DRUG DISCOVERY AND DEVELOPMENT
M. Hanafi
Puslit Kimia LIPI
Kawasan PUSPIPTEK, Serpong
Research Phases in Drug
Development
Target Identification
And Validation
Search of Lead
Structure
Optimization of
Lead Structure
Preclinical
Development
Idea
Lead Structure
Candidate for
Development Product
Development Product
DEVELOPMENT of NOVEL DRUGS
from NATURAL PRODUCT
1. Screening of Natural Compounds for Biological Activity :
Soil, plants, fungi, etc
2. Isolation and Purification of Active Principle
3. Determination of Structure : NMR, IR, MS
4. Structure-Activity relationships(SAR) :
Identification of Pharmacophore
5. Synthesis of Analogues :
Increase activity, reduce side effects
6. Receptor Theories : binding site information
7. Design and Synthesis of Novel Drug Structure
Lead compounds from Natural Products
O
O
O
O
O
O
O
O
N
HO
HO
OH
R
O
O
N
N
N
O
H
N
O
O
N
N
N
N
HO
OH
Vincristine (R = -CHO) –
Vinblastine (R = -CH3)
Vinca rosea (Catharanthus roseus)
(Apocynaceae)
Camptothecin
Camptotheca acuminata
Topotecan
Discovery from Natural Products
HO
O
H
O
O
N
H
N
O
O
Lovastatin
O
OH
O
Aspergillus tereus
O
O
H
O
R
O
Anticholesterol -
UK-3A
Streptomycesp sp. 517-02
Cytotoxic to P338, KB
COOCH3
N
O
OH
O
HO
OCH3
HO
CH3
N
HO
HO
O
Phenazine carbioxylate
Pseudomonas pycocyaneae
O
O
Sulochrin - Antidiabetes
Aspergillus terreus
O
Calanone
Callophyllum tesmanii
Lead Compounds
O
O
MeO
OMe
Curcumin
HO
OH
O
O
Piperine
N
O
O
MeO
HO
Gingerol
OH
Rational drug design



X-ray crystallography has developed so that the
determination of the 3-D crystal structures of proteins
and receptors is coming easier.
The Protein Data Bank (see
http://pdb.ccdc.cam.ac.uk/pdb/) has data for
hundreds of published structures which are all freely
available
Coupled with advances in computing power and
molecular modelling the so-called rational or structurebased drug design.
database
/genes
protein chemical
targets diversity
identify
‘hit’
optimize
‘hit’ structure
test s
anim
s
Diagram 1. Natural Product Drug Development from
new information to new therapy (Guo et al., 2006)
Influencing Bio-molecular
Processes
Target = enzyme, receptor, nucleic acid, …
Ligand = substrate, hormone, other messenger, ...
Protein BcL-xL -
Visualisasi enzim α-Glukosidase
Binding site prediction
Positon of ligand
in enzym target
Enzym HMG-CoA Reductase
Virtual Screening by in Silico Docking
New Technologies and should Enable Parallel
Process and Faster Time to Market at Lower Cost
Drugs Fail Because of two Major Reason
39 % fail due to deficiencies in
Absorption, Distribution, Metabolism &
Elimination (ADME)
30% fail due to lack of efficacy
11% fail due to animal toxicity
10% fail due to adverse effects in man
5% fail due to commercial reason
5% miscellaneous
Design of DHODH Inhibitors
H-bonding, electrostatic and hydrophobic interactions can be identified
and, hopefully, optimised by “in silico” design.
hydrogen bonding
hydrophobic π-stacking
interaction
Properties of orally Available
Drug-like Compounds
The Lipinski : Rule of five criteria





Molecular weight 500 Da
Log P ≤ 5
Hydrogen bond donors (OH and NH) ≤5
Hydrogen bond acceptors (lone-pairs of
hetero-atoms, like O and N)
Number of heavy atoms 10–70
5'
O
6'
HO 4'
2'
7
1
1'
H3CO 3'
O
2
3
4
5
Curcumin
H3CO
OH
PGV-0
OCH3
H3CO
O
HO
OH
6
OCH3
OCH3
O
HO
OH
H3CO
PGV-1
OCH3
O
O
OCH3 H3CO
H3CO
HO
OH
HGV-0
OCH3
HO
OH
OCH3
HGV-1
OCH3
Cytotoxic effect of curcumin, PGV-0 and
PGV-1 on some cell’s types (IC50 , M)
Myeloma Log P
Compound
HeLa
T47D
Raji
MCF-7
Curcumin
15.76
20
14
20*
6
2.56
PGV-0
7.60
10
3
10*
3
3.19
PGV-1
ND
1.5
ND
2.5*
ND
2.94
* Concentrations to induce cell apoptosis
as indicated by PARP cleavage
Direct and structural analogues
For “direct analogues”, a new lead must normally
promise improvements in properties over an existing
drug to be pursued. They are sometimes known as
“me-too compounds”. For example ACE inhibitors:
Ph
HS
N
EtO2C
O
CO2H
Captopril
Log P 0.24
N
N
H
Enalapril
Log P 3.09
O
CO2H
Success inspires competition
Since the discovery of captopril many new ACE inhibitors have been discovered.
The active site model of ACE was significantly improved, and the development
of enalaprilat (enalapril) showed that carboxylates could be used as the
zincbinding motif if the structure benefited from additional hydrophobic
binding.
IC50
IC50
N
N
HO2C
75
N
H
HO2C
O
CO2H
O
Log P -0.92
CO2H
4.08
Log P -0.1
Ph
HO2C
N
O
Log P -0.52
18.33
CO2H
EtO2C
N
N
H
Enalapril
Log P 3.09
O
CO2H 1
 DEVELOPMENT
OF
LOVASTATIN FoR
ANTICHOLESTEROL
Find and Optimized a Lead
Compound: Lovastatin
» Minimise energy of structure :
Chem3D, Gaussian, Mopac,
» QSAR (hub. Struktur Aktivits) : HyperChemPro
» Direct Ligand Design (HMG-CoA rductase):
Arguslab 4.0
» Synthesis
» Bioaactivity Test
METHODOLOGY
O
O
O
O
Sintesis
QSAR
Parameter
Identificatio
n
Active
Activity evaluation
In vivo
Anticholesterol
compound
Drug Design
Hyperchem &Docking
Evaluation Results Total
cholesterol(mg/dl)
Evaluation Results: HDL
(mg/dl)
HIPOTESIS
“Perubahan Polaritas/Sterik
HO
O
H
O
O
HO
COOR
H
OH
HO
O
O
H
H
O
O
OH
H
Simvastatin
O
H
Ester Statin
R = Bu, Hex, Oct, dst.
Lovastatin
“… makin mudah
menembus dinding usus halus”
= makin tinggi aktivitasnya
DESAIN 2:
Mengisi pusat aktif enzim [docking]
Lovastatin fit terhadap enzim
melalui 4 buah interaksi:
Tabernero et al. J. Biol. Chem., 2003
SIMVASTATIN &
LOVASTATIN DERIVATIVES AND LOG P
HO
Log P 5.68
O
Log P 3.77
O
HO
Log P 5.73
O
O
O
O
O
H3C
C30H46O6
Exact Mass: 502.33
H
O
H3C
CH3
O
H3C
H
CH 3
Lovastatin (1)
HO
H
H
CH 3
Simvastatin (2)
10
Log P 4.8
O
O
O
O
Log P 4.6
OH
H
O
H
O
H
O
H
O
CH3
H3C
OH
O
O
H3C
H3C
O
CH3
H 3C
18
H3C
H
CH 3
HyperChem 7.0
ArgusLab 4.0
Interaction Dehydrolovastatin
(grey)
and the active site of HMG-CoA reductase
(dark)
INTERACTION ENERGY WITH HMG CoA REDUCTASE AND LOG P
NO
Compounds
1 Substrat (HMG-CoA)
Interaction Energy (kcal /
mol)
Log P
- 10,5055
2 Dehydrolovastin
- 9.95
4.80
3 Lovastatin (1)
- 9,48
3.77
4 Simvastatin (2)
5 Buthyl ester (Lovastatin)
- 8,86
- 9,91
5.73
4,92
Synthesis
Dehydrolovastatin
HO
O
O
O
O
O
O
pTsOH, Cyclohehane
H3C
O
H3C
O
CH3
CH3
H3C
H3C
Lovastatin
Dehydrolovastaton
88,3 % (EtOH)
Lovastatin
Heksan:EtOAC (4:1)
Evaluation Results of Antihiperlipidemic Activity
on Rat for Lipistatin and Simvastatin
Parameter
Total
cholesterol
(mg/dl)
(%)
Trigliseride
(mg/dl)
(%)
LDL-cholesterol
(mg/dl)
(%)
HDLcholesterol
(mg/dl)
(%)
Simvastatin
Normal Hiperlipi(7,2 mg/
demic
200 g bw)
control
Lipistatin
(7,2 mg/
200 g bw)
Lipistatin
(14,4 mg/
200 g bw)
111,79
156,66
112,03
(28,49%)
106,64
(31,93 %)
105,54
(32,55 %)
106,29
172,53
102,28
(40,72%)
103,85
(40,0%)
94,79
(45,06%)
32,34
72,99
30,23
(58,58%)
25,00
(65,75%)
28,77
(60,58%)
58,20
49,16
61,34
(24,77%)
60,87
(23,82%)
57,81
(17,60%)
Development of UK-3A analog potential for
Breast cancer treatment
Structure Analog Design UK3A
in silico
Lipinski Rule
Hyperchem Program
MW < 500 g/mol; log P < +5
Virtual Interaction
(molecular docking)
ArgusLab program
Sel Normal vs Sel Kanker
Sel Payudara Normal

Protein-protein anti-apoptosis (a.l.
Bcl-xL) diinhibisi oleh protein-protein
pro-apoptosis yang sama banyaknya
Sel Kanker Payudara

Protein-protein anti-apoptosis (a.l.
Bcl-xL) berlebih, sehingga ada
yang tidak terinhibisi
Akibat:
Sel payudara rusak tidak alami
apoptosis; terus tumbuh dan
membelah tidak terkendali (kanker)
Simstein et al, 2003.
44
Inhibisi Bcl-xL dengan Obat
Bila kelebihan Bcl-xL diinhibisi, sel
rusak akan alami apoptosis secara
spesifik >> tidak jadi kanker
OBAT
OBAT
Ricci, et al, 2006.
Ghobrial, et al, 2005.
Ferreira, et al, 2002.
45
Optimum Conformation(Emin)Chem3D Ultra 10
Chem3D
46
Konformasi PDBGE
Konformasi PDOGE
HyperChem Pro (QSAR Parameter) &
ArgusLab 4.0 (Ebinding)
HyperChem Pro 7.0
ArgusLab 4.0
47
Interaction of Protein BcL-xL & Analog UK-3
DEVELOPMENT OF ANALOG UK-3A
POTENTIAL FOR BREAST CANCER TREATMENT
OH
O
O
O
N
N
H
PSMOE
PSMOE
O
O
O
UK-3A
O
BcL-xL Protein
OH
O
O
N
A
OH
O
C
UK-3A Ring opening (Analog UK-3A)
O
H
N
O
O
N
H
B
N
O
O
O
O
Analog UK-3A : PSMOE
QSAR Parameter & Cytotoxic
Test Results
N
O
N
H
N
O
H
N
HClg/MeOH
O
OH
O
OH
O
O
R
O
OH
CH3
O
O
UK-3A
Log P -1.18
Ebinding = -7.1 kcal/mol
IC50 = >100 g/ml
Log P 1.61
Ebinding = -11.65 kcal/mol
P388 : IC50 = 38 g/ml O
O
O
OH
O
H
N
O
HN
O
OHHO
O
NH
O
O
O
O
O
O
O
OH
H
O
Taxol
Log P 1.67
Ebinding = -10.39 kcal/mol
O
O
Antimycin A3
O
O
O
Log P 1.30, Ebinding = -10.24 kcal/mol
KB
:IC50 = 0.23 mg/ml
YMB-1:IC50 = 0.015 mg/ml
Cytotoxic Test Results to P388, KB and YMB-1
N
Ebinding=-9.66 kcal/mol),
O
H
N
O
OH
O
PDBGE : R = Butyl
O
N
O
Log P 1.5
IC50 34 g/ml (P388)
IC50 2.28 g/ml (KB)
IC50 1.83 g/ml (YMB-1)
OH
O
H
N
Ebinding=-10.29 kcal/mol);
O
O
NDBGE : R = Butyl
O
O
Log P 1.62
IC50 38 g/ml (P388)
IC50 1.92 g/ml (KB)
IC50 5.46 g/ml (YMB-1)
N
O
H
N
O
OH
O
PDOGE : R = Octryl
O
O
IC50 9.8 g/ml (P388)
IC50 9.84 g/ml (KB)
IC50 147.0 g/ml (YMB-1)
Log P 3.32
Ebinding -13.538
SAR Parameter & Cytotoxic Test
Results P388, KB and YMB-1
H
N
OH
O
Log P 3.29
Ebinding = -10.21 kcal/mol
P388 :IC50 = 7.75 g/ml
KB :IC50 = 0.6 g/ml
YMB-1:IC50 =2.97 g/ml
Calanone derivatives and Its
Cytotoxic Activity
O
O
O
HO
HO
O
O
O
O
HO
O
O
R
O
HO
Calanone
Ester Calanol
Log P -0.42
Log P 0.43
Against colon cancer cells
HCT116: IC50 > 20 µg/mL
L1210 : 59.4 µg/mL
P388 : IC50 = 15
Against colon cancer cells
HCT116: IC50 > 20 µg/mL
L1210 : 70.0 µg/mL
P388 : IC50 = 15
Log P 2.32
Against colon cancer cells
HCT116:
IC50 = 1.29 µg/mL
P388 : IC50 = 7,5 µg/mL
Cisplatin IC50 = 1.02 µg/ml
Molegro Virtual Docking (MVD)
Alignment of analog compound to ligand
Determination of binding site “pocket” in the
enzyme
Calculation of docking energy value of
compound candidate to fill the “pocket”
Compound candidate synthesis
Inhibitor α-Glukosidase
HO
H
H
C
C
OH
CH2OH
OH
S
CH2
HO
HO
HO
H3C
OH
O
CH2OH
OH
HN
HO
OH
Salacinol
O
O
OH
HO
akarbose
OH
O
OH
O
OH
HO
OH
OH
HO
HO
H3C
OH
N
O
HO
NH
HO
HO
OH
OH
1-deoksi-nojirimicin
OCH 3
nojirimisin
OSO3
Sulochrine Derivatives
OH
O
COOCH3
OH
Br
O
OCH3
Br
OH
COOCH3
O
H3C
OH
OH
O
H3C
H3CO
Br
Br
A
H3COOC
O
B
OH
HO
OCH3
HO
O
CH3
OCH3
I
OH
O
COOCH3
5 (sulochrin)
O
H3C
COOCH3
O
OH
COOCH3
O
OCH3
OH
H3COOC
I
OH
C
O
O
CH3
H3CO
1
OCH3
HO
2
COOCH3
O
OH
O
OCH3
H3CO
D
CH3
O
H3C
COOCH3
H3C
OCH3
Cl
H3CO
O
OH
Cl
H3COOC
O
E
OH
HO
O
CH3
H3CO
4
3
COOCH3
O
OH
HO
COOCH3
OH
O
OCH3
dioxybenzene
OCOCH3
O
OH
OH
H3CO
H3CO
OCH3
H3CO
6
CH3
H3CO
OCH3
H3COCO
7
OH
CH3
O
OCH3
OH
oxybenzone
CH3
benzophenone-6
Similarity Calculation Score of the
ligan to MVD
Ligan
Similarity
Score
Salacinol
B
C
E
S3
1
-494.341
-420.861
-420.769
-420.347
-407.934
-399.824
Sulochrin
-385.956
IC50
22.4
80.4
Ligan
Similarity
Score
4
5
2
7
6
Benzophenone6
dioxybenzene
-377.17
-357.712
-370.041
-369.389
-366.136
-362.692
-359.89
KESIMPULAN
1. Tanaman Obat dapat dijadukan sumber Ide (Lead Compound)
2. Protein/Enzim tertentu dapat digunakan untuk stimulasi
interaksi dengan ligan
3. Drug design sangat membantu dalam mempercepat dalam
pengembangan obat
4. Parameter QSAR (Log P) dan Energi dcking dapat dijadikan
indikator Optimasi lead Compound
QSAR PARAMETER
PARAMETER
Log P
Refractivity
(Ao)
Polarizability
(Ao)
Surface area
(approx)
Surface area
(grid)
Volume
Geometry
Optimazatio
n(kcal/mpl)
Molecular
dynamic(kca
l)
Calanon Calanol C.Octanoat
e
C. 2,2-diMe-butirat
C.Phepropionat
Taxol
0.43
133.1
-0.42
133.7
2.32
170.5
1.96
161.2
1.2
176.4
2.25
233.6
45.4
49.7
61.7
58.07
62.2
87.8
312.1
432.9
576.1
393.1
437.8
122.2
477.5
603.5
634.8
574.8
646.9
819.4
861.8
1068.
4
1163.6
1056.8
1172.5
146.0
149.2
148.4
1532.
1
287.9
224.3
219.0
219.9
400.5
146.2
31.2
200.5
80.5
Citra Interaksi Substrat (bola)
dengan Pusat Aktif HMG-CoA reduktase
(kawat)
Citra Interaksi Lovastatin terdehidrasi
(kawat abu-abu)
dengan pusat aktif HMG-CoA reduktase
(kawat gelap)
Drug targets
Drug targets are most often proteins, but nucleic acids, may also
be attractive targets for some diseases.
TARGET





Enzyme Inhibitor
Receptor*
Nucleic acid
Ion channels*
Transporters*
MECHANISM
: reversible or irreversible
: Agonist or antagonist
: Intercalator (binder), modifier
(alkylating agent) or substrate mimic.
: Blockers or openers
:Uptake inhibitors
*present in the cell membranes
Rational Drug Design
Physiological target where drugs act.
1. Enzymes :
where new molecules are made in tissue
2. Receptors
where circulating messengers, eg. Biogenic amines and
peptides, act to alter cellular activity
3. Transport systems
the selectivity permit access through membranes into and
out of cells, eg. ion channels, transporter moleculed
4. Cell replication and protein synthesis
controlled by DNA and RNA
5. Storage sites
where molecules are kept in an inactive form for
subsequent release and re-uptake, eg. Blood platelets, neurons
Prodrugs - examples
1. The antibiotic chloramphenicol is very bitter, but the palmitate ester does not
get absorbed by the tongue so much when taken orally and so is more
palatable. The succinate ester on the other hand makes it more soluble making
intravenous formulation more effective. Once absorbed the esters are quickly
hydrolysed.
2. The ACE inhibitor enalaprilat is potent in vitro, but is poorly absorbed and so
not very effective in vivo. The ethyl ester enalapril, however, is absorbed much
better but is a weak ACE inhibitor. It is hydrolyzed to the carboxylic acid by
esterase enzymes in the blood, which is where ACE is found.
Drug candidates


Bind to specific protein, usually receptors or
enzymes
Ease of absorption, distribution, metabolism,
and excretion (ADME)
Drug development
Structure-based drug design
65
The Contribution of IT to Drug
Discovery is Increasing
Drug designing based on 3D
structural information



Binding model: Lock and key
Small molecule: complement in
shape and electronic structure
Molecule features obey Lipinski’s rule
–
–
–
–
Mw < 500
Σ hydrogen bond donors < 5
Σ hydrogen bond donor acceptors <10
Partition coefficient (log P) < 5
67
Apoptosis:
Tumor Promotion
Cell cycle:
P53
CyclinD1
Bax
Bcl-2
Akt/PKA
NFB
Candidate
drugs
NFB
COX-2
Kinase
Transcription
factors
Caspase-3
Metastasis and
Angiogenesis
α-Amilase structure
B domain
Ca2+
Binding pocket
A domain
C domain
α-Amilase structure
Structure-based drug design
PDB 7TAA
69
HIV protease structure
Binding pocket
D25
D25
Catalytic residues
HIV protease structure
Structure-based drug design
70
PDB 1HSH