Post on 14-Jan-2016
DRUG DISCOVERY AND DEVELOPMENT
M. HanafiPuslit Kimia LIPI
Kawasan PUSPIPTEK, Serpong
Target IdentificationAnd Validation
Search of Lead Structure
Optimization of Lead Structure
PreclinicalDevelopment
Research Phases in Drug Development
Idea
Lead Structure
Candidate for Development Product
Development Product
DEVELOPMENT of NOVEL DRUGSDEVELOPMENT of NOVEL DRUGSfrom NATURAL PRODUCTfrom 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
NR
N
O
OH
HN
NHO
OO
O
OO
O
Vincristine (R = -CHO) – Vinblastine (R = -CH3)Vinca rosea (Catharanthus roseus) (Apocynaceae)
O
O
O
H O
N
N
Camptothecin
Camptotheca acuminata
O
O
O
H O
N
N
O H
N
Topotecan
Lead compounds from Natural Products
Discovery from Natural Products
H
O
O
HO
H
O
O
Lovastatin Aspergillus tereus Anticholesterol -
O
O
O
OOR
HN
N
OOH
O
UK-3A
Streptomycesp sp. 517-02Cytotoxic to P338, KB
N
N
HO O
Phenazine carbioxylatePseudomonas pycocyaneae
O
O O
O
HO
CalanoneCallophyllum tesmanii
OCOOCH3 OH
HO OCH3
HO
CH3
Sulochrin - AntidiabetesAspergillus terreus
O OH
HO
MeO
O
O
O
N
Gingerol
O O
OMe
OH
MeO
HO
Curcumin
Piperine
Lead Compounds
Rational drug designRational drug design
X-ray crystallography X-ray crystallography has developed so that the has developed so that the determination of the 3-D crystal structures of determination of the 3-D crystal structures of proteins and receptors is coming easier.proteins and receptors is coming easier.
The Protein Data Bank The Protein Data Bank (see (see http://pdb.ccdc.cam.ac.uk/pdb/) has data for http://pdb.ccdc.cam.ac.uk/pdb/) has data for hundreds of published structures which are all hundreds of published structures which are all freely availablefreely available
Coupled with advances in Coupled with advances in computing power and computing power and molecular modellingmolecular modelling the so-called the so-called rational or rational or structure-based drug designstructure-based drug design..
database /genes
protein targets
chemical diversity
identify ‘hit’
optimize ‘hit’ structure
Diagram 1. Natural Product Drug Development from new information to new therapy (Guo et al., 2006)
Influencing Bio-molecular Influencing Bio-molecular ProcessesProcesses
Target = enzyme, receptor, nucleic acid, …Ligand = substrate, hormone, other messenger, ...
Protein BcL-xL -Protein BcL-xL -
Visualisasi enzim α-α-GlukosidaseGlukosidase
Binding site prediction
Positon of ligand in enzym target
Enzym HMG-CoA Reductase
Virtual Screening by Virtual Screening by in Silico in Silico DockingDocking
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
H-bonding, electrostatic and hydrophobic interactions can be identified and, hopefully, optimised by “in silico” design.
hydrogen bonding
hydrophobic π-stacking interaction
Design of DHODH Design of DHODH Inhibitors Inhibitors
Properties of orally Properties of orally Available Available
Drug-like CompoundsDrug-like Compounds
The Lipinski : Rule of five criteriaThe Lipinski : Rule of five criteria Molecular weight 500 Da Molecular weight 500 Da Log Log PP ≤ 5 ≤ 5 Hydrogen bond donors (OH and NH) ≤5 Hydrogen bond donors (OH and NH) ≤5 Hydrogen bond acceptors (lone-pairs of hetero-Hydrogen bond acceptors (lone-pairs of hetero-
atoms, like O and N) atoms, like O and N) Number of heavy atoms 10–70 Number of heavy atoms 10–70
O O
H 3 C O O C H 3
O HH O
6 '5 '
4 '
3 '
1 '
2 '
1
23
45
6
7
Curcumin
O
O C H 3
O HH O
H 3 C O
PGV-0
O
O C H 3
O HH O
H 3 C O
H 3 C O O C H 3
H 3 C O O C H 3
H O O H
O
HGV-0
PGV-1
HGV-1
H 3 C O O C H 3
H O O H
O
O C H 3 O C H 3
Compound HeLa T47D Raji MCF-7 Myeloma
Curcumin 15.76 20 14 20* 6
PGV-0 7.60 10 3 10* 3
PGV-1 ND 1.5 ND 2.5* ND
Cytotoxic effect of curcumin, PGV-0 and PGV-1 on some cell’s types (IC50 ,
M)
* Concentrations to induce cell apoptosis as indicated by PARP cleavage
Log P
2.56
3.19
2.94
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:
Direct and structural analogues
H S
O
N
C O 2 H
NH
O
N
C O 2 H
E t O 2 C
P h
Captopril
Log P 3.09
EnalaprilLog P 0.24
Since the discovery of captopril many new ACE inhibitors have been Since the discovery of captopril many new ACE inhibitors have been discovered. The active site model of ACE was significantly improved, discovered. The active site model of ACE was significantly improved, and the development of enalaprilat (enalapril) showed that and the development of enalaprilat (enalapril) showed that carboxylates could be used as the zincbinding motif if the structure carboxylates could be used as the zincbinding motif if the structure benefited from additional hydrophobic binding.benefited from additional hydrophobic binding.
Success inspires competition
H O 2 C
O
N
C O 2 H
O
N
CO2H
HO2C
IC50
75
18.33
NH
O
N
C O 2 H
E t O 2 C
P h
IC50
4.08
1
NH
O
N
CO2H
HO2C
Log P -0.92
EnalaprilLog P 3.09
Log P -0.1
Log P -0.52
DEVELOPMENT OF DEVELOPMENT OF LOVASTATIN FoR LOVASTATIN FoR
ANTICHOLESTEROLANTICHOLESTEROL
Find and Optimized a Lead Find and Optimized a Lead Compound: Compound: LovastatinLovastatin
» 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
Sintesis
Activity evaluationIn vivo
Drug DesignHyperchem &Docking
ActiveAnticholesterol
compound
QSAR Parameter Identificatio
n
O
O
O
O
Evaluation Results Total
cholesterol(mg/dl)Evaluation Results: HDL (mg/dl)
HIPOTESIS
“… makin mudahmenembus dinding usus halus”
= makin tinggi aktivitasnya
H
O
O
HO
H
O
O
Lovastatin
H
OH
OH
COOR
HO
H
Ester StatinR = Bu, Hex, Oct, dst.
H
O
O
HO
H
O
O
Simvastatin
“Perubahan Polaritas/Sterik
DESAIN 2:DESAIN 2:Mengisi pusat aktif enzim [docking]Mengisi pusat aktif enzim [docking]
Tabernero et al. J. Biol. Chem., 2003
Lovastatin fit terhadap enzimmelalui 4 buah interaksi:
C H 3
O
H 3 C
H 3 C
O
H O O
O
L o v a s t a t i n ( 1 )
H
C H 3
O
H 3 C
H
H 3 C
O
H
H O O
O
H 3 C C H 3
S im v a s t a t i n ( 2 )
C H 3
O
H 3 C
O
O
O
1 8
Log P 3.77Log P 5.73
Log P 5.68
SIMVASTATIN & LOVASTATIN DERIVATIVES AND LOG P
Log P 4.8
H
OH
H
OH
H
HO O
O
Log P 4.6
H
CH3
O
H3C
H
CH3
H3C
O
H
O O
O
O
10
C30H46O6Exact Mass: 502.33
Interaction Dehydrolovastatin (grey)
and the active site of HMG-CoA reductase (dark)
NONO CompoundsCompounds Interaction Energy (kcal / Interaction Energy (kcal / mol)mol)
Log PLog P
11 Substrat (HMG-CoA)Substrat (HMG-CoA) - 10,5055- 10,5055
22 DehydrolovastinDehydrolovastin - 9.95- 9.95 4.804.80
33 Lovastatin (1)Lovastatin (1) - 9,48- 9,48 3.773.77
44 Simvastatin (2)Simvastatin (2) - 8,86- 8,86 5.735.73
55 Buthyl ester Buthyl ester (Lovastatin)(Lovastatin)
- 9,91- 9,91 4,924,92
INTERACTION ENERGY WITH HMG CoA REDUCTASE AND LOG P
ArgusLab 4.0
HyperChem 7.0
Synthesis Synthesis DehydrolovastatinDehydrolovastatin
CH3
O
H3C
H3C
O
O
O
CH3
O
H3C
H3C
O
O
O
Lovastatin
HO
pTsOH, Cyclohehane
Dehydrolovastaton
88,3 % (EtOH)
Heksan:EtOAC (4:1)
Lovastatin
Parameter Normalcontrol
Hiperlipi-demic
Simvastatin
(7,2 mg/200 g bw)
Lipistatin(7,2 mg/
200 g bw)
Lipistatin(14,4 mg/200 g bw)
Total cholesterol
(mg/dl)(%)
111,79 156,66 112,03 (28,49%)
106,64 (31,93 %)
105,54 (32,55 %)
Trigliseride (mg/dl)
(%)
106,29 172,53 102,28 (40,72%)
103,85 (40,0%)
94,79 (45,06%)
LDL-cholesterol
(mg/dl)(%)
32,34 72,99 30,23 (58,58%)
25,00 (65,75%)
28,77 (60,58%)
HDL-cholesterol
(mg/dl)(%)
58,20 49,16 61,34 (24,77%)
60,87 (23,82%)
57,81 (17,60%)
Evaluation Results of Antihiperlipidemic Activity on Rat for Lipistatin and Simvastatin
Development of UK-3A analog potential for Breast cancer treatment
Lipinski Rule Hyperchem Program MW < 500 g/mol; log P < +5
Virtual Interaction (molecular docking) ArgusLab program
Virtual Interaction (molecular docking) ArgusLab program
Structure Analog Design UK3A in silico
Structure Analog Design UK3A in silico
Sel Normal vs Sel KankerSel Normal vs Sel KankerSel Payudara NormalSel Payudara Normal
Protein-protein anti-apoptosis (a.l. Protein-protein anti-apoptosis (a.l. Bcl-xL) diinhibisi oleh protein-Bcl-xL) diinhibisi oleh protein-protein pro-apoptosis yang sama protein pro-apoptosis yang sama banyaknyabanyaknya
Sel Kanker PayudaraSel Kanker Payudara
Protein-protein anti-apoptosis Protein-protein anti-apoptosis (a.l. Bcl-xL) berlebih, sehingga (a.l. Bcl-xL) berlebih, sehingga ada yang tidak terinhibisiada yang tidak terinhibisi
Akibat:Akibat:Sel payudara rusak tidak alami Sel payudara rusak tidak alami apoptosis; terus tumbuh dan apoptosis; terus tumbuh dan membelah tidak terkendali membelah tidak terkendali (kanker)(kanker)
Simstein Simstein et alet al, 2003., 2003.
4444
Inhibisi Bcl-xL dengan ObatInhibisi Bcl-xL dengan Obat
4545
OBATOBAT
Bila kelebihan Bcl-xL diinhibisi, sel rusak akan alami apoptosis secara spesifik >> tidak jadi kanker
Ricci, et al, 2006.Ghobrial, et al, 2005.Ferreira, et al, 2002.
Optimum Conformation(EOptimum Conformation(Eminmin)- )- Chem3D Ultra 10Chem3D Ultra 10
4646
Konformasi PDBGE Konformasi PDOGE
Chem3D
HyperChem Pro (QSAR Parameter) & ArgusLab 4.0 (Ebinding)
47
HyperChem Pro 7.0ArgusLab 4.0
Interaction of Protein BcL-xL & Analog UK-3
N
OH
NH
O
OO
O
A
B C
O
Analog UK-3A : PSMOE
N
OH
NH
O
O
O
O
O
O
OUK-3A
UK-3A Ring opening (Analog UK-3A)
DEVELOPMENT OF ANALOG UK-3A POTENTIAL FOR BREAST CANCER TREATMENT
DEVELOPMENT OF ANALOG UK-3A POTENTIAL FOR BREAST CANCER TREATMENT
PSMOEPSMOE
HN
O
O O
N
OOH
O
BcL-xL Protein
QSAR Parameter & Cytotoxic Test Results
N
OH
HN
O
OH
O OCH3
O
O
O
OOR
HN
N
OOH
O
UK-3A
Log P -1.18Ebinding = -7.1 kcal/molIC50 = >100 g/ml
Log P 1.61Ebinding = -11.65 kcal/molP388 : IC50 = 38 g/ml
O
O
O OH
OO
O
OOHO
O
O
OH
NH
OTaxol
Log P 1.67Ebinding = -10.39 kcal/mol
O
OHN
OOH
HN
O
O
OH O
OAntimycin A3
Log P 1.30, Ebinding = -10.24 kcal/molKB :IC50 = 0.23 mg/mlYMB-1:IC50 = 0.015 mg/ml
HClg/MeOH
Cytotoxic Test Results to P388, KB and YMB-1
NHN
O
O
OH
O
OO
PDBGE : R = Butyl
N
HN
O
O
O
OO
OH
NDBGE : R = Butyl
IC50 34 g/ml (P388)IC50 2.28 g/ml (KB)IC50 1.83 g/ml (YMB-1)
IC50 38 g/ml (P388)
IC50 1.92 g/ml (KB)IC50 5.46 g/ml (YMB-1)
Ebinding=-9.66 kcal/mol), Log P 1.5
Ebinding=-10.29 kcal/mol);
Log P 1.62
NHN
O
O
OH
O
OO
PDOGE : R = Octryl
IC50 9.8 g/ml (P388)IC50 9.84 g/ml (KB)IC50 147.0 g/ml (YMB-1)
Log P 3.32Ebinding -13.538
SAR Parameter & Cytotoxic Test Results P388, KB and YMB-1
O H
HN
O
P388 :IC50 = 7.75 g/mlKB :IC50 = 0.6 g/mlYMB-1:IC50 =2.97 g/ml
Log P 3.29Ebinding = -10.21 kcal/mol
Calanone derivatives and Its Cytotoxic Activity
O
O O
HO
HO
O
O O
O
HO
Calanone Ester Calanol
Log P 2.32Against colon cancer cells HCT116: IC50 = 1.29 µg/mL P388 : IC50 = 7,5 µg/mL
Log P 0.43Against colon cancer cells HCT116: IC50 > 20 µg/mL L1210 : 59.4 µg/mL P388 : IC50 = 15
Cisplatin IC50 = 1.02 µg/ml
O
O O
R
HO
Log P -0.42
Against colon cancer cells HCT116: IC50 > 20 µg/mL L1210 : 70.0 µg/mL P388 : IC50 = 15
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
O
HO
OH
H3C
O
O
HO
OH
OH
O
HO
OH
OH
OH
O
HO
HO
OH
OH
HN
N
HO
HO
OH
OH
O
HO
OCH3
OH
H3C
HO
HO
NH
OH
OH
1-deoksi-nojirimicin
akarbose
nojirimisin
S
HO
HO
CH2OH
CH2 C
H
OH
C
H
CH2OH
OSO3
Salacinol
Inhibitor α-Inhibitor α-GlukosidaseGlukosidase
OCOOCH3 OCOCH3
H3CO OCH3
H3COCO
CH3
OCOOCH3 OH
H3CO OCH3
HO
CH3
OCOOCH3 OH
H3CO O CH3
OCOOCH3 OH
H3CO OCH3
H3CO
CH3
OCOOCH3 OH
HO O CH3
OCOOCH3 OH
H3CO OH
HO
CH3
OCOOCH3 OH
HO OCH3
HO
CH3
7
3
21
6
4
5 (sulochrin)
OH O COOCH3
OH
H3CO
H3C
OH
Br
Br
OH O COOCH3
OCH3
H3CO
H3C
OCH3
O
OCH3
OH3COOC
OOH
Br
H3C
Br
O
OCH3
OH3COOC
OOH
I
H3C
I
O
OCH3
OH3COOC
OOH
Cl
H3C
Cl
D
C
E
BA
OH
O OH
OCH3
OH
O OH
OCH3
H3CO
O OH
OH
dioxybenzene
benzophenone-6
oxybenzone
Sulochrine Derivatives
Ligan Similarity Score
IC50 Ligan Similarity Score
Salacinol -494.341 4 -377.17
B -420.861 22.4 5 -357.712
C -420.769 2 -370.041
E -420.347 7 -369.389
S3 -407.934 6 -366.136
1 -399.824 Benzophenone-6
-362.692
Sulochrin -385.956 80.4 dioxybenzene -359.89
Similarity Calculation Score of the ligan to MVD
KESIMPULAN1. Tanaman Obat dapat dijadukan sumber Ide (Lead Compound)2. Protein/Enzim tertentu dapat digunakan untuk stimulasi interaksi dengan ligan3. Drug design sangat membantu dalam mempercepat dalam pengembangan obat4. Parameter QSAR (Log P) dan Energi dcking dapat dijadikan indikator Optimasi lead Compound
QSAR PARAMETERQSAR PARAMETERPARAMETERPARAMETER CalanonCalanon CalanolCalanol C.OctanoatC.Octanoat
eeC. 2,2-di-C. 2,2-di-
Me-butirat Me-butirat C.Phe-C.Phe-
propionatpropionatTaxol Taxol
Log P Log P 0.430.43 -0.42-0.42 2.322.32 1.961.96 1.21.2 2.252.25Refractivity Refractivity (A(Aoo) )
133.1133.1 133.7133.7 170.5170.5 161.2161.2 176.4176.4 233.6233.6
Polarizability Polarizability (A(Aoo) )
45.445.4 49.749.7 61.761.7 58.0758.07 62.262.2 87.887.8
Surface area Surface area (approx) (approx)
312.1312.1 432.9432.9 576.1576.1 393.1393.1 437.8437.8 122.2122.2
Surface area Surface area (grid) (grid)
477.5477.5 603.5603.5 634.8634.8 574.8574.8 646.9646.9 819.4819.4
Volume Volume 861.8861.8 1068.1068.44
1163.61163.6 1056.81056.8 1172.51172.5 1532.1532.11
Geometry Geometry OptimazatioOptimazation(kcal/mpl)n(kcal/mpl) 146.2146.2 31.231.2
146.0146.0 149.2149.2 148.4148.4 287.9287.9
Molecular Molecular dynamic(kcadynamic(kcal)l)
200.5200.5 80.580.5 224.3224.3 219.0219.0 219.9219.9 400.5400.5
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 targetsDrug targetsDrug targets are most often proteins, but nucleic acids, may Drug targets are most often proteins, but nucleic acids, may
also be attractive targets for some diseases.also be attractive targets for some diseases.
TARGET MECHANISMTARGET MECHANISM
Enzyme Inhibitor : reversible or irreversibleEnzyme Inhibitor : reversible or irreversible Receptor* : Agonist or antagonistReceptor* : Agonist or antagonist Nucleic acid : Intercalator (binder), modifierNucleic acid : Intercalator (binder), modifier
(alkylating agent) or substrate mimic.(alkylating agent) or substrate mimic. Ion channels* : Blockers or openersIon channels* : Blockers or openers Transporters* :Uptake inhibitorsTransporters* :Uptake inhibitors
*present in the cell membranes*present in the cell membranes
Rational Drug DesignPhysiological target where drugs act.
1. Enzymes : where new molecules are made in tissue2. Receptors where circulating messengers, eg. Biogenic amines and
peptides, act to alter cellular activity3. Transport systems the selectivity permit access through membranes into and out of cells, eg. ion channels, transporter moleculed4. Cell replication and protein synthesis controlled by DNA and RNA5. Storage sites where molecules are kept in an inactive form for subsequent release and re-uptake, eg. Blood platelets, neurons
1. The antibiotic chloramphenicol is very bitter, but the palmitate ester does notget 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 sonot very effective in vivo. The ethyl ester enalapril, however, is absorbed muchbetter but is a weak ACE inhibitor. It is hydrolyzed to the carboxylic acid byesterase enzymes in the blood, which is where ACE is found.
Prodrugs - examples
Drug developmentDrug development Structure-based drug designStructure-based drug design 6565
Drug candidatesDrug candidates
Bind to specific protein, usually Bind to specific protein, usually receptors or enzymesreceptors or enzymes
Ease of absorption, distribution, Ease of absorption, distribution, metabolism, and excretion (ADME)metabolism, and excretion (ADME)
The Contribution of IT to The Contribution of IT to Drug Discovery is Drug Discovery is IncreasingIncreasing
6767
Drug designing based on Drug designing based on 3D structural information3D structural information
Binding model: Lock and keyBinding model: Lock and key Small molecule: complement in Small molecule: complement in
shape and electronic structureshape and electronic structure Molecule features obey Lipinski’s Molecule features obey Lipinski’s
rulerule– Mw < 500 Mw < 500 – ΣΣ hydrogen bond donors < 5 hydrogen bond donors < 5– ΣΣ hydrogen bond donor acceptors <10 hydrogen bond donor acceptors <10– Partition coefficient (log P) < 5Partition coefficient (log P) < 5
Candidate drugs
Apoptosis:
P53
Bax
Bcl-2
Akt/PKA
NFB
Caspase-3
Cell cycle:
CyclinD1
NFB
COX-2
Kinase
Transcription factors
Metastasis and
Angiogenesis
Tumor Promotion
α-Amilase structureα-Amilase structure Structure-based drug designStructure-based drug design 6969
A domain
C domain
B domain
Binding pocketCa2+
αα-Amilase structure-Amilase structure
PDB 7TAA
HIV protease structureHIV protease structure Structure-based drug designStructure-based drug design 7070
Binding pocket
D25 D25
Catalytic residues
HIV protease structure
PDB 1HSH