© Strand Life Sciences 2008 Systems toxicology – Predicting Drug Induced Liver Injury...

© Strand Life Sciences 2008

Systems toxicology – Predicting Drug Induced Liver Injury

Kalyanasundaram Subramanian, Ph.D.Strand Life Sciences


2

Overview

• The Hepatotoxicity problem

• Modeling Approach

• Validation

• Summary


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


7

• 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

Why is hepatotoxicity prediction hard?


8

Biotransformation can cause toxic metabolites to be formed

Phase I(CYP450)

Phase II(transferases)

excreted

Glutathione,Glucuronic acid,

Sulphate,Glycine

Glutamine,acetylation

excreted

RH

Oxidation,dealkylation


9

People respond differently to the same drug


10

estrogen

Physiological/disease factors


11

Hepatotoxicity is the result of complex interactions

physiology/disease

drug/metabolites

patient


12

Liver toxicity is inferred from blood parameters

Detected via blood analysisNon-specific, non-unique

Toxin/Virus

Biomarkers: AST, ALT, bilirubin


13

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?


14

Building a Top-Down Systems Model

target

clinicalendpoint

cascade ofbiological pathways

linking target toclinical endpoints

increasing detail

Liver Lobule

Liver cellsLiver cells

ProteinsProteins


15

ExplicitExplicithypotheseshypotheses

reversereverseengineered to fillengineered to fillknowledge gapsknowledge gaps

Top-down Model Development Leads to Novel Insights

Clinical dataClinical data

High throughput High throughput datadataIn vitroIn vitro data dataAnimal model Animal model datadata


16

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


17

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)


18

Drugs can cause cell death (necrosis) to the same extent as IschemiaATP depletion is one of the root causes of IschemiaSo ATP depletion could be a root cause of drug induced cell death

Clues from biomarkers on the injury mechanisms


19

ATP and Glutathione depletion can lead to necrosis

ATP,GSH,Ca...

bleb


20

Transporter Inhibition can lead to impaired Bile Flow

KidneyGall

Bladder

Bile Duct

Systemic Circulation

Portal Systemic Shunt

Liver

Intestine

X


21

Fat import and export is an important function of the liver


22

Handling Metabolism

• Modeling ATP depletion also addresses metabolism effects implicitly

• How?


23

Drug Metabolism linked to Injury


24

The liver is functionally asymmetric

Zone 1High OxygenHigh gluconeogenesis Zone 3

Low oxygen High CYPs,glycolysis

Toxicity could be linked to metabolism


25

Handling Physiology Effects

Physiological Factors which exacerbate toxicity• Obesity• Diabetes


26

Handling Patient Variations

• Models can handle genetic variations in key proteins involved

• Key enzymes can also point to source of variability


Equation Types & Roadblocks


1- Rate of change of ATP in cytosol: = V

ant+V

pk+V

pgk+V

adk-V

utilisation(cytosol)

2- Rate of change of ATP in mitochondria: = (V

fof1atpase- V

utilisation(mitochondria)-V

ant)* R

cm

3- Rate of change of ADP in cytosol: = V

utilisation(cytosol)- V

ant-V

pk-V

pgk-2*V

adk

4- Rate of change of Pi in cytosol: = V

utilisation(cytosol)- V

picarrier-V

pk-V

pgk

e: cytosol.m: mitochondria.R

cm: cell volume/mitochondrial volume

d[ATP]e

dt

d[ATP]m

dt

d[ADP]e

dt

d[Pi]e

dt

Differential Equations


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1. Asum,e

= Total adenine pool in the cytosol= ATPe+ADP

e+AMP

e=constant

2. Asum,m

=Total adenine pool in the mitochondria=ATPm+ADP

m=constant

Psum

=3ATPe+2ADP

e+AMP

e+Pi

e+(3ATP

m+2ADP

m+Pi

m)/R

cm=constant

PHOSPHATE POOL IN THE CELL

ADENINE NUCLEOTIDE POOL IN THE CELL

Asum

= ATPe+ADP

e+AMP

e+ +(ATP

m+ADP

m+AMP

m)/R

cm=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

ADENINE NUCLEOTIDE POOL IN THE MITOCHONDRIA

e- cytosolm- mitochondria

Conservation Laws


Flux V= f[reactants]Reactants considered either variables or as constant parameters.

e.g. The kinetic expression for fof1atpase

Enzyme Kinetics


33

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


Modules


41

Definition of homeostasis for a minimal model

bile acidsBilirubin

Actin skeleton

cell viability

Steatosis/fatty liver

Cytotoxicitycell death

Cholestasis/impaired bile flow

fatty acids

Hep

ato

toxic

ity in t

he c

linic p

red

ictive m

od

el in

silico


42

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).


43

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.


44

Fatty Acid Metabolism

• Scope– 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)

• Major pathways– 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.


45

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 bile-

transporter function


46

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)


Validation Studies

1) Validate homeostasis• Module level and whole system level

2) Validate effect of drugs and toxins3) Validate known genetic diseases4) Look for insights


48

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 Oxidative- phosphorylation

66% 57%#

* Eur J Biochem 1978, 84:413-420 # Eur J Biochem 1999 263:671-685

Validation – HomeostasisATP module


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Validation – HomeostasisActin 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


50

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


52

Validation – Effect of DrugGSH module

Simulation

12

3

Experiment

2

1

3

Drug: Ethacrynic Acid (EA): Target Glutathione-S-transferase

Mitochondria depletion of GSH is also reproduced


53

Validation – Effect of DrugBile-salt module

• 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

Drug: Fusidate: Target: Bile Salt Export Pump (BSEP)

1 Bode KA, et. al., Biochem Pharmacol. 2002 Jul 1;64(1):151-82 Taburet AM et. al., J Antimicrob Chemother. 1990 Feb;25 Suppl B:23-31


Literature Simulations

UGT activity 10-33% wild type 20% activity

UCB in serum <70 µM 50 µM

Validation – Genetic DiseaseBilirubin module


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Biological InsightsThe capacity of the liver to recover from reactive hydrogen shock

Novel InsightsGSH module


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Enhancing the model using NLPDrugs involved in Cholestasis


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Assay Panel Liver Model

),...(][

etcGSHft

ATP

assayresults

toxic pathwaystoxic concentrationsbiomarkersetc

drug candidate

The Overall Hepatotoxicity Platform..


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Extensions

• Acute to Chronic

• Idiosyncrasy

• Organ architecture

• Other toxicity endpoints


63

Team

• 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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