Molecular Targeting for Lung
Cancer Prevention and Therapy
Ruiwen Zhang, MD, PhD, DABT
Professor, Pharmacology and Toxicology
Director, Cancer Pharmacology Laboratory
University of Alabama at Birmingham
Birmingham, AL 35294
Ruiwen.zhang@ccc.uab.edu
UCLA, April 14, 2007
In the short 30 min...
Our view on…
Drug Discovery and Development
in Cancer Prevention and Therapy
Who we are…
&
What we are doing…
(3 Examples)
Drug Discovery & Development
The Players

Basic Scientists/Academic Researchers


Chemist
Biologist
Pharmacologist
Geneticist
Medical Professionals




Big Pharmaceutical Companies
Small Development Companies
Biotech/Start-up Companies
Contract Research/Management Organizations



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Developers
Entrepreneurs
Regulatory Agencies (US FDA, SFDA, etc.)
Government Research
Consumers (Patients, Community)
Overview of
Drug Discovery
Decision
Filter
(Go/No Go?)
Overview of Drug
Development
Decision
Filter
(Yes/No?)
10,000-50,000
Chemical
Development
Drug
Development
FDA
Pharmacology
Toxicology
Clinical
Development
NDA Submission
Decision Filter
1,000
10
1
~ $ 250M$500M
Chemical
Development
Pharmacology
Toxicology
~ $ 250M$500M
Clinical
Development
NDA Approval
1.8 yr
Decision Filter
4.4 yr
6.6 yr
2.2 yr
FDA Drug Approvals
New molecular entities (NMEs) and biologic license applications
approved by the US FDA by year.
The number of NMEs approved in 2006 stayed the same as in 2005, with
a slight increase in the number of approved biologics.
Joanna Owens Nature Reviews Drug Discovery 6, 99–101 (February 2007)
Importance of Target Validation




Reality Check: The Apparent decline in success rates
for new pharmaceuticals in recent years is consistent
with a theory that the development of new
technologies that posits in effect that the low-lying
fruit will tend to be picked first.
Hope or Hype?
 Post-genome era
 Second genomics?
 “***” Omics?
 Systems Biology?
 Individualized Medicine (Drug, Pharmaceuticals)
Key Factors:
 New Technology,
 New Targets,
 Early Decision (go/no go?)
 New approaches to clinical trial, e.g., Phase 0
Predictive Models are urgently needed:
 Fail Early, Fast and Often
Target Selection
Drug May Target at Various Subjects:
Foreign Pathogens
Disease
Host Disease-causing
Genes/Proteins
Host Internal and
External Environments
Target Discovery Process
Target Validation Process


Methods
 Molecular/Genetic/Genomic
 Biochemical/Proteomic
 Physiological/functional
 Pharmacological/Toxicological
 Population-based
System
 Cell-free in vitro
 Cell
 Organ (in vitro and in vivo)
 Small animals
 Non-human primates
 Humans
Target Validation Process
Criteria






Causal relation between
target and disease
Correlation with disease
status
Specificity (Specific/Nonspecific)
Affinity
Mode of action (onset,
short-term/long-term)
Regulation of effects
Data Interpretation
 Genotype vs.
Phenotype
 In vitro vs. In vivo
 Animals vs. Humans
 Healthy subjects vs.
Patients
 Other host factors:
sex, age, race, etc.
 Other Limitations,
e.g., dose-range
 Research tools vs.
Drug class/entity
Animal Disease Models
Toxicology
What
and
Cancer
Why
Cardiovascular
HIV
Who We Are…
&
What We Are Doing…
Birmingham: Steel City
Birmingham: The beautiful
Birmingham: Magic City
American Idol Taylor Hicks
Miss UAB 2005
Cancer Drug Discovery and Development







Target Validation
In Vivo Disease Models
PK/PD
Toxicology
Delivery
Combination Therapy
 Chemosensitization
 Radiosensitization
 Antibody/Immunother
apy
 Vaccine
 Gene Therapy
Clinical Trials & Clinical
Pharmacology






Molecular Targets:
 p53
 PKA
 VEGF
 ICAM-1
 MDM2
 XIAP
 BCL-2
 β-catenin
 E2F1
CpG Oligos (IMOs)
Small Molecules
Natural Products
Imaging agents
Antibiotics
Examples:
Preclinical and Clinical Drug Development








In
In
In
In
vitro Pharmacology
vivo Pharmacology
vitro Toxicology
vitro Toxicology
Pharmacogenomics
Toxicogenomics
Drug Delivery
Biomarker







Clinical Trials :
Phase 0 Trials/Biomarcker
Phases I /Clinical
Pharmacology
Phase II Trials
Phase III Trials
Phase IV/Surveillance
Prevention Trials
Example 1: Toll-Like Receptor Agonists

(TLR)-mediated Immune responses




Substrate/ligand specific
Cell type dependent
Ap-1/NFkappaB pathways
TLR9-mediated Immune stumulation by CpG ODNs



Structure dependent
Cell type dependent
Multiple responses
(Wang et al: Current Pharmaceutical Design 2005;
Molecular Cancer Therapeutics 2006)
Bacterial lipoproteins (BLP)
Lipoarabinomannan (LAM)
LPS binding protein (LBP)
Lipoteichoic (LTA)
Peptideglycans (PGN)
MALP-2, Zymosan
?
Small
anti-viral
compounds
LPS, LTA, Taxol,
HSP60/HSP70?
F protein, Lipid A
dsRNA
Flagellin
Bacterial DNA,
Synthetic DNA,
Plasmid DNA
TLR4
TLR8
TLR10
TLR1
TLR6
TLR3
TLR2
MD2
TLR5
TLR7
TLR9
CD14
?
Toll-like
Receptors
(TLR), their
Ligands and
Related
Signal
Transduction
Pathways
CpG
DNA
MyD88
TIRAF
Caspase-1
Endosome
IRAK
IRF3
Pre-IL-18
•TNF-: Adhesion molecules on endothelial cells; IL-6
upregulation; macrophage activation
IL-18
TRAF6
•IL-6: B-cell differentiation; antibody section; class switch
•IL-12: IFN- productin by NK and Th1 cells; Th1 cell
differentiation; Th2 cell suppression
MAPK
AP1
IkB
NF-kB
•IFN-: APC activation; Th1 development; MHC-I
upregulation
•IFN-/: MHC-I upregulation; antigen processing and
presentation
•IL-1: Adhesion molecule upregulation on endothelial cells;
upregulation of IL-6
NF-kB
•IL-10: Inhibitor of IL-12 and IFN- production
•MHC-I: Antigen presentation to CD8+ cells
P
Nucleus
•CD40: Co-stimulatory signal, IL-12 secretion
IRF3
•CD86: Co-stimulatory signal
•CD69: Co-stimulatory signal
Transcription of immune response genes
Activation of
Inflammatory
cytokines
MOA: CpG-TLR9 Signaling
CpG DNA
Wang H, Rayburn E, and Zhang R. Current Pharmaceutical Design 11 (22): 2889-2907.
Strategies to Improve CpG ODN Properties
• In Vivo Stability
• Immunostimulation (A, B, C – class ODN)
Backbone Modification
• N – Base modifications
5’- NNNNNNNNNNNCpGNNNNNNNNNNN - 3’
5’ modifications
5’ Immunomodulatory
Moieties (Including
polyG Nucleobase
Deletion)
CpG
Core
Species
Selectivity
•3’ modifications
•3’ – 3’ link
3’ Immunomodulatory Moieties
• Multiple CpG
• Synthetic motifs
(CpG, YpG, CpR, YpR)
Novel IMOs: Chimeric IMOs
In Vivo Anti-tumor Activity
Saline
of IMO in Human Lung
Cancer H358 Xenograft
IMO
Models Following Treatment
of IMO Alone or in
IMO + Gemcitabine
Combination with
Gemcitabine
Gemcitabine
Saline
Saline
Control IMO
IMO
Gemcitabine
B lymphocyte
MDA-MB-231
PC3
ACHN
A549
BT474
MCF7
Western Blot
Control
IMO (100 nM)
Control IMO (100 nM)
Control
IMO (100 nM)
Control IMO (100 nM)
HCT116 (p53-/-, p21-/-)
HCT116 (p21-/-)
HCT116 (p53-/-)
HCT116 (p53+/+)
U87MG
U2-0S
MIA-PaCa2
PANC-1
MCF-7
A549
DU145
PC3
DLD-1
HCT116 (p53-/-)
HCT116 (p53+/+)
TLR9 mRNA and protein expression
in various cancer cell lines
RT-PCR
RT-PCR
A549 Cells
506 bp
298 bp
PC3 Cells
506 bp
298 bp
β-actin (~ 600 bp)
TLR9 (~ 259 bp)
β-actin (~ 600 bp)
TLR9 (~ 259 bp)
IM O / Lip ofectin (+)
IM O / Lip ofectin (-)
Control IM O / Lip ofectin (+)
Control IM O / Lip ofectin (-)
0
0
20
40
60
80
100
Lipofectin (+)
150
100
50
0
Control
Concentration (nM)
B: U87MG
Apoptotic Index (% of Control)
Cell Survival (%)
120
90
60
IMO / Lipofectin (+)
IMO / Lipofectin (-)
Control IMO / Lipofectin (+)
Control IMO / Lipofectin (-)
30
0
0
20
40
60
80
250
Lipofectin (-)
200
Lipofectin (+)
150
100
50
0
Control
100
Concentration (nM)
Apoptotic Index (% of Control)
120
Cell Survival (%)
C: HCT116
(p53 +/+)
90
60
IMO / Lipofectin (+)
IMO / Lipofectin (-)
Control IMO / Lipofectin (+)
Control IMO / Lipofectin (-)
30
0
0
20
40
60
80
250
200
150
100
50
0
Control
100
Apoptotic Index (% of Control)
Cell Survival (%)
90
60
IMO / Lipofectin (+)
IMO / Lipofectin (-)
Control IMO / Lipofectin (+)
Control IMO / Lipofectin (-)
0
0
20
40
60
80
250
Lipofectin (-)
200
Lipofectin (+)
100
50
0
Control
100
60
IMO / Lipofectin (+)
IMO / Lipofectin (-)
Control IMO / Lipofectin (+)
Control IMO / Lipofectin (-)
0
20
40
60
80
Concentration (nM)
100
Apoptotic Index (% of Control)
Cell Survival (%)
90
0
Control IMO
IMO (100nM)
(100nM)
120
30
IMO (100nM)
150
Concentration (nM)
E: PC3
Control IMO
(100nM)
120
30
Control IMO IMO (100nM)
(100nM)
Lipofectin (-)
Lipofectin (+)
Concentration (nM)
D: HCT116
(p53 -/-)
Control IMO IMO (100nM)
(100nM)
250
Lipofectin (-)
200
Lipofectin (+)
150
100
50
0
Control
Control IMO IMO (100nM)
(100nM)
Proliferation Index (% of Control)
30
Lipofectin (-)
200
Proliferation Index (% of Control)
60
250
Proliferation Index (% of Control)
90
Proliferation Index (% of Control)
Cell Survival (%)
120
A: A549
Proliferation
Proliferation Index (% of Control)
Apoptosis
Apoptotic Index (% of Control)
Survival
Lipofectin (-)
140
Lipofectin (+)
120
100
80
60
40
20
0
Control
Control IMO IMO (100nM)
(100nM)
Lipofectin (-)
140
Lipofectin (+)
120
100
80
60
40
20
0
Control
Control IMO IMO (100nM)
(100nM)
Lipofectin (-)
140
Lipofectin +)
120
100
80
60
40
20
0
Control
Control IMO IMO (100nM)
(100nM)
Lipofectin (-)
140
Lipofectin (+)
120
100
80
60
40
20
0
Control
Control IMO IMO (100nM)
(100nM)
Lipofectin (-)
140
Lipofectin (+)
120
100
80
60
40
20
0
Control
Control IMO IMO (100nM)
(100nM)
Effects of
IMO alone
on cell
survival,
apoptosis
and
proliferation
in various
cancer cell
lines
Lung cancer xenograft tumors treated with an anti-VEGF Antisense
oligonucleotide +/- chemotherapy
3000
3000
Tumor Mass (mg)
Saline
2500
Saline
2500
Control ODN (20 mg/kg)
Gemcitabine
AS-VEGF (10 mg/kg)
2000
1500
Control ODN + Gemcitabine
2000
AS-VEGF (20 mg/kg)
1500
A
1000
1000
500
500
0
0 3
6 9 12 15 18 21 24 27 30 33 36
Day
AS-VEGF + Gemcitabine
B

The anti-VEGF ASO
and control ODN
were given by ip
injection 5d/wk

Gemcitabine (160
mg/kg) was
administered by ip
injection on days 4
and 11.
0
0 3
6 9 12 15 18 21 24 27 30 33 36
Saline
Saline
Control ODN
(20 mg/kg)
Saline
AS-VEGF
(10 mg/kg)
Cntl ODN
(20 mg/kg)
AS-VEGF
(20 mg/kg)
AS-VEGF
(20 mg/kg)
Gemcitabine
IMO inhibits NSCLC tumor growth in animal models (1)
4000
3000
Saline
Control Oligo
IMO (1 mg/kg)
3500
3000
2000
2500
A. H520
Tumor Mass (mg)
2000
Saline
Control Oligo
IMO (1 mg/kg)
2500
B. H358
1500
1500
1000
1000

500
500
0
0
0
3
6
9 12 15 18 21 24 27
1000
0 3 6 9 12 15 18 21 24 27 30 33 36
2000
Saline
800
Saline
IMO (0.5 mg/kg)
1500
The IMO was
administered at
doses of 0.5 or
1.0 mg/kg by sc
injection 3d/wk
IMO (0.5mg/kg)
600
C. A549
D. H1299
1000
400
500
200
0
0
0
7
14
21
28
35
0
7
14
21
28
35
Day
Wang H et al. Mol Cancer Ther. 2006 5: 1585-92.
IMO inhibits NSCLC tumor growth in animal models (2)
4500
4000
3500
3000
Tumor Mass (mg)
2500
2000
A. H520
1500
1000
500
Tumor Mass (mg)
2000
Saline
Control Oligo
IMO
Gemcitabine
Control Oligo + Gemcitabine
IMO + Gemcitabine
Saline
Control Oligo
IMO
Alimta
Control Oligo + Alimta
IMO + Alimta
1500
1000
6
9
12 15 18 21 24 27
0
3
6
9
12 15 18 21 24 27 30
3000
2500
2000
1500
Saline
Control Oligo
IMO
Gemcitabine
Control Oligo + Gemcitabine
IMO + Gemcitabine
Saline
500
IMO
0 3 6 9 12 15 18 21 24 27 30 33 36

Alimta (100
mg/kg) was
administered by
ip injection on
days 11, 18 and
25.
Gemcitabine
Saline
Cntl oligo
0
Gemcitabine
(160 mg/kg)
was
administered by
ip injection on
days 4 and 11.
Day
D. H358
B. H358
1000

C. H520
0
3
The IMO or
Control oligo
was
administered at
1 mg/kg by sc
injection 3 d/wk
500
0
0

Day
Wang H et al. Mol Cancer Ther. 2006 5: 1585-92.
Example 2:
ECPKA As A Cancer
Marker:
A Population Study
Wang et al: Cancer Epidemiology Biomarker and Prevention, 2007 April 1 Issue
Purpose: The present study was designed to investigate the
population distribution of extra-cellular activity of cAMPdependent protein kinase (ECPKA) and its potential value in
cancer detection.
Background: PKA may have a role in tumorigenesis and
cancer growth. Elevated PKA expression has been reported
in patients with cancer, and PKA inhibitors have been tested
in clinical trials as novel cancer therapy.
Methods: The population distribution of ECPKA activity was
determined in serum samples from normal healthy subjects
and cancer patients in a Chinese population, consisting of a
total of 603 subjects (374 normal healthy volunteers and 229
cancer patients). The serum ECPKA was determined by a
validated sensitive radioassay and its diagnostic values
(positive and negative predictive values) were analyzed.
MOA of cAMP-Dependent PKA
ATP
ADP
cAMP
R-subunit C-subunit
C-subunit
Active
Inactive
ATP
[C··· R] + cAMP -> C + R ··· cAMP
Inactive
Active
Phosphorylation (Arg, Val, Ser, Val)
Histone H1
ECPKA Assay:
Affinity Ultrafiltration Separation Assay
Reaction Mixture:
•ATP
•32P-ATP
•Kemptide
•CAMP
•Reaction Buffer
Blood
sample
PKA
Reaction
Adding Avidin
Plasma
32P-ATP
Ultrafiltration system
Recover 32P-labeled,
biotinylated substrate
Ultrafiltration & Washing
Radioactivity Counting
ECPKA in Normal Population and Cancer Patients
N
Mean
Standard
Deviation
Median
Range
Overall
603
5.50
10.90
2.12
0 – 108.45
Cancer Patients
229
10.98
15.84
5.42
0 – 108.45
Controls
374
2.15
2.95
1.02
0 – 25.19
ECPKA in Normal Population and Cancer Patients
25
Control (Total)
Cancer Patient (Total)
15
10
5
ECPKA Activity (U/mL)
>=14
13-
12-
11-
10-
9-
8-
7-
6-
5-
4-
3-
2-
1-
0.01-
0
UD
Frequency (%)
20
ECPKA in Normal Population and Cancer Patients
30
30
Control (Female)
Control (Female)
25
Cancer Patient (Female)
ECPKA Activity (U/mL)
LDH Activity (U/L)
280-
260-
240-
220-
200-
180-
140-
120-
100-
>=14
13-
12-
11-
10-
9-
8-
7-
6-
5-
4-
3-
0
2-
0
1-
5
0.01-
5
80-
10
60-
10
15
40-
15
20
20-
Frequency (%)
20
UD
Frequency (%)
Cancer Patient (Female)
160-
25
ECPKA in Normal Population and Cancer Patients
30
35
30
Control (Male)
25
Control (Male)
Frequency (%)
Cancer Patient (Male)
20
15
10
5
25
20
15
10
5
ECPKA Activity (U/mL)
LDH Activity (U/L)
280-
260-
240-
220-
200-
180-
160-
140-
120-
100-
80-
60-
40-
0
20-
>=14
13-
12-
11-
10-
9-
8-
7-
6-
5-
4-
3-
2-
1-
0.01-
0
UD
Frequency (%)
Cancer Patient (Male)
cC
a
Al
lC
a
ha
ge
al
Ca
Co
lor
ec t
al
Ca
Lu
ng
Ca
Ga
str
ic
Ca
He
pa
tom
a
Es
op
nc
rea
ti
Pa
l
tro
st C
a
Br
ea
Co
n
ECPKA Activity (U/mL)
ECPKA in Normal Population and Cancer Patients
25
20
15
10
5
0
Al
lC
a
He
pa
tom
a
a
Ca
Ca
Lu
ng
ect
al
Ga
str
ic
C
Co
l or
a
lC
a
a ti
cC
a
ol
st C
Es
op
ha
gea
Pa
nc
re
Br
ea
Co
n tr
LDH Activity (U/L)
LDH in Normal Population and Cancer Patients
120
90
60
30
0
Example 3: Natural Products
Ginseng
Wang/Zhao et al: Med Chem 2007; Cancer Chemother Pharm 2007
Top 10 Dietary Supplements In the US Market
Rank
天然产物
Name
1
紫锥花
Echinacea
2
人参
Ginseng
3
银杏
Ginkgo
4
大蒜
Garlic
5
葡萄糖胺
Glucosamine
6
金丝桃
St. John’s wort
7
薄荷
Peppermint
8
鱼油
Fish oil
9
生姜
Ginger
10
大豆
Soy
From: Barnes et al: Advance Data Report 343, 2004
Identification of Ginsenosides
Fruits of P. ginseng
Extracted with 75% EtOH
EtOH extract
Evaporated in vacuum
Resin column
Eluted with 70% EtOH
Total saponins
Extracted with CHCl3 and 1-BuOH
CHCl3 fraction
CHCl3-MeOH
CH3CN-H2O
1-BuOH fraction
CHCl3-MeOH-H2O
Silica gel column
reverse-phase HPLC
MeOH-H2O
Compounds 1-4
Compounds 5-11
Identification and
characterization by EI-MS, IR, 1HNMR, and 13C-NMR
1. 20(R)-dammarane- 3β,12β, 20, 25–
tetrol
2. 20(R)-dammarane-3β, 6α,12β, 20, 25 pentol
3. 20(S) -protopanaxadiol
4. Daucosterin
5. 20(S)-ginsenoside-Rh2
6. 20(S)-ginsenoside-Rg3
7. 20(S)-ginsenoside-Rg2
8. 20(S)-ginsenoside-Rg1
9. 20(S)-ginsenoside-Rd
10. 20(S)-ginsenoside-Re
11. 20(S)-ginsenoside-Rb1
Scheme for isolation and identification of compounds 1-11
SAR of Ginsenosides
Compound
Name
Structure
OH
HO
OH
1
20(R)-dammarane-3β, 12β,
20, 25-tetrol (25-OH-PPD)
HO
OH
2
20(R)-dammarane-3β, 6α,
12β, 20, 25-pentol (25-OHPPT)
4
β-sitosterol- 3-O-β-Dglucopyranoside
(daucosterin)
HO
OH
C2H5
HO
OH
glc
O
R2O
R1
R2
20S
H
H
OR2
Glc
H
Glc2-Glc
H
Glc2-Glc
Glc
Glc2-Glc
Glc6-Glc
R1
R2
Glc2-Rha
H
Glc
Glc
Glc3-Rha
Glc
OH
3
20(S) -PPD
5
20(S) -Rh2
12
3
6
20(S) -Rg3
9
20(S) –Rd
R1O
20R
PPD-type saponin
11
20(S) -Rb1
RO
2
OH
12
7
8
10
20(S)-Rg2
20(S)-Rg1
20(S)-Re
20S
OR2
3
6
HO
20R
OR1
PPT-type saponin
Identification and Purification of Two Novel Ginsenosides
20(R)-25-羟基-达玛烷-3β,12β,20,25-四醇
20(S)-25-甲氧基-达玛烷-3β, 12β, 20-三醇
[20(R)-25-OH-PPD]
[20(S)-25-OCH3-PPD]
HT Screening for Anticancer Activity
Cell-based Assay
No.
MCF-7 （CV%）
1μM
1
25-OH-PPD
2
25-OH-PPT
3
PPD
4
Daucosterol*
5
Rh2
6
Rg3
7
Rg2
8
Rg1
9
Rd
10
Re
11
Rb1
10μM
100μM
H838 （CV%）
1μM
10μM
100μM
LNCaP （CV%）
1μM
10μM
100μM
PC3 （CV%）
1μM
Highest concentration was 50 μM.
Cell viability (CV) data: inhibition <20%, in black; 20-90%, in green; >90%, in red.
*
10μM
100μM
SAR (IC50, μM)
SAR (IC50, μM)
Cancer Type
Cell Lines
Glioma
A172
38.3
303.0
T98G
5.0
397.0
HPAC
5.8
>500
PANC-1
7.8
180.3
A549
5.7
369.1
H1299
4.9
357.2
H358
8.1
470.0
H838
11.7
293.0
MCF-7
13.5
361.2
MDA-MB-468
18.2
153.1
LNCaP
PC3
12.0
5.6
302.1
266.5
Pancreatic Ca
Lung Ca
Breast Ca
Prostate Ca
25-OCH3-PPD
Rg3
Lung Cancer Model
Cell Viability (%)
A549
H1299
120
120
100
100
80
80
60
60
40
40
20
20
0
0
0
10
20
30
40
50
0
10
H838
20
30
40
50
H358
140
160
120
140
100
120
100
80
25-OH-PPD
80
60
25-OH-PPT
60
40
PPD
40
20
20
0
0
0
10
20
30
40
50
Rh2
Rg3
0
10
20
30
40
50
Concentration (µM)
Cytotoxicity of ginsenosides to human lung cancer cells in culture
Lung Cancer Model
120
400
350
H838
H838
100
300
80
200
Apoptotic Index (% of Control)
150
100
50
0
0
400
350
1
10
25
50
H358
300
250
200
150
100
50
0
0
1
10
25
Concentration (μM)
50
Proliferation Index (% of Control)
250
60
40
20
0
0
10
20
30
40
50
H358
180
160
140
120
100
80
60
40
20
0
25-OH-PPD
25-OH-PPT
PPD
Rh2
Rg3
0
10
20
30
40
50
Concentration (μM)
Induction of apoptosis and anti-proliferative effects of ginsenosides
Lung Cancer Model
H838
60
60
60
60
40
40
40
40
40
20
20
20
20
20
% of Cells
H838(PS38)
80
Rh2
% of Cells
80
H838(PS41) 100
80
PPD
100
100
100
H838(PS26) 100
80
25-OH-PPT
100
H838(PS18)
25-OH-PPD
80
60
0
0
G1
H358
S
0
G1
G2/M
G2/M
0
0
G1
25-OH-PPT
H358(PS26) 100
100
25-OH-PPD
H358(PS18) 80
80
S
S
G2/M
G1
H358(PS41) 100
PPD
80
S
G1
G2/M
H358(PS38) 100
Rh2
80
60
60
60
40
40
40
40
20
20
20
20
20
0
0
0
0
40
G1
S
G2/M
G1
S
G2/M
G1
S
G2/M
S
G2/M
H358(PS36)
Rg3
80
60
60
H838(PS36)
Rg3
0
G1
S
G2/M
G1
S
G2/M
Effect of ginsenosides on the cell cycle progression of lung cancer cells
Lung Cancer Model
200
0
1
10
25
50
400
200
0
1
H358
50
0
100
60
30
25
50
1
120
90
60
30
0
0
1
10
25
50
100
25
50
1
10
25
50
400
200
100
0
60
30
60
30
0
50
100
90
60
30
0
0
100
25
120
90
0
10
BEAS-2B-PS25
H520-PS25
1
10
25
50
concentration(μM)
1
concentration(μM)
120
0
600
concentration(μM)
90
100
BEAS-2B
800
0
0
120
concentration(μM)
concentration(μM)
10
A549
150
100
50
concentration(μM)
Proliferation index (%
of control)
Proliferation index
(% of control)
Proliferation index
(% of control)
90
0
1
10
25
concentration(μM)
50
100
0
1
10
25
50
Concentration(μM)
H358(PS25)
BEAS-2B(PS25)
100
100
H358(PS25) 100
H838(PS25) 100
A549(PS25) 100
H520(PS25) 100
80
80
80
80
80
0 μM
60
60
60
60
100
60
1 μM
40
40
40
40
20
20
20
20
0
0
G1
S
G2/M
0
G1
S
G2/M
40 80
20
0
G1
S
G2/M
Cell Cycle Phase
% of Cells
% of Cells
25
100
0
H838
120
10
10
150
0
concentration(μM)
150
1
300
0
100
concentration(μM)
0
600
Proliferation Index
(% of Control)
0
600
1000
H520
Apoptotic Index (%
of control)
400
200
900
Proliferation Index
(% of Control)
600
800
1200
Apoptotic Index
(% of control)
800
H838
1000
Apoptotic index (%
of control)
Apoptotic Index (% of
control)
Apoptotic Index (% of
control)
A549
H358
1000
G1
S
G2/M
10 μM
60
25 μM
0
40
G1
S
G2/M
20
0
G1
S
PS25 Induces Apoptosis, Inhibits Cell Proliferation and Arrest
Cells in the G1 Phase in Lung Cancer Cells.
G2/M
Gene Expression Profiling
PS25
PS25
PS25
Lung Cancer Model
1400
1200
1200
C o n t ro l
P S 2 5 (5 m g / kg )
1000
P S 2 5 ( 10 m g / k g )
800
600
400
1000
P a c lit a xe l+P S 2 5
800
600
400
0
800
600
400
200
9 12 15 18 21 24 27 30 33 36 39 42
0
0
3 6
Day
30
P S 2 5 (1 m g / kg )
10
P S 2 5 (5 m g / kg )
P S 2 5 ( 10 m g / k g )
5
0
BodyWeight (g)
C o n t ro l
20
20
15
15
C o n t ro l
P a c lit a xe l ( 10 m g / k g )
10
P a c lit a xe l+P S 2 5
6
9 12 15 18 21 24 27 30 33 36 39 42
Day
C o n t ro l
10
RT
R T +P S 2 5
5
5
0
0
3
Day
25
25
15
9 12 15 18 21 24 27 30 33 36 39 42
30
30
20
3 6
Day
35
25
0
9 12 15 18 21 24 27 30 33 36 39 42
Body weight (g)
3 6
PS25(5days/week):
10mg/kg/day: 35.2%
Paclitaxel: 11.6%
+PS25: 40.1%
RT: 8.8%
+PS25: 42.3%
RT
R T +P S 2 5
0
0
0
1000
200
200
Body Weight (g)
C o n t ro l
P a c lit a xe l ( 10 m g / k g )
Tumor Mass (mg)
P S 2 5 (1 m g / kg )
Tumor Mass (mg)
Tumor Mass (mg)
1200
C o n t ro l
0
3
6
9 12 15 18 21 24 27 30 33 36 39 42
Day
0
3
6
9 12 15 18 21 24 27 30 33 36 39 42
Day
• Neither of
these dosing
procedures
resulted in any
appreciable
effect on the
body weight of
the mice
PS25 inhibits the growth of Lung xenograft tumors and
sensitizes tumors to treatment with chemotherapy or radiation
In vivo
Antitumor
Activity
35
1500
Saline
1200
30
25-OCH3-PPD (5mg/kg)
900
25-OCH3-PPD (10mg/kg)
25
25-OCH3-PPD (20mg/kg)
20
Saline
15
600
25-OCH3-PPD (5mg/kg)
10
25-OCH3-PPD (10mg/kg)
300
5
0
25-OCH3-PPD (20mg/kg)
0
0
3
6
9
12
15
18
21
24
27
0
1500
3
6
Saline
1200
12
15
18
21
24
27
30
25-OCH3-PPD (5mg/kg)
25
25-OCH3-PPD (10mg/kg)
900
20
600
15
Saline
10
25-OCH3-PPD (5mg/kg)
300
5
25-OCH3-PPD (10mg/kg)
0
0
0
3
6
9
12
15
18
21
24
27
1500
Saline
T exot ere (15mg/kg)
1200
T exot ere + 25-OCH3-PPD
900
600
300
0
0
3
6
9
12
15
18
21
24
0
30
Body Weight
(g)
Tumor
Mass (mg)
9
35
27
3
6
9
12
15
18
21
24
27
30
27
30
35
30
25
20
15
Saline
10
Texotere (15mg/kg)
Texotere + 25-OCH3-PPD
5
0
30
0
3
6
9
12
15
18
21
24
35
1500
Saline
30
Gemcit abine (160mg/kg)
1200
25
Gemcit abine + 25-OCH3-PPD
900
20
600
15
Saline
10
Gemcitabine (160mg/kg)
300
Gemcitabine + 25-OCH3-PPD
5
0
0
0
3
6
9
12
15
18
21
24
27
30
0
3
6
9
12
15
18
21
24
27
30
24
27
30
35
1500
Saline
30
RT (3Gy )
1200
25
RT + 25-OCH3-PPD
900
20
600
15
Saline
10
RT (3Gy )
300
5
0
RT + 25-OCH3-PPD
0
0
3
6
9
12
15
18
Day
21
24
27
30
0
3
6
9
12
15
18
Day
21
SAR of P450 modulation-CYP2C9
Strength
Ginsenosides C3
C6
C20
C25
25-OCH3-PPD
-OH
-H
-OH
-OCH3
25-OH-PPD
-OH
-H
-OH
-OH
Rg3
-O-G2-G
-H
-OH
Rh2
-O-G
-H
-OH
PPD
-OH
-H
-OH
Re
-OH
-O-G3-Rha
-O-G
Rd
-O-G2-G
-H
-O-G
25-OH-PPT
-OH
-OH
-OH
Rg2
-OH
-O-G2-Rha
-OH
Rg1
-OH
-O-G
-O-G
Rb1
-O-G2-G
-H
-O-G6-G
-OH
Take-Home Message


Accelerating Drug Discovery and
Development by Novel Approaches to Failing
Early, Fast, and Often
4R’s:





Right
Right
Right
Right
Target
Models
Approaches
Timing for Decision-Making (go/no go)
After ALL (You love or hate):


Pharmacology & Toxicology
Regardless of Targets/Diseases/Products
The partnering of industry, academia, health organizations, and
government agencies provides optimal utilization of emerging
science, resulting in enhanced regulatory decision making,
expedited drug development, and improved patient care
S Buckman et al. Clinical Pharmacology & Therapeutics 141-144 ( February 2007)
Grants
NIH/NCI R01 CA 80698
NIH/NCI R01 CA 112029
NIH/NCI N01 CM 47015-45
DoD W81XW04-10845
Hybridon/Idera, Inc.
Zhang Laboratory
H. Wang, MD, PhD
D. Hill, PhD
M. Li, MD
G. Prasad, PhD
Z. Zhang, MD, PhD
M. Haslinger
V. Schachinger
A. Adaim
J. Wu, MD
D. Chen, PhD
Y. Li, MSc
W. Wang, MD
Y. Li, PhD
E. Rayburn
Collaborators
•Dr. S. Agrawal
Hybidon, Inc./Idera
•Dr. J. Chen
Univ. South FL
•Late Dr. Y. Cho-Chung
NIH/NCI
•Dr. C. Deng
NIH/NIDDK
•Dr. J. Buolamwini
Univ. Tennessee
•Dr. R. B. Diasio
Mayo Clinic
•Dr. J. Bonner
UAB Radiation Oncology
•Dr. X. Chen
UC Davis
•Dr. C. Elmets
UAB Dept of Dermatology
•Dr. S. Lee
Harvard University
•Dr. J.J. Rinehart
Univ Kentucky
•Dr. J.R. Lindsey
UAB Dept of Genomics & Pathobiology
•Dr. J He
Chinese Academy of Medical Sciences
•Dr. H. Wang
Chinese Academy of Sciences
•Dr. Y. Zhao
Shenyang Pharmaceutical Univ