Application of Systems Pharmacology to Analyze Risk of ...insysbio.com/sites/default/files/2009 poster NSAID.pdf · Analyze Risk of Thrombosis Resulted from Administration of Anti-Inflammatory

Application of Systems Pharmacology to

Analyze Risk of Thrombosis Resulted from

Administration of Anti-Inflammatory Drugs

Yuri Kosinsky, Sergey Smirnov and Oleg Demin

Institute for Systems Biology SPb, Moscow, Russia

Shanghai, IDDST2009

Outline

• Biomarkers vs disease progression vs drug

efficacy and safety

• Systems Pharmacology Modeling Strategy

• Application of the Strategy to the problem

of adverse effects of Non Steroidal Anti

Inflammatory Drugs

Biomarkers vs disease progression vs drug efficacy and safety

A biomarker, or biological marker, is in general a substance used as an

indicator of a biological state. It is a characteristic that is objectively

measured and evaluated as an indicator of normal biological processes,

pathogenic processes, or pharmacologic responses to a therapeutic

intervention

Two challenges interconnecting disease progression, drug efficacy

and biomarker issues:

• HOW to identify an optimal set of biomarkers which is reliably

describe drug efficacy and safety at any state of disease

• HOW to understand what knowledge about drug and disease can

be extracted from measurements of a given set of biomarkers

Our solution: Systems Pharmacology Modeling Approach

Systems Pharmacology Modeling (SPM) Strategy

1. Identification of biological system of interest. Mining all possible information about the system

(organs, tissues and types of cells involved, intercellular and extracellular interactions, structural and kinetic properties of

proteins/enzymes involved)

2. Identification of the main players (proteins, enzymes low molecular weight molecules) and reconstruction

of events (signaling, metabolic and genetic networks…) and regulations at different levels (inhibitions,

activations, induction, repression …)

3. Development of appropriate mathematical model of the biological system and its verification

against experimentally established facts and reliable data

4. Development of mathematical model describing PK of the drug, its influence on intracellular

pathways and possible PD endpoints

5. Application of the model (i) to understand functioning and regulation of the system at normal

and pathological states, (ii) to distinguish between drug and system determinants of a

pharmacological response, (iii) to understand mechanisms underlying adverse effects and (iv)

to address the “biomarker” challenges

Modeling techniques combined in SPM:

• pathways modeling

• mechanism based PK/PD modeling

• PBPK modeling

• cell dynamics modeling

• physiological modeling

Final Model

represents system of Algebraic and

Ordinary Differential Equations

Demin O., Goryanin I. Kinetic Modelling in Systems Biology.

Taylor & Francis (United States), (2008), pp.360

Example:

•NSAIDs side effects

NSAIDs safety problem • NSAIDs – popular drugs for pain relief and antipyretic, more

recently started to be used in cancer and even depression.

• Main targets – COX1,2

• Wide range of adverse effects

• Aspirin – risk of gastro-intestinal bleeding

• Selective COX-2 inhibitors (Coxibs) - efficient in pain relief but with unfavourable side effects (heart attacks)

• The exact mechanism of NSAID action, and the origin of many undesirable adverse effects still remain poorly understood.

• Biomarkers to characterize Coxibs adverse effects: prostacyclin (PGI2) and thromboxane A2 (TXA2)

AIM OF THE MODELING EXERCISE: Applying our strategy (1)

to understand mechanism of Coxibs mediated increase in

probability of clot formation and (2) to demonstrate that PGI2 and

TXA2 are reliable biomarkers able to follow this adverse effect in

vivo

What we know about biological system of interest

• What cells contribute mainly to prostanoid (PGI2 and TXA2) concentrations

in blood?

- Endothelium cells produce PGI2

- Platelets produce TXA2

• Concentrations of PGI2 and TXA2 in plasma can modulate the intracellular

Ca2+ concentration in both endothelium cells and platelets via signaling

pathways.

• The production rate of PGI2 and TXA2 is governed by intracellular Ca2+

concentration.

• Risk of clot formation is proportional to concentration of Ca2+ in platelets

• All these cells belong to blood circulation system

• Model of Prostaglandin H synthase taking into account its inhibition with

various NSAIDs has been developed and verified against in vitro

experimental data.

• Models of all individual enzyme catalyzed and degradation processes

involved in biosynthesis and signaling pathways initiated by PGI2 and

TXA2 in platelets and endothelium cells have been developed and verified

against in vitro experimental data

• Models of biosynthesis of prostanoids and signaling pathways initiated by

them in platelets and endothelium cells (EC) have been developed on the

basis of the models of individual processes and verified against

experimental data measured in cell culture.

• Model of human blood circulation system has been developed. Models of

endothelium cells and platelets developed at “cellular level” have been

integrated into model of blood circulation

Reaction level

Cellular level

Organ level

What modeling efforts have been done

Mogilevskaya E., Bagrova N., Plyusnina T., Gizzatkulov N., Metelkin E., Goryacheva E., Smirnov S., Kosinsky Y.,

Dorodnov A., Peskov K., Karelina T., Lebedeva G., Goryanin I. and Demin O. Kinetic modeling as a tool to integrate

multilevel dynamic experimental data. Methods Mol Biol. (2009), 563, 197-218

Explanation of Coxibs mediated increase in probability of clot formation

PGI2 and TXA2 are reliable biomarkers able to follow this adverse effect in vivo

The Cyclooxygenase Reaction

Arachidonic acid + 2O2 PGG2 + H2O PGG2 PGH2

The enzyme has two activities: Cyclooxygenase and peroxidase

Cyclooxygenase (COX) is a membrane bound enzyme responsible for the

oxidation of arachidonic acid to Prostaglandin G2 (PGG2) and the subsequent

reduction of PGG2 to prostaglandin H2 (PGH2).

reaction level cellular level organ/organism level

Main assumptions in our model:

• COX is a bifunctional enzyme with two distinct activities: cyclooxygenase (COX) and peroxidase (POX)

• Radical mechanism of COX functioning

• Self-inactivation of COX and POX activity

• Two isoforms COX-1 and COX-2

23 enzyme states and 55 reactions considered in the model


Goltsov A., Maryashkin A., Swat M., Kosinsky Y., Humphery-Smith I., DeminO., Goryanin I., Lebedeva G. Kinetic

modelling of NSAID action on COX-1: focus on in vitro/in vivo aspects and drug combinations (2009) Europ J

Pharmac Sciences. 36(1), 122–136.

The model of Prostaglandin H synthase has already been presented at IDDST-2009 by

Dr. Alexey Goltsov (section 3-13 “Current Strategies of Bioequivalence…”)

Kinetic model of Cox-1/2 catalytic cycle


ODE system corresponding to the catalytic cycle: 23 equations, 22 parameters

120 110 100 90 80 70 60 50 40 30 20 10 0

AA

co

nsu

mption

., m

kM

2,2

2

1,8

1,6

1,4

1,2

1

0,8

0,6

0,4

0,2

0

Time, s

[AA]=20 mkM

2 mkM

1 mkM

0.5 mkM

Time, s

300 250 200 150 100 50 0

[Adre

nochro

me], m

kM

160

140

120

100

80

60

40

20

0

1.08 mkM

0.81 mkM

0.54 mkM

0.27 mkM

0.16 mkM

[COX-1]=1.61 mkM

Validation of the COX model

The COX model was developed and successfully validated on more than 150

independent studies globally

Rate constant Identified value Literature value

k1 40 M-1 s-1 k_1/k1= 1-3 M

k5 1.1 M-1 s-1 -

k6 0.7 M-1 s-1 -

k7 18 M-1 s-1 14 M-1 s-1

k9 332 s-1 350 s-1

k_in1 0.011 s-1 0.013 s-1

All kinetic parameters (22 in total) of COX catalytic cycle were identified:

•Validated kinetic model can be used for analysis / prediction of the enzyme interaction with the inhibitors


Effects of inhibitors (NSAIDs)

introduced to the COX model:

•Aspirin + - 1,2

•Indomethacin + + 1,2

•Naproxen 1-,2+ + 1,2

•Diclofenac + + 1

•Ibuprofen - + 1,2

•Celecoxib 1-,2+ + 2

•Rofecoxib 1-,2+ + 2

Time

dependence Reversibility

of binding

Selectivity to

COX1,2

1- COX1; 2 - COX2


The model allows for consistent description of experimental data on

inhibitory effects of different types of NSAIDs in vitro

1,2 Preincubation time, sec

Aspirin

0.8 mM

2.35 mM

4.36 mM

0

0,2

0,4

0,6

0,8

1

1,2

0 1000 2000 3000 4000 5000

Rela

tive

CO

X a

cti

vit

y

Preincubation time, sec

Indomethacin

2.2 M

3.8 M

5.4 M

0

0,2

0,4

0,6

0,8

1

0 500 1000 1500

1.4 M

Rela

tive

CO

X a

cti

vit

y

0

20

40

60

80

100

0 200 400 600 800 1000 1200

Ibuprofen concentration, M

Ibuprofen

Rela

tive

CO

X a

cti

vit

y

Experimental data from:

Varfolomeev S.B.

Prostaglandins - molecular

biological regulators. 1985.

Publishing Moscow State

University. in Russian

Points – experimental data; Curves – model predictions

1

Re

l C

OX

-2 a

cti

vit

y

Celecoxib

0.5 M

1 M

2 M

0

0,2

0,4

0,6

0,8

0 10 20 30 40 50 60 70

Experimental data

from: Gierse J. K. et

al Kinetic basis for

selective inhibition of

cyclo-oxygenases.

Biochem. J. (1999)

339, 607-614

Preincubation time, sec


Conclusions derived from COX modeling (presented at

IDDST talk of Dr. Alexey Goltsov; section 3-13 “Current

Strategies of Bioequivalence…”):

• Model explains the discrepancy between in vitro/in vivo estimates of IC50 for Aspirin

• Model predicts that selectivity for Celecoxib in vivo depends on substrate concentration


AA

PLA2

COX-1,-2

PGH2

TXAS

HHT TXA2

TXB2

inactivation…

Ph.Lip.-AA

PGH2 (ext)

TXB2 (ext)

AA (ext)

cAMP

ATP

PKA

IP3R

Ca2+

AC

Ca2+ ER

PLC

IP3

PIP2

AMP

Ca-

ATPase

degradation

PGE2 (ext)

R1

Gq

R2

Gs

thrombin,

TXA2 (ext)

TXA2 (ext)

PKC

degradation

DAG

PGE2

PGES

PGI2 PGI2 (ext)

PGIM (ext)

PGIS

PGI2 (ext) ,

iloprost (IP);

PGD2(ext) (DP)

Endothelium cell model • prostanoid biosynthesis (PGI2, PGE2, TxA2)

• transmembrane transport

• signalling pathways activated by prostanoids

• Ca2+ fluxes involved in EC activation

• NSAIDs action on cyclooxygenase


ODE system:

47 equations,

69 rate laws,

184 parameters

Model validation: I. Endothelium cells response to thrombin stimulation Data from Journal of Cellular Physiology

(1988), 136: 54-62 Ca Time-dependence, model description

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0 0,5 1 1,5 2 2,5 3

Time, min

Ca

, u

M

0,01 uM, TXA2

0,1

1

10

0,05

0,5

II. Prostaglandin biosynthesis by HUVEC cell culture as a response to addition of 25 M AA

Data from Circ. Res. 1998;83;353-365

Синтез PGH2 в HUVEC

0

200

400

600

800

1000

1200

0 20 40 60 80 100AAext, uM

PG

H2e

xt, p

mol

/10^

6 ce

lls

exp

model

Thrombosis Research 99 (2000) 155–164

PGD2 (ext)

TXA2 (ext)

AA

PLA2

COX-1

PGH2

TBXAS

HHT TXA2

TXB2

inactivation

Ph.Lip.-AA

PGH2 (ext)

TXB2 (ext)

AA (ext)

cAMP

ATP

PKA

IP3R

Ca2+

AC

Ca2+ ER

PLC

IP3

PIP2

AMP

Ca-

ATPase

degradation

PGE2 (ext)

R1

Gq

R2

Gs

thrombin,

ADP,

TXA2 (ext)

PGI2 (ext) ,

iloprost (IP);

PGD2(ext) (DP)

PKC

degradation

DAG

Platelet model • TXA2 biosynthesis

• transmembrane transport

• signalling pathways activated by prostanoids

• Ca2+ fluxes involved in platelet activation

• NSAIDs action on cyclooxygenase


ODE system:

42 equations,

62 rate laws,

152 parameters

Model validation: Platelet response to thrombin stimulation is

inhibited by iloprost (prostacyclin stable analog)

Model demonstrates that high dose of iloprost

effectively inhibits platelet activation by thrombin.

Stimulation of platelet IP receptors by iloprost leads to cAMP-dependent

PKA activation that phosphorilates IP3-dependent calcium channels (IP3R)

on endoplasmic reticulum (ER). This results in decrease of IP3R sensitivity

to IP3 and inhibition of Ca2+ flux from ER to cytoplasm

Model demonstrate that preincubation of

platelets with iloprost completely inhibits their

sensitivity to thrombin…

So, platelets model demonstrate at least qualitively

agreement with in vitro experimental data…

Time

10 9 8 7 6 5 4 3 2 1 0

Co

nc

en

tra

tio

n,

M

4

3.8 3.6

3.4

3.2 3

2.8 2.6

2.4 2.2

2 1.8

1.6 1.4 1.2 1

0.8

0.6 0.4

0.2

0

Time 10 9 8 7 6 5 4 3 2 1 0

Co

nc

en

tra

iotn

,

M

4 3.8

3.6

3.4 3.2

3 2.8

2.6

2.4 2.2

2 1.8

1.6 1.4

1.2

1 0.8

0.6 0.4

0.2

0

Data from JBC(2002)277:29321

Ca2+

iloprost

thrombin

COX-1

IP3 cAMP

thrombin

iloprost

Ca2+

cAMP

IP3

COX-1

“Two cell model” takes into account:

1) Three compartments: endothelium cell, platelet and blood

2) Platelets express COX-1 only

3) Endothelium cells express COX-1 at normal conditions and both COX-1

and COX-2 at inflammation

4) Endothelium cells produce prostacyclin but platelets produce TXA2

5) Both endothelium cells and platelets produce PGD2, PGE2 and PGF2a

6) Both endothelium cells and platelets can export/import PGH2

Models of endothelium cell and platelet

have been combined

The aim of development of the model is to understand

how clot formation depends on COX-1 and COX-2 inhibition


ODE system: 95 equations, 137 rate laws, 325 parameters


System:

platelets (COX1) +

inflammatory EC

(COX1,2)

Inhibitors:

COX2 selective

(selectivity =

Kd_Cox1/Kd_Cox2);

selectivity of celecoxib

and rofecoxib is equal to

10 and 100,

correspondently.

Risk of clot formation is

proportional to

concentration of Ca2+

in platelets

Rofecoxib

Celecoxib

Model predicts

(1) Ca2+ concentration in platelets is able to increases with increase in

COX2 selective inhibitor concentration

(2) More selective inhibitor results in more significant increase in platelet

Ca2+ concentration

Calculated dose dependence of intracellular platelet Ca2+ concentration


Model explains that

(1) Administration of COX2 selective inhibitor results in decrease in PGI2 synthesis and, consequently,

PGI2 extracellular concentration

(2) Decrease in PGI2 concentration results in decrease in inhibition of Ca2+ release from ER

(3) Decrease in inhibition of Ca2+ release results in increase in Ca2+ concentration in platelets

(4) increase in platelet Ca2+ concentration results in increase in risk of clot formation

How “two cell” model explains this phenomena

Ca2+ Ca2+

COX-1

COX-2 COX-1

PGI2

TXA2

Ca2+ Ca2+

COX-1

COX-2 COX-1

PGI2

TXA2

COX-2 selective

inhibitor

no inhibitor

platelet endothelium cell

On the basis of the MODEL we can predict response of the platelets

and endothelium cells to inflammation and different NSAIDs

BUT

we cannot calculate concentrations of biomarkers (PGI2 and

TXA2) and compare their profiles with risk of clot formation (Ca2+

concentration in platelets) at different parts of blood circulation

system in vivo.

“Classical” PBPK and PK/PD models do not allow us to solve the

problem, i.e. predict probability of clot formation and biomarker

concentrations on the basis of knowledge about intracellular

processes.


We developed mathematical model of blood circulation and

integrated into it kinetic models of platalets and endothelium cells


Heart

muscle

Head

(Brain)

AORTA

LUNG Upper

body

(arms)

Kidneys

GIT

Live

r VENA CAVA

Lower body

(Legs)


Kinetic model of

endothelium cell

Endothelium

cells

Platelets Kinetic model

of platelet

Model of blood circulation takes into

account 1) Several phases (immobile, mobile,…)

2) Several cell types

3) Interaction between different cells via secreted

metabolites

4) Kinetic description of intracellular processes of

each cell type

5) Anatomical/geometrical features of the system

6) Changes in properties of the cells located at

different parts of the system

8 organs have been taken into

account in our models

The model of blood circulation system has been applied

1) To monitor ability of plateles to form a clot (risk of clot formation) in

different parts of blood circulation system

2) To monitor TXA2/PGI2 ratio in different parts of blood circulation

system

3) To compare the profile of biomarker concentration ratio

(TXA2/PGI2) with risk of clot formation at different part of

circulation system and to demonstrate that TXA2/PGI2 is reliable

biomarker able to follow this Coxibs adverse effect in vivo



Scheme of virtual experiment 1) Wound in leg capillaries

2) Release of soluble PAFs –

platelet activation factors

(TXA2,…)

3) Distribution of the PAFs

resulted from blood stream

4) Monitoring state of platelets

moving along with PAFs

Heart

muscle

Head

(Brain)

AORTA

LUNG Upper

body

(arms)

Kidneys

GIT

Liver

VENA CAVA Lower body

(Legs)

Thrombus

Platelets

Soluble platelet activating

factors (TXA2)

WOUND

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

iTX

A2

co

nce

ntr

ati

on

(n

M) T = 0.75 min

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

iTX

A2

co

nce

ntr

ati

on

(n

M) T = 1.5 min

PAFs introducing

(activation zone) Spatial distribution of PAFs (TXA2,…) in leg compartments at

different time after initial point

flow direction A

ort

a

Ve

na c

ava

arteries

capillaries venules veins

1) PAFs wave is damped due to PAFs degradation and dilution

2) It takes about 1.5 - 2 minutes that PAFs wave arrives at place of its initiation (capillaries of legs)


Heart

muscle

Head

(Brain)

AORTA

LUNG Upper

body

(arms)

Kidneys

GIT

Liver

VENA CAVA Lower body

(Legs)

Thrombus

Platelets

Soluble platelet activating

factors (TXA2)

WOUND

Monitoring state of platelets moving along with PAFs

Way passed by Platelets:

Legs – Vena Cava – Lung –

Heart muscle/Arms

Dynamic of platelet intracellular Ca2+ concentration after local activation

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.5 1 1.5 2 2.5 3

Time after activation (min)

Intr

acellu

lar

Ca2+ c

on

cen

trati

on

(µM

)

ca

pil

lari

es

& v

en

ule

s

veins

lung

ve

na

ca

va

ao

rta

ve

na

ca

va

lower body (legs) c

ap

illa

rie

s

art

eri

es

&

art

eri

ole

s

Activation

(TXA2)

veins

ca

pil

lari

es

& v

en

ule

s

upper body (arms)


Monitoring state of platelets moving along with PAFs

1) Maximal risk of clot formation (Ca2+ concentration in platelets) is observed in leg veins

2) Capillaries of lungs and arms decrease risk of clot formation substantially

Spatial distribution of TXA2/PGI2 ratio (at 1.65 min)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

TX

A2

/PG

I2 r

ati

o

upper body (arms)

ve

na c

ava

lung

heart

ve

na c

ava

ao

rta

ao

rta

Art

eri

es

Art

eri

ole

s

Cap

illa

rie

s

Cap

illa

rie

s

Cap

illa

rie

s

Venules Veins


1) TXA2/PGI2 ratio increases substantially in veins of arms

2) Capillaries of lungs, heart and arms decrease substantially TXA2/PGI2 ratio

CONCLUSIONS

• Systems Pharmacology Modeling allows us to understand

regulatory mechanisms underlying adverse effects of anti-

inflammatory drugs at the reaction, cellular and organ levels

• We have demonstrated that ratio of TXA2/PGI2 is reliable

biomarker able to follow Coxibs adverse effect in vivo at any part of

blood circulation system. In particular, capillaries of lungs, heart

and arms decrease substantially both TXA2/PGI2 ratio and Ca2+

platelet concentration, i.e. risk of clot formation.

http://www.insysbio.ru

Thank you for attention!

Our team:

Modelers Yuriy Kosinskiy

Galina Lebedeva

Alexey Goltsov

Tatiana Plyusnina

Ekaterina Mogilevskaya

Evgeniy Metyolkin,

Aleksandr Dorodnov

Kirill Peskov

Tatiana Karelina

Ekaterina Goryacheva

Sergey Smirnov

Nataliya Bagrova

Anton Maryashkin

Acknowledgements: Edinburgh University, Biosystems

Informatics Institute

http://www.insysbio.ru

Scientific programming

Nail Gizzatkulov

Aleksandr Galchenko

Documents

Application of Systems Pharmacology to Analyze Risk of ...insysbio.com/sites/default/files/2009 poster NSAID.pdf · Analyze Risk of Thrombosis Resulted from Administration of Anti-Inflammatory