Molecular docking and 3d QSAR study on PfENR inhibition by triclosan derivatives

Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Preliminary study:
Molecular Docking and 3D-QSAR Study on
Pf
ENR Inhibition by Triclosan Derivatives
Department of Medicinal Chemistry
University of Medicine and Pharmacy at Ho Chi Minh City
The 7th PharmaIndochina Conference
(14.01.2011)
Dr Pharm Khac-Minh Thai

Dr Khac-Minh Thai
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
VOLUME 10 | DECEMBER 2011 | 887
GSK: RTS,S
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Introduction
3D QSAR models of triclosan derivatives on
Pf
ENR inhibition
Docking study on
Pf
ENR
Conclusions
Contents

Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
The global distribution of malaria, showing areas where
Plasmodium falciparum resistance to the most
commonly used
antimalarial drugs, chloroquine and sulphadoxine-pyrimethamine
(Nature 2004 - WHO 2008)
- Killing 1–3 M people per year
- Causing disease in 300–500 M people per 1 year
Malaria
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
(
Nat Rev Drug Dis,
2010, 9(7), 511-2)
Development of a malaria vaccine
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Selected antimalarial drugs in development
(
Nat Rev Drug Dis,
2010, 9(7), 511-2)
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Targets for antimalarial chemotherapy
(

Nat Rev Drug Dis,
2004, 3(6), 509-20)
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Pathway of fatty acid biosynthesis
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
P. falciparum
Enoyl Acyl Carrier Protein Reductase
Pf
ENR
Pf
ENR
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
1. Collection of Triclosan Database
from Literature
- the
in vitro
PfENR inhibition data IC
50
value (74/83)
2. Generation 3D- Structure of
triclosan Database
EM/MD
3. 3D Structures Alignment
4. Calculation of 3D Descriptors

CoMFA/CoMSIA
6. PLS Analysis on Training Set
- r
2,
, SEE
- Crossvalidated q
2
, SEE (LOO)
7. Validation QSAR Model
on test sets and external sets
5. Definition of Training & Test Sets
Training set: 59
Test set: 15
Sybyl/Tripos
3D QSAR
A B
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Models
Descriptor
field
Column
filter
Optimum
number of
component
q
2
(LOO)

SEE
R
2

(training
set)
r
2
(test
set)
CoMSIA 1 Hydrophobic
1 6 0.588 0.279 0.915 0.642
5 6 0.593 0.279 0.915 0.642
CoMSIA 2
Steric,
hydrophobic
1 6 0.591 0.241 0.937 0.566
3 6 0.594 0.241 0.937 0.566
CoMSIA 3
Hydrophobic,
H-bond
acceptor
1 3 0.507 0.368 0.844 0.485
CoMSIA 4
Hydrophobic,
H-bond
acceptor
4 4 0.529 0.301 0.897 0.551
5 4 0.538 0.301 0.897 0.551
CoMSIA 5

Steric,
hydrophobic,
H-bond
acceptor
1 4 0.500 0.269 0.918 0.602
5 4 0.544 0.269 0.918 0.602
Summary of 3D QSAR Models
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
3D QSAR CoMSIA Model 1:
Hydrophobic
(Observed versus calculated
Pf
ENR pIC50
plots)
R
2
= 0.92 and SEE = 0.28 (49 training set compounds)
r
2
= 0.64 and SEE = 0.23 (15 test set compounds)
Triclosan
(pIC
50

observed
= 1.1367;
pIC
50 calculated

= 1.1470)
JBC_2007_20
(pIC
50

observed
= 1.1487;
pIC
50 calculated
= 1.3260)
Hydrophobic: yellow mesh
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
3D QSAR CoMSIA Model 2:
Hydrophobic and Steric
(Observed versus calculated
Pf
ENR pIC
50
plots)
R
2
= 0.94 and SEE = 0.24 (49 training set compounds)
r
2
= 0.57 and SEE = 0.23 (15 test set compounds)
Triclosan
(pIC
50

observed
= 1.1367;
pIC
50 calculated
= 1.1920)
Hydrophobic: yellow mesh
Steric: green mesh
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
3D QSAR CoMSIA Model 3:
Hydrophobic and H-bond acceptor
(Observed versus calculated
Pf
ENR pIC
50
plots)
R
2
= 0.90 and SEE = 0.30 (49 training set compounds)
r
2
= 0.55 and SEE = 0.23 (15 test set compounds)
BMC_2005_50
(pIC
50

observed
= 0.8539;

pIC
50 calculated
= 1.1840)
Hydrophobic: yellow mesh
H-bond acceptor: blue mesh
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
3D QSAR CoMSIA Model 5:
Hydrophobic, Steric and H-bond acceptor
(Observed versus calculated
Pf
ENR pIC
50
plots)
R
2
= 0.92 and SEE = 0.27 (49 training set compounds)
r
2
= 0.60 and SEE = 0.24 (15 test set compounds)
BMC_2005_50
(pIC
50

observed
= 0.8539;
pIC
50 calculated
= 1.2320)

Hydrophobic: yellow mesh
Steric: green mesh
H-bond acceptor: blue mesh
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
P. falciparum
Enoyl Acyl Carrier Protein Reductase
Pf
ENR
Docking studies on
Pf
ENR
pdb 3am3
Ala217,
Asn218,
Ala219
Tyr277
Ala319
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
BMC_05_35
IC
50
= 0.11 μM
BMC_05_38
IC
50
= 0.30 μM

Docking studies on
Pf
ENR
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
BMC_05_44
IC
50
= 0.17 μM
BMC_05_46
IC
50
= 0.25 μM
Docking studies on
Pf
ENR
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
DESIGN INFORMATION
The positive effects on
Pf
ENR inhibition of triclosan
derivatives from QSAR and docking results
A B
2

Methyl,
Ph with a

halogen moiety
-CH
2
-NH-CH
2
-R,
-CH
2
-NH-C(O)-R
Maximum R is 2 rings
-C(O)-NH-R = Me or heterocylic
containing nitrogen
-NH-C(O)-R
-NH-CH2-R
–NH-SO2-R with R = Ar
5
A B
2
4
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Conclusions
Analogue Design as a Means of Discovering New Drugs:
(i) New Uses for Old Drugs
(ii)The PASS Program (Prediction of Activity Spectra for Substances)
(iii) New Leads from Old Drugs: The SOSA Approach
(Selective Optimization of Side Activities)
(Camille G. Wermuth)
The information obtained from 3D QSAR and docking

studies on
Pf
ENR might useful to guide the design of
new
Pf
ENR inhibitors which targeted in malaria.
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Target Identification and Validation
For human ABC-transporter, the dataset of Gottesman is the main source
for substrate patterns. For infectious deseases targets are identified via
screening of bacterial and fungal genomes for ABC-pumps.
Considerations on drugability includes analysis of the regulatory networks
Hit Identification
Hit identification comprises use of artificial
neural networks, autocorrelation vectors
and VolSurf descriptors
Virtual Screening
Virtual screening of large compound
libraries is performed on basis of our
recently developed SIBAR-technology,
which utilises similarity based algorithms.
Lead Optimisation
For lead optimisation, we use conventional 2D-
and 3D-QSAR methods as well as pharmacophore
modeling and molecular holograms
Structure based Design
Up to now no high resolution structure of a
human ABC-transporter is available.

Photoaffinity labeling and protein homology
modeling pave the way for structure-based
design and molecular basis of function.
Protein-Protein Interactions
Selectivity profiling is performed on a on a
systemic level and includes modeling of protein-
protein interactions.
Emerging Focus
Pharmacoinformatics
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12
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9
8
4
3
2
5
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1
Drug Design - Pharmacoinformatics
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
FUND:
The Vietnam's National Foundation for Science and Technology Development -
NAFOSTED (Grant # 104.01.21.09 to Khac-Minh Thai)
Prof. Dr. Tran Thanh Dao
Dr. Huynh Thi Ngoc Phuong

Nguyen Dac Chi
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam
Dr Khac-Minh Thai
University of Medicine and Pharmacy
at Ho Chi Minh City, Vietnam