Systems Pharmacology: An Application of Systems Biology

2. Systems Pharmacology: An Application of Systems Biology Keith O. Elliston, PhD Genstruct, Inc.

3. Systems Pharmacology Background • Systems Biology • Defining the biological networks perturbed by disease and drug action • Translational Medicine • Capturing research knowledge and translating it into actionable clinical results and mechanistic biomarkers • Drug Development • Defining molecular mechanisms and biomarkers for efficacy, toxicity and stratification, facilitated clinical trials and accelerated approvals

4. COMPOUND APPLIED RESULTS Systems Pharmacology: The Problem Something Happens What biomarkers can we use? What is the mechanism of action? Why are we seeing toxicity? What is the target?

5. COMPOUND APPLIED RESULTS Phenotypes are the result of complex molecular interactions in biological networks Achieving System Level Understanding Specific Mechanisms

6. The Problem is Complexity • Drugs exert their effects at the molecular level • Agonism and antagonism of molecular targets • Drug development assesses drugs at the animal level • Assessment of clinical phenotypes • High failures are due to a primitive understanding of the molecular actions of drugs • Imagine testing an airplane without any understanding of aerodynamics • A better understanding of molecular action will help to reduce failures • An increased understanding of aerodynamics lead the wright brothers (bicycle mechanics) to design a workable airplane We need to open up the molecular black box, and look inside

7. But in an experiment where tens of thousands of measurements result in hundreds or thousands of observed changes, the relevant networks are impossibly complex The Challenge: Harnessing Complexity for Knowledge Synthesis and Reasoning Scientists are effective in reasoning about moderate numbers of interacting components

8. Incomprehensible Biology Comprehension Global Biological Measurements

9. Causal System Modeling • Causal Modeling (Capturing Knowledge) • The “Right Way” to model biological systems • Based on cause and effect relationships between molecules • Much more comprehensive than any other approach • Automated Reasoning (Interrogating Data) • Uses Artificial Intelligence to interrogate hundreds of thousands of possibilities • Augments the innate abilities of the biologist • Expert Biologists (Hypothesis Generation and Refinement) • Biologists trained in the use of artificial intelligence and causal modeling to define drug mechanisms • Experts in biology first… • Real Results • Over 30 successful commercial programs to date • Biomarkers for marketed anti cancer compounds • Biomarker validation presented to FDA by partner

10. What Is Causality? What is Consequence? What is Cause?

11. Causality and Biology X tyrosine phosphorylates Z, activating its catalytic activity Z’s catalytic activity causes the cleavage of protein N N kaof(X) Z {P@Y} catof(Z) N → Ncleaved Ncleaved What is Cause? What is Consequence? These cause and effect relationships can be readily defined emperically, and are reported in the scientific literature along with the key evidence supporting the findings.

12. Drug Q N exp(A) kaof(X) Z {P@Y} catof(Z) N → Ncleaved Ncleaved Process G exp(B) X {P@Y} A exp(C) M {P@Y} taof(M) B exp(D) C D Causal Modeling • Capture biological and biochemical reactions using causal logic • Abstract reactions and ‘Cause – Effect’ relationships • Enable ‘Causal Reasoning’ on causal models • Logical evaluation of cause-effect relationships to reconstruct networks and define mechanisms • Integrate Biological Networks • Gene networks, protein networks, metabolic networks, all integrated into a single, functional network model

13. Diagnostic Reasoning (Reverse Causal Reasoning) Use causality to diagnose what happened (mechanisms) Requires experimental data and pre-existing knowledge Choose among the world of possibilities The more data and knowledge you have, the more accurate the results Predictive Inference (Forward Causal Reasoning) Use causality for predictions Must have great confidence in the causal relationships Efficiency of the system The better you define the cause – effect relationships the more accurate the results Cause Cause Diagnostic Reasoning Predictive Inference Effect Effect Reasoning Using Causality

14. Drug Q N exp(A) kaof(X) Z {P@Y} catof(Z) N → Ncleaved Ncleaved Process G exp(B) X {P@Y} A exp(C) M {P@Y} taof(M) B exp(D) C D Diagnostic Reasoning • Capture biological and biochemical reactions using causal logic • Abstract reactions and ‘Cause – Effect’ relationships • Enable ‘Causal Reasoning’ on causal models • Logical evaluation of cause-effect relationships to reconstruct networks and define mechanisms • Integrate Biological Networks • Gene networks, protein networks, metabolic networks, all integrated into a single, functional network model

15. Drug Q N exp(A) kaof(X) Z {P@Y} catof(Z) N → Ncleaved Ncleaved Process G exp(B) X {P@Y} A exp(C) M {P@Y} taof(M) B exp(D) C D Diagnostic Reasoning • Capture biological and biochemical reactions using causal logic • Abstract reactions and ‘Cause – Effect’ relationships • Enable ‘Causal Reasoning’ on causal models • Logical evaluation of cause-effect relationships to reconstruct networks and define mechanisms • Integrate Biological Networks • Gene networks, protein networks, metabolic networks, all integrated into a single, functional network model

16. Biologists Biologists Biologists Biological Expertise is Essential Analysis + Modeling = Context and Relevance Data Analysis Causal Modeling Mechanism Discovery

17. Knowledgebase of Biology • Compact representation of biological knowledge • Biological components and causal relationships • Inspired by the common sense reasoning project developed by Doug Lenat • Causal relationships are directed empirical facts • e.g. Kinase activity of AKT1 leads to phosphorylation of FOXO3A • Knowledge of 1000’s of Scientists captured, integrated and applied • 275,000 causal relationships in Human Knowledgebase • 35,000 references • Provides the Substrate for Automated Reasoning

18. Biology Specific Inferencing System Logical evaluation of cause-effect relationships to define molecular mechanisms Automatically generated Hypothesis • Diagnostic Reasoning (Reverse Causal Reasoning) • Use causality to diagnose what happened (molecular mechanisms) • Requires experimental data and pre-existing knowledge • Choose among the world of possibilities • The more data and knowledge you have, the more accurate the results • Predictive Inference (Forward Causal Reasoning) • Use causality for predictions • Must have great confidence in the causal relationships • Efficiency of the system • The better you define the cause – effect relationships the more accurate the results • Computer generated findings provide substrate for Biological Interrogation • Millions of Hypotheses evaluated nightly by the inferencing system • Hundreds of Hypotheses evaluated by Biological Staff • Dozens of Hypotheses integrated into defined molecular mechanisms Individual Hypotheses integrated into Coherent Biological Mechanism

19. The Value of the Network • Metcalfe’s Law • The value of a network increases with the number of connections • The human genome is a network of genes, proteins and metabolites • 20,000 genes, 100,000 protein forms, 5,000 metabolites • The value of a model of human biology grows with the number of defined connections • Genstruct’s knowledgebase currently contains 300,000 empirically derived connections • Up from 150,000 18 months ago, and 12,000 4 years ago Metcalfe’s Law

20. Why Does Causal System Modeling Work? • It captures the complexity of complete biological systems • The relationships between every gene, protein, metabolite and phenotype • It uses the right ‘level of abstraction’ for modeling • Only causal effects can be modeled at the scale of complete systems, but still maintain molecular detail • It combines the power of computation with the flexibility of human cognition • Artificial intelligence AND natural intelligence • It leverages high value, high-throughput experimental data • Gene expression profiling, protein profiling, phosphoprotein profiling, metabolite profiling, clinical chemistry, phenotypic analysis, etc. • It combines computation, human reasoning, and experimental data, exponentially expanding the ability to generate and test hypotheses • It uses artificial intelligence to augment human cognition • It leverages a vast collective intelligence to define molecular mechanisms

21. Systems Pharmacology Candidate compounds for target X Process level compound response • Define networks activated by each independent drug • Use systems approaches and causal system modeling to define biology of each drug in class • Competitive drug modeling • Causal analysis of compounds for the same target or for the same indication • Process level comparison of the effects of multiple compounds • Highlights differential mechanisms • Identifies commonalities in mechanism, despite variation in transcriptional response. Class 1 Common Processes Class 2 Competitive compounds for target X Competitor 1 Competitor 2

22. The Experiment • Goal: To define the underlying mechanisms for drug efficacy for EGF inhibitors • Experiment • Treat cancer cell lines with drug, and measure changes in gene expression, protein abundance, and protein phosphorylation • Build Causal System Models to define these mechanisms at the molecular level • Define the key on-target and off-target mechanisms

23. Causal Model of Cancer

24. Network Activated by EGF Receptor Antagonist 1

25. Network Activated by EGF Receptor Antagonist 2

26. Network Activated by EGF Receptor Antagonist 3

27. Blue = Drug 1 Yellow = Drug 2 Green = Drug 3 Black = 2 of the above Red = All three Union of all 3 networks activated by EGF drugs

28. Blue = Drug 1 Yellow = Drug 2 Green = Drug 3 Black = 2 of the above Red = All three Intersection of all 3 networks activated by EGF drugs

29. Key “on-target” effects activated by EGF drugs

30. Key “off target” effects of these drugs

31. The Combined “Systems Profile” for EGF Inhibition

32. Systems Pharmacology of Drug 1

33. Systems Pharmacology of Drug 2

34. Key Questions Answered • What is the molecular mechanism that leads to drug efficacy? • ie., What is the ‘Perfect Drug’? • How well does my drug fit the efficacy model? • How well does it correspond to the ‘perfect’ profile? • What are the key risk factors (off target effects) for the drug? • And how do they impact efficacy? • What are the key biomarkers for efficacy and stratification? • What do I screen against to find the ‘perfect drug’

35. Applications for Causal System Modeling • Defining drug mechanisms for efficacy • Defining drug mechanisms for toxicity • Defining response biomarkers • Defining stratification biomarkers • Defining toxicity biomarkers • Assessing the risk of ‘on-target’ and ‘off-target’ effects

36. Applications for Systems Pharmacology and Systems Toxicology • Assess risks of key molecular mechanisms of drugs • As individual drugs, and as a class • Eg., mechanism-based tox, non-mechanism based tox • Identify profiles for ‘perfect drugs’ • And key molecular tests needed to identify them • Examples of what can be avoided • Vioxx, Tysabri, Torcetrapib, Avandia, etc.

37. Current pharmacology When drugs fail, we often do not understand why We often cannot use the knowledge gained to assess other related drugs, or to develop new drugs Systems Pharmacology Define the mechanisms for efficacy, and define the risks associated with ‘on target’ and ‘off target’ effects Define the toxicity mechanisms in advance, to understand the risks Open the black box for each failure, and read the information Reading the “Black Box” of Pharmacology

38. Strategic Portfolio Management • Compare and contrast molecular mechanisms for compounds by modeling competitor drugs • Identify key strategic advantages for therapies of interest • Provides compelling insights for key development, clinical and marketing decisions • The more you know about your drug, the better you can position it in the marketplace

39. Cancer Drug Mechanisms Mechanisms for efficacy, resistance and toxicity Cholesterol drug efficacy mechanisms Clinical phase compound Drug Toxicity mechanisms Support for FDA filings Drug Efficacy Mechanisms Anti-angiogenensis drugs Drug Rescue Mechanisms to achieve approval Partnership Successes

40. Summary • Addressing a critical need in drug development • Identifying molecular mechanisms for Efficacy and Toxicity • A rich and refined knowledgebase of molecular biological reactions • Every gene, protein and metabolite in human, mouse and rat • Over 300,000 individual reactions to date • An efficient biological inferencing platform • Leveraging biological knowledge to interrogate global measurements to define molecular mechanisms • A proven methodology • Over 20 successful pharmacology modeling programs to date, with top tier pharma companies, innovative biopharma’s and leading academics • Proven success • Successful and growing partnerships with top tier pharma • Cash flow breakeven achieved in 2006

41. One Alewife Center Cambridge, MA 02140 (617)547-5421 kelliston@genstruct.com