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Systems toxicology – Predicting Drug Induced Liver Injury Kalyanasundaram Subramanian, Ph.D. Strand Life Sciences PowerPoint Presentation
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Systems toxicology – Predicting Drug Induced Liver Injury Kalyanasundaram Subramanian, Ph.D. Strand Life Sciences

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

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

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

  2. Overview • The Hepatotoxicity problem • Modeling Approach • Validation • Summary

  3. 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 Liver is highly susceptible to toxicity

  4. 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

  5. Biotransformation can cause toxic metabolites to be formed Phase II (transferases) Phase I (CYP450) Glutathione, Glucuronic acid, Sulphate, Glycine Glutamine, acetylation Oxidation, dealkylation RH excreted excreted

  6. People respond differently to the same drug

  7. Physiological/disease factors estrogen

  8. Hepatotoxicity is the result of complex interactions drug/metabolites patient

  9. Liver toxicity is inferred from blood parameters Toxin/ Virus Biomarkers: AST, ALT, bilirubin Detected via blood analysis Non-specific, non-unique

  10. 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?

  11. 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

  12. 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

  13. 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

  14. 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)

  15. 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

  16. ATP and Glutathione depletion can lead to necrosis bleb ATP,GSH,Ca...

  17. Transporter Inhibition can lead to impaired Bile Flow X Kidney Gall Bladder Systemic Circulation Bile Duct Portal Systemic Shunt Liver Intestine

  18. Fat import and export is an important function of the liver

  19. Modeling ATP depletion also addresses metabolism effects implicitly How? Handling Metabolism

  20. Drug Metabolism linked to Injury

  21. The liver is functionally asymmetric Zone 1 High Oxygen High gluconeogenesis Zone 3 Low oxygen High CYPs, glycolysis Toxicity could be linked to metabolism

  22. Handling Physiology Effects Physiological Factors which exacerbate toxicity • Obesity • Diabetes

  23. Handling Patient Variations • Models can handle genetic variations in key proteins involved • Key enzymes can also point to source of variability

  24. Equation Types & Roadblocks

  25. Differential Equations 1- Rate of change of ATP in cytosol: = Vant+Vpk+Vpgk+Vadk-Vutilisation(cytosol) 2- Rate of change of ATP in mitochondria: = (Vfof1atpase- Vutilisation(mitochondria)-Vant)* Rcm 3- Rate of change of ADP in cytosol: = Vutilisation(cytosol)- Vant-Vpk-Vpgk-2*Vadk 4- Rate of change of Pi in cytosol: = Vutilisation(cytosol)- Vpicarrier-Vpk-Vpgk e: cytosol. m: mitochondria. Rcm: cell volume/mitochondrial volume d[ATP]e dt d[ATP]m dt d[ADP]e dt d[Pi]e dt

  26. 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 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

  27. Enzyme Kinetics Flux V= f[reactants] Reactants considered either variables or as constant parameters. e.g. The kinetic expression for fof1atpase

  28. Modeling Fluxes: Typical Roadblocks & solutions

  29. Modules

  30. Definition of homeostasis for a minimal model cell viability Cytotoxicity cell death bile acids Bilirubin Actin skeleton Cholestasis/ impaired bile flow Hepatotoxicity in the clinic predictive model in silico fatty acids Steatosis/ fatty liver

  31. 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).

  32. 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.

  33. 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.

  34. 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

  35. 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)

  36. Validation Studies Validate homeostasis Module level and whole system level Validate effect of drugs and toxins Validate known genetic diseases Look for insights

  37. Validation – HomeostasisATP module * Eur J Biochem 1978, 84:413-420 # Eur J Biochem 1999 263:671-685

  38. 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

  39. 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 * Ontko, J A. JBC,1972,vol:247,1788-1800

  40. Validation – Effect of DrugGSH module Simulation 1 2 3 Drug: Ethacrynic Acid (EA): Target Glutathione-S-transferase Mitochondria depletion of GSH is also reproduced Experiment 2 3 1

  41. Validation – Effect of DrugBile-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

  42. Validation – Genetic DiseaseBilirubin module

  43. Biological Insights Novel InsightsGSH module The capacity of the liver to recover from reactive hydrogen shock

  44. Enhancing the model using NLPDrugs involved in Cholestasis

  45. The Overall Hepatotoxicity Platform.. drug candidate toxic pathways toxic concentrations biomarkers etc assay results Assay Panel Liver Model

  46. Extensions • Acute to Chronic • Idiosyncrasy • Organ architecture • Other toxicity endpoints

  47. 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.