Title: Contents

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(No Transcript)
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Contents
?. Life Sciences Drug Discovery ?. Drug
Discovery Process ?. Target Identification /
Validation ?. Lead Generation / Optimization ?.
Assessment and Decision ?. LG Life Sciences
3
?. Life Sciences Drug Discovery
4
1. Where are we ?
? ?? ?? ??
? ?
1?
2?
3?
7?
8?
12?
13?
GNI (02?, U B) (Gross National Income)
?? 10,446
?? 3,993
?? 1,987
???? 1,186
??? 735
?? 477
???? ???? (02?, ?)
?? 26.8?
?? 6.8?
?? 6.6?
?? 1.5?
-
-
-
?? ???? ?? (01?, ?)
?? 40,003
?? 13,616
?? 11,846
???? 3,187
?? 2,318
-
-
5
1. Where are we ?
? ?? ?? ?? ??
2002? ?? ?? ??? ??
1?
2?
3?
4?
5?
6?
7?
Shipbuilding (Mil CGT)
?? 6.82
?? 6.54
?? 1.36
Electronics (U B)
?? 304
?? 220
?? 60
Auto (Mil Unit)
?? 12.24
??
??
??
?? 3.14
Internet User (per 100 People)
????? 69
???? 60
?? 55
Steel (Mil Ton)
?? 149.25
?? 103.77
?? 91.32
??? -
?? 45.39
Foreign Reserve (U B)
?? 121.41
6
1. Where are we ?
? ?? ?? (??? ? ???)
?? U M
??
??? ? ???
??
??
1999 2000 2001 2002
? 2,206 ? 2,533 ? 2,129 ? 2,153
455 688 924 826
2,661 3,221 3,053 2,979
7
1. Where are we ?
? ?? ??
?? 1?? GNI (U20,000)
?? 1?? GNI (U10,000)

??? ?? ??
? ?? ????? ?? ??

02? U 10,013
- OECD ??? 60
- OECD 30? ? 24?

1?? GNI ??
10,000
5,000
94
95
96
97
98
99
00
01
02
8
Macro Trend
2. Why Life Sciences ?

?? ?? ? ??.?? ??
?.?.? ? Life Style
??? ? ????. ????

9
Unlimited Commercial Possibilities
2. Why Life Sciences ?
? Selective breeding ? Mendels studies of
heredity (1800s) ? Watson Cricks discovery of
the molecular structure of DNA (1950s) ?
Mapping of the human genome (1990 )
Life Sciences Evolution
? Nutrition health ? Agricultural
productivity ? Drug discovery process ? Bio-based
materials process ? Convergence of industries
Unlimited Commercial Possibilities
10
Impact on Employment
2. Why Life Sciences ?
Employment in Biotechnology and Selected
Industries
250

200
Jobs (Thousands)
150

100
50
0
Toys Sporting Goods
Biotechnology
Cable Other Pay TV
Prepackaged Software
Drugs
ltSource Ernst Young, May 2001gt
11
????? ??
2. Why Life Sciences ?
(?? ??, 1 1,250?)
Company Sales Volume (2002) Profit (2002) Market Value (03.8)
WalMart 2,445 80 2,481
GE 1,317 151 2,938
???? 480 59 378
Pfizer 324 95 2,455
Merck 518 71 1,287
Amgen 55 ?14 803
Source Forbes 2003, Internet website etc.
12
3. ?? ?? ?? ??
?? 10?? ? 10 ?? ? ?? (???? 30 ??) - ??? ??
?? ?? Strategic Alliance ?? - ?? ?? ??? ??
?? ? ?? ???? ?? Outsourcing ?? MA ??? -
Global ??? ?? ??? ??? ?? / Globalization
13
?? ????? ???
3. ?? ?? ?? ??
???? 325B (2000?)
14
MA ??
3. ?? ?? ?? ??
?? (??)
?? ?
?? ?
??
MA ? ??
Amgen
Amgen Immunex
01.12
160
??? ? ????? ?? - Enbrel? ??
Pfizer
Pfizer Warner Lambert
00. 0
902
????? RD ?? - Lipitor? ??
GSK
GlaxoWellcome SmithKline Beecham
00. 1
817
?? ? ??? ?? ?? ?? ??
Aventis
Hoechst Rhone-Poulenc
99.12
219
?? ? ? ?? 6? ?? ?? ?? ?? ?? ??
AstraZeneca
Astra Zeneca
98.12
350
???? ? 3? ???? ??
Sanofi-Synthelabo
Sanofi Synthelabo
98.12
300
7? ???? ? ?? ? ?? ?? ??
Novartis
Ciba Geigy Sandoz
96. 3
299
??? ?? ?? ??
15
500? ?? ? ???? ??
3. ?? ?? ?? ??
Source Forbes 3/28. 2003, Internet website
etc.
16
?? ?? ??
3. ?? ?? ?? ??
17
4. ?? ?? ?? ??

???? ????? 1.2
400? ??? ?? ?? ?? Generic ?? ??
? ?? ???? MNC ?? (?? ?? ? ????)
?? ?? ?? - ??? ?? ??
? ?? ?? ??? ? ?? ?? ??
??? 5 ??? ??? ??? ?? ??? 45 ??
??? RD ?? - ?? ? ?? ??

18
4. ?? ?? ?? ??
?? ?? ??
?? ?? ?? ?? ?? ??
??? ??(??) 1,490 890 480 40
1? ?? ??(??) 400 130 50 2.5
??? (??? ??,) 1025 1025 1015 3
?? ???, 1035 1035 1020 3
?? ? ??? ? 5?5?? 5?5?? 3?2?? 20120?
?? ?? ??(?) 60 100 30 10
?? ???(?/?) 11 17 10 -
ltSource IMS Health World Review 00gt
19
4. ?? ?? ?? ??
??? ??(???? vs ?? ???)
(?? ??, 1 1,200?)
?? ?? ??? ??? (??) Blockbuster (10?? ??) ?? ? RD ??? (??)
1 Merck 404 8 24
2 JJ 291 4 29
3 GSK 274 9 38
4 Pfizer 265 8 44
1 ???? 3.2 1?? ?? ??? ?? 0.1
2 ??? 2.2 1?? ?? ??? ?? 0.1
3 ??? 1.9 1?? ?? ??? ?? 0.1
4 ???? 1.7 1?? ?? ??? ?? 0.1
ltSource IMS World Review, 2001gt
20
4. ?? ?? ?? ??
?? ????? MNC ??
2000??? ?? ?? ???? ??, ??? ?? ??? ?? ??? ????
????
? ? ???? (???) MNC (???) ? ?
????? (ETC) 10? ?? 3? (N/A) 7? (88) ? ?? ???? 232? ? MNC ???? 1,200? ??? 410?
????? (OTC) 10? ?? 9? (4) 1?(76.8) ? ??? 2,055? ? ???? 357? ? ???? 211?
21
4. ?? ?? ?? ??
Bio Venture ??
?? 2? ?? ?? ??? ??? ??? ???? ?????, ??? ??? ???
?? ?? ? ? ? ? ? ? (2001)
?? ?? 2000 2001 ? ? (2001)
??? ?? ??? 400 600 1,400
???? ?? ??? 140 220 930
??? ? (?? ? ??) 11 21 lt 50
IPO ??? 10 16 65
?? ?? ?? ?? ?? ?? 75 ?? ?? 21 ?? ?? ?? ?? 75 ?? ?? 21 ??? Major Biotech ??? 90 ??
ltSource ????? 2001? ??? ?? ?? ?? ??,
biospace.comgt
22
4. ?? ?? ?? ??
??? RD ?? ??
??? ??? ?? RD ?? ?? (2003)
U/1150?
? ? ? ? ? ?
? RD ?? 115 100.0 4.8 100.0
- National Defense - Health - Space RD - Energy - IT/NT/ET - Others 57.4 50.0 27.4 23.8 10.0 8.7 8.0 6.9 NA NA 12.2 10.6 0.7 14.6 0.4 8.3 0.1 2.1 NA NA 0.8 16.7 2.8 58.3
Amount Billion Dollar
Amount Billion Dollar
Composition ()
Composition ()
23
How to Approach?
??? ??
???? ??
? ??? ?? ?? ?? ?? - ??? ? ??? ?? - ???
?? ?? - ????? cGMP? ? Worldwide ????? ??
- ??? RD ?? - ??? ?? ?? ? ??/??? ??? ???
Must Be Globally Unique Product
24
How to Approach?
?? ??
????
??
?? / ????

??? ?? ???
RD? ??? ??
?? ?? ??
Risk Manager
(Venture ??)
?? ??? ??????
?????? ???? ??

Globally Unique Product
Globally Advanced
Platform Technology ??
??? ??? Alliance??
Global Marketing?? ??
Generic Product ???
??

New Idea / Concept
Generation
Basic RD ??
Biologist, Chemist,
Computer Scientist ?
?? ?? ??? ??

25
How to Approach?
Conclusion
? Globally New Products ? Advanced Technology
from Global Standpoint ? Conventional Growth
Strategy Protectionism will not work
26
5. ?? ?? ?? ??
? High Entry Barrier
1?? ??? ???? ???? 15? ??? ???? ??? ???? ?? ????
?? ?? ???
15? ??? ???? ?? ? Safety ?
Efficacy ? Regulation ?? ??? ?
5,00010,000? screened ? 250? ??? ?? ?
5? ?? ?? ? 1? FDA ??
ltSource?????? 2001gt
27
5. ?? ?? ?? ??
? Huge RD Cost
1?? ?? ??? ??? ?? ?????? 3.4???? ?? ????? ????
??? ? 2.6??? ?? ?????? 78? ???
1?? ?? ??? ?? ?? ???? ?? (2000)
???
Phase I
Phase II
Phase III
??
?? ?? ?? (?? ?? only)
73.5 M
36.5 M
65.5 M
163.5 M
1.0 M
265M (78)
340 M
ltSource McKINSEY Report 2000gt
28
5. ?? ?? ?? ??
? Global Product
6? ?? ????? ???? ?? 10?? ??? Blockbuster ??? ?
??? ????? ???? ???? ??? 50? ?? ??
6? ?? ????? Global Products ??(2001)
?? ??
No. of Blockbusters
RD Expenses (2001?, ?? ??)
Blockbusters Sales/ Ethical Sales ()
???
1 2 3 4 5 6
Pfizer GSK Merck J J
AstraZeneca BMS
8 9 5 4 2 4
79.8 50.7 63.0 56.3 47.8 49.8
48 42 26 29 27 22
ltSource IMS World Review, 2002gt
29
5. ?? ?? ?? ??
?? ?? Paradigm ??
??? ?? ?? ?? ? Genomic /Proteomics Ex) Novartis?
???
?? ?? ??

???? ???, ???? (?? ??)
??? ??? ?? ???, ???? ???
???, AIDS ???, ???? ???

???? RD ??

Global ??? ?? ??? ??
- ? ??? 1520 ??

30
RD ?? vs New Drug Launches
5. ?? ?? ?? ??
Drug companies are spending more but producing
less
Indexes, 1992100
200
RD Spending
150
New Drug Launches
100
50
1992
02
93
94
95
96
97
98
99
00
01
Note Based on 48 companies, including 15
with RD spending exceeding 1 billion
each in 2000. Source Center for Medicines
Research International
31
?? ?? ?? ??
6. ?? ?? ?? ??
?? ??? ??? ?? ?? ? ?? ??? ??? ?? ??? ??
???? Global Player?? ?? ???? ?? ???
Partner
???? ??
???
??
??? ??
??? ???
LGLS
1991.1
????
???? ?? ???
????
1997.4
???? ???
SB
LGLS
1997.5
??
??? ???
????
2000.1
SB
??? ???
????
2000.9
???
Alza
???
2000.10
2001.3
??? ???
?????
????
2000.10
SR-hGH
BioPartners
LGLS
32
?? ?? ??
6. ?? ?? ?? ??
? ?
????
????
? ?
???? ??? LB20304a
? ?
Approval(??)
LG????
??? SK 12503R (???)
???
????(??)
SK???
EGF ???, ???
???
????(??)
????
??, ???? ??? ??? DW-116HC (????)
???
????(??)
????
??? ??? (???)
???
????(??)
????
??? ??? DA-125
???
??2?(??)
????
??? ??? G009
???
??2?(??)
????
??? ??? DW-116
???
??2?(??)
????
??? ??? YU-439
???
??2?(??)
????
???? ??? CFC-222
???
??2?(??)
????
??? ??? YKP10A
? ?
??2?(??)
SK
33
?. Drug Discovery Process
HTS
Pharmacogenomics
Combinatorial Chemistry
Genomics Bioinformatics
Proteomics SNPs
Sequencing
ADME
Patient Management
Clinical Trials
Preclinical Candidates
Lead Identification
Target Validation
Target Identification
?? ???? Research ??
?? ??? Development ??
? RD Factory ? HTS/LSDP
? Integration of Platform Technologies ? Data
Management ??? ??
? HTS (High Throughput Screening) LSDP (Large
Scale Data Processing)
34
Target Discovery
Target
Discovery
ID
VD
Lead Generation
Lead Optimization

Enzyme assay
Receptor assay
in vitro cell toxicity
in vitro pharmacology

Virtual screening
SAR by NMR
QSAR
X-ray crystallography
Modeling
HTS assay development
HTS

Stable cell line
Molecular transporter
Transgenic mouse,
etc

Bioinformatics
Gene expression
profile
Proteomics

Biology/Structure

Medicinal chemistry (parallel syn. ??)
Physicochemistry
Prodrug research

Virtual library
Focused library
Combi-chem
Chemoinformatics
HTS Hit analysis

Chemistry

Pharmacokinetics
in vitro vivo metabolism test
Protein-binding
Pre-formulation
in vivo pharmacology
Preliminary Acute Animal Toxicity

Predictive ADMET

Pharm/Tox
35
Preclinical Clinical Stage
Phase ?
Phase ?
Clinical Phase ?
Preclinical
??

?? ??
???? ? ??
??? ??
?? ??
?? ??

??? ??
(2080?)
Safety ??
??? ??

?? ??
(100300?)
Safety
Efficacy
?? ??

?? ?? ??
(1,0005,000?)
Safety
Efficacy

36
NDA Phase ?
Phase ? (PMS)
NDA (New Drug Application)

??? ?? ??
?? ? ???? ?? ???? ???
???? ? ??? ?? ?? ??
- ?? 5? ?? 3,000 Case
- ?? ?? ? ?? Case ??

?? ?? ?? 13?
??? ???/??? ?? ??

?? ??
?? ?? ?? ??
FDA? ??? ??? ??

? PMS (Post Marketing Surveillance)
37
Hurdles to drug development
However, preclinical study is not that simple
38
The RD Productivity
39
Challenges of new treatment paradigms
40
Golden rules of drug discovery

Unmet medical needs
Choosing appropriate disease and therapeutic area
Creating a suitable research environment and
appropriate attitudes
Reviewing research activities and allocating
resources
Stimulating innovation
Advancing compounds into development

41
?. Target Identification and Validation
42
Consideration points
43
Target Identification and Validation

Advances in genomics
and proteomics have
given rise to a range of
solutions for improving
target identification and
validation
Gene knockout and gene
silencing, expression profiling
and population genetics based
approaches are now being
employed in a complimentary
fashion across the industry.

44
Genomics can save the cost and time
Cost to drug
Time to drug
Pre-genomics
14.7
Pre-genomics
880
Post-genomics Target ID
Post-genomics Target ID
13.8
740
Plus in silico chemistry
Plus in silico chemistry
13.0
610
Plus preclinical And clinical advances
Plus preclinical And clinical advances
12.7
590
0
5
10
15
0
200
1,000
400
600
800
Time (years)
Cost (M)
45
Genomics technology assumptions
Development
Biology
Chemistry
Target Validation
Preclinical
Clinical
Target ID
Screening
Optimization
Target identification
Screening
Preclinical (ADME/tox)

Limited numbers of genes
Molecular biology and biochemistry techniques

Parallel synthesis for library design
Assay development for HTS
High-throughput screening(HTS)

Animal testing

Pre-genomics
Target validation
Chemical optimization
Clinical

Cell and tissue studies
Mouse knockouts

Bench synthesis
Parallel synthesis

Patient trials

Target identification
Screening
Preclinical (ADME/tox)

Large numbers of genes
Industrialized techniques(e.q., gene chip
expression)
Bioinformatics(e.g., database searches for
homologies)

Structural biology (target structure)
SAR profiling of library
Assay development for HTS
Virtual screening and HTS

Animal testing
In silico ADME/tox
In vitro toxicology
Surrogate markers

Post-genomics
Target validation
Chemical optimization
Clinical

Cell and tissue studies
Mouse knockouts

In silico-supported bench synthesis
In silico early ADME/tox

Patient trials
Surrogate markers

46
Genomics reveals potential drug targets
Human Genome - size 30 times larger than worm
and fly - gene 3 times as many as fly and
worm - However, encodes more than 100,000
proteins because of extensive alternative
splicing
New opportunities for drug target identification
- currently 800 targets identified - More than
7,000 targets are waiting to be discovered
- Most proteins and their function(s) not yet
identified
Genotyping Expression Profiling (Transcriptomics,
Proteomics) Metabolomics
47
However, we need to know about targets
48
Hot spots for drug discovery
49
Target validation and druggability are
effective Predictors of lead generation success
50
Balancing the risk in drug discovery
Do-ability of target Classes
51
?. Lead Generation and Optimization
52
Leads, drug-likeness, and drugs ?
Leads Compounds that have a binding affinity
with the micro-molar range or less Starting
compounds for drug development Drug like
compounds Compounds that have sufficiently
acceptable pharmacokinetic and toxicity
properties to survive through the completion of
human phase I clinical trials The Rule of
Five MWlt500, No. of H-bond acceptors lt10, No. of
H-bond donorslt 5, Calculated logPlt5.0 Drugs
Compounds that have been approved for marketing
for human disease therapy by a regulatory agency.
53
Leas discovery is a bottleneck
54
Lead Generation
Finding molecules matching biological and
chemical structure spaces
Lead
55
Lead generation rope contains multiple strands
56
Accelerating Lead Generation and Optimization
1. High Throughput Screening (HTS) 2.
Combinatorial Chemical Library (CCL) 3. Modeling
/ Virtual Screening 4. Structure Studies 5.
Quality of lead compounds
57
3 Key Elements in HTS
HTS (High Throughput Screening)
Hardware
Software
Chemical Library

Fast Changing!
Fast Improvement!!
Most Important factor for High Throughput
!!!

Increasing !
Most important factor for successful
discovery !!

Moderately changing!
Important when Data becomes larger
larger!!

58
The Screening Analysis Process
59
Plate Formats
96
384
864
1536
60
Detection Range in LGLS HTS Platform

1. Enzyme Assay
Fluorescence Intensity
Fluorescence Resonance Energy Transfer
Fluorescence Polarization
Absorbance

2. Receptor Assay
SPA (Scintillation Proximity Assay)
Filter binding Assay
Luminescence (Reporter Gene Expression)

61
HTS Strategy for BACE Inhibitors
I. Cloning and Expression of BACE IgG Fc in HEK
293 T Cells
BACE (Ectodomain)
IgG Fc
CMV
II. FRET (Fluorescence Resonance Energy Transfer)
Assay
Fluorophore
Quenching group
EDANS -Ser-Glu-Val-Asn-Leu-Asp-Ala-Gl
u-Phe-Arg-Lys DABCYL -Arg-Arg-NH2
EDANS -Ser-Glu-Val-Asn-Leu
Asp-Ala-Glu-Phe-Arg-Lys DABCYL -Arg-Arg-NH2
High-Throughput Screening
BACE
Screening of 30,000 compounds per day with 384
wells
62
LG Life Science Lead Generation Group
Source of Drug Targets
Source of Drug Targets
BPD
support
Support SAR
Key Function
Biology Group (10 targets/year) Genomics-Based Ou
tsourcing (10 targets/year)
Lead !!! MediChem
Assay Development
Assay Development
Cloning Team
Protein Team
HTS
HTS

Chemical Library
Assay Miniaturization Hit Confirmation
Chemical Library
Robotics
Assay Miniaturization Hit Confirmation
support
Chem- informatics
Chem- informatics
Target Generation Group
Combinatorial Chemistry
63
Molecular Diversity Sources of MD
Molecular Diversity (Background Rationale)

Classical synthetic techniques (100
compounds/yrlt)
Cost (5,00010,000/compound)
Explosion in targets from molecular
biologyGenomics
Decrease timecost to discover/develop
biologically active molecules

Sources of Molecular Diversity

Natural products
Corporate collections
University researchers
Synthetic combinatorial librariesManual
Time-consuminginefficientAutomated
Mixturesparallel synthesis of discrete compounds

64
Biological Diversity-Chemical Diversity
Chemical Diversity
Chemical Diversity
Chemoinformatics
Chemical Diversity
Chemical Diversity
T1
T1
T2
T3
T4
T5
T6
T7
Selected Gene family targets from Genomics
Selected single target
Lead
Leads

Gene family Kinases, Proteases (Cysteinyl-,
Aspartate-), Phophatases, etc.

65
Libraries Generation versus Optimization

Small focused libraries
SAR expansion
Lead validation
Rapid analoging
Discrete compounds
Target sythesis

10,000s of compounds
Library size
Complex Chemistry
More demanding chemistry Make what you need
to Make, not what your Technology allows
Limited Chemistry
100-1,000 compounds
Lead Generation
Lead Optimization
66
Lead Generation Library for Chemical Diversity
67
Example Scaffold

A scaffold is defined by the 3-dimensional
disposition of
Charged Group (s)
Lipophilic Group (s)
H-bond doner (s)
H-bond aceptor (s)
Relative to the core functionality
Space Filling
Stereochemistry

68
LGLS Combinatorial Chemical Library?? ???
Compound ??
Building block Selection
? ?
Analysis
Evaporation
Purification
ACD Afferent

Combiflex
Miniblock
Chem speed

TLC
HPLC
LC-Mass

GeneVac
SpeedVac

Combi Flash
Parallel
Crystalization

LC-Mass
Isis data base

69
Accelerating Lead Generation and Optimization
1. High Throughput Screening (HTS) 2.
Combinatorial Chemical Library (CCL) 3. Modeling
/ Virtual Screening 4. Structure Studies 5.
Quality of Lead Compounds
70
?? ??? ?? ? ??

QSAR?? ??? ?? ???? ????? ???? ???? ??? ???? ???
DockingReceptor ? active site ? ???? fit ??? ??
De Novo DesignPharmacophore ?? ???? ??? ???? ???
?? ??
Virtual Screening (?? ????)?? ??? ?? (QSAR,
docking, de novo ???) ?? ???? ??? ?????? ?? ??
library ? ???? ?? ????? ???? ???

71
Docking ???

Receptor ? ligand ? binding ? lock key ??? ??
(? ??? ????? receptor ? ??? ???? ?? ? ??)

Receptor
Ligand
Receptor-Ligand Complex
Docking ? ??

Docking ???? ???? ???? ?? ?? ?? (? ligand) ? ????
binding site ? ????? ???.

72
Ligand-based drug design (3D pharmacophore search)

Pharmacophore (pharmacophoric pattern)
An ensemble of steric and electronic features in
3D required for binding a particular protein.
Can be considered as the largest common
denominator shared by a set of active molecules.
Pharmacophoric features
Elements that define a pharmacophore
Hydrogen bond donors
Hydrogen bond acceptors
Positive charge centers
Aromatic ring centers
Hydrophobic centers

Pharmacophore search
A pharamacophore
hit
73
De Novo ligand design
Fragment replacement
Fragment growing linking
Merck0431
Fragments are enumberated from fragment
libraries, .e.g., ACD, MDDR, etc.
74
Structure-based drug design
A lead optimization method by growing fragments
from the existing scaffold or linking fragments
that fit into the active site.
Performs a conformational search on each fragment.
Scores each fragment.
Goes through fragments in the database.
75
New de novo design strategy
Validation of Small Fragments by X-ray
In-Silico Screening
DNA Gyrase assay
Clustering
NMR/X-ray
14 clusters(needles)
350,000
3,000
150 hits
3D Optimization
DNA Gyrase inhibition
Boehm J. Med. Chem. 2000, 43, 2664.
76
Accelerating Lead Generation and Optimization
1. High Throughput Screening (HTS) 2.
Combinatorial Chemical Library (CCL) 3. Modeling
/ Virtual Screening 4. Structure Studies 5.
Quality of Lead Compounds
77
Example in drug discovery
5000 fold
Relenza (zanamivir) GlaxoSmithKline Dry-powder
inhalation
Tamiflu (oseltamivir) Gilead/Hoffmann-La
Roche oral
Neuraminidase
78
LGs structures
Thrombin
FTase
Caspase 8
PTP1B
FGFR
FXa
KDR
PPARa
TCPTP
FabG
CDK2
Caspase 3
PPARg
PDF
DPPIV
79
PPARa/g dual agonists
PPARa / Compound A
PPARg / Compound A
PPARa EC500.2mM PPARg EC500.02mM
80
KDR inhibitors
NS41415 NMR Screening Hits
KDR IC503.16mM
LB60613 Introducing Pyrimidine Ring
KDR IC505.62mM
81
KDR inhibitors
LB60675 Introducing Cyclopropyl and Aniline
Groups
KDR IC5020nM
Further modifications to improve the solubility
and the efficacy are in progress
82
Accelerating Lead Generation and Optimization
1. High Throughput Screening (HTS) 2.
Combinatorial Chemical Library (CCL) 3. Modeling
/ Virtual Screening 4. Structure Studies 5.
Quality of Lead Compounds
83
Lead quality is multi-dimensional
84
Pharmaceutical Profiling Assays
Optimize several parameters of a lead structure
simultaneously to discover a Development Compound
Lead
Drug-like
85
Main Reasons for attrition in development
86
Oral Bioavailability
Membrane Transfer
Liver Extraction
Solid Drug
Dissolution
Drug in Solution
Absorbed Drug
Systemic Circulation
Solubility
Permeability
Metabolism
87
Properties Affect Discovery Bioassay Data

Better planning and interpretation of biology
88
High throughput screening of absorption
89
Comparison of PAMPA and Caco-2 Assay
90
High throughput screening of CYP metabolism
91
Assessment and Decision
92
Key drivers for right decision
93
Go/No-Go Decision
Discard those compounds most likely to
fail Concentrate your resource on those most
likely to succeed Speed drugs to the
market Produce additional income to company
94
LGs decision gate system
95
LGs decision gate system
RD Productivity Can Be Increased by Decision
Gates
Target Identification
Gate Keeping Systems and Criteria for better
decision
RD process
Go/No go decision
Lead Identification
Target validation, Technical feasibility,
Strategy
Lead Optimization
Proof of concept, Druggable lead, Key success
factor
Preclinical studies
Competitiveness, DMPK, pre-tox issue, Develop
value
Phase I (safety, tolerance, PK)
First in man, Safety, Possibility
Phase II/III (dose optimization, safety, efficacy)
Clinical proof of concept, Effectiveness in man
96
Factive, Success story with right decision
No. of cpds
Target Identification
RD process
Go if following elements are positive
Lead Identification
Me-Too approach for the proven target (DNA gyrase)
350 new cpds 150 Back-ups
Lead Optimization

- Efficacy Active against respiratory tract
pathogens, including
resistant strains
- Patent World-wide coverage
PK Once daily, iv and oral

Preclinical studies

- Safety Balance of effects/side effects
Chemistry and formulation

1
Phase I (safety, tolerance, PK)

Marketing Cost, sales price, competitors
Licensing Pipeline of major Pharm.

Phase II/III (dose optimization, safety, efficacy)

Clinical proof of concept, effectiveness in man
Society FDA policy

Factive
97
Clinical trials
? ?
? ?
PK (?? ??, ??, ??)? ???? ?? side effects ??
20-100 ?? ??? volunteers
??? ?? ????? health-impaired patients ??? ?? ?? ??
???? ??
??? ?? benefit/risk relationship? ?? ??? ??? ???
?? ?? ??
?? ???? ??
?? ?? ???? ?? ?? ? ??? ??
???? ??
98
Co-development strategy
99
LGs strategic alliance
100
LGs NCE pipelines
New Chemical Entity
Factive Neovastat Thymitaq LB80380 LB30870 LB110
58 LB84318 MCR DPIV HCV inhibitor KDR inhibitor
Oscient Aeterna Eximias Anadys Yamanouchi
CAP, AECB Lung cancer Liver cancer Hepatitis
B DVT Anti-MRSA Liver Disease Obesity Diabetes
Mellitus Hepatitis C Cancer
HBV Pol Thrombin Caspase MCR DP IV HCV Pol KDR
In-licensed product
101
LG Life Sciences
Leading Global Life Sciences Company
102
LG Group

Founded Lucky Chemical Industrial Co.
(present LG Chem) in 1947 and
established Goldstar Electronic Co.
(present LG Electronics Co.) in 1958.
2nd largest business group in Korea
? USD 74 billion in revenues in 2003
? Business domains in
Chemicals Energy, Electronics
Telecommunications and Services
? Worldwide business operations
130,000 employees in 49 countries

Headquarter LG Twin Tower, Seoul, Korea
103
LG Life Sciences
Establishment
LG Corporation (Holding company)
LG Chem Investment (Holding company)
LG Chemical

- BUSINESS AREAS
Chemicals Polymers
Industrial Materials
Electronic Materials
Household Healthcare
Pharmaceuticals
Agrochemicals

LG Chem
LG Life Sciences
LG Household Healthcare
Apr. 1st, 2001
Aug. 1st, 2002
104
History of LG Life Sciences

1983 Initiated as a genetic engineering
department in LG Chem.
1984 LBC opened in US, for joint RD with
Chiron
1990 First launch of biopharmaceutical in Korea
(Intermax-?)
1994 Reorganized to focus on small molecule
drug discovery and biopharmaceutical
development
1997 Strategic alliances initiated for
worldwide development
2002 Demerged from LG Chem Investment as LG
Life Sciences
2003 Factive US FDA approval

? aINF(92), rHBV Vaccine(92), hGH(93),
bST(94), GM-CSF(95), EPO(99), HA(97)
Of FactiveTM with SmithKline Beecham
105
RD Institute and Plants
RD institute, Daejeon
Iksan Manufacturing Plant
Onsan Manufacturing Plant

? Site 3,060,000 ft2,
? Floor area 1,140,000 ft2
? 318 scientists

? Bio-pharmaceuticals
? FACTIVE plants
? Finished pharmaceutical products

? Pharmaceutical Intermediates ? Agrochemicals
(Ph.D 20, MS 65)
106
RD area
LG Life Sciences
Drug Discovery Development
Biopharmaceuticals

Recombinant Proteins- hGH, EPO, etc.
Vaccines- Hepatitis B, Combination Vaccine
Fermentation Products- Hyaluronic Acid.
Drug Delivery System- SR-hGH

Anti-infectious- Antibiotics, Antiviral Agents
Cancer
Life Style Drugs- Obesity, Cholesterol
Lowering Agents, Diabetes

107
RD Organization
LG Life Sciences RD consists of 6 functional
groups
Life SciencesRD
108
Strategy
Small molecule drug
Focusing on drug discovery research andstrategic
cooperation with leading pharmaceutical companies
Lead Optimization
Focused Disease Area

Cancer
Metabolic disease
Cardiovascular

CCL
HTS
Chemo Informatics
Assay Development
Molecular Biology

Medicinal Chemistry
Structure(X-ray, NMR, Modeling)
Pharmacology
PK

109
Strategy
Biopharmaceuticals
Worldwide Market Share of Major
Biopharmaceuticals
LGs Strength

Worldwide development of internal products with
leading biopharmaceutical companies (Biopartner)
Develop novel biopharmaceutical products for
global market (DDS, Therapeutic Antibody)

Sales(2000) 20 Billion USD
Sales(2008) 67 Billion USD

110
Factive
Success story in LG Life Sciences
Approval in S. Korea (Dec. 2002)
NDA Submission (1999)
Quinolone Research Initiated
Approval in New Zealand (Jun. 2002)
Clinical Study Initiated
NDA Resubmission
Approval by FDA (Apr. 3, 2003)
LG
GSK - LG
GeneSoft
2003
2002
1991
1997
Licensing Partners
Factive
111
Factive
Currently the only antimicrobial approved by the
FDA for use against multidrug resistant S.
pneumoniae
Competitors
Narrow spectrum activity
Three times daily
Side effects on CNS, Cardiovascular
112
Fast Active Factive
113
Development Pipeline
New Chemical Entity
Success stories will be continued
Target
Project
Partner
Indication
Discovery Preclinical I II III
NDA Market
Factive Neovastat Thymitaq LB80380 LB30870 LB110
58 LB84318 KDR inhibitor HCV inhibitor MCR DPIV
CAP, AECB Lung cancer Hepatic cancer Hepatitis
B DVT Anti-MRSA Liver Disease Cancer Hepatitis
C Obesity Diabetes Mellitus
HBV Pol Thrombin Caspase KDR HCV Pol MCR DP IV
Oscient Aeterna Eximias Anadys Yamanouchi
In-licensed product
114
Development Pipeline
Bio-Pharmaceuticals
Success stories will be continued
Project
Partner
Indication
Discovery Preclinical I II III
NDA Market
HBV, HCV, etc. Short stature ? Combo-vaccine Anemi
a Infertility
IFN-a hGH LB03002 (sr-hGH) DTaP-HepB EPO FSH sr-I
FN- a
BioPartners BioPartners BioPartners Kaketsuken Bio
Partners
115
Strategic Alliances
Success stories will be continued