Roger L. Chang November 22, 2010

1. Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model Roger L. Chang November 22, 2010 (Chang et al PLoS Comput Biol 2010)

2. × Catalytic site Path of Drug Action Efflux Uptake Metabolic network Systemic response × × × × × × × × Affect protein function Enzyme inhibition Target binding Drug molecules

3. Computational Evaluation of Drug Off-Target Effects Proteome Drug binding site alignments SMAP Predicted drug targets Drug and endogenous substrate binding site analysis Competitively inhibitable targets Inhibition simulations in context-specific model COBRA Toolbox Predicted causal targets and genetic risk factors

4. Polypharmacology • Drug promiscuity predominates. • Comprehensive experimental detection of drug targets currently impractical. (Paolini et al Nat Biotech 2006) Edges connect proteins if ≥1 drug binds both.

5. SMAP for Prediction of Drug Off-Targets Identify drug binding site of primary target Identify off-targets by binding site similarity (SOIPPA) Dock drug to off-targets (Ren et al Nucleic Acids Res 2010)

6. L E R V K D L L E R V K D L Sequence-order Independent Profile-Profile Alignment (SOIPPA) Structure A Structure B • Build association graph from graph representations of two structures. Weight nodes using similarity matrix. • Maximum-weight clique corresponds to optimum structural alignment. (Xie et al Proc Natl Acad Sci USA 2008)

7. Binding Site Analysis for Inhibitability • Overlap between drug-binding sites (SMAP) and native active sites (PDB, Catalytic Site Atlas) suggests inhibitability. • Estimation of relative binding affinities (docking) suggests strength of competitive inhibition. Endogenous substrate Drug Drug Endogenous substrate

8. Constraint-based Metabolic Modeling Metabolic network reactions Flux space Steady-state assumption S · v = 0 Perturbation constraint Flux Matrix representation of network Change in system capacity

9. Flux Balance Analysis (FBA) 1. Define system v3 v4 stated objective  max b2 2. Contraints form solution space Metabolic flux (v2) internal reactions S · v= 0 , 0 ≤ vi≤ vi,m min ≤ bi≤ max inputs & outputs Metabolic flux (v1) 3. Objective function v1=1 v1 v2 v2=1 bi=1 bi b2 b2=1 input input output output Particular solution (optimal) max b2=1 Metabolic flux (v3) Solution space Z is user specified by choosing the ci In example system where b2 is to be maximized, c = (0,0,0,0,0,1) 4. Optimization v3=0 v4=0 Z is optimized (max|min) as by linear programming (LP)

10. Recon1: A Human Metabolic Network Global Metabolic Map Comprehensively represents known reactions in human cells Reactions (3,311) Compounds (2,712) Pathways (98) Genes (1,496) Transcripts (1,905) Proteins (2,004) Compartments (7) http://bigg.ucsd.edu (Duarte et al Proc Natl Acad Sci USA 2007)

11. Context-specific Modeling (with GIMME) Metabolic network Expression data • GIMME algorithm finds network most consistent with expression data and required metabolic functions (RMFs), objectives for the model • Assigns inconsistency score (IS) to each deleted rxn and then aims to reassemble a functional network minimizing total IS Remove reactions below threshold Reduced model Reinsert reactions needed for RMFs Functional context-specific model (Becker et al PLoS Comput Biol 2008)

12. Human Context-specific Modeling • Human tissues have differing metabolism due to gene expression and physiological environment. • Context-specific algorithms (GIMME, Shlomi) fit allowable network flux state to expression data. • Missing: • Defined system inputs and outputs • Defined physiological objective • Multiple omics data • Content curation based on literature

13. metabolomic biofluid & tissue localization data metabolic network gene expression data constrain exchange fluxes preliminary model normalize & set threshold model refine based on capabilities set flux constraints GIMME metabolic influx set minimum objective flux objective function literature metabolic efflux Context-specific Modeling Pipeline

14. Case Study: Torcetrapib Side Effect • Cholesteryl ester transfer protein (CETP) inhibitors treat cardiovascular disease by raising HDL and lowering LDL cholesterol (Torcetrapib, Anacetrapib, JTT-705). • Torcetrapib withdrawn due to occasional lethal side effect, severe hypertension. • Cause of hypertension undetermined; off-target effects suggested. • Predicted off-targets include metabolic enzymes. Renal function is strong determinant of blood pressure. Causal off-targets may be found through the modeling kidney metabolism.

15. Renal Objective Function

16. Kidney Model Subsystem Distribution • Kidney model: 228 genes, 448 reactions • Largest subsystem is membrane transporters, expected for renal filtration. Remainder involved in indirect reabsorption and secretion synthesis. Renal objectives

17. = Max perturbed flux Max unperturbed flux Degree of phenotype = Max flux # exchanges Perturbation Phenotype Simulation Secretions Absorptions Objective flux × × Perturbation constraint System boundary constraint

18. Gene-deficient Renal Phenotypes • Predicted gene-deficient renal disorders also constitute potential risk factors for treatment. • Disorders caused by 20 out of 118 simulated gene deficiencies clinically validated in literature. 100 Gene deficiencies 0

19. Torcetrapib Renal Response Phenotypes Cryptic genetic risk factors - phenotype only under combined gene-deficient, drug-treated perturbation. Deficiency and target could be isozymes or in parallel pathways. Drug off-targets Severity of phenotype increases when combined with cryptic gene-deficiency.

20. Predicted Hypertension Causal Drug Off-Targets

21. ROC Curves for Gene-deficient Phenotype Prediction • Clinical positives = 58 Clinical negatives = 4 • AROC = 0.7565 p-value = 8.71×10-70 • Mean TPR = 0.2859 p-value = 3.29×10-127

22. Conclusions • Torcetrapib hypertension side effect may result from renal metabolic off-target effects. • Framework for perturbation phenotype simulation capable of predicting metabolic disorders, causal drug targets, and genetic risk factors for drug treatment (including cryptic risk factors). • Pipeline established for in silico prediction of systemic drug response.

23. Acknowledgements Lei Xie Li Xie Phil Bourne Bernhard Palsson