1. BMED 3510 Systems Biology in Medicine and Drug Development Book Chapter 13

2. Buzzwords: Personalized Medicine And Predictive Health “Personalized”: Different treatments for different people. Make custom-tailored predictions of one’s health “trajectory” Sounds good, but are these goals feasible? 2

3. How Different Are We? Genetic similarities Naïve view: ~50% of genes from mom, ~50% from dad Hence: brothers and sisters differ by ~ 50% (or not?) Yet: Genomes of humans and chimpanzees are very similar (98.8%!) Two people should exhibit much greater similarity than that! False deduction due to similarity between mom’s and dad’s genome Differences mostly due to SNPs (single nucleotide polymorphisms) and some genomic rearrangements. If every 100 th nucleotide could have a SNP and we have 3 billion nucleotides, then we could have 4 30,000,000 different people! 3

4. Differences and their Importance SNPs Some important, others not really; often combinations are important Other sources of variability Some gene rearrangements, deletions, duplications Epigenetics (DNA unchanged, but transcription (frequency) affected) Important consequence for medicine Every person potentially responds differently to treatment Diseases and their treatments should be individualized, but are usually not 4

5. Personalized Medicine Status quo: Medicine is based on averages (either from epidemiology or from animal experiments; later) Task: Need to progress from average input-output correlations to a deeper understanding of in individuals disease processes in individuals Challenges: 1. Get the right data from individuals 2. Analyze them appropriately (i.e., with (sophisticated) modeling) Hope: Analogy with engineering We do not need to take apart every machine if we understand the principles we encounter, if we understand the principles that make this type of machine functional. 5

6. Personalized Disease Modeling Concept Develop dynamical model of a healthy system (person; pathway; …) Determine parameter values These are usually based on population averages Replace average parameter values with person-specific values, as much as possible Study effects of the personalized combination of parameter values Many parameter changes incur no significant symptoms Scan the model for options of counteracting the disease 6

7. Example Illustration Pathway What is the same within a population; what is different? Topology probably the same (What does it represent?) Parameter values probably different (What do they represent? What if p i = 0) How big are typical changes? Do changes in parameter values make a big difference? sensitivity analysis (typically one change at a time; guaranteed for infinitesimally small changes) simulations (simultaneous changes) Homeostasis / allostasis Personalized model 7

8. Example 8

9. 9 Simulation: @ t = 10, V max5 increased by 20%

10. Example 10 Simulation: Start at steady state; @ t = 10, K I6 increased by 20%

11. Example 11 Simulation: @ t = 10, h 352 increased in magnitude by 20%

12. Example 12 Simulation: @ t = 10, b 6 increased in magnitude by 20%

13. Example 13 Simulation: @ t = 10, K I6 and b 6 increased in magnitude by 20%

14. Example 14 Simulation: @ t = 10, h 351 and h 352 doubled Run longer

15. Personalized, Predictive Health Two issues 1.Identify differences between personal “parameters” and what’s “normal” 2.Investigate which differences (or combinations) are significant 3.Ideally identify significant difference before disease manifests Search for biomarkers Proteins (e.g., cytochrome p450 enzymes), genes, metabolites, blood pressure, abnormal fingernails (kidney, liver, thyroid disease, …) Big Q: Which biomarkers are symptomatic and which are causative? 15

16. Combinations of Biomarkers One Biomarker: (A to T) - SNP in HgbS Sickle Cell Anemia Hierarchical Networks of Biomarkers: Many Biomarkers: Oncotype DX Test (21 genes) Remission of Breast Cancer Disease 16

17. Biomarkers, Health and Disease Simplexes One dimension: “normal range” (“U-box”) Two dimensions: combined normal ranges 17 biomarker normal biomarker 1 normal biomarker 2 normal

18. Biomarkers, Health and Disease Simplexes 18 biomarker 1 normal biomarker 2 normal Two dimensions: combined normal ranges + constraints (Two extremes are not tolerable; compensation between variables) Result: linear bounds (reasonable approximation)

19. Biomarkers, Health and Disease Simplexes 19 biomarker 1 normal biomarker 2 normal biomarker 1 normal biomarker 2 normal

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

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

22. Classification of Health & Disease Would like to say: x : sick (like PSA > 4) x y z 22

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

24. Health and Disease Trajectories (2-d) Premorbidity Treatable or Self-healing Disease Temporary Illness (fever, dehydration, …) Health 24

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

26. Personalized Disease Models 26 Molecular Biology Biochemistry Physiology Hypothesized Risk-Factor~Disease Associations Physiological Mechanism Epidemiology Clinical Trials “Averaged” Treatment Model Design Perturbation Numerical Solution Sensitivity, Robustness Health-Disease Classification Simulation Computational Systems Biology Experimental Systems Biology “Averaged” Model Personalized Treatment Personalized Health Model Personalized Risk Profile Suggested Prevention Personalized Simulation Personalized Health Prediction Voit & Brigham, Open Path. J., 2008 Process Parameters

27. Modeling in Drug Development “Drug Development Pipeline” Target ID Hit ID, Lead ID Lead Optimization Development of Drug Candidate Clinical Phase I FDA Approval Process Launch Clinical Phase II Clinical Phase III Discovery Preclinical Development Clinical Development Postclinical Development Note: 1 NCE out of ~10,000 makes it; 10-20 years; ~ 1 Billion $ 27

28. Modeling in Drug Development 6. Seek FDA Approval Discovery Preclinical Development Clinical Development Postclinical Development TIHitLeadDCCP1CP2CP3FDAL!! 1. Identify biological target and molecules (potential drugs; “hits”) that affect the target; screen for the most promising hit 2. Optimize the most promising hit; Formulate as drug with desirable properties 3. Test safety on healthy individuals 4. Test efficacy on small patient cohort 5. Test on large patient cohort 7. Launch 28

29. Modeling in Drug Development Discovery Preclinical Development Clinical Development Postclinical Development TIHitLeadDCCP1CP2CP3FDAL!! relatively cheapvery expensive Generic strategy: try to weed out as many molecules as possible as early as possible, if they are not likely to make it to the end Use models (and experiments) for screening process 29

30. Models in Drug Development NCE (New chemical entity) screening QSAR (Quantitative Structure-Activity Relationships) Binding prediction (molecular dynamics) TIHitLeadDCCP1CP2CP3FDAL!! 30

31. Modeling of Receptor Binding Many drugs work by binding to proteins. Here, the FDA approved drug Indinavir docks into the cavity of an HIV protease in a lock-and-key mechanism (PDB: 10DW and 1HSG). Courtesy of Juan Cui and Ying Xu, University of Georgia.

32. Modeling of Receptor Binding ReceptorLigandAntibody TIHitLeadDCCP1CP2CP3FDAL!! ??

33. Modeling of Receptor Binding A C2C2 R kRkR – kRkR + kAkA – Inject L C1C1 C3C3 kLkL – kLkL + k1k1 – k1k1 + k2k2 – k2k2 + k3k3 – k3k3 + 33

34. Compartment Models in Drug Discovery TIHitLeadDCCP1CP2CP3FDAL!! 34

35. Compartment Models in Drug Discovery essentially linear; easy to estimate from data

36. Models in Drug Discovery: PBPK TIHitLeadDCCP1CP2CP3FDAL!! Much used model in pharmaceutical research: Physiologically-Based Pharmacokinetic Model First goal: Determine “ADME”: Absorption, Distribution, Metabolism, and Excretion; Extrapolation to other species Routes of drug administration Dosage 36

37. Models in Drug Discovery: PBPK Each organ and blood modeled as a compartment with its specific features: Initially a simple mass action model with influx, retention, efflux. E.g., fat tends to retain lipophilic drugs for a longer time than lung. Volumes are taken into account. Liver tends to degrade drug metabolically: May include a metabolic model for this compartment; may account for break-down products. Kidney, liver, lung provide possible exit routes 37

38. Pathway Screening Concept: Develop dynamic model of a physiological system Introduce changes leading to disease Systematically scan the model for means of disease treatment TIHitLeadDCCP1CP2CP3FDAL!!

39. Pathway Screening Example: Simplified model of purine metabolism TIHitLeadDCCP1CP2CP3FDAL!!

40. More Detailed Purine Metabolism PRPP IMPS-AMPXMP RNADNA Xa UA Ade SAM dGMP dGDP dGTP GMP GDP GTP Gua Guo dGuo dAdo dAMP dADP dATP Ado AMP ADP ATP HX Ino dIno R5P v prpps v pyr v aprt v ade v gprt v hprt v adrnr v x v ua v hprt v impd v gmps v den v grna v rnag v gmpr v arna v rnaa v trans v asli v ampd v polyam v asuc v mat v gnuc v gdrnr v dgnuc v dnag v gdna v dnaa v adna v inuc v hx v gprt v gua v hxd v xd v ada v dada P i P i P i P i Curto et al., Math. Biosc., 1998 What does it take to set up such a model? Lots of time and effort!! Here: Over 30 variables Dozens of parameters, … 40

41. PRPP IMPS-AMPXMP RNADNA Xa UA Ade SAM dGMP dGDP dGTP GMP GDP GTP Gua Guo dGuo dAdo dAMP dADP dATP Ado AMP ADP ATP HX Ino dIno R5P v prpps v pyr v aprt v ade v gprt v hprt v adrnr v x v ua v hprt v impd v gmps v den v grna v rnag v gmpr v arna v rnaa v trans v asli v ampd v polyam v asuc v mat v gnuc v gdrnr v dgnuc v dnag v gdna v dnaa v adna v inuc v hx v gprt v gua v hxd v xd v ada v dada P i P i P i P i Curto et al., Math. Biosc., 1998 Tasks: Diagnostics Do responses make sense? Stability Sensitivities Typical analyses Bolus experiments Changes in enzyme activities Changes in parameter values Diseases Treatments 41 More Detailed Purine Metabolism

42. e.g.: UA   Xa  e.g., PRPPS superactivity or, HGPRT deficiency Suppose too much UA reduce UA production PRPP IMPS-AMPXMP RNADNA Xa UA Ade SAM dGMP dGDP dGTP GMP GDP GTP Gua Guo dGuo dAdo dAMP dADP dATP Ado AMP ADP ATP HX Ino dIno R5P v prpps v pyr v aprt v ade v gprt v hprt v adrnr v x v ua v hprt v impd v gmps v den v grna v rnag v gmpr v arna v rnaa v trans v asli v ampd v polyam v asuc v mat v gnuc v gdrnr v dgnuc v dnag v gdna v dnaa v adna v inuc v hx v gprt v gua v hxd v xd v ada v dada P i P i P i P i 1.Explain: 2.Intervene: 3.Side effects? Curto et al., Math. Biosc., 1998 42 More Detailed Purine Metabolism

43. Potential Application: Disease Simulators Analogy: Flight simulator Disease Simulator: Enter virtual person, symptoms, vital signs, disease history, biomarker signals, … interactively simulate effects of drugs, treatment options, … 43

44. Summary Modeling has been used in drug development for some while New approaches possible, due to better data and methods Personalized medicine one of the hallmark goals of systems biology Personalizing a model easy in principle (difficult in actuality) Future: Disease simulators 44