The document discusses finding lead compounds for drug design and development. It provides the following key points: - A lead compound is a compound that shows potential therapeutic activity and is used as a starting point for drug optimization. Lead compounds can be found through screening natural or synthetic compounds or via molecular modeling. - Lead compounds can come from various sources, including natural ligands, existing drugs, and high-throughput screening. High-throughput screening involves screening large chemical libraries against biological targets using automated assays. - Several approaches are used to search for hit compounds from libraries, including traditional library screening, fragment-based screening, and virtual screening techniques like docking and pharmacophore modeling. Hit compounds then undergo further evaluation and optimization to

1. Finding a Lead
apt. Rudi Hendra Sy., M.Sc., PhD

2. The lead compound
• A compound demonstrating a property likely to be therapeutically useful
• The level of activity and target selectivity are not crucial
• Used as the starting point for drug design and development
• Found by design (molecular modelling or NMR) or by screening compounds (natural or
synthetic)
• Need to identify a suitable test in order to find a lead compound
• Active principle - a compound that is isolated from a natural extract and which is
principally responsible for the extract’s pharmacological activity. Often used as a lead
compound.

3. Lead Compounds from a Variety of Sources
4. Natural Ligands
5. Existing Drugs
6. High Throughput Screening (HTS)
N
S
O
N
H
R
O
O
OH
Penicillin
O
OH
O
O
O
O
O
O
OH
O
O
O
O
H
NH
O
H
Taxol
N
H
N
N
N
O
O
S
N
O O
N Viagra
1. Chance Discovery
2. Natural Products
3. Clinical Observation

4. 4. Natural Ligands
O
H
O
H
OH
N
H
R
R=H adrenaline
R=Me noradrenaline
Catechol
bioisostere
(toxicity)
Increased size
(selectivity and duration)
Catechol
bioisostere
(toxicity)
Increased size
(selectivity and duration)
N
H
O
H
OH
N
H
O
O
H
Formoterol
AstraZeneca
O
H
OH
N
H
O
H
Salbutamol
GlaxoSmithKline

5. N
O
O
N
N
H
O
O
Cialis
Eli Lilly
N
H
N
N
N
O
O
S
N
O O
N
Levitra
Bayer
5. Existing Drugs
Also known as the “Me-Too” or “Me-Better” Approach
Issues: short duration
Multiple side effects and
incompatibility with other drugs
N
H
N
N
N
O
O
S
N
O O
N
Viagra
Pfizer
Fewer side effects and
incompatibility with other drugs
36h duration (“the weekend pill”)
BEWARE: Patent Issues!!

6. 6. High Throughput Screening (HTS)
• Validated, tractable targets
• target selection for HTS
• Industrialised process
• HTS assay technologies
and automation
• Chemical diversity
• sample selection for HTS
How?
“An industrialised process which brings together validated,
tractable targets and chemical diversity to rapidly identify
novel lead compounds for early phase drug discovery”
50-70% of new drug projects originate from a HTS

7. High-Throughput Screening
Plate explorer + Opera imaging system ultraHTS system
Evotec-Technologies/Perkin-Elmer
Up to 100,000 samples/24 hours

9. Drug Candidate
safety testing
Animal Studies
- relevant species
- transgenic KO/KI
mice
- conditional KOs
- agonists/antagonists
- antibodies
- antisense
- RNAi
Studies of
Disease Mechanisms
Human Studies
Phases I,II, III
Target
-receptor; -ion channel; -transporter;
-enzyme; - signalling molecule
Lead Search
-Develop assays (use of automation)
-Chemical diversity
-Highly iterative process
Molecular Studies
The Drug Discovery Process
Lead optimization
-selectivity
-efficacy in animal models
-tolerability: AEs mechanism-
based or structure-based?
-pharmacokinetics
-highly iterative process
Drug Approval
and Registration
Target selection &
validation
Discovery Development

10. Target
Identification
HTS or Comp
Hit-to-Lead
(HtL)
New Lead
Optimisation
Projects (LO)
Candidate
Drug (CD)
Active-to-Hit
(AtH)
3 months to
2 years!
3-4 months
3 months
6-9 months
2 years
The Drug Discovery Process

13. Approaches to Searching for Hit Compou
1. Traditional Library Screening

14. Types of reactions
amide coupling
sulphonamide formation
reductive amination
Boronic acid coupling
Multicomponent reaction (3 variants so far)
Sulphonamide arylation
Ester hydrolysis
Acyl sulphonamide formation
Urea formation
Epoxide opening
Anhydride opening
Condensation to form benzamidazoles
Mitsunobu
N-, O- and S-Alkylation
Sulfonylurea formation
benzoxazinone formation
Pyridone formation
tetrazole formation
Boc or t-butyl deprotection
cyclization to heterocycles (21 types - see list)
Nucleophilic aromatic substitutions (2 types)
aminopyrazoles
imidazopyridines
imidazothiazoles
imidazopyrimidines
aminothiazoles
aminooxadiazoles
triazolopyrimidines
aminotriazoles
aminobenzimidazoles
triazolopyridines
pyrazolopyrimidine
3-aminoquinolines
triazolopyridazines
triazolopyrazines
thiazolidin-4-one
3-amino-1,2,4-triazoles
pyrimidin-2-ones
triazolo[1,5-c]quinazoline
imidazolidin-2-one
quinazolinone
1,2,4-oxadiazole
CCE – Library Chemistry
3 most commonly used reactions-
Amide coupling
Reductive amination
Sulphonamide formation
Compound Collection Enhancement

15. CCE – Common Combinatorial Reactions
• Amide Coupling
R
3
O
H
O
N
R
2
H
R
1
N
R
2
R
1
R
3
O
+
HATU, Et3N
NMP
• Sulphonamide Formation
N
R
2
H
R
1
N
R
2
S
R
1
R
3
O O
S
Cl R
3
O O
+
Et3N
NMP
• Reductive Amination
N
R
2
H
R
1
N
R
2
R
1
R
3
H R
3
O
+
AcOH, NMP
Na(AcO)3BH
N N
N
N
O
N
N
+
PF6
-
N
O
HATU
NMP

16. Mechanism
Amide
Coupling
R
3
O
H
O
O
N
N
+
N
N
N
N
PF6-
Sulphonamide
Formation N
R
2
H
R
1
S
Cl R
3
O O
Reductive
Amination
N
R
2
H
R
1
H R
3
O
H
+
N
R
2
R
1
OH
R
3
-H+
H
+
N
+
R
2
R
1
R
3
-H2O
B
H
O
O
O
O
O
O
Na
+
N
R
2
R
1
R
3
N
R
2
S
R
1
R
3
O O
N
+
H Cl
-H+
+
R
3
O
O
N
+
N
PF6-
+H+
-H+
N
R
2
H
R
1
N
R
2
R
1
R
3
O
+H+
-H+

17. DATABASE COMPOUND
265,242
570,187
35 million

18. INTERESTING DATABASE COMPOUND
http://tcm.cmu.edu.tw

19. 2. Fragment-Based Screening

20. Example 1: Inhibition of Stromelysin
Stromelysin is a zinc-dependent protease that is responsible for breaking down and
re-forming connective tissues, including collagen.

21. Example 2. Inhibition of Cyclin-Dependent Kinase

23. Library for the Protein kinase inhibitor
N
H
NOCH3
O
N OCH3
O
N OCH3
OH
N OCH3
S
N OCH3
N OCH3
OH
OH
N
N
OCH3
Cl
O
N OCH3
Cl
Cl
N OCH3
N OCH3
OH
N
S N
O
O
N
OCH3
O
N OCH3
OH
1
2
3 4
5
6
9 10
7
8
11 12
D.J.Maly, I.C.Choong and J.A, Ellman,Proc.Natl.Acad.Sci.USA,2000,97,2419-2424
Example 3. Protein kinase inhibitor

24. Library for the Protein kinase inhibitor
D.J.Maly, I.C.Choong and J.A, Ellman,Proc.Natl.Acad.Sci.USA,2000,97,2419-2424
Ki =41µM
N
H
NOCH3
O
N OCH3
O
N OCH3
OH
N OCH3
S
N OCH3
N OCH3
OH
OH
N
N
OCH3
Cl
O
N OCH3
Cl
Cl
N OCH3
N OCH3
OH
N
S N
O
O
N
OCH3
O
N OCH3
OH
1
2
3 4
5
6
9 10
7
8
11 12

25. Library of protein kinase inhibitor
D.J.Maly, I.C.Choong and J.A, Ellman,Proc.Natl.Acad.Sci.USA,2000,97,2419-2424
F
F
F
F
F
N OCH3
O
Br
Br
O
N OCH3
N
OH
N OCH3
N
N OCH3
O
N OCH3
HO
HO
N OCH3
O
O
O
N OCH3
S
N OCH3
N OCH3
S
N
OCH3
H3CO
C6H5H2CO
N
OCH3
N
OCH3
16
17
18 19
20
13
14 15
21 22 23 24
Ki= 40M

26. Fragment-Based Design : Protein Kinase Inhibition
D.J.Maly, I.C.Choong and J.A, Ellman,Proc.Natl.Acad.Sci.USA,2000,97,2419-2424
Compound IC50µM
C-Src Fyn Lyn Lck
[7] 41± 5 >1000 >1000 >1000
[16] 40± 16 64± 50 400± 170 >500
[7,16] 0.064± 0.038 5.0± 2.4 13 ± 2.4 >250
N
N OCH3
N
N
O
N
O
HO
OH
OH
OH
N
H3CO
7 16

27. Entry
1
2
3
4
6
7
8
Compound
7,16, n=2
Linker c-Src IC50, M
(CH2)n
7,16, n=3
7,16, n=4
7,16, n=5
7,16, n=6
7,16, cis
7,16, trans (1R,2R)
(CH2)n
(CH2)n
(CH2)n
(CH2)n
0.0640.038
1.10.2
6.53.0
6.50.8
5.32.1
1.20.6
0.620.02
N
N N
O
HO
OH
O
linker
Correlation of linker structure with IC50 values for c-Src Inhibition

28. HO
N
H
N
O
HO
N
O
HN
O
HO
Kd=17 mM
Kd=0.2 mM
IC50=57 nM
Haiduk, P.J. et al. JACS. 1997,119, 5818-5827
Puerta, D.T, Lewis J.A. JACS. 2004, 126, 8389
Example 4. Matrix Metalloproteinase Inhibitors

29. 3. Virtual Screening
Lipinski’s rule screening
Pharmacophore screening
ADMET based screening
Docking, consensus scoring,
visual inspection
Schematic illustration virtual screening strategy
Hit Compound
IC50 prediction, in silico ADME
prediction, in silico toxicity
prediction (TOPKAT)

30. 2D/3D Substructure Searching
Selection of compounds based on
chemical similarity to known active
compounds using some similarity
measure
a. Compound similarity

31. b. Pharmacophore Searching

32. Filtering Hits to Lead Compounds
1.Pharmacodynamics and Pharmacokinetics
2. Biological Assays
3. Lipinski’s Rules and Related Indices
4. Etc.

33.  Classic medicinal chemistry
 Improve the classic properties of
o Potency
o Selectivity
o Toxic side effect
o Pharmacokinetic
Computer-Aided Drug Discovery:
o Docking,
o 2D-, 3D-, 4D- QSAR
o 3D-QSAR Pharmacophore
o Dynamic simulation
o The drug discovery pipeline
o ADMET profiling
o TOXICITY Profiling
Leads Optimization

34. Structure-based
know receptor,
don’t known ligands
There are two major types of drug design. The first is referred to as Structure-based
drug design and the second, Ligand-based drug design
What will be happy in there???
Ligand-based
don’t know receptor,
known ligands
Protein/ligand interactions
structure/biophysics
docking Statistical analysis (eq. QSAR) of
what group(s) are important for
biological activity
Structure modeling
(Homology/experimental X-
ray/NMR/neutron)
Get a structure

35. Structure-based drug design

36. Structure-based drug design

37. Watch Video
https://www.youtube.com/watch?time_continue=14&v=rxzjswuAZBo
Structure Based Design

38. TERIMA KASIH