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PwC: Pharma 2020: Virtual R&D (Mathematical Modeling )

Virtual Organs and Humans. Biology: Phenomenological Science Predictive Science. PwC: Pharma 2020: Virtual R&D (Mathematical Modeling ). The Virtual R&D Challenge. Complexity. Handling Complexity. Computers for Exploration Fabricate for Confirmation. The Joys of a Model.

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PwC: Pharma 2020: Virtual R&D (Mathematical Modeling )

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  1. Virtual Organs and Humans Biology:Phenomenological Science Predictive Science PwC: Pharma2020: Virtual R&D (Mathematical Modeling)

  2. The Virtual R&D Challenge • Complexity

  3. Handling Complexity • Computers for Exploration • Fabricate for Confirmation

  4. The Joys of a Model Explore Hypotheses

  5. Systems Perspectives: Self-protection

  6. Paradigm Change in Biology • ExperimentsComputersfor Exploration • Experiments for Confirmation

  7. Visualization, Mining & Prediction Interface Compound/Material DB Predictive Methods Compounds-Targets DB Activity, Binding Generate Novel Insights Predict consequence of hypothesis Profile “in silico” Design Experiments Targets DB KnowledgeBases Explore More, Experiment Less Design Experiments Experimental Data

  8. Building a Top-Down Systems Model increasing detail cascade of biological pathways linking target to clinical endpoints target clinical endpoint Liver Lobule Proteins Liver cells

  9. drug candidate toxic pathways toxic concentrations biomarkers etc assay results Assay Panel In Silico Liver Model Schematic of the Strand Virtual Liver

  10. Our Approach • Build a comprehensive model of liver homeostasis (normal or steady state) • Treat disease or toxicity as a case of drug/gene/environment induced perturbations • Computationally mine the network to identify key pathway (combinations) • Create assays that measure effect of drug/metabolite on the pathways • Predictive platform is a combination of assays and model

  11. Validation – HomeostasisActin Cytoskeleton module Rate of filament growth is linear and constant both at the pointed and the barbed end, Pollard, J. Cell Biol. 1986 (103) 2747-54

  12. Simulation 2 1 3 Validation – Effect of DrugGSH module Drug: Ethacrynic Acid (EA): Target Glutathione-S-transferase Experiment 1 3 2

  13. Validation – Genetic DiseaseBilirubin module

  14. Mechanistic Insights Combinatorial Effects • The impact of cyclosporine on BSEP activity along with other changes leads to bile salt accumulation in the serum1 • However, mere BSEP inhibition is not enough to produce large increases in bile salt accumulation • The effect is enhanced by concomitant reduction in intracellular ATP which greatly enhances the cholestatic effect2 1. Gastroenterology 93: 344-51, 1987 2. Gastroenterology 107: 255-65, 1994

  15. How is the Virtual Liver Used? • Assessing target liabilities • Does the target or similar/homologous targets have the potential of injuring the liver? • Chemical hazard & risk assessment • Test chemicals/series through the platform to understand potential risks • Problem solving (understanding toxic mechanisms) • Given there exists a problem in a molecule or lead series, understand what exactly the liability is and the potential to design around it • Use “omic” data, to generate testable hypotheses for the chemical’s impact on the liver

  16. SLS HepTox: Patented Prediction Platform of Drug Induced Liver Injury (DILI) with Mechanistic Insights Proof of concepts in progress with Amgen, Pfizer, J&J , Unilever etc. Identify Pathways Curate pathway parameters from literature Model and identify sensitive entities to measure Design appropriate assays to measure entities d [-GC] /dt = VGCS – VGS Feed measurements back to model and simulate for prediction U.S. Patent No. 8,645,075 A METHOD FOR PREDICTING ORGAN TOXICITY AND A SYSTEM THEREOF

  17. Levels of Biological InformationIntegrated Biology at Strand Ecologies Societies/Populations Individuals Organs Tissues Cells Protein and gene networks Protein interaction networks Protein mRNA DNA Hood

  18. Inflections

  19. Diagnostics for Precision Medicine Vision: Bring insights from sequencing to bear on disease, treatment & wellness Precision Medicine Right Drug Right Disease Right Time Right Dosage

  20. Rapidly Changing Standards of Care 2004 1987 • Precision Medicine for Lung Cancer 2009 Traditional

  21. Heralding Individualized Medicine Neuromuscular disorder Schwarz-JampelSyndrome or Sodium Channelopathy? Sequence relevant genes to rule out Schwartz-Jampel. X-linked Rare Disease Bangalore Fetal Medicine Centre Both children born with multiple disorders including systemic hypertension. Both died in second year of life. DNA samples of Trio (second born and parents). Diagnosis of culprit gene FLNA by a process of elimination. Parents have a third child – a healthy daughter – born August 2013 . Larry SmarrUCSD, Calit2Predictive, Preventive, Personalized,ParticipatoryP4 Medicine Nicholas Volker Wisconsin Medical One in a Billion Sequence helped to identify XLP and Gut Disorder. Cord blood transplant worked..

  22. Virtual Humans: Helping Facilitate Breakthroughs in Medicine AAAS Chicago 2014-02-14 sriram@nist.gov; jain@ics.uci.edu; henson@gwu.edu

  23. Virtual Humans: Symposium GoalAAAS 2014 To discuss current state of the art and future directions in computer-based modeling of humans as a hierarchy of systems and how these computational models are aiding new discoveries in medicine. Leroy Hood , Systems Medicine: Reactive to Proactive Transformation of Clinical Care Vijay Chandru, Virtual Liver: Towards Drug Discovery Using In Silico Biological Pathways Terrance Stewart, Whole-Brain Simulation for Modeling Neural Disorders and Diseases Peter V. Coveney, Computational Biomedicine: Towards the Virtual Human Nadia Magnenat-Thalman, Predicting Hip Deformations Through Computer Modeling Christian Jacob, Bringing Interactive Anatomy and Physiology into the Classroom

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