Dr. Galina Lebedeva
12th March 2010
Systems Biology for
Drug Discovery as applied to NSAID safety problem
Key To Future Medical Breakthroughs Is Systems Biology,
Say Leading European Scientists
• High Complexity of biochemical networks underlying cell functioning
• If disturbed can result in a disease (diabetes, cancer..- network diseases)
• Conventional approaches of biology are not suitable for the analysis of these elaborate webs of interactions, which is why drug design often fails
• Knocking out one target in a pathway is not productive – disease will by-pass the drug (network robustness)
• Systems Biology approaches (computational modelling and analysis) should be engaged to develop smarter therapeutic strategies and predict drug safety and efficacy.
ScienceDaily (Jan. 13, 2009)
Key To Future Medical Breakthroughs Is Systems Biology
• High Complexity of biochemical networks underlying cell functioning
• If disturbed can result in a disease (diabetes, cancer..- network diseases)
• Conventional approaches of biology are not suitable for the analysis of these elaborate webs of interactions, which is why drug design often fails
• Knocking out one target in a pathway is not productive – disease will by-pass the drug (network robustness)
• Systems Biology approaches (computational modelling and analysis) should be engaged to develop smarter therapeutic strategies and predict drug safety and efficacy.
Reality of molecular complexity
Pathways do not end … they interconnect
Health, disease and therapeutic intervention in terms of network modelling
• Input – output behaviour of biological networks • Health: balance of input and output, adequate response to
perturbation • Disease: balance is disturbed, due to suppression or
activation of certain stages, resulting in inadequate output • Goal of therapy: to restore the normal balance by targeting
key components/checkpoints of networks by drugs • Therapeutic resistance – loss of the sensitivity of the
output to the drug
Key To Future Medical Breakthroughs Is Systems Biology,
Say Leading European Scientists
• High Complexity of biochemical networks underlying cell functioning
• If disturbed can result in a disease (diabetes, cancer..- network diseases)
• Conventional approaches of biology are not suitable for the analysis of these elaborate webs of interactions, which is why drug design often fails
• Knocking out one target in a pathway is not productive – disease will by-pass the drug (network robustness)
• Systems Biology approaches (computational modelling and analysis) should be engaged to develop smarter therapeutic strategies and predict drug safety and efficacy.
ScienceDaily (Jan. 13, 2009)
Systems medicine
• aimed at exploitation of systems biology approaches to develop prognostic and predictive models for diagnostics and therapeutic applications
• there is not yet an established arsenal of computational approaches, methods, and techniques applicable for model-based diagnostics and design of therapeutic strategies
• specific techniques of model analysis need to be developed to allow for application of general models in the context of therapeutic research.
• Takes into account all properties of the biosystem (protein structure, mechanism, stoichiometry, dynamics and
regulation)
• Clearly describes the key properties of the biosystem in terms of easily understandable and measurable parameters (Vmax,
Km, Kd, Ki, IC50..)
• Reproduces all known responses of the biosystem to external and internal perturbations and therapeutic interventions
Kinetic Modeling (KM) Approach
Kinetic Modeling in Systems Biology, Demin, Goryanin Chapman & Hall/CRC, 2008
Kinetic modelling approach 1. Pathway reconstruction and static model development:
elucidation of stoichiometry of the network, identifying key cross-talks and regulations
2. Generation of the system of ODEs describing dynamics of the metabolic/signalling network:
dx/dt=N·v(x;e,K) Here, x=[x1,…xm] is vector of compound concentrations and v=[v1,…vn] is vector of rate laws
3. Modelling metabolic, signalling and transport processes: Detailed description of the catalytic cycles of key proteins, derivation of rate equations
4. Prameterisation of the model (literature, experiments, fitting) 5. Validation of submodels on the base of in vitro and cell
extract data 6. Validation of the whole model using in vivo dynamic data
(genomics, proteomics, metabolomics)
Applica'onofthevalidatedmodeltoprac'calproblems:e.g.inpharmaceu'calindustry‐drugresistance,safety
Practical applications
• Various analyses of the validated model, e.g.: – In silico experiments to test various hypothesis on the
mechanisms of disease and drug action • E.g. test how modifications within the network can affect its input-
output behaviour – Local and global sensitivity analysis to generate ideas on
• Drug targets • Biomarkers • Combination therapies
Challenges in translating theory into clinical practice:
• Extrapolation to a multi-layered context, from molecular to cellular, tissue,… and organism level
• High level of individual variability of the networks, e.g. in cancer – to be addressed by personalised medicine
• Complex dynamical aspects – pharmaco-kinetics and pharmaco-dynamics effects, – drug scheduling, – circadian rhythms…
• Incomplete knowledge on the biological networks underlying disease onset and progression
• limitations imposed by the number of elements which a tractable model can include
Context layers to consider…
• Intracellular microenvironment – regulation of protein activity by local concentration of substrates/
products and co-factors… • Larger network context
– Subsystem embedded in a larger network is subject to higher level regulation
• Cell-specific protein expression and gene regulation – Pathway structure and dynamics vary in different cell types…
• Organ/ organism level effects – – Spatial aspects
• ….
“Too many pharmacological agents have entered into clinical practice
for which considerable and potential life-threatening outcomes
were recognized only AFTER a large number of patients had been treated”
Wall Street Journal – Thursday, January 26, 2006 quoting an editorial from same week’s New England Journal of Medicine
Drug Safety Problem
Example: Application of Systems Biology Approach to
(NSAIDs) safety problem
• NSAIDs – popular drugs for pain relief and antipyretic, more recently started to be used in cancer and neurophysiology (depression).
• Main target – COX1,2 • COX1 – constitutive, COX2 – induced at inflammation • Aspirin (targeting both COX1 and COX2)– risk of gastro-intestinal
bleeding at medium/high dose • Selective COX-2 inhibitors (Coxibs) – developed to overcome GI
side effects: – efficient in pain relief but with new dangerous side effects (heart
attacks) – Vioxx withdrawal from the market – cost Merck $billions, with ongoing
legal costs – FDA suggests Vioxx has contributed to >20 000 heart attacks &
sudden cardiac deaths during its stay on market • The exact mechanism of NSAID action, and the origin of many
undesirable adverse effects still remain poorly understood.
Non-Steroidal Anti-Inflammatory Drugs • Aspirin
• Ibuprofen
• Naproxen
• Indomethacin
• Celebrex …
(NSAIDs) safety problem
Non-Steroidal Anti-Inflammatory Drugs • Aspirin
• Ibuprofen
• Naproxen
• Indomethacin
• Celebrex …
• Static pathway reconstruction • Intracellular level models:
– Catalytic cycle of Drug Target (COX) – NSAID action on COX – Upstream and downstream metabolic and
signalling pathways involved in response to NSAIDs
• Cell level models – NSAID action on platelets – NSAID action on endothelium cells – Combined platelet-endothelium-plasma
• Model of Blood circulation system • Coupling with pharmacokinetic profiles
“Vioxx” project
Biosystems Informatics Institute, Newcastle Moscow States University, ISB SPb
Intracellular
Cell-type – specific
Organ level
Pathway reconstruction for biosynthesis of prostanoids and leukotriens NSAIDs inhibit PGH2 production
Arachidonate PGG2 PGH2
PGI2 PGE2 PGD2 TXA2
6-KetoPGF1α
6-KetoPGE1
PGF2α
PGA2 PGC2
PGB2
PGJ2
Dihydro-PGJ2
TXB2
Dihydro-TXB2
5-HPETE
LTA4
LTC4
LTD4
LTB4
20-OH-LTB4
20-COOH-LTB4
12-Keto-LTB4
Lecithin
15-HPETE
12-HPETE
5-HETE
2O2
NADPH
NADP
AccH2 Acc
FpH2
Fp
NADP
NADPH
O2
O2
1.14.99.1 1.14.99.1
5.3.99.4
1.1.1.189
5.3.99.3 5.3.99.2
5.3.99.5
1.1.1.184
1.1.1.188
1.13.11.34
1.13.11.34
Glutathione
Glutamate
4.4.1.20
2.3.2.2
3.3.2.6
1.11.1.7
3.1.1.4
1.13.11.33 1.13.11.31
1.14.13.30
PGHS-1,2 PGHS-1,2
PTGIS
PTGES2 PGDS
TBXAS1
DHRS4
CBR1 AKR1C3
ALOX5
ALOX12 ALOX15
PLA262D
ALOX5
LTC4S
GGT1
LTA4H
CYP4F3
LPO
NSAID
• PGI2 and PGD2 inhibit platelet aggregation; • TXA2 induces platelet aggregation; • PGE2 potentiates it
The overall response to NSAIDs results from complex interplay of inductions/inhibitions in different branches of prostaglandin synthesis and further signalling
Prostanoid
Prostanoid Receptors (GPCRs)
G-proteins
signaling pathways
PGI2 PGE2 PGD2 TXA2 PGF2α
Gq Gi Gs
Raf
MEK
MAPK
PLC
PKC Ca2+
AC
cAMP
PKA
CREB1
ATP
GSK-3
β-catenin
p38
Ras
IP EP1 EP2 EP3 EP4 FP DP1 TP DP2
IP3
PLA2
Rac PLD
PPARγ PPARγ
COX2 AA
Pathway reconstruction for prostanoid signalling network
• Static pathway reconstruction • Intracellular level models:
– Catalytic cycle of Drug Target (COX) – NSAID action on COX – Upstream and downstream metabolic and
signalling pathways involved in response to NSAIDs
• Cell level models – NSAID action on platelets – NSAID action on endothelium cells – Combined platelet-endothelium-plasma
• Model of Blood circulation system • Coupling with pharmacokinetic profiles
“Vioxx” project
Biosystems Informatics Institute, Newcastle Moscow States University, ISB SPb
Intracellular
Cell-type – specific
Organ level
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).
Kinetic model of COX-1/2 catalytic cycle (as for purified enzyme)
• Detailed ODE-based model for COX-1,2 catalysis, with all kinetic parameters accurately cross-validated. (>30 ODE, >60 rate equations, >25 parameters)
Heme in the POX site: Fe and PP
Tyr385
Substrate/ inhibitor binding site (COX)
Catalytic mechanism of drug target elucidated and understood Structural knowledge as a base for mechanistic description of COX catalysis
molecular level cellular level organ/organism level
From Model to Simulation
Implement the ODE set in DBSlove or/and MATLAB (via SBML conversion)
Write this as set of ordinary differential equations (ODEs), e.g.:
d[C4] dt
= v4 +v57+v53+v13-v1-v9
d[D5] dt
= v1 - v2 –v14-v29+v18
step 2
step 3
step 1
Transform the scheme into a set of reactions and reaction rates, e.g.:
R2: D5 → E4 ν2 = k2 • D5
R1: C4 → D5 ν1 = k1 • C4 • AA
……..
Time, s 300 250 200 150 100 50 0
[Adr
enoc
hrom
e], 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
Parameters of the COX catalytic cycle identified on the base of experimental data available from literature
120 110 100 90 80 70 60 50 40 30 20 10 0
AA
cons
umpt
ion.
, mkM
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
[COX-1]=1.61 mkM
Model Validation. Identification of kinetic parameters.
• Static pathway reconstruction • Intracellular level models:
– Catalytic cycle of Drug Target (COX) – NSAID action on COX – Upstream and downstream metabolic and
signalling pathways involved in response to NSAIDs
• Cell level models – NSAID action on platelets – NSAID action on endothelium cells – Combined platelet-endothelium-plasma
• Model of Blood circulation system • Coupling with pharmacokinetic profiles
“Vioxx” project
Biosystems Informatics Institute, Newcastle Moscow States University, ISB SPb
Intracellular
Cell-type – specific
Organ level
Figure A: Extended Cox-1/2 model. Bottom panel – default Cox-1/2 model; Top panel – inhibition of Cox-1/2 by Aspirin and/or second Inhibitor.
Figure B: Cox cartoon of the simplified Cox1/2 inhibition mechanism.
Extended Cox-1/2 Model for Drug Action Modeling
AA
AA
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
• Vioxx 1-,2+ + 2
Time dependence
Reversibility of binding
Selectivity to COX1,2
1- COX1; 2 - COX2
0 20 40 60 80
100
0 200 400 600 800 1000 1200 Ibuprofen concentration, M
Ibuprofen
1,2 Preincubation time, sec
Aspirin
Consistent description of experimental data on inhibitory effects of different NSAIDs
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
Rel
ativ
e C
OX
activ
ity
Rel
CO
X-2
activ
ity
Preincubation time, sec Preincubation time, sec
Celecoxib
0.5 M
1 M 2 M
0
0,2
0,4
0,6
0,8
1
0 10 20 30 40 50 60 70
Indomethacin
2.2 M 3.8 M
5.4 M
0
0,2
0,4
0,6
0,8
1
0 500 1000 1500 2000
1.4 M
Rel
ativ
e C
OX
activ
ity
Rel
ativ
e C
OX
activ
ity
NSAID binding parameters used to cross-validate and predict
inhibitor effects on COX
- No curve fitting was employed -
• Selective COX2 inhibitor can block aspirin effect – experimental phenomena observed, not previously explained
50% inhibition plane
- For both single drug and drug combinations:
- Aspirin, Ibuprofen, Naproxen, Celecoxib, Indomethacin, Diclofenac,…
Prediction of NSAID action on target
High Dose Aspirin GI side effects
High Dose Coxib CV side effects
Coxib [M]
ASA [M]
Safer ASA/Coxib Combinations?
• Model based analysis allows for prediction of safer drug combinations
COX in the context of intracellular micro- conditions
COX degradation
Description includes:
• Detailed catalytic cycle of COX
• COX self-inactivation
• COX synthesis and degradation
• In-fluxes and out-fluxes of substrates and products
Allows for properties of COX and NSAID inhibition to be translated into in vivo conditions
COX catalysis and NSAID effects in real time and within real physiological substrate / product concentration range
Intracellular microenvironment controls COX activity and dictates sensitivity to NSAIDs model allowed to explain/predict many experimental phenomena:
• Discrepancies between in vitro / in vivo estimates of IC50 for Aspirin
• Origin of variability of in vivo experimental values of Aspirin IC50 – intracellular micro- environmental concentrations of substrates
• Variability in COX-1/COX-2 selectivity – results from intracellular conditions
• Attenuation of ASA effect by Celecoxib
COX activity is controlled by intracellular micro-environmental conditions: Arachidonic Acid and Reducing Co-substrate concentration
AA, M RC, M
Velo
city
of P
GH
2 pr
oduc
tion
Functionally active
Non-functional
In vivo COX-1 Whole blood assay 1.3 [1], Platelet 1.3 [2] Endothelial cells 1.5 [3] Fibroblasts 2.6 [4]
In vitro COX-1 Purified enzyme: 30-200 [5]
“in vivo” model
IC50=2 M
IC50=300 M
in vitro model
ASA, uM
First valid explanation of discrepancies between
in vitro / in vivo estimates of IC50 for Aspirin
Experimental estimates of IC50 (M) for Aspirin:
PGH
2 pr
oduc
tion,
rel.u
nits
1. Warner T., Giuliano F., et al PNAS 96, 1999, 7563 2. Quellet M., Riendeau D., Percival M. PNAS 98, 2001, 14583 3. Mitchell J. , Akarasereenont P., et al PNAS 90, 1994, 11693 4. Chulada P., Langenbach R. J. Pharm. Exp. Ther. 280, 1997,606 5. Kargman S., Wong E. et al Bioch. Pharm. 52, 1996, 1113
Accumulation of acetylated COX gives rise to additional COX inhibition due to retained peroxidase activity
Modelling drug action on target under various regimes of drug administration
Low dose Aspirin, once daily
ASA ASA ASA ASA
A higher dose Aspirin reduced with time
• Static pathway reconstruction • Intracellular level models:
– Catalytic cycle of Drug Target (COX) – NSAID action on COX – Upstream and downstream metabolic and
signalling pathways involved in response to NSAIDs
• Cell level models – NSAID action on platelets – NSAID action on endothelium cells – Combined platelet-endothelium-plasma
• Model of Blood circulation system • Coupling with pharmacokinetic profiles
“Vioxx” project
Biosystems Informatics Institute, Newcastle Moscow States University, ISB SPb
Intracellular
Cell-type – specific
Organ level
Detailed scheme
Inactive AC Active AC
ATP
Inactive PKA Active PKA
CREB1
CREB1-phosphate
Protein Phosphatase
Gi Gs
cAMP PPi
AMP
Cyclic-nucleotide Phosphodiesterase
ATP
ADP
Pi
TRANSCRIPTION
Abbrevations: Gs = stimulating Gα-protein Gi = inhibiting Gα-protein AC = Adenylyl Cyclase PKA = cAMP-dependent Protein Kinase CREB1 = cyclic AMP response element
binding protein-1 RED – constant metabolites (parameters) BLUE – variable metabolites
Example: Adenylate Cyclase and Protein Kinase A Signalling
Input Signal
Output Signal
1. AC activation
2. cAMP synthesis
3. PKA activation
4. Protein phosphorylation
AC AC-Gi
AC-Gs AC-Gi-Gs
Gs Gs
Gi
Gi
K_AC_Gi
K_AC_Gs_Gi
K_AC_Gi_Gs
K_AC_Gs
ATP cAMP PPi
ATP cAMP PPi
Identification of constants for AC activation model
* Chen-Goodspeed M, Lukan AN, Dessauer CW; Modeling of Galpha(s) and Galpha(i) regulation of human type V and VI adenylyl cyclase; J Biol Chem. 2005; 280(3):1808-16
Gi concentration (µM) 3 2 1 0 In
itial
rate
of c
AM
P sy
nthe
sis
(pm
ol/m
in/m
g)
2 1.8 1.6 1.4 1.2
1 0.8 0.6 0.4 0.2
0
Gs = 3.0 µM
Gs = 1.0 µM
Gs = 0.25 µM
Gs = 0.1 µM Gs = 0.05 µM Gs = 0.02 µM Gs = 0.01 µM
• Static pathway reconstruction • Intracellular level models:
– Catalytic cycle of Drug Target (COX) – NSAID action on COX – Upstream and downstream metabolic and
signalling pathways involved in response to NSAIDs
• Cell level models – NSAID action on platelets – NSAID action on endothelium cells – Combined platelet-endothelium-plasma
• Model of Blood circulation system • Coupling with pharmacokinetic profiles
“Vioxx” project
Biosystems Informatics Institute, Newcastle Moscow States University, ISB SPb
Intracellular
Cell-type – specific
Organ level
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
Time, min
uM IP3
cAMP
Ca2+
COX-1
thrombin iloprost
Part of “active” IP3R
High dose of iloprost effectively inhibits platelets activation by thrombin
Stimulation of platelets by iloprost leads to cAMP-dependent PKA activation that phosphorilates IP3-dependent calcium channels (IP3R) on endoplasmic reticulum (ER). This results in in the inhibition of IP3R sensitivity to IP3, and a decrease in Ca2+ outflux from ER
Data from JBC(2002)277:29321
Platelet model validated on perturbation experimental data with receptor agonists
• Static pathway reconstruction • Intracellular level models:
– Catalytic cycle of Drug Target (COX) – NSAID action on COX – Upstream and downstream metabolic and
signalling pathways involved in response to NSAIDs
• Cell level models – NSAID action on platelets – NSAID action on endothelium cells – Combined platelet-endothelium-plasma
• Model of Blood circulation system • Coupling with pharmacokinetic profiles
“Vioxx” project
Biosystems Informatics Institute, Newcastle Moscow States University, ISB SPb
Intracellular
Cell-type – specific
Organ level
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
• Static pathway reconstruction • Intracellular level models:
– Catalytic cycle of Drug Target (COX) – NSAID action on COX – Upstream and downstream metabolic and
signalling pathways involved in response to NSAIDs
• Cell level models – NSAID action on platelets – NSAID action on endothelium cells – Combined platelet-endothelium-plasma
• Model of Blood circulation system • Coupling with pharmacokinetic profiles
“Vioxx” project
Biosystems Informatics Institute, Newcastle Moscow States University, ISB SPb
Intracellular
Cell-type – specific
Organ level
Endothelial cell
Arachidonic acid
(exogenous)
PGH2
,PGD2, PGE2, PGF2a
PGI2 (prostacyclin)
BLOOD PLASMA Platelet
Arachidonic acid
Phospholipids
AA transporter
PGH2
PGI2 (prostacyclin)
PGH2 transporter
AA transporter
Phospholipase A2
PGD2, PGE2, PGF2a
Transporter
COX-1 COX-2
Prostacyclin synthase
Nonenzymatic
Arachidonic acid
Phospholipids
AA transporter
PGH2
TXA2 Thromboxane A2
PGH2 transporter
TXA2 transporter
Phospholipase A2
PGD2, PGE2, PGF2a
Transporter
COX-1
Thromboxane synthase
Nonenzymatic TXA2
Thromboxane A2 Nonenzymatic
Platelet-Endothelium-Plasma model allows to assess potential cardiovascular risk after NSAID administration
molecular level cellular level organ/organism level
Inflammatory EC
Non-inflammatory EC
Risk of clot formation increases with increase in COX2 selective inhibitor concentration under inflammatory conditions
Dose-response of the platelet-endotelium plasma system. Prediction of clotting risks for NSAIDS and combinations
molecular level cellular level organ/organism level
Heart muscle
Head (Brain)
AORTA
LUNG Upper body (arms)
Kidneys
GIT
Liver VENA CAVA
Lower body (Legs)
Kinetic model of endothelium cell
Endothelium cells
Platelets Kinetic model of platelet
• Several phases (immobile, mobile,…) • Several cell types • Interaction between cells via secreted
metabolites • Models of intracellular processes in each cell
type • Anatomical/geometrical features of the system • Changes in properties of the cells located at
different parts of the system
8 organs have been taken into account in our models
Scaling up to organism level: Blood circulation
capi
llarie
s &
ven
ules
veins
lung
vena
cav
a
aort
a
vena
cav
a
lower body (legs)
capi
llarie
s
arte
ries
&
arte
riole
s
Activation (TXA2)
veins
capi
llarie
s &
ven
ules
upper body (arms)
molecular level cellular level organ/organism level
Monitoring state of platelets in different parts of blood circulation
1) Clotting risks monitored (proportional to intracellular Ca2+ concentration in platelets) 2) Maximal risk of clot formation is observed in leg veins 3) Capillaries of lungs and arms decrease risk of clot formation substantially
• In silico methods help to understand the efficacy, mode of action and potential dangerous side effects of drugs • Model-based analysis and prediction of drug safety and efficacy • Informed advice on drug dosing, scheduling and combinations
Systems Approach applied to drug safety
Acknowledgement
Dr. Oleg Demin
Dr. Yury Kosinsky and team
Dr. Jörg Lippert
Prof. Tim Warner
Dr. Alexey Goltsov
Dr. Maciej Swat
Prof. Igor Goryanin University of Edinburgh