Risk of Late-Onset Alzheimer's Disease by Plasma Cholesterol: Rational in Silico Drug Investigation of Pyrrole-Based HMG-CoA Reductase Inhibitors

Article abstract of DOI:10.1089/adt.2017.804

Alzheimer's disease (AD), a worldwide renowned progressive neurodegenerative disorder, is the most common cause of dementia. There are several studies on the important role of cholesterol metabolism in AD pathogenesis, which indicated that the high concen

Full text of DOI:10.1089/adt.2017.804

O R I G I N A L A R T I C L E
Risk of Late-Onset Alzheimer’s
Disease by Plasma Cholesterol:
Rational In Silico Drug Investigation
of Pyrrole-Based HMG-CoA
Reductase Inhibitors
Sajad Shahbazi,1 Jagdeep Kaur,1 Ananya Kuanar,2
12.17 kcal/mol and binding energy -94.10 kcal/mol through 5
Dattatreya Kar,2 Shikha Singh,2 and Ranbir Chander Sobti1,3
hydrogen bonds. It showed the best ADME and toxicity profil-
ing and higher affinity values than other potent candidate and
market drugs such as atorvastatin and rosuvastatin. Therefore, it
is suggested for further in vivo investigation, the druggability of
5j and its cholesterol regulatory impact on AD.
1Department of Biotechnology, Panjab University,
Chandigarh, India.
2Centre of Biotechnology, SOA University, Bhubaneswar, India.
3Babasaheb Bhimrao Ambedkar University, Lucknow, India.
Keywords: Alzheimer’s disease, plasma cholesterol, HMGCR
inhibitor, 3-sulfamoylpyrroles 5, ADMET, docking score
ABSTRACT
Alzheimer’s disease (AD), a worldwide renowned progressive
neurodegenerative disorder, is the most common cause of de-
mentia. There are several studies on the important role of cho-
lesterol metabolism in AD pathogenesis, which indicated that
the high concentrations of serum cholesterol increase the risk of
AD. Biosynthesis of the plasma cholesterol and other isoprenoids
is catalyzed by 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR)
through the conversion of HMG-CoA to mevalonic acid in me-
valonate pathway. Normally, the high level of plasma cholesterol
is downregulated by HGMCR inhibition as the result of deg-
radation of LDL, but in abnormal conditions, for example, high
blood glucose, the HMGCR over activated resulting in uncon-
trolled blood cholesterol. Selective HMGCR inhibitor drugs such
as statins, which increase the catabolism of plasma LDL and
reduce the plasma concentration of cholesterol, have been in-
vestigated as a possible treatment for AD. In the present study,
we have identified the binding modes of 22 various derivatives of
3-sulfamoylpyrroles 16, prepared via a [3 + 2] cycloaddition of
a mu¨nchnone with a sulfonamide-substituted alkyne, by using
efficient biocomputational tools. Out of 22, 5 ligands, with code
numbers 5b, 5c, 5d, 5i, and 5j, possessed most absorption,
distribution, metabolism, and excretion (ADME) and toxicity
profiles in acceptable ranges. Among ligands, 5j (sodium (3R,5R)-
7-(3-(N,N-dimethylsulfamoyl)-5-(4-fluorophenyl)-2-isopropyl-
4-phenyl-1H-pyrrol-1-yl)-3,5-dihydroxyheptanoate) could inhibit
HMGCR enzyme in inhibitory binding site with affinity value -
INTRODUCTION
he brain is a lipid-rich organ. Since *50% of brain
dry mass constituted lipids, specifically cholesterol,
the dysregulation of brain cholesterol metabolism
T
results in brain diseases, in particular Alzheimer’s
disease (AD).1 AD, an age-related neurodegenerative disease,
impairs cognitive functions, particularly memory. AD is
known as the major form of dementia and diagnosed by age-
dependent amyloid plaque deposition and neurofibrillary
tangles.2 After discovery of the isoform 4 (e4) of the choles-
terol transport protein (apolipoprotein E) synthesized in the
liver and the central nervous system (CNS),3 most attention
focused on the role of cholesterol metabolism pathway on AD
development.4
The role of cholesterol in many critical aspects of AD
neuropathology has been proved by various evidence derived
from genetic, epidemiological, and biochemical studies. It is
discovered that a number of genes involved in cholesterol
homeostasis caused late-onset AD.5,6 There is evidence that
cholesterol homeostasis dysregulation can significantly in-
fluence amyloid beta (Ab) production, formation of amyloid
plaques, Ab toxicity, tau hyperphosphorylation, and other
mechanisms leading to sporadic AD.7,8
One of the well-studied cholesterol homeostasis dysregu-
lation is hypercholesterolemia. Hypercholesterolemia, known
DOI: 10.1089/adt.2017.804
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SHAHBAZI ET AL.
as a risk factor for vascular dysfunction (VaD) and AD, can
be controlled in every step of cellular cholesterol synthe-
sis.9 Cellular cholesterol is synthesized from acetyl-CoA in
a multistep pre- and postsqualene mevalonate pathway.
The presqualene mevalonate pathway starts by formation of
acetoacetyl-CoA from two moles of acetyl Co-A in the pres-
ence of acetoacetyl-CoA thiolase, followed by synthesis of
HMG-CoA from one mole of acetoacetyl-CoA and acetyl-CoA
through 3-hydroxy-3-methylglutaryl (HMG) CoA synthase
(HMGS) activity. Subsequently, 3-hydroxy-3-methylglutaryl-
CoA reductase (HMGCR) converts HMG-CoA to mevalonic acid.10
HMGCR is the rate-limiting enzyme of the mevalonate
pathway. The presence of two SRE motifs in the HMGCR
promoter leads to a higher level sterol-dependent regula-
tion.11 HMGCR inhibition is a rapid (within 1 h) switch off of
cholesterol synthesis. Thus, HMGCR is the main target for
developing various cholesterol-synthesis inhibitory drugs
such as statins.11 Information on the status and regulation of
HMGCR in AD is very limited. The crucial role of HMGCR in
cholesterol regulation in both the brain and plasma has great
impact on dementia and AD development directly via plaque
formation in the brain or indirectly through hypercholester-
olemia and VaD.12
MATERIALS AND METHODS
Selection of Proteins
The HMGCR structures (PDB Id: 2Q1L) were obtained from
the RCSB Protein Data Bank (www.rcsb.org/pdb) with X-ray
diffraction resolutions in 2.00 angstroms.
Preparation of Ligands and Proteins
‘‘LigPrep 2.5’’ module of Schrodinger Suite 2011 was uti-
lized for ligand preparation using OPLS 2005 forcefield at
biologically relevant pH. The preparation process included the
assignment of the protonation states: disconnecting of group I
metals in simple salts, protonating strong bases, and depro-
tonating strong acids, by adding explicit hydrogens and
topological duplicates. The pathway of 4-sulfamoyl pyrrole
synthesis13 and the molecular properties of all compounds are
presented in Figure 1 and Table 1, respectively.
The preparation of retrieved target protein was performed
using Protein Preparation Wizard of Schrodinger Suite 2011
¨
(Schrodinger Suite; Epik version 2.2; Impact version 5.7;
¨
Prime version 2.3, Schrodinger, LLC, New York, NY, 2011).
OPLS 2005 forcefield with RMSD as 0.30 was used for geo-
metrical optimization and energy minimization of target
protein. The receptor binding site was predicted based on the
pose of presented ligands in crystalographic structure of
protein extracted from PDB file.
The focus of the present study was to investigate the im-
pact and mode of action of 22 various derivatives of
3-sulfamoylpyrroles 16 on HMGCR inhibition in hypercho-
lesterolemia patients. Schrodinger Suite 2011 and TOPKAT
approach of Accelrys technology, two most powerful and
well-known tools for in silico drug investigation, have been
used to elucidate the molecular mechanism, biological ac-
tivity, and toxicity properties of druggable molecules.
Pharmacodynamic, Pharmacokinetic,
and Toxicity Properties
The pharmacodynamic and pharmacokinetic study and
quantitative prediction of absorption, distribution, metabo-
lism, and excretion (ADME) have been carried out using
Fig. 1. The pathway of 4-sulfamoyl pyrrole synthesis: (A) acetic anhydride, toluene at 60ꢀC, 5 h, 60–80%; (B) 30 (v/v) % TFA/DCM, room
temperature, 2 h, quantitative; (C) one equivalent of 1 N NaOH/THF, room temperature, 4 h, quantitative (Ref. US Patent No. 7250444 B2).
2
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Table 1. Two-Dimensional Structure and Molecular Properties of Ligands
Name
NR1R2
R3
mol MW
donorHB
accptHB
QPlogPC16
QPlogPoct
QPlogPw
QPlogPo/w
5a
H
588.69
2
10.6
17.106
29.157
15.548
4.937
5b
5c
5d
5e
H
H
H
F
586.717
572.69
2
2
2
5
8.9
8.9
17.779
17.01
28.796
28.3
14.082
13.825
14.081
21.862
5.973
5.568
5.973
4.328
586.717
655.712
8.9
17.778
20.384
28.796
36.886
11.4
5f
F
F
669.739
628.687
5
4
11.4
9.65
20.563
19.121
37.182
32.884
24.629
18.31
4.19
5g
5.248
5h
5i
F
626.714
601.732
546.653
558.664
564.643
550.616
3
2
2
2
2
3
8.9
10.9
8.9
19.373
18.272
16.528
16.835
16.105
16.342
31.887
30.401
27.264
27.35
16.02
6.395
2.974
5.035
5.46
H
H
H
F
16.146
13.949
13.471
13.724
15.409
5j
5k
5l
8.4
8.9
27.51
5.264
4.8
5m
F
8.9
28.329
5n
5o
F
F
622.741
691.761
2
5
9.4
17.174
21.171
29.275
38.571
14.233
23.885
5.841
3.592
13.4
5p
F
655.712
5
11.4
20.458
36.886
21.896
4.288
5q
5r
5s
5t
F
652.752
640.741
622.75
2
2
2
3
8.9
8.9
8.9
8.9
19.789
19.884
20.271
18.393
31.642
30.833
30.74
15.13
7.169
7.006
6.719
6.169
F
14.846
15.113
16.095
H
F
630.678
31.258
5u
5v
F
612.687
594.697
3
3
8.9
8.9
18.795
19.197
31.003
30.748
16.315
16.534
5.935
5.702
H
accptHB, estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution: R.V. = 2.0–20.0; donorHB,
estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution: R.V.:0.0–6.0); mol_MW, molecular weight:
recommended value (R.V.):130–725; QPlogPC16, predicted hexadecane/gas partition coefficient: R.V. = 4.0–18.0; QPlogPoct{, predicted octanol/gas partition
coefficient: R.V. = 8.0–35.0; QPlogPw, predicted water/gas partition coefficient: R.V. = 4.0–45.0; QPlogPo/w, predicted octanol/water partition coefficient:
R.V. = -2.0–6.5.
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SHAHBAZI ET AL.
¨
QikProp module of Schrodinger Suite 2011 (Schrodinger
Press: QikProp 3.4 User Manual, LLC, 2011). These criteria for
each compound were assessed by #start parameter, as the
overall ADME acceptance score for drug likeness for 95% of
known drugs.14 #start includes the following: SASA/Smol
(300-1,000), FISA (7-330), FOSA (0-750), PISA (0-450), total
solvent- accessible volume (volume), Glob (0.75-0.95 for
95% of drugs), molecular weight (mol_MW, 130–725), do-
norHB (0-6), accptHB (2-20), partition coefficient, including
QPlogPo/w (octanol/water, 2-6.5), QPlogPoct{ (octanol/gas,
8-35), QPlogPw (water/gas, 4-45), and QPlogPC16 (hex-
adecane/gas, 4-18), number of likely metabolic reactions
(Metab; 1-8 for 95% of drugs), QPlogKhsa (-1.5 to 1.5),
QPlogHERG (concern, <-5), QPlogKp (-8 to -10), QPPMDCK
(nm per sec; <25 poor, >500 great),15 CNS activity (-2 to 2),16
QPlogBB (-3 to 1.2),17 QPPCaco (<25 poor, >500 great),18 PM3
calculated ionization potential (IP(eV); 7.9-10.5), PM3 cal-
culated electron affinity (EA(eV); -0.9 to 1.7), the human oral
absorption level, the maximum transdermal transport rate
(Jm; Kp XMWXS; mgcm-2h-1), and the number of violations
of Lipinski’s rule of five19 of the various 3-sulfamoylpyrroles
16 derivatives.
Visualization of Interaction Between Top Score
Candidate/s and Residues in Receptors
Candidates with the best ADMET (T, toxicity) profiles and top
dock scores have been selected and their complexes with residues
in binding site of receptor were visualized using XP visualizer
approaches of Schrodinger 2011. The receptor surface was figured
based on the electrostatic potential of residuesin binding packet of
˚
protein and truncated in 5 A from ligand with 20% transparency.
RESULTS AND DISCUSSION
The most common HMGCR inhibitor marketed drugs are
within the statin family of drugs. However, there are various
reports regarding the side effects of most statins, among which,
myalgia is the main adverse effect, manifested by muscle
stiffness, muscle weakness, fatigue, and cramps.24 Myalgia is
thought to be a consequence of inhibition of myocytic HMG-
CoA reductase.25 Also, it is reported that statins with greater
hepatoselectivity may reduce the side effects due to less
availability to muscle tissues.26,27 Therefore, statins have been
modified to increase the hepatoselectivity by utilizing or-
ganic anion transporting polypeptides, the hepatocyte-specific
transporters.28 Hydrophilic statins, by reducing the passive
diffusion of nonselective ones into all cells, can increase se-
lectivity for cells and internalize statins through active trans-
port. Figure 2 represents the role of statins or other HGMCR
inhibitors in regulating the presqualene mevalonate pathway.
Derivatives have been developed from sodium (3S,5S)-7-
(2-(4-fluorophenyl)-5-isopropyl-3-phenyl-4-sulfamoyl-1H-
pyrrol-1-yl)-3,5-dihydroxyheptanoate, yielding modifying
atorvastatin in multiple steps. The 5-member heterocyclic
core in atorvastatin structure has been retained as a key scaf-
fold for interacting within the active site of the enzyme. For
synthesizing the different 4-sulfamoyl pyrrole analogs, the
2-(N-(2-((4S,6S)-6-(2-(tert-butoxy)-2-oxoethyl)-2,2-dimethyl-
1,3-dioxan-4-yl)ethyl)isobutyramido)-2-(4-fluorophenyl)acetic
acid was prepared stepwise from 2-(4-fluorophenyl)acetic acid
The qualitative and quantitative toxicity properties of
compounds were predicted using online TOPKAT approaches
of Accelrys Environmental Chemistry and Toxicology Work-
bench, Accelrys Inc. (San Diego, CA; https://ect01.accelrysonline
.com/webport/ECT/main.htm). TOPKAT features provide the
accurate toxicity properties of compounds based on similarity
with compounds registered in two largest chemical and drug
data banks, FDA and NTP. Toxicity profiling includes predic-
tion of rodent carcinogenicity from the FDA and NTP data sets
for both female and male (v3.1), weight of evidence (WOE)
(v5.1), developmental toxicity potential (DTP), mutagenicity
(Ames test v3.1), ocular irritation (v5.1), skin sensitization
(guinea pig maximization test) and irritancy (v6.1), aerobic
biodegradability (v6.1), and EC50, LD50, and TD50.20
The ADME and toxicity results of all compounds are listed in
Table 2 and Supplementary Table S1 (Supplementary Data are
available online at www.liebertpub.com/adt), respectively.
¨
and heated in acetic anhydride/toluene to form munchnone
¨
in situ; subsequently, munchnone interacted with alkynyl sul-
fonamides to afford the pyrroles 3 in situ.13 By treating the pyr-
roles 3 with trifluoroacetic acid, the trimethoxybenzyl group has
been eliminated from the sulfonamide nitrogen and cleaved the
tert-butyl esters and acetonide to form the lactones 4. By treating
the lactone with one equivalent of 1 N NaOH and lyophilizing, the
pyrrole sodium salts 5 has been yielded. Finally, various 4-
sulfamoyl pyrrole analogs have been prepared by interacting
different residues with the sulfonamide moiety of pyrrole sodium
salts 5. The various residues and their physiochemical properties
are presented in Table 1 and the pyrrole sodium salts 5 synthe-
sizing pathway is depicted in Figure 1.
Receptor—Ligand Interactions
Dock scores and Molecular Mechanics/Generalized Born
Surface Area (MMGBSA) values21 for selected compounds
in complex with receptor were computed using ‘‘Glide 5.7’’
module in Extra Precision (XP) mode22,23 and Prime 3.0
application of Schrodinger Suite 2011 (Suite 2012: Prime,
¨
Version 3.1, Schrodinger, LLC, New York, NY, 2012), respec-
tively. Supplementary Table S2 represents docking scores and
MMGBSA values.
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ALZHEIMER’S DISEASE BY PLASMA CHOLESTEROL
Table 2. Absorption, Distribution, Metabolism, and Excretion Properties
Name QPlogS CIQPlogS QPlog HERG QPP Caco QPlog BB QPP MDCK QPlog Kp QPlog Khsa #metab HOA RF RT
Jm
5a
5b
5c
5d
5e
5f
-6.08
-7.32
-6.64
-7.32
-6.86
-5.40
-6.90
-7.77
-6.44
-6.18
-6.72
-6.53
-6.45
-7.18
-6.66
-6.95
-8.74
-8.48
-8.01
-7.76
-7.40
-7.04
-7.99
-8.57
-8.29
-8.57
-9.01
-8.65
-9.26
-9.62
-7.08
-7.73
-7.98
-8.09
-7.73
-9.17
-8.96
-9.01
-10.29
-10.00
-9.63
-9.70
-9.33
-8.97
-4.11
-4.52
-4.05
-4.52
-4.90
-3.40
-4.89
-5.03
-5.36
-4.13
-4.27
-4.00
-4.23
-4.04
-5.09
-4.96
-5.40
-5.59
-5.67
-4.96
-5.09
-5.23
32.47
36.13
29.91
36.14
1.71
-2.11
-2.16
-2.14
-2.16
-3.79
-3.49
-2.93
-2.59
-1.85
-2.24
-2.25
-2.14
-2.38
-1.99
-4.37
-3.88
-2.17
-2.43
-2.57
-2.39
-2.49
-2.58
28.02
31.45
25.64
31.46
2.10
-3.17
-3.00
-3.27
-3.00
-5.23
-4.66
-3.84
-3.16
-5.16
-3.38
-3.32
-3.51
-3.79
-3.43
-5.79
-5.33
-2.61
-2.53
-2.49
-3.27
-3.14
-3.00
0.41
0.85
0.73
0.85
0.39
0.16
0.57
0.94
0.56
0.52
0.69
0.55
0.42
0.70
0.11
0.38
1.22
1.08
1.03
0.81
0.77
0.73
5
4
4
4
6
7
6
5
5
4
4
4
4
4
4
5
6
5
5
4
5
5
2
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
1
2
2
2
1
2
3
2
2
2
2
2
2
2
1
1
1
1
2
2
2
2
2
1
1
1
2
1
2
2
1
1
1
2
2
2
0.000333
0.000028
0.00007
0.000028
0.000001
0.000059
0.000011
0.000007
0.000002
0.000151
0.000051
0.000051
0.000032
0.000015
0
1.36
3.79
5g
5h
5i
7.87
10.96
24.82
6.87
16.76
8.05
5j
24.47
26.30
24.52
16.63
27.73
0.81
21.03
22.32
38.14
24.90
76.55
0.96
5k
5l
5m
5n
5o
5p
5q
5r
1.53
1.87
0
34.27
28.38
24.86
17.53
17.51
17.50
53.77
44.30
21.21
47.15
26.02
14.37
0.000003
0.000006
0.00002
0.000006
0.000018
0.000054
5s
5t
5u
5v
CIQPlogS, conformation-independent predicted aqueous solubility; -6.5 < x<0.5; CNS, central nervous system activity -2, -1, 0, 1, 2: -2=completely inactive, -1=very low
activity, 0 =low activity, 1 =medium activity, 2 =completely active, 3 =high; HOA, human oral absorption level; 1,2, 3; 1 =low, 2 =medium; Jm, maximum transdermal transport rate;
QPlogBB, predicted brain/blood partition coefficient; -3.0 to 1.2; QPlogHERG, predicted IC50 value for blockage of hERG K+ channels; <-5=concern; QPlogKp, predicted skin
permeability; range =-8 < x < -1; QPlogKhsa, prediction of binding to human serum albumin; -1.5 to 1.5; QPlogS, prediction of aqueous solubility level; recommended range -6.5 <
x<0.5; QPPCaco, predicted apparent gut-blood barrier permeability; <25 =poor, >500 =great; QPPMDCK, predicted apparent Madin-Darby canine kidney cell permeability; <25 =poor,
>500 =great; #Metab, number of likely metabolic reactions; 1–8; RF, the number of violations of Lipinski’s rule of five; RT, the number of violations of Jorgensen’s rule of three.
Protein and Ligand Preparation
ADME Prediction
The structures of HMGCR, PDB Ids: 2Q1L, have been
downloaded from the Protein Data Bank website (www.rcsb
.org/pdb) and prepared by using ProPrep and Grid Generation
The main parameter of druggability is the ability of a
compound to cross the oral and intestinal barriers, enter the
blood circulation for delivery to its target wherever in the
body, be metabolized by the target, and excrete from the body
during a period of time. The Lipinski’s rule of 5 (ro5) was
utilized to predict the drug likeness of orally administered
compounds.29 ro5 includes standard ranges for molecular
weight (MW <500), number of hydrogen bond donor (<5),
¨
Wizard of Schrodinger 2011 by optimizing the tertiary
structure and predicting the binding sites for the ligands.
The ligands, also, have been prepared using LigPrep 2.5 of
¨
Schrodinger 2011 and their molecular properties have been
computed via QikProp 3.4 (Table 1).
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SHAHBAZI ET AL.
Fig. 2. Presqualene mevalonate pathway and the role of statins or other HGMCR inhibitors.
number of hydrogen bond acceptor (£10), and predicted oc-
tanol/water partition coefficient (logP < 5). Lipinski’s criteria
are presented in the scatter plots of correlation between MW
and rest of parameters for all compounds, depicted in Figure 3.
According to the result, none of the compounds had molecular
weight in the recommended range by ro5 and could not satisfy
drug likeness criteria. Approximately, 31.82%, 72.73%, and
100% of all compounds are in the recommended range for
LogP, accptHB, and donorHB of drug likeness, respectively.15
However, all MW values are in the recommended range for
95% of known drugs indicated by #star.20 The ADME prop-
erties of all compounds are presented in Table 2.
sorption, the percentage of human oral absorption, and the
conformation-independent aqueous solubility, CIlog S. CIlog
S is computed based on the similarity of compounds with their
close analogs. The adjusted formula is given in Equation (1)
for similarity >0.9.
Ppred = SPexp + ð1 - SÞPQP,
(1)
where S is the similarity, Pexp and PQP are the respective
experimental and QikProp predictions for the most similar
molecule within the training set. There are several parameters
that have to be considered for prediction of bioavailability of a
compound such as Log S, predict the aqueous solubility levels,
#metab, number of likely metabolic reactions, and Caco-2, the
gut/blood barrier permeability that is a nonactive transport in
nm/s, respectively.30 The results indicate that 36.36% of com-
pounds possessed QPPCaco in recommended range 25–500 nm/s
and rest showed low gut permeability. All compounds showed
metabolic behaviors in recommended range and the aqueous
solubility levels of all compounds were out of recommended
range -6.5 to 0.5. Increasing the hydrophilicity could dra-
matically decrease the gut permeability of compounds and in
turn reduce the bioavailability of orally administered drugs,
but can increase the specificity and selectivity of statins.13
Bioavailability
Absorption and the first-pass metabolism of the liver are
two processes by which the bioavailability of each compound
can be predicted. The absorption influenced by effective fac-
tors, including the solubility of compounds, the gut wall
permeability to the compounds, and the ability of compound
to interact with shuttles in the gut wall such as transporters
and metabolizing enzymes, depended on the functional
groups in the compound structure.
The computational methodology for oral absorption pre-
diction has been offered by Jorgensen, known as ‘‘Rule of
Three’’ (ro3). The parameters of oral availability likelihood
include log S>-5.7, QPPCaco >22 nm/s, and primary metab-
olites, #metab <7. However, there are other important pa-
rameters that directly affect the bioavailability of compounds
such as the prediction of the qualitative human oral ab-
The Prediction of Blood/Brain Penetration (QPlogBB)
The prediction of the blood/brain barrier (BBB) permeability
using QPlogBB was used to check the accessibility of com-
pounds for CNS based on the polarity of compounds.31 For the
blood/brain penetration (logB/B) as well as CNS activity,
6
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ALZHEIMER’S DISEASE BY PLASMA CHOLESTEROL
Fig. 3. Scatter plots of correlation between MW and the rest of the parameters for all compounds. MW, molecular weight.
Madin-Darby canine kidney (MDCK) was used to compute the
level of BBB penetration to druggable molecules.31 Results
indicated that none of compounds was active in CNS (pre-
dicted value -2), except 5e, 5f, 5o, and 5p, almost all com-
pounds were in the recommended range for the logB/B
prediction (-3.0 to 1.2) and *54.54% of all compounds were
in the recommended range for the prediction of nonactive
transportation through the MDCK (25–500).
The Prediction of Metabolism
The accessibility level of compounds for their target after
entering into the blood stream, as the number of likely met-
abolic reactions, can be computed by QikProp. The #metadata
were used to predict the average number of possible metabolic
reactions of each compound. Based on #metadata, all com-
pounds possessed #metavalues within the recommended
range of metabolic reaction 1-8.
The Prediction of Plasma Protein Binding
`
The Prediction of Blockage of Human Ether-a-go-go-
The plasma proteins such as glycoproteins, human serum
albumin, lipoproteins, and globulins (a, b, and c) are targets
for the prediction of pharmacodynamics of a druggable
molecule by investigating the ability of ligands to bind to
blood proteins, which can directly affect the efficacy of a
drug.32 On the contrary, the availability of drug for target is
in direct relation to the rate of plasma protein binding. The
high plasma protein binding results in reducing the rate of
distribution of drug through general blood circulation.33
Therefore, for designing a drug, a less degree of plasma
protein binding is desirable. QPlogKhsa was used as a unique
parameter for the estimation of tendency level of a com-
pound to bind to plasma proteins. The results showed that all
compounds were in the recommended range (-1.5 to 1.5) for
plasma protein binding.
Related Gene Potassium Channel
One of the most important targets for testing the cardiac
toxicity of druggable molecules is the human Ether-a`-go-go-
related gene (hERG) because of its role in the electrical activity
during systolic and diastolic periods of the heart by encoding
the potassium ion (K+) channel.34 This channel has modula-
tory function for the nervous system as well35 and involves
neurocardiac disorders such as long QT syndrome (Torsade de
pointes).36 Therefore, blockage of hERG K+ channel can be
considered as the potential toxicity of a compound for the
nervous and cardiac system, which is indicated by IC50 in drug
designing.37 The QPlogHERG indicator was used by QikProp
module of Schrodinger Suite 2011 to simulate the IC50 values
of hERG channel toxicity of a compound. The values showed
that 63.63% of compounds had no toxicity potential for
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SHAHBAZI ET AL.
neurocardiac systems and their values fell in the recommended
probability of 0.97–1.00. None of compounds was degradable
through an aerobic biodegradability process with a proba-
bility of 0.19–0.48. The levels of maximum tolerated dose by
feeding, oral LD50, inhalation and fathead minnow LC50 for
rat, Daphnia EC50, TD50 for mouse and rat were evaluated and
are presented in Supplementary Table S1.
range of IC50 (>-5) for blockage of hERG K+ channels.
Toxicity
Accelrys Environmental Chemistry and Toxicology Work-
bench invented TOPKAT online approaches for in silico in-
vestigation of the most important pharmacological toxicity
parameters of compounds. TOPKAT toxicity profiling includes
carcinogenicity, mutagenicity, skin and ocular sensitizing and
irritancy, lethal dose for oral administration, effective con-
centration, tolerable dose, lethal concentration for inhalation,
and toxicity potential based on structural similarity of com-
pounds with structures available in both the FDA (U.S. Food
and Drug Administration) and NTP (National Toxicology
Program) databases. The carcinogenicity of all compounds for
male mouse (MM), male rat (MR), female mouse (FM), and
female rat (FR) was predicted. Based on the NTP database, only
13.64% all compounds were carcinogenic for FM with a
probability of 0.58–0.64 and just 10% carcinogenic for MR
0.61–0.64 and none of compounds showed carcinogenicity
for MR and FM. According to the FDA database, around
63.64% of all compounds were carcinogenic for MR with a
probability of 0.31–0.42 and no carcinogenicity for FM, MM,
and FR. The predicted data indicated that all compounds were
nonmutagenic and noncarcinogenic based on the WOE for
rodent carcinogenicity. Around 68.18% of all compound DTP
with a probability of 0.49–0.58. All compounds were nonir-
ritant for skin, however, around 90% of all showed weak to
strong skin sensitizing with a probability of 0.75–0.84 and
approximately 81.82% of all compounds irritate oculus with a
In a glance, all compounds with big molecular sizes and
more hydrophilic modification were not recommended for
oral administration and could not satisfy Lipinski’s and
Jorgensen’s criteria, but by having good distribution and
metabolism in the body can be used in suggested doses as
interveinal injections. Among them, 5b (sodium (3R,5R)-7-(2-(4-
fluorophenyl)-5-isopropyl-3-phenyl-4-(piperidin-1-ylsulfonyl)-
1H-pyrrol-1-yl)-3,5-dihydroxyheptanoate), 5c (sodium
(3R,5R)-7-(2-(4-fluorophenyl)-5-isopropyl-3-phenyl-4-(pyrrolidin-
1-ylsulfonyl)-1H-pyrrol-1-yl)-3,5-dihydroxyheptanoate), 5d
(sodium (3R,5R)-7-(2-(4-fluorophenyl)-5-isopropyl-3-phenyl-
4-(piperidin-1-ylsulfonyl)-1H-pyrrol-1-yl)-3,5-dihydroxy-
heptanoate), 5i (sodium (3R,5R)-7-(2-(4-fluorophenyl)
-5- isopropyl-4- ((4-methylpiperazin-1-yl)sulfonyl) -3-phenyl-
1H-pyrrol-1-yl) -3,5-dihydroxyheptanoate), and 5j (sodium
(3R,5R)-7-(3-(N,N-dimethylsulfamoyl)-5-(4-fluorophenyl)-2-
isopropyl-4-phenyl-1H-pyrrol-1-yl)-3,5-dihydroxyheptanoate)
showed better ADME and toxicity profiling and can be selected
for further study.
Docking Calculations Using Schrodinger 2011
The five selected compounds have been docked to the
binding site of HMGCR enzyme via Glide v.5.7 feature of
Schrodinger Suite 2011. The result indicated that among all
Fig. 4. Dock scores of five selected compounds in comparison with two marketed drugs, atorvastatin and rosuvastatin.
8
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ALZHEIMER’S DISEASE BY PLASMA CHOLESTEROL
five compounds, 5j with affinity value -12.17 kcal/mol and
binding energy -94.10 kcal/mol has a significantly better
dock score than both the marketed drugs atorvastatin and
rosuvastatin with docking scores -10.55 and -11.43 kcal/mol,
respectively. The dock scores of five selected compounds in
comparison with two marketed drugs, atorvastatin and rosu-
vastatin, have been depicted in Figure 4.
Glu559, and Lys735with bond distances 1.872, 1.912, 1.880,
˚
1.726, and 1.601 A, respectively. The cocrystal of 5j with HMG-
CoA reductase shown in Figure 5, salient features of the
binding of (sodium (3R,5R)-7-(3-(N,N-dimethylsulfamoyl)-5-
(4-fluorophenyl)-2-isopropyl-4-phenyl-1H-pyrrol-1-yl)-3,5-
dihydroxyheptanoate) to the enzyme include strong hydrogen
bonding interactions provided by the 3,5,7-trihydroxy hepta-
noic acid fragment of 5j; the isopropyl group fitting into a small
lipophilic pocket. The number of hydrogen bonds and related
residues for all compounds are presented in Supplementary
Table S2.
Visualization of Interaction Between Compounds
and Residues in Receptors
In a glance, 5j (sodium (3R,5R)-7-(3-(N,N-dimethylsulfamoyl)-
5-(4-fluorophenyl)-2-isopropyl-4-phenyl-1H-pyrrol-1-yl)-3,5-
dihydroxyheptanoate) among five eligible compounds with the
best dock score, ADME, and toxicity profiling has been selected
for visualizing the interaction with the residues in binding
packet of HMGCR with PDB id 2Q1L.
CONCLUSION
The important role of cholesterol metabolism in AD path-
ogenesis has been reported that the high concentrations of
serum cholesterol can increase the risk of AD. Since the
biosynthesis of plasma cholesterol and other isoprenoids is
initiated by catalytic function of 3-hydroxy-3-methyl-glutaryl-
CoA reductase (HMGCR) through the conversion of HMG-CoA
to mevalonic acid in the mevalonate pathway, HMGCR is now
considered as an interesting target for AD drug development
and treatment. In this article, we have focused on modified
statin derivatives, which act selectively on hepatocyte to re-
duce the risk of myalgia, the well-known side effect of statins.
There have been designed 22 varieties of 4-sulfamoyl pyr-
roles, the statin derivatives and the biological activities and
pharmacological properties of such compounds have been
predicted. Among all compounds, 5j (sodium (3R,5R)-7-(3-
(N,N-dimethylsulfamoyl)-5-(4-fluorophenyl)-2-isopropyl-4-
phenyl-1H-pyrrol-1-yl)-3,5-dihydroxyheptanoate) showed
the best ADME and toxicity profiling and higher affinity
values than other potent candidates. It could inhibit the
HMGCR enzyme in inhibitory binding site with affinity value
-12.17 kcal/mol and binding energy -94.10 kcal/mol through
five hydrogen bonds, much better than atorvastatin and ro-
suvastatin. Most of ADMET properties were in recommended
ranges and can be considered as potent hepatoselective pyr-
role drugs for cholesterol and AD treatment.
All compounds have been docketed in same binding poc-
ket of HMGCR enzyme with PDB ids: 2Q1L, in which 5j
interacted with residue inhibitory binding pocket, including
GLU559, CYS561, LEU562, SER565, ARG568, LYS735, ALA751,
HIS752, ASN755, LEU853, ALA856, HIS861, ARG590, MET657,
SER661, VAL683, SER684, ASP690, LYS691, and LYS692
through five hydrogen bonds with Arg590, Asn755, Asp690,
ACKNOWLEDGMENT
The authors are grateful to Dr.Shikha Singh, Centre of Bio-
technology, SOA University, Bhubaneswar, Orissa, India, for
providing technical support and encouragement throughout.
DISCLOSURE STATEMENT
No competing financial interests exist.
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Fig. 5. Cocrystal of 5j with HMG-CoA reductase. Color images
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Abbreviations Used
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ADME ¼ absorption, distribution, metabolism, and excretion
Ab ¼ amyloid beta
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BBB ¼ blood/brain barrier
CNS ¼ central nervous system
DTP ¼ developmental toxicity potential
FM ¼ female mouse
FR ¼ female rat
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hERG ¼ human Ether-a`-go-go-related gene
HMGCR ¼ 3-hydroxy-3-methylglutaryl-CoA reductase
MDCK ¼ Madin-Darby canine kidney
MM ¼ male mouse
MMGBSA ¼ Molecular Mechanics/Generalized Born Surface Area
MR ¼ male rat
MW ¼ molecular weight
VaD ¼ vascular dysfunction
WOE ¼ weight of evidence
XP ¼ extra precision
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