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“Systems Perspectives: Indigenous Knowledge in Global Health "
Jan van der Greef
TNO Systems Biology&
Center for Medical Systems Biology
Leiden University
2
Outline of the presentation
• A few words on System Science
• Thoughts for the workshop
Integration of “IK-based” and “Western” Medicine
Concepts of Systems thinking
3
The perspectives
“IK-based” “Western” Medicine Medicine
Holistic Reductionistic(treatment & diagnosis) (treatment &
diagnosis)Personalized Generic approach
Treatment of disease Treatment of symptoms Synergetic active components 1 drug – 1 target
but this perspective has changed by Systems Science
4
……………. what are Systems ????
MetaverseCosmos
MetagalaxyGalaxy
BiosphereEcosystemOrganism
OrganCell
MoleculeAtom
Quantum
The
con
nect
ivity
hyp
othe
sis
Last century : non-localityCoherence/Correlation
This century :Embryonic paradigm shift ?
biology/coherence Quantum BiologyQuantum Biology
5
……………. what is Systems Science ?
MetaverseCosmos
MetagalaxyGalaxy
BiosphereEcosystemOrganism
OrganCell
MoleculeAtom
Quantum
The challenge :Human Systems Biology
Complexity & Connectivity
The
con
nect
ivity
hyp
othe
sis
: Science of organized complexity
6
• Parallel analyses of mRNA, proteins & metabolites from complex samples
• Discovery of Biomarkers of disease and of drug/nutritional effects
• Translation into Systems Pathology and Systems Pharmacology/Toxicology
• Parallel analyses of mRNA, proteins & metabolites from complex samples
• Discovery of Biomarkers of disease and of drug/nutritional effects
• Translation into Systems Pathology and Systems Pharmacology/Toxicology
Biomarkers
Pathways
SystemKnowledge
Biomarkers
Pathways
SystemKnowledge
Info
rmat
ics
Transcripts
Proteins
Metabolites
Proteins
Metabolites
Systems Biology
Systems
Analysis
Tissue
Cell Organism Measurement Interpretation
Data Information Knowledge
7
Some Philosophy… “Targeted” versus “Holistic”
An “System” Approach Means Broadening the “Scope”Cannot Simply Target the Known
8
D:\Documents\...\Human Plasma_1 27/05/2003 09:50:06 Human Plasma No1, LCrun, manual injection of 5 ultargetfsms4e5
RT: 0.0000 - 21.9416
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Time (min)
0
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
11.5643 19.5967
15.911012.5526
9.4713
10.85746.6989 20.332917.4764
15.20531.0502 9.93107.5684 18.4599
13.9725 21.16212.3065
5.1317 8.53594.88222.8708
NL:1.57E7
TIC F: MS Human Plasma_1
Human Plasma_1 #10-709 RT: 2.03-21.93 AV: 644 NL: 7.48E4T: FTMS + p ESI Full ms [ 150.00-2000.00]
150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360 370
m/z
0
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
214.0896
260.1855
218.8295
366.1756204.1230
195.0877 334.0986231.1161
279.1591254.1598 263.2043 298.1259235.1110188.1645 339.0539178.0896 319.0878 352.0739
328.9980
288.1362246.1698 274.1217 307.1323175.2444157.7074
Body fluid fingerprinting & Pattern recognition
A new diagnostic tool for IK-based intervention
Body fluid fingerprinting & Pattern recognition
A new diagnostic tool for IK-based intervention
ppm0123456789
NMR
LC - LTQ-FTMS
GC & LC-MS/MS
10
A B C
1
2
3
2.5
3.0
2.0A B C
1
2
3 2.53.0
2.0
B
AC
PARC Pattern Recognition - Principal Component Analysis
12
A B C
1
2
3 3.0
1.5
1.0
A B C
1
2
3 2.53.0
2.0
B
AC
PARC Pattern Recognition - Principal Component Analysis
14
• Principal Component Analysis: search for trends in data
PARC Pattern Recognition - Principal Component Analysis
15
PC1
PC2
• Principal Component Analysis: create new co-ordinate system PC1 and PC2
A B C
factor spectrum“Fingerprint of Disease”
PARC Pattern Recognition - Principal Component Analysis
Healthy
Disease IK-based Treatment effect
16
Disease
Predispositionmarkers
Onset ofdisease/effect
Early biomarkers of disease/effect
Late biomarkersof disease/effect
Prognosticmarkers
Diagnosticmarkers
Changes in pathway dynamicsto maintain homeostasis
BIOMARKERSFrom normality, to dysfunction, to disease
Pharma
Nutrition Healthy
Homeostasis, the self-regulatory mechanism (feedback)that allow
organisms to maintain themselves in a state of dynamic balance
with the variables fluctuating between tolerance limits
17
• |Cij|>0.8 METABOLITE
PROTEIN
GENE
NuclearRibonucleoprotein H1(transcription splicing)
TranslationInitiation Factor 2(protein synthesis)
Protein Kinase C ε(cell signaling)
Apoptosis InhibitoryProtein 6
Down-regulatedUp-regulated
DKK 1
Pyruvate Kinase
Hemopoietic CellPhosphatase PLA2 VII
HemeOxygenase 2
InositolMonophosphatase
TG
GlutathioneS-transferase
PC C1818:O
PPAR
Fatty Acid Binding GeneApolipoprotein A1
LysoPC
Murinoglobulin 2
Fatty Acid Binding Protein Diacylglycerol
32
Systems Biology :A diagnostic and intervention platform for Holistic Intervention
Combinatorialoptions
18
The current status of modern “Western” Medicine:
In other words 50-70% of the patients are experiencingadverse drug effects without any benefit !!
Iatrogenesis: estimates are difficult, but very significant
Benefits Risks
2003 : Allen Roses, Worldwide Senior VP GSK of genetics:
> 90% of the drugs work only in 30-50% of the patients
> We need to get the right drugs to the right patient in the right dose
Note: Western medicine is best in acute diseases and not good in chronic diseasesreductionistic < > system-based
19
MedicineNutraceuticals
Functional Foods(1st, 2nd, 3rd generation)
Herbal Medicine…….
Systems Biology :From Medicine to Nutrition
Perturbing complex systems with multi-factorial inputs
Com
ple
xity
Single target, single component
Multiple targets, multiple components
In contemporary Systems Science, we look at a number ofdifferent things and interactions and note their behavior
as a whole under different influences
20
Metabolic Syndrome
HealthyMetabolicSyndrome
Insulin resistance
Obesity
Diabetes II
Dyslipidemia
Hypertension
Heart Diseases
Diabesity
21
Disease
Predispositionmarkers
Onset ofMetabolic Syndrome
Early biomarkers of disease/effect
Late biomarkersof disease/effect
Prognosticmarkers
Diagnosticmarkers
Insulin resistance
Healthy
Are there opportunities to bringthe system back in homeostasis for early
stage chronic diseases?
Herbal Medicine
22
Multidimensional Pharmacologyof complex mixtures
(multiple bioactive low affinity components)
Combinatorial effects
Synergeticeffects
Antagonisticprinciples
screening on purified components or usingsingle targets prohibits the discovery of the mostimportant synergetic nature of natural products
23
Example of synergetic and combinatorial effects:
0.1 1 10 100
25
50
75
100
Berberine
5’-MHC (+Berb at 30 ug/ml)
% G
row
th in
hib
ition
“Growth inhibition of S. aureus”
ug/ml
Synergy in a medicinal plant: Antimicrobial action of berberine potentiated by 5’-methoxyhydnocarpin a multidrug pump inhibitor Stermitz et al. PNAS 97, 1433-1437 (2000)
24
The history of Diabetes in Chinese Medicine
The name Xiao ke appears in the Nei Jing (Inner Classic, 400 BC), where several different though related conditions are mentioned : Xiao ke, wasting and thirsting, Xiao dan, pure heat wasting, Ge xiao, diaphragm wasting and Xiao zhong, central wasting.
In terms of disease cause, the authors of the Nei Jing recognized that overeating of sweets and fats, emotional stress, weakness of the five viscera and obesity are all closely related to this disease.
25
TCM product optimization : SUB 885xIn
su
line
res
ista
nc
e
Control(pre-diabetic)
SUB 885ASUB 885B
SUB 885C
Low
High
0
1020
30
40
5060
70
80
GIR
(µ
L/m
in.k
g)
Metformin90
100
110
120
130
140
Chow diet level
26
Herbal Fingerprint Biomarker Fingerprint
Biological Response
Scientific EvidenceQuality Control
The core of Herbal Medicine research :linking complex fingerprints
Disease patternBatch variations
gs01 #1342-2766 RT: 13.54-24.06 AV: 357 NL: 3.36E4F: FTMS + c NSI Full ms [ 190.00-1900.00]
200 400 600 800 1000 1200 1400 1600 1800m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
789.34
775.45
834.53
656.35
619.42
921.51337.19
757.44
280.13
557.61
1857.34
1174.77947.50547.33
358.12
1061.77
1255.46 1634.50 1795.611414.22
Zooming in
Herbal Medicine profilingby FTMS
gs01 #1383-2715 RT: 13.89-23.65 AV: 333 NL: 5.82E3F: FTMS + c NSI Full ms [ 190.00-1900.00]
950 1000 1050 1100 1150 1200m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rela
tive A
bundance
1174.77217
947.49640
979.56559
1061.77190
1144.69308996.58553
1082.73447
1045.15823
1184.66356
1230.953741107.73541
1212.47951
Mo
re a
bu
nd
an
tIn
Ap
oE
3L
es
s a
bu
nd
an
tIn
Ap
oE
3
Genes (index)N=4000
Peptides (m/z)N = 1024
Metabolites (m/z)N = 950
Diff
ere
ntia
l Pro
file
(no
rma
lize
d s
ign
ifica
nce
)
100
50
75
25
0
25
50
75
100 Variable Index
500 1000 1500 2000 2500
500
1000
1500
2000
2500
Measured Bioactivity
Pre
dic
ted
Bio
acti
vity
PLS Prediction of Using 3 Factors R=0.982
M1WC1
M1WC2
M1WT1M1WT2
M2WT1
M2WT2M3WT1
M3WT2
M8WT1
M8WT2
TNO-Winlin V2.5
Linking Herbal Composition with Bioactivity
27
Herbal Fingerprint Biomarker Fingerprint
Biological Response
Scientific EvidenceQuality Control
The core of Herbal Medicine:linking complex fingerprints
Reversed Pharmacology
Disease patternBatch variations200 400 600 800 1000 1200
feature
-3E-006
-2E-006
-1E-006
0
1E-006
2E-006
3E-006
4E-006
value UnWeighted PLS Regression Vector 3 factors 80.97 %Y expl
19
1.1
37
92
00
.32
07
20
9.1
32
72
09
.13
34
22
5.1
20
8
25
5.2
42
3
32
9.3
77
9
36
7.4
22
73
82
.13
61
41
7.1
80
34
17
.24
74
47
6.1
79
94
76
.24
77
48
8.3
17
3
51
9.1
33
05
20
.13
35
54
9.1
88
45
49
.25
21
57
7.2
18
75
93
.17
08
65
8.3
77
26
58
.37
86
70
6.2
35
3
73
6.2
33
6
80
7.4
18
48
19
.39
66
82
3.4
03
88
34
.18
00
83
7.3
93
78
39
.39
42
84
8.3
65
3
87
9.3
88
48
80
.38
77
94
5.4
30
2
96
4.3
98
09
83
.40
20
10
43
.18
00
10
70
.38
80
10
98
.18
90
TNO-Winlin V2.5
200 400 600 800 1000 1200feature
-3E-006
-2E-006
-1E-006
0
1E-006
2E-006
3E-006
4E-006
value UnWeighted PLS Regression Vector 3 factors 80.97 %Y expl
19
1.1
37
92
00
.32
07
20
9.1
32
72
09
.13
34
22
5.1
20
8
25
5.2
42
3
32
9.3
77
9
36
7.4
22
73
82
.13
61
41
7.1
80
34
17
.24
74
47
6.1
79
94
76
.24
77
48
8.3
17
3
51
9.1
33
05
20
.13
35
54
9.1
88
45
49
.25
21
57
7.2
18
75
93
.17
08
65
8.3
77
26
58
.37
86
70
6.2
35
3
73
6.2
33
6
80
7.4
18
48
19
.39
66
82
3.4
03
88
34
.18
00
83
7.3
93
78
39
.39
42
84
8.3
65
3
87
9.3
88
48
80
.38
77
94
5.4
30
2
96
4.3
98
09
83
.40
20
10
43
.18
00
10
70
.38
80
10
98
.18
90
TNO-Winlin V2.5Identification key bioactive peaks
28
What is essential for IK-projects ?
>>IK-based Medicine and Western Medicine are using a different diagnostic model !!!
>>So never use a traditional western diagnostic model to evaluate IK-based Medicine !!!!
Use a system-based diagnostic model (biomarker fingerprints)
Use a QC-system in-line with the complexity of Herbal Medicine
Use patient inclusion criteria that ideally fit both perspectives
Note the TANGA project is ideal as there is little or no debate on opportunistic infections,
But note the patient background: HIV/AIDS, Hepatitis A/B, Malaria……..
29
Future perspectives
ofSystems Science
Future perspectives
ofSystems Science
# Systems based intervention strategies
a.o. Combinatorial strategies (incl Herbal Medicine)
# Towards Health promotion (today Disease
management)
a.o. focus on prevention and sub-health : Nutrition <> Pharma
# Focus on Psychology <> Physiology (body-mind-spirit)
# Personalized Medicine
Medicine will change
fundamentally………………………….
30
Further empower the TANGA-project
Take limited extra projects on board (stay focused)
Enhance the strong position of IK with scientific-based evidenceEmphasize the strong position of IK in chronic diseasesEmphasize the strong position of IK for children
Realize that the integration of the different knowledge perspectives is much more than the sum :
important benefits for patients QoL & sustainable model.
Thoughts for the IK-workshop