Transcript Systems toxicology – Predicting Drug Induced Liver Injury Kalyanasundaram Subramanian, Ph.D. Strand Life Sciences © Strand Life Sciences 2008

Systems toxicology – Predicting
Drug Induced Liver Injury
Kalyanasundaram Subramanian, Ph.D.
Strand Life Sciences
© Strand Life Sciences 2008
Overview
• The Hepatotoxicity problem
• Modeling Approach
• Validation
• Summary
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Liver is highly susceptible to
toxicity
•
60% liver failures are due to
toxicity.
•
2-4% jaundice is associated
with drugs.
•
Main Problems
– Loss of Functional Liver Cells
via Cell Death
– Impaired Bile Flow
– Faulty Fat Processing
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Why is hepatotoxicity prediction
hard?
• A drug may not cause toxicity but its metabolites might
• People may respond differently to the same drug
• Physiological status (e.g., obesity) may modulate toxic
response
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Biotransformation can cause toxic
metabolites to be formed
Phase I
(CYP450)
Oxidation,
dealkylation
RH
Phase II
(transferases)
Glutathione,
Glucuronic acid,
Sulphate,
Glycine
Glutamine,
acetylation
excreted
excreted
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People respond differently to the
same drug
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Physiological/disease factors
estrogen
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Hepatotoxicity is the result of
complex interactions
drug/metabolites
physiology/
disease
patient
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Liver toxicity is inferred from blood
parameters
Toxin/
Virus
Biomarkers: AST, ALT, bilirubin
Detected via blood
analysis
Non-specific, non-unique
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Specific Problems to be Addressed
• Given an NCE, can I predict the concentration range in
which the drug is safe?
• Can I predict a toxic dose range?
• Can I predict the mechanism by which the drug will injure
the liver?
• Can I identify specific biomarkers associated with each
injury mechanism?
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Building a Top-Down Systems
Model
increasing detail
cascade of
biological pathways
linking target to
clinical endpoints
target
clinical
endpoint
Liver Lobule
Proteins
Liver cells
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Top-down Model Development
Leads to Novel Insights
Clinical data
Explicit
hypotheses
reverse
engineered to fill
knowledge gaps
High throughput
data
In vitro data
Animal model
data
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Our strategy for building a
predictive model
• Strategy
– Build a comprehensive model of liver homeostasis (normal or
steady state)
– Treat toxicity as a case of drug-induced perturbations
– Computationally mine the network to identify key pathway
(combinations)
– Create assays that measure effect of drug/metabolite on the
pathways
– Predictive platform is a combination of assays and model
– Generate mechanism specific biomarkers
• Alternatives
– QSAR
– genomics
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Identifying Mechanisms
• Identify drugs reported to be liver toxic in literature
• Identify the molecular mechanism of toxicity for each such
drug (e.g., cell death, impaired bile flow etc)
• Identify root causes for these mechanisms (e.g., oxidative
stress, transporter inhibition)
• Model these root causes (identify pathways for each, and
kinetics for each pathway)
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Clues from biomarkers on the
injury mechanisms
Drugs can cause cell death (necrosis) to the same extent as Ischemia
ATP depletion is one of the root causes of Ischemia
So ATP depletion could be a root cause of drug induced cell death
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ATP and Glutathione depletion can
lead to necrosis
ATP,GSH,Ca...
bleb
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Transporter Inhibition can lead to
impaired Bile Flow
Systemic Circulation
Bile Duct
X
Portal
Systemic
Shunt
Kidney
Liver
Gall
Bladder
Intestine
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Fat import and export is an
important function of the liver
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Handling Metabolism
• Modeling ATP depletion also addresses metabolism effects
implicitly
• How?
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Drug Metabolism linked to Injury
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The liver is functionally asymmetric
Zone 1
High Oxygen
High gluconeogenesis
Zone 3
Low oxygen
High CYPs,
glycolysis
Toxicity could be linked to metabolism
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Handling Physiology Effects
Physiological Factors which exacerbate toxicity
• Obesity
• Diabetes
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Handling Patient Variations
•
•
Models can handle genetic variations in key proteins
involved
Key enzymes can also point to source of variability
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Equation Types & Roadblocks
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Differential Equations
1- Rate of change of ATP in cytosol:
d[ATP]e = V +V +V
ant
pk
pgk+Vadk-Vutilisation(cytosol)
dt
2- Rate of change of ATP in mitochondria:
d[ATP]m
= (Vfof1atpase- Vutilisation(mitochondria)-Vant)* Rcm
dt
3- Rate of change of ADP in cytosol:
d[ADP]e = Vutilisation(cytosol)- Vant-Vpk-Vpgk-2*Vadk
dt
4- Rate of change of Pi in cytosol:
d[Pi]e = Vutilisation(cytosol)- Vpicarrier-Vpk-Vpgk
dt
e: cytosol.
m: mitochondria.
Rcm: cell volume/mitochondrial volume
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Conservation Laws
PHOSPHATE POOL IN THE CELL
Psum=3ATPe+2ADPe+AMPe+Pie+(3ATPm+2ADPm+Pim)/Rcm=constant
ADENINE NUCLEOTIDE POOL IN THE CELL
Asum= ATPe+ADPe+AMPe+ +(ATPm+ADPm+AMPm)/Rcm=constant
1)
AMP DOES NOT TRAVERSE THROUGH THE MITOCHONDRIAL MEMBRANE.
(Dransield & Aprille Arch. Biochem. Biophys. 313:156-165)
2)
ADP & ATP ARE EXCHANGED BETWEEN THE CYTOSOL AND MITOCHONDRIA
VIA ANT ANTIPORT
TWO CONSERVATION LAWS FOR ADENINE NUCLEOTIDE POOLS IN THE CELL
ADENINE NUCLEOTIDE POOL IN THE CYTOSOL
1. Asum,e= Total adenine pool in the cytosol= ATPe+ADPe+AMPe=constant
ADENINE NUCLEOTIDE POOL IN THE MITOCHONDRIA
2. Asum,m=Total adenine pool in the mitochondria=ATPm+ADPm=constant
e- cytosol
m- mitochondria
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Enzyme Kinetics
Flux V= f[reactants]
Reactants considered either variables or as constant
parameters.
e.g. The kinetic expression for fof1atpase
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Modeling Fluxes: Typical
Roadblocks & solutions
Problems
Solutions
Hepatocyte data unavailable
Use non-dimensional parameter values (e.g. [S]/Km)
from other sources.
Km value needs to be estimated for single substrate
MM kinetics
For a cascade of reactions the homeostatic flux
value of the cascade can be equated to the flux
value of any enzyme in the cascade at steady state
Km values for the substrates that take part in more
than one important metabolic network e.g., ATP,
NADPH
Important metabolites operate near saturation,
hence two substrate enzyme kinetics can be
modified to single substrate kinetics
For mitochondrial enzymes and transporters,
experiments are usually done in isolated
mitochondria
Protein content ratio of cell protein to mitochondrial
protein is used to express the flux value with respect
to whole cell
In vitro experiment does not mimic the in vivo
combinations of effects of cellular regulators
Simulate the in vivo condition with the help of
experimental information
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Modules
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Cytotoxicity
cell death
cell viability
bile acids
Bilirubin
Cholestasis/
impaired bile flow
Actin skeleton
Steatosis/
fatty liver
fatty acids
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predictive model in silico
Hepatotoxicity in the clinic
Definition of homeostasis for a
minimal model
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Glutathione-ROS-Lipid Peroxidation
•
Scope
– To capture intracellular GSH and ROS metabolism, the lipid peroxidation
process
– the interdependence among the three modules in homeostasis to predict
drug metabolism induced changes in [GSH] and intracellular effects of
increased [ROS].
•
Major pathways
– Intracellular antioxidant interactions
– Basic scheme of lipid peroxidation
– GSH synthesis, efflux and the redox cycle
•
Upon completion, the model will predict
– GSH depletion caused by increased ROS (due to drug metabolism) or the
conjugation of the drug with GSH (eg. EA, Acetaminophen)
– The increase in lipid peroxidation caused by increased ROS and imbalance
of antioxidant levels (including GSH).
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ATP Conservation
•
Scope
– Metabolic network for ATP synthesis
– Understanding the regulation and connections among the different
pathways involved
•
Major pathways
– Glycolysis, malate-aspartate shuttle, Tri-carboxylic acid (TCA) cycle,
Oxidative phosphorylation
•
Upon completion, this module will predict
– target (or targets) which when perturbed can cause drug induced necrotic
death of cell due to ATP depletion.
– the distribution among different pathways (e.g. Glycolysis and Oxidative
phosphorylation) for the total ATP pool in the cell, under normal and
perturbed state
– Time scale of cell survival under toxic exposure.
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Fatty Acid Metabolism
•
Scope
–
•
Major pathways
–
•
To understand the partitioning of free fatty acid flux in the hepatocyte to
identify the key event(s) and/or metabolite(s) concentrations that could lead
to the development of fatty liver (steatosis)
mitochondrial beta oxidation, triglyceride synthesis and storage, ketone
body formation, fatty acid synthesis
Upon completion, the module can explain
–
–
–
the development of steatosis from the inhibition of any of the above
processes. For e.g., tetracycline, amiodarone, inhibit -oxidation leading to
steatosis.
Alcohol-induced steatosis
Hormonal control of VLDL secretion from the triglyceride stores.
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Actin Cytoskeleton
•
Scope
– Quantity and rate of actin polymerization, the number and length of
filaments and degree of branching
– The impact of the cytoskeletal function on bile-flow related processes
•
Major pathways
– the actin polymerization pathway with the role of six actin binding proteins,
pH, electrolytes
– second messengers that modulate the pathway (e.g. PIP2)
•
Given quantitative data, the module can explain
– Effects of drugs that alter the above mentioned modulators and hence, actin
architecture & function
– The degree of impact on canalicular contractility, microvilli integrity and biletransporter function
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Bile Salt, Bilirubin, Bicarbonate
• Scope
– To understand the metabolism and transport of bile-salts, bilirubin
and bicarbonate ions in the hepatocyte
– To understand the bile-salt dependant and independent flow of bile
in the body
• Major pathways
– Bile salt and bilirubin metabolism
• Upon completion this module will explain
– Cholestasis and necrosis due to dysregulation of the pathways
modeled
– The impact of drugs on these pathways (given in vitro data)
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Validation Studies
1) Validate homeostasis
•
Module level and whole system level
2) Validate effect of drugs and toxins
3) Validate known genetic diseases
4) Look for insights
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Validation – Homeostasis
ATP module
Simulations
Experimental Results
Cytosolic [ATP]
2950 mM
2760 mM*
Cytosolic [ADP]
200 mM
315 mM*
Mitochondrial [ATP]
9000 mM
10380 mM*
Mitochondrial [ADP]
7000 mM
5380 mM*
Cytosolic [Pi]
3375 mM
3340 mM*
Mitochondrial [Pi]
14000 mM
16800 mM*
Cytosolic [AMP]
60 mM
130 mM*
ATP generated by Glycolysis
33%
38%#
ATP generated by Oxidativephosphorylation
66%
57%#
* Eur J Biochem 1978, 84:413-420
# Eur J Biochem 1999 263:671-685
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Validation – Homeostasis
Actin Cytoskeleton module
•
Rate of filament growth is linear and constant both at the pointed
and the barbed end, Pollard, J. Cell Biol. 1986 (103) 2747-54
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Validation – Homeostasis
Steatosis module
• We examined how the fatty acid flux was distributed
between esterification and β-oxidation in differing
nutritional states and compared against known values in
the literature
% of flux entering mitochondrial oxidation
Nutritional State
Simulations
Experimental Value
Fed
74.6
70*
Fasted
35
30*
* Ontko, J A. JBC,1972,vol:247,1788-1800
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Validation – Effect of Drug
GSH module
Drug: Ethacrynic Acid (EA):
Target Glutathione-S-transferase
Simulation
Experiment
2
2
1
3
3
Mitochondria depletion of GSH is also reproduced
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Validation – Effect of Drug
Bile-salt module
Drug: Fusidate:
Target: Bile Salt Export Pump (BSEP)
• ATP Dependent transport of taurocholate inhibited by
fusidate with a Ki of 2.2 M 1
• Simulate effect of 100 mg/Kg dose given intravenously, use
PK data from literature 2
• Simulations show that the rate of transport of taurocholate
inhibited by 85%
• Compares well with experimental value of 80% 1
1
Bode KA, et. al., Biochem Pharmacol. 2002 Jul 1;64(1):151-8
2 Taburet AM et. al., J Antimicrob Chemother. 1990 Feb;25 Suppl B:23-31
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Validation – Genetic Disease
Bilirubin module
Literature
Simulations
UGT activity
10-33% wild type
20% activity
UCB in serum
<70 µM
50 µM
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Novel Insights
GSH module
Biological Insights
The capacity of the liver to recover from reactive hydrogen shock
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Enhancing the model using NLP
Drugs involved in Cholestasis
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The Overall Hepatotoxicity
Platform..
drug candidate
toxic pathways
toxic concentrations
biomarkers
etc
assay
results
[ ATP]
 f (GSH ,...etc)
t
Assay Panel
Liver Model
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Extensions
• Acute to Chronic
• Idiosyncrasy
• Organ architecture
• Other toxicity endpoints
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Team
•
•
•
•
•
•
•
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•
•
Anupama Rajan Bhat
R. Rajesh, Ph.D.
Dr. Nalini, R.
Dr. Narasimha, M.K., Ph.D.
Rajeev Kumar
Sai Jagan Mohan, Ph.D.
Sonali Das, Ph.D.
Sowmya Raghavan, Ph.D.
Raghunathan Srivatsan, Ph.D.
Kas Subramanian, Ph.D.
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