Eberhard O. Voit

1. Integrative BioSystems I Institute B S I The Role of Systems Modeling in Drug Discovery and Predictive Health Eberhard O. Voit & Department of Biomedical Engineering Atlanta, Georgia Unither Nanomedicine & Telemedical Technology Conference February 23-26, 2010 Quebec, Canada 1

2. Overview General Caveat: Most technical details will be skipped The Grand Challenge of Systems Biology The Drug Pipeline and its Challenges Systems Modeling in Drug Discovery: PBPK Models Receptor Models Pathway Models Predictive Health

3. % model_ode function dy = PP_ode(t,y) … dy(1) = y(1) * (b1 - b2 * y(1) - b3 * y(2)); dy(2) = y(2) * (b4 * y(1) + b5 - b6 * y(2)); end www.alternative-cancer.net/ “Grand Challenge” of Comp. Sys. Biol. science.nationalgeographic.com www.ornl.gov/

4. % model_ode function dy = PP_ode(t,y) … dy(1) = y(1) * (b1 - b2 * y(1) - b3 * y(2)); dy(2) = y(2) * (b4 * y(1) + b5 - b6 * y(2)); end www.alternative-cancer.net/ “Grand Challenge” of Comp. Sys. Biol. science.nationalgeographic.com www.ornl.gov/

5. Rewards for Solving the Grand Challenge Testing incomparably faster and cheaper Explain root causes of observations Make reliable predictions Understand design and operating principles of biological systems Create novel biological systems Reduce animal experiments Improve medicine

6. Drug Discovery Pipeline Preclinical Development Clinical Development Postclinical Development Discovery Lead Optimization Clinical Phase I Clinical Phase III Target ID Launch Hit ID, Lead ID Development of Drug Candidate Clinical Phase II FDA Approval Process Note: 10-20 years; ~ 1 Billion $ 6

7. Models in Drug Discovery: Structural Biology, Bioinformatics TI Hit Lead DC CP1 CP2 CP3 FDA L!! NCE (New chemical entity) screening QSAR Binding prediction (molecular dynamics) 7

8. Models in Drug Discovery: PBPK TI Hit Lead DC CP1 CP2 CP3 FDA L!! ADME: Absorption, Distribution, Metabolism, and Excretion; Extrapolation; Routes; Dosage 8

9. ? ? Drug Discovery: Receptor Binding TI Hit Lead DC CP1 CP2 CP3 FDA L!! Receptor Antibody Ligand

10. C3 Inject A L R C2 C1 – – + + kL kR kR kL + – – + + – – k1 k1 k2 k3 k2 k3 kA Receptor Binding

11. Drug Discovery: Systems Analysis TI Hit Lead DC CP1 CP2 CP3 FDA L!!

12. PRPP Suppose too much UA e.g., PRPPS superactivity GMP GDP or, HGPRT deficiency GTP RNA reduce UA production DNA e.g.: UA  Xa  Drug Treatment (Purine Metabolism) R5P v prpps v pyr v ade Ade P v i gprt v v polyam den SAM v v hprt aprt v mat • Explain: v trans v asuc v v gmps impd XMP IMP S-AMP P i Ado v v gmpr asli AMP v ampd ADP ATP v v rnag rnaa v v P v v gdrnr grna i arna adrnr v 2. Intervene: gnuc v v dAdo v dnag dnaa gprt dGMP dAMP v ada dGDP dADP dGTP dATP v v gdna adna v v dada dgnuc P i v hprt v inuc 3. Side effects? HX Gua Ino Guo v v gua hxd Xa dIno dGuo v xd v x v hx UA v ua

13. The Task of Personal Medicine Status quo: Medicine is based on averages (either from epidemiology or from animal experiments) Task: Need to progress from average input-output correlations to a deeper understanding of disease processes in individuals Challenges: 1. Get data 2. Analyze them appropriately (i.e., per (sophisticated) modeling) Hope: Analogy with engineering We do not need to take apart every machine we encounter, if we understand the principles that make this type of machine functional.

14. Modeling Approaches Biomarker associations (statistics, epidemiology) Biomarker networks (biomarkers predictive of later biomarkers; graph theory) Parameter variations in disease models Long-term development of health and disease simplexes; personalized disease trajectories time

15. Many Biomarkers: Oncotype DX Test (21 genes) Remission of Breast Cancer Disease Biomarker Modeling One Biomarker: (A to T) - SNP in HgbS Sickle Cell Anemia Hierarchical Networks of Biomarkers:

16. Dynamic Disease Modeling Develop “physiological” model (of a disease prone system) Set up equations Identify parameter values for average, healthy individuals “Personalize” models by altering parameters (singly or in combination) and study “disease” outcome (targeted changes or MC simulations) Model diseases and develop (personalized) countermeasures

17. Epidemiology “Averaged” Model Experimental Systems Biology Computational Systems Biology Process Parameters Sensitivity, Robustness Personalized Treatment Personalized Simulation Simulation Perturbation Model Design Health-Disease Classification Suggested Prevention Personalized Health Model Numerical Solution Personalized Health Prediction Personalized Risk Profile Personalized Disease Models Molecular Biology Biochemistry Physiology Hypothesized Risk-Factor~Disease Associations Physiological Mechanism Clinical Trials “Averaged” Treatment Voit & Brigham, Open Path. J., 2008

18. Two dimensions: combined normal ranges normal biomarker 2 biomarker 1 normal Biomarkers, Health and Disease Simplexes One dimension: “normal range” (“U-box”) biomarker normal Voit, Math. Biosci. (2009)

19. Where does the Simplex Come from? Biomarkers, Health and Disease Simplexes Two dimensions: combined normal ranges + constraints (Two extremes are not tolerable; compensation between variables) Result: linear bounds (reasonable approximation) normal biomarker 2 biomarker 1 normal

20. Biomarkers, Health and Disease Simplexes Many dimensions: polygon becomes a simplex Note: Simplex can be computed from a model

21. “Disease Simplex” Classification of Helath & Disease Ideal Solution (in full “biomarker space”): Clear separation between health and disease simplexes z “Health Simplex” y x

22. Classification of Helath & Disease Would like to say: x <  : healthy; x >  : sick (like PSA > 4) z y  x

23. “Don’t know” “Diseased” Classification of Helath & Disease In reality, there is no unique because disease status also depends on other biomarkers, such as y and z. z y “Healthy”  x Consequence: Looking at one biomarker insufficient.

24. Premorbidity Treatable or Self-healing Disease Temporary Illness (fever, dehydration, …) Health and Disease Trajectories Health

25. Premorbidity Treatable or Self-healing Disease Temporary Illness (fever, dehydration, …) Health and Disease Trajectories Health

26. Summary Modeling complements experimental systems biology Quite a few technical challenges (e.g., parameter estimation), but potential is clear Systems modeling can play roles at several points of the drug pipeline Models are needed to assess personalized states, diagnoses, treatments, and predictions

27. Acknowledgments The 2008 Crew Funding: NIH, NSF, DOE, Woodruff Foundation, University System of Georgia, Georgia Research Alliance Information: www.bst.bme.gatech.edu