An iterative algorithm for metabolic network-based drug target identification

1. An iterative algorithm for metabolic network-based drug target identification Padmavati Sridhar, Tamer Kahveci, Sanjay Ranka Department of Computer and Information Science and Engineering www.cise.ufl.edu/~tamer

2. Disease Disease Target enzyme Target compound(s) Metabolic Network Potential compounds (drugs) Lead compounds Data mining Preclinical testing Target enzyme(s) Phase I – III trials Drug Discovery Process

3. Why Drugs? • Excessive production or lack (or a combination of the two) of certain compounds may lead to disease. • Example: Malfunction (Phenylalanine hydroxylase) => accumulation of phenylalanine => Phenylketonuria => mental retardation. • Drugs can manipulate enzymes to reduce or increase the production of compounds !

4. An Example: Targets for Affecting Central Nervous System Drug: Phenylbutazone (Therapeutic category = 1144)

5. Goal • Given a set of target compounds, find the set of enzymes whose inhibition stops the production of the target compounds with minimum side-effects.

6. Directed Graph Model Edges Vertices Catalyzes Enzyme Reaction Produces Compound Consumes Target compound

7. C1 Simple Metabolic Network E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4

8. Target compound removed C1 Three Non-target compounds removed Damage (E1) = 3 Inhibit E1 E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4

9. Target compound removed C1 Inhibit E2 or/and E3 E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4 Damage (E1) = 3 Damage (E2) = 0 Damage (E3) = 0 • What is the best enzyme combination? • Number of combinations is exponential ! Damage (E2, E3) = 1

10. How can we find the right enzyme set? • Iterative method • Initialization: Remove each node (reaction or compound or enzyme) from graph directly. • Iteration: Improve (reduce damage) each node by considering its precursors until no node improves.

11. C1 Initialization: Enzymes E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4 T, 3 E1 E2 E3 F, 0 F, 0 =

12. C1 Initialization: Reactions E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4 {E1}, T, 3 R1 R2 R3 R4 T, 3 E1 E2 E3 {E1}, T, 3 = F, 0 F, 0 = {E2}, F, 0 {E3}, F, 0

13. C1 Initialization: Compounds E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4 {E1}, T, 3 C1 C2 C3 C4 C5 {E1}, T, 3 R1 R2 R3 R4 T, 3 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 E1 E2 E3 {E1}, T, 3 = F, 0 F, 0 = = {E2}, F, 0 {E3}, F, 0 {E2, E3}, T, 1

14. C1 {E1}, T, 3 {E2}, F, 0 {E3}, F, 0 {E1}, T, 3 R1 R2 R3 R4 {E1}, T, 3 = {E2}, F, 0 {E3}, F, 0 Iterations: Reactions E2 R3 R1 = min{R1, C5} = min{3, 1} C5 E3 R4 R1 C2 {E2, E3}, T, 1 R1 R2 R3 R4 = C3 R2 E1 C4 {E1}, T, 3 C1 C2 C3 C4 C5 T, 3 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 E1 E2 E3 F, 0 F, 0 = = {E2, E3}, T, 1

15. C1 {E2, E3}, T, 1 {E2, E3}, T, 1 C1 C2 C3 C4 C5 R1 R2 R3 R4 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 = {E2}, F, 0 = {E3}, F, 0 {E2, E3}, T, 1 Iterations: Compounds E2 R3 C1 = min{C1, R1} = min{3, 1} C5 E3 R4 R1 C2 C3 R2 E1 C4 {E1}, T, 3 C1 C2 C3 C4 C5 T, 3 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 E1 E2 E3 F, 0 F, 0 = = {E2, E3}, T, 1

16. How many iterations? Number of iterations is at most the number of reactions on the longest path that traverses each node at most one

17. Experiments: Datasets

18. Experiments: Accuracy • Average damage for one, two, and four randomly selected target compounds • 10 + 10 + 10 runs for each network

19. Experiments: Running Time

20. Experiments: Number of Iterations