.943 0.974 0.971 0.987
r

The correlation coefficient of OD and VC for all three experiments
display a good correlation, meaning accepted results for these experiments
(Table 4.5).
Also the correlation between OD and VC for every experiment (0.873,
0.665, 0.930 for isolates No. 56, 58, 10 respectively) indicates a good result
with a non- slime producer and moderate correlation with slime producers.
This is because the OD values of the spectrophotometer are affected by
slime production and cells that are dead and alive can affect the turbidity
of the culture (Schoonover, 2009).
Statistical evidence can be demonstrated for the log phase using pearsons
linear regression, as the result for the three experiments was (0.995)
indicating that the log phase does not end after 6 or 7 hours. This can lead
to the proposal that peak bacterial growth exceeds 1x107 and may reach
1x108 or more before reaching the stationary phase.
Slime production begins due to quorum sensing phenomena where there
is deficiency in nutrient and oxygen and an increase in crowding and waste
products (i.e induction of different types of stresses). The triggering of
quorum sensing systems has been shown to be responsible for a variety of
physiological behavior in the bacteria including bioluminescence,


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production of antibiotics, release of virulence factors and biofilm formation
(Hentzer et al., 2004; Nadell et al., 2008; Parsek and Greenberg, 2005).
This cooperative behavior is generally regarded to be controlled by cell
density, but other circumstances, such as nutritional availability and
environmental conditions, can affect quorum sensing behavior (Horswill
et al., 2007).
The mean correlation coefficient between slime and VC for slime
producers indicate moderate correlation (0.7). This can be explained by the
fact that slime production begins after the QS phenomenon and at the early
stationary phase (i.e not from the lag phase).
For isolate No. 58, the growth curve does not differ much from that of
No.56, but slime production was greater, this might be due to various
factors. Generation times for each isolate were calculated according to the
equations below (Al-Khafaji, 2008):
n = (log Nt log N0) / 0.301
where: n = No. of generations at time period from t0 to t

Nt = cells number at time (t)

N0 = cells number at initial time (t0)

t = end point time

t0 = initial time

generation time (gt) = t/n


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Table 4.6 Generation times of the three isolates studied

Isolate No. Generation time (min.)
56 63
58 48
10 (Non biofilm producer) 71

According to the obtained results, it is obvious that biofilm producer
isolates had shorter generation times than non-biofilm producer ones.

4.5 Antibiofilm Activity Estimation:
In the drug design section and out of the thirty-seven molecules that gave
positive docking results (negative binding free energies) and passed
through Lipinski rules and toxicity/mutagenicity test, only seven molecules
were selected to be tested in vitro as an antibiofilm. These molecules were:
1. Acetaminophen
2. Acetylsalicylic acid
3. Acetic acid
4. Diacetyl
5. Ferric ammonium citrate
6. Ibuprofen
7. Thymol
The selection was made on the basis of: drug likeness, low side effects in
humans, market availability, low cost, ease of handling in the laboratory.
The experiments were done on these molecules to investigate the biofilm
production and viable count of the bacteria after exposure to gradient


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concentrations of each molecule. However biofilm monitoring only is not
enough because the aim of these experiments was to disarm the virulence
factor of the bacteria, not to kill them. Isolate No. 56 was used in these
experiments.
4.5.1 Molecules Results:
All molecule results are shown in (Table 4.7)
Table 4.7 Results of viable count and slime production after treating with different
concentrations of each molecule
Contro
l
Slime
productio
n
)
570nm
(OD
V.C.
Cfu/m
l
Concentratio
n µg/ml
properties
Molecule
0.05
1.18
3 x
6
10
0
Formula C2H4O2
M.W= 60.05 g/mol
Solubility: miscible
In water
Log p= -0.3
Hypothetical Binding
energy to sarA = -
23.01 Kcal/mol
Acetic acid
1
3.21
2 x
7
10
0.02
2.72
2 x
7
10
0.5
3.60
1.7 x
7
10
5
3.23
1.3 x
7
10
25
2.10
1 x
7
10
100
2.53
5.2
6
x10
200
2.41
2.7 x
6
10
1000
0.10
7 x
3
10
3300
0.07
3.23
2 x
7
10
0
2
NO
9
H
8
Formula C
Acetaminophe
n
2

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3.03
1.7 x
7
10
0.02
M.W=151.17 g/mol
Solubility:12.78mg/
ml
In water
Log p= 0.5
Hypothetical Binding
energy to sarA = -
5.37 Kcal/mol
3.33
2 x
7
10
1
3.13
1.3 x
7
10
10
3.03
1.3 x
7
10
50
3.13
2 x
7
10
200
3.63
2 x
7
10
1000
2.93
1.7 x
7
10
2000
2.63
1.3 x
7
10
4000
1.43
1.7 x
7
10
9000
0.13
1 x
7
0
1
10000
0.05
1 x
7
10
11000
0.06
1.0
2 x
7
10
0
2
O
6
H
4
Formula C
86.0892
M.W=
g/mol
Solubility: Soluble in
4 parts of water
Log p= -1.3
Diacetyl
3
1.34
2 x
7
10
0.2
1.74
1.3 x
7
10
10
1.54
9 x
6
10
100
1.34
4 x
6
10
200
1.24
2.3 x
5
10
500

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0.01
1.4 x
4
10
600
Hypothetical Binding
energy to sarA = -
12.63 Kcal/mol 0
0
1600
0.05
3.75
2.6 x
7
10
0
4
O
8
H
9
Formula C
180.157
M.W=
g/mol
Solubility: 3 mg/ml
In water
Log p= 1.2
Hypothetical Binding
energy to sarA = -
0.05 Kcal/mol
Acetylsalicylic
acid
4
3.25
0.7 x
7
10
10
2.95
1 x
7
10
50
2.95
1.6 x
7
10
200
2.45
0.8 x
7
10
320
1.65
1.5 x
7
10
440
1.65
1.3 x
7
10
650
1.25
0.9 x
7
10
770
1.75
0.6 x
7
10
880
1.95
0.5 x
7
10
1000
0.11
0.4 x
7
0
1
1600
0.08
3 x
6
10
2300
0.05
3.25
8.7 x
7
10
0
O
14
H
10
Formula C
M.W= 150.22 g/mol
Solubility: insoluble
In water
Log p= 3.3
Thymol
5
2.65
1.5 x
8
10
10
3.15
1 x
8
10
50
2.75
1 x
8
10
200

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