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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

“Systems Perspectives: Indigenous Knowledge in Global Health " Jan van der Greef TNO Systems Biology & Center for Medical Systems Biology Leiden University

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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

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• 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

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100

Re

lativ

e A

bu

nd

an

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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

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PARC Pattern Recognition

• How does one analyze complex Fingerprints ?

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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

11

A 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

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PARC Pattern Recognition - Principal Component Analysis

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• Principal Component Analysis: search for trends in data

PARC Pattern Recognition - Principal Component Analysis

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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

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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

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Systems Biology :A diagnostic and intervention platform for Holistic Intervention

Combinatorialoptions

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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

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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

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Metabolic Syndrome

HealthyMetabolicSyndrome

Insulin resistance

Obesity

Diabetes II

Dyslipidemia

Hypertension

Heart Diseases

Diabesity

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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

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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

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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)

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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.

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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

L/m

in.k

g)

Metformin90

100

110

120

130

140

Chow diet level

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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

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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

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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

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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……..

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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………………………….

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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

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“Integrating Indigenous and Scientific knowledge will create an important stepping stone

towards better Global Health"

Enjoy the workshop !