Causal Models for Drug and Biomarker Development

2. Topics The Complexity Challenge The Genstruct Approach

3. Topics The Complexity Challenge So much data, so little unified reasoning The Genstruct Approach

4. Topics The Complexity Challenge So much data, so little unified reasoning The Genstruct Approach A system that knows biological causes and can suggest reasons for effects seen in experiments

5. Systems Biology Systems Biology has as its ultimate goal, the development of a complete working model of the human body the structure and function of subcellular structures molecular networks in cells cellular interactions in organs and tissues the interactions of organs and so on Systems biology has been enabled by the ‘Omics revolution The development of a ‘catalog’ of parts (genes, proteins, metabolites) The ability to measure large numbers and types of molecules and their changes in a system The ability to use a systems approach to biology relies upon the implementation of an integrating, systems framework that can: Capture the complexity and richness of biology Can integrate high-throughput measurements Can generate testable biological hypotheses for experimentation That can scale to the scope of complete biological systems

6. Drug Development Process Current drug discovery and development assess the activity of drugs at the organism level By observing clinical phenotypes Current drug discovery and development assess the activity of drugs at the organism level By observing clinical phenotypes

7. Drug Development Process Drugs have their effects at the molecular level: agonism and antagonism of molecular targets There is no real attempt to understand its molecular function beyond simple protein binding We have little understanding of on and off target effects Drugs have their effects at the molecular level: agonism and antagonism of molecular targets There is no real attempt to understand its molecular function beyond simple protein binding We have little understanding of on and off target effects

8. Drug Development Process Drug development can be improved through the understanding of the molecular mechanisms of drugs Leading to less failures and more successesDrug development can be improved through the understanding of the molecular mechanisms of drugs Leading to less failures and more successes

9. The Causal Modeling Approach Computer-aided causal analysis provides a practical means of modeling biology using very large data sets The combination of comprehensive measurement with a causal model of biology will identify mechanisms of action, resistance and toxicity Causal System Modeling is an efficient method of identifying evidence-driven hypotheses that define biological mechanisms and downstream activities Explaining the general Genstruct approachExplaining the general Genstruct approach

10. Causal vs. Mathematical Approaches Causal Modeling Qualitative Comprehensive Scales to 100’s of thousands of concepts Models biology as whole networks Easily adapted and modified Rapid lifecycle Mathematical Modeling Quantitative Kinetic Data limited and expensive Difficult to scale to or beyond 100’s of concepts Models biology as circuits Time consuming

11. Causal Modeling of Molecular Mechanisms

12. Biological states can be predicted by reasoning through state changes

13. Biological States from panomic measurements enhance the predictive ability of the model

14. DEFINING THE HURDLES (and finding the right solutions)

15. The Problem of Complexity The more data you have, the worse the problem gets.The more data you have, the worse the problem gets.

16. The Challenge: Managing the Complexity of Knowledge Synthesis and Reasoning

17. A Matter of Scale

18. Genstruct Background Genstruct partners with pharmaceutical companies to advance their drug development programs. Genstruct employs a systems biology methodology that combines computational modeling with experimental evidence to define mechanisms and biomarkers. Genstruct has three top tier pharmaceutical partners and a developing pipeline. derive maximum value from –omics data generate novel, actionable scientific insights solve critical problems in drug discovery and development reducing or eliminating barriers to development of therapeutics derive maximum value from –omics data generate novel, actionable scientific insights solve critical problems in drug discovery and development reducing or eliminating barriers to development of therapeutics

19. The Genstruct Model Modeling Causal Framework for representing biochemical reactions Causal Reasoning methodology to define biological mechanisms Manipulation Large-scale omic’s experiments Genes, Proteins, Metabolites Measurement Biological State Changes Change in state of a biological process Mining Re-use causal relationships through Causal Knowledge Repository

20. The Genstruct Model A Computational Systems Modeling Platform Incorporates all critical molecular components for a biological problem of interest Maps qualitative functional relationships among the molecular components Builds a graphical network that integrates those relationships and supports computational modeling of complex biological knowledge A Hypothesis Generation Methodology Utilizes a computational system of automated reasoning Generates inferences through causal analysis Produces actionable end products: Testable Hypotheses

21. The Process Define specific scientific question Given the problem statement, a plausible question could be: Define the mechanism of action for compound X which will explain the observed glucose metabolism effects Design experiments to monitor the specific effects of the compound Identify state changes evidenced by the data as key readouts of molecular networks Explore each of the identified state changes for their regulation and downstream effects Define the key steps that lead to the different effects in different tissues Capture and evaluate the defined biology through causal system modeling Evaluate the Causal System Model for biomarkers for efficacy, resistance and toxicity The application of our approach in your problemThe application of our approach in your problem

22. REAL WORLD SUCCESSES

23. Summary of Successes Discovery Molecular mechanisms for type II diabetes Novel molecular switches controlling breast cancer tumor growth Molecular mechanisms for prostate cancer proliferation Safety / Tox Molecular mechanisms controlling drug-induced vascular injury Biomarkers for toxicity Development Molecular mechanisms for cancer drug resistance Molecular mechanisms of cardiovascular drug action

24. Novel Target Identification Mapping of disease networks and key control mechanisms Identification of druggable control points Lead Selection Model compound activities and differentiate compound sets Identify most efficacious compound with least adverse effects Safety / Toxicity Define response and toxicity mechanisms Identify biomarkers for assessment and stratification Clinical Development Identify mechanisms and markers for efficacy Identify mechanisms and markers for resistance Identify mechanisms and markers for toxicity Stratify patients and define early endpoints Commercial Successes Along the Value Chain

25. Commercial Projects Along the Value Chain

26. PDE-4 Inhibitor Toxicity: Mesenteric Vascular Injury & Inflammation in Rats

27. Drug-Induced Vascular Injury An Issue in Animal Toxicity Testing of Drugs froma Wide Range of Pharmacological Classes

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29. Causal Systems Model for PDE-4 Inhibitor-Induced Vascular Injury

31. Activated Pathways in PDE4-Inhibitor InducedVascular Injury and Inflammation

32. Summary Causal models can now successfully analyze omics data in light of known molecular relationships to drive hypothesis-driven, mechanism-based R & D. Genstruct is aiding drug and biomarker discovery by applying causal system modeling to a diverse range of biological and model systems in Oncology Metabolic Disorders Inflammation & Toxicology The models get even better as the knowledge base grows and as more types of data are generated

33. Genstruct’s Core Expertise Oncology Solid Tumors (breast, colo-rectal) Prostate Cancer Cancer Biomarkers & Drug Resistance Mechanisms Metabolic Disorders Type II Diabetes Metabolic Syndrome Dyslipidemia Inflammation Atherosclerosis Vascular Inflammation

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