Preston Hensley Skolkovo biotech vision

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  • All the 4 factors are known to be involved in liver metabolism. C/EBP alpha is a main regulator of all three categories and FOXA1 and 2 are more associated mainly with glucose metabolism. PPAR alpha is associated with lipid metabolism and fatty acid uptake. Under each factor at the two conditions, you will find the no. of bound genes that are associated with categories. The arrows represents the association of genes bound by the factor to the categories. The numbers in the end of each arrow represent the number of genes bound by the factor in each category. Over all you may interpret that since C/EBP alpha do not bind genes in those three categories under the insulin resistance state (type 2 diabetes mellitus), and that the number of bound genes by the other factors (except FOXA2) is reduced, there is a significant reduction in the transcription of genes that normally expressed in the normal condition. The fact that FOXA2 binds more genes in the Insulin resistance state (type 2 diabetes mellitus), is not sufficient to overcome the lack of the other factors (except FOXA1).
  • Here is a comparison table between the IHC score and AQUA system. For IHC score, pathologists distinguish the tumour areas in tissue through morphological changes or patterns. The +1, +2 and +3 positivity is subject to the person scoring. As a result the scoring is discrete and nominal. In addition, it take a long time to score a batch of TMA. However, for the AQUA system most of the drawbacks of the IHC can be overcome. It’s directly quantitative and capable of calculations based on colocalisation. The time consumed is also reduced significantly.
  • Preston Hensley Skolkovo biotech vision

    1. 1. Vision for Skolkovo Biotech Sector – Pharma Discovery Preston Hensley Lotus Translational Medicine, LLC 3 February 2011 Skolkovo Vision
    2. 2. What are the drivers? Chronic disease impacts US national economy 3 February 2011 Skolkovo Vision Combined treatment and productivity costs for US in 2003 Milken Institute 2008 Total: 1.3 T US$
    3. 3. How Big is 1.3 T$ per Year? (US Numbers, $ per year) <ul><li>1/10 th of entire GDP – 14 T US$ </li></ul><ul><li>~ 1000 Dallas Cowboys Football Stadiums (1.3 B$ ea) - 20 in each state of the USA </li></ul><ul><li>> 3X Dependence on foreign oil (420 B$) </li></ul><ul><li>> 6X Wars in Iraq and Afghanistan (190 B$) </li></ul><ul><li>~ 2X Banking industry bailout (700 B$) </li></ul><ul><li>> 20 X Automotive industry bailout (60 B$) </li></ul><ul><li>> Economic stimulus package (~1,000 B$) </li></ul>3 February 2011 Skolkovo Vision
    4. 4. What are the drivers? Unmet therapeutic need 3 February 2011 Skolkovo Vision Unmet Need – Unrealized profit Adverse Events – Cost Ineffective drugs - Waste
    5. 5. This is a big issue 3 February 2011 Skolkovo Vision Jerel Davis, McKinsey & Co http://www.dnapolicy.org/images/issuebriefpdfs/PGx%20IB.pdf Aspect Numeric 2008 Rx spend, US 292 B US$ (of 800 B US$ WW) Percent Rx not effective 20 – 90% (average 50%) Adverse events, US ~2,000,000 Fatal adverse events, US ~100-125,000 Cost adverse drug events, US 45-135 B US$ Percent events avoidable 20 – 35% <ul><ul><li>Percent of care decisions informed by diagnostics </li></ul></ul>70% <ul><ul><li>Diagnostics as percent of healthcare market </li></ul></ul>3-4%
    6. 6. US Drivers: Summary 3 February 2011 Skolkovo Vision
    7. 7. Confounding issue: High failure rate/cost to discover new medicines 3 February 2011 Skolkovo Vision High risk process 1/100 ideas get to market 12-15 years Fully amortized cost US$3.9B per drug
    8. 8. Productivity of pharma industry 3 February 2011 Skolkovo Vision ~20 NMEs / yr ~ 7 NMEs / yr Bernard Munos, NATURE REVIEWS | Drug Discovery VOLUME 8 | DECEMBER 2009 | 963
    9. 9. Discovery steps largely in good shape 3 February 2011 Skolkovo Vision HP Prang: Drug Discovery and Development
    10. 10. Reason for failure: complex biology of safety and efficacy is not well understood 3 February 2011 Skolkovo Vision <ul><li>Reasons for failure: </li></ul><ul><li>30% Efficacy </li></ul><ul><li>30% Clinical Safety and Toxicology </li></ul><ul><li>20% Commercial </li></ul><ul><li>20% other reasons </li></ul>I Kola, CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 83 NUMBER 2 | FEBRUARY 2008
    11. 11. US National Resource Committment <ul><li>Efficacy – NIH budget, ~30 B US$ </li></ul><ul><li>Safety – fraction of FDA budget, ~0.160 B US$ </li></ul><ul><li>Safety/tox failure = efficacy failure </li></ul><ul><li>Safety/toxicology support off by a factor of nearly 200 </li></ul>3 February 2011 Skolkovo Vision
    12. 12. Outline of plan 3 February 2011 Skolkovo Vision
    13. 13. Where to focus? Causes of death (US, 2005) 3 February 2011 Skolkovo Vision Jernal, et al., CA Cancer J Clin 2008;58;71-96,Feb 20, 2008
    14. 14. Breast Cancer in Russia <ul><li>23,718.5 deaths per year </li></ul><ul><li>16.5 deaths per 100,000 persons per year ( twice world average, 7.7 ) </li></ul><ul><li>288,048 life years lost per year </li></ul><ul><li>200 life years per 100,000 persons per year </li></ul>3 February 2011 Skolkovo Vision
    15. 15. What have we learned from cancer genomics? <ul><li>GWAS </li></ul><ul><li>‘ Although statistically compelling associations have been identified, there is an enormous gap in the ability to provide the biological explanation for why a genomic interval tracks with a complex trait.’ </li></ul><ul><li>Kelly A. Frazer </li></ul><ul><li>Scripps Translational Science Institute and The Scripps Research Institute </li></ul><ul><li>Cancer genomics </li></ul><ul><li>‘ Therefore, these genetic analyses can only identify candidate genes that may play a role in cancer and do not definitively implicate any gene in the neoplastic process.’ </li></ul><ul><li>Bert Vogelstein </li></ul><ul><li>Johns Hopkins Kimmel Cancer Center </li></ul>3 February 2011 Skolkovo Vision Cancer genomics ‘ What necessarily follows will be the detailed functional characterization of individual candidate cancer genes, to determine whether and how they contribute to a tumorigenic phenotype. Daphne Bell National Human Genome Research Institute, NIH
    16. 16. Global, unbiased, discovery technologies to more deeply understand biology 3 February 2011 Skolkovo Vision 100’s 10,000’s 1000’s <ul><li>Other Omics </li></ul><ul><li>Metabolomics </li></ul><ul><li>RNAi screens </li></ul><ul><li>Lipidomics </li></ul><ul><li>Global Protein </li></ul><ul><li>Turnover </li></ul><ul><li>Etc. </li></ul>
    17. 17. Global Unbiased Aceto-Proteomics Can Now Significantly Increase Resolution <ul><li>Mouse adipocytes + insulin </li></ul><ul><li>> 100 proteins change acetylation state </li></ul><ul><li>Mostly non-histone </li></ul>15 January 2010 Fraunhofer Center for Molecular Biotechnology Forest White, MIT
    18. 18. ResponseNet algorithm for identifying response networks 3 February 2011 Skolkovo Vision Ernest Fraenkel, Nat Genet. 2009 March, 41(3) : 316–323 Assay Genomic Data Proteomic Data Probabilistic interactome Computational tools
    19. 19. Chip-Seq Data for Four Transcription Factors Ernest Fraenkel MIT 15 January 2010 Fraunhofer Center for Molecular Biotechnology
    20. 20. Study hepatotoxicity using LiverChip technology 3 February 2011 Skolkovo Vision Steve Tannenbaum, Linda Griffith, MIT
    21. 21. Phenotype-driven Rx and Dx discovery Drug Targets Are Proteins 3 February 2011 Skolkovo Vision Genome T T T T T T T T Complex Biology Phenotype-driven Drug Discovery In Cancer, Genome is Altered T Primary Cancer Targets Appear Proteome Drug Resistance Resistance Targets Appear X Proteome T T T T
    22. 22. Tamoxifen and Breast Cancer Resistance 3 February 2011 Skolkovo Vision Based on Narmanno et al. Endocrine-Related Cancer 2005
    23. 23. Experimental approaches: Quantitative phosphoproteomics 3 February 2011 Skolkovo Vision Forest White, MIT Follow time course under multiple conditions
    24. 24. Globally follow many 100’s of events – Quantitative time courses 3 February 2011 Skolkovo Vision Forest White, MIT
    25. 25. New RTK signaling seen with Tamoxifen resistance – directly defines a new target class 3 February 2011 Skolkovo Vision Forest White, MIT Indicates an increase in tyrosine phosphorylation as a result of acquiring Tamoxifen resistance. Defines SRC as a potential resistance target. SRC-directed therapeutics may revert Tamoxifen resistance.
    26. 26. SRC-directed therapeutics revert Tamoxifen resistance – functionally define a new target and therapy class 3 February 2011 Skolkovo Vision Forest White, MIT
    27. 27. Phenotype directs target/lead, toxicity pathway and biomarker identification 3 February 2011 Skolkovo Vision
    28. 28. Lack of therapeutic efficacy affects large populations 3 February 2011 Skolkovo Vision Unmet Need – Unrealized profit Adverse Events – Cost Ineffective drugs - Waste
    29. 29. Solution: Diagnostics to stratify populations by drug response 3 February 2011 Skolkovo Vision Diagnostic test positive Likely to benefit from therapy Diagnostic test negative Not likely to benefit from therapy Toxicity test positive Likely to have toxic response Dx+ Dx- Tx+
    30. 30. Genomic Dx methods have had success: Oncotype DX ® segregates breast cancer populations 3 February 2011 Skolkovo Vision
    31. 31. Other Commercially Available Genomic Assays 3 February 2011 Skolkovo Vision Christos Sotiriou and Lajos Pusztai, n engl j med 360, february 19, 2009
    32. 32. Quantitative immunofluorescence tools can stratify patients 3 February 2011 Skolkovo Vision Recurrence score independent of grade and stage in multivariate analysis – added value to histopathology Algorithm being optimised for AQUA technology Further validation in independent cohort from another institution David Harrison and Dana Faratian
    33. 33. Quantitative PTEN protein expression is associated with Trastuzumab resistance in vivo 3 February 2011 Skolkovo Vision AQUA fluorescent analysis of PTEN expression in a TMA core, showing mainly cytoplasmic localization of PTEN (red) and masking of tumor areas for quantification by cytokeratin (green) Kaplan-Meier survival curves for patients treated with Trastuzumab for low (blue) and high (red) protein expression of PTEN. Dana Faratian, et al ., Cancer Res 2009; 69: (16). August 15, 2009 Low PTEN High PTEN PTEN is a tumor suppresser
    34. 34. Stratification using aptamer-based serum proteomic platform <ul><li>Highly multiplexed,>1000-plex today </li></ul><ul><li>Works on cells, tissues, serum </li></ul><ul><li>300–500 patient samples per day </li></ul><ul><ul><li>Equivalent to 425,000 ELISAs per day </li></ul></ul><ul><li>Needs ~14 µl sample </li></ul><ul><li>3-4 log unit dynamic range </li></ul><ul><li>10 -14 M lower limit of detection </li></ul><ul><li>Customizable Arrays – 200 new aptamers in 2-3 months </li></ul>3 February 2011 Skolkovo Vision SomaLogic
    35. 35. Aptamers Unlock Biomarker Discovery in The Human Proteome SomaLogic 3 February 2011 Skolkovo Vision
    36. 36. Multiplexed aptamer technology for clinical Dx discovery SomaLogic 3 February 2011 Skolkovo Vision
    37. 37. Example: Lung Cancer Diagnostic SomaLogic 3 February 2011 Skolkovo Vision
    38. 38. Aptamer-based diagnostics being developed <ul><li>Oncology </li></ul><ul><ul><li>Lung cancer   </li></ul></ul><ul><ul><li>Pancreatic cancer   </li></ul></ul><ul><ul><li>Ovarian cancer   </li></ul></ul><ul><ul><li>Mesothelioma     </li></ul></ul><ul><li>Neurology </li></ul><ul><ul><li>Amyotrophic lateral sclerosis (ALS)   </li></ul></ul><ul><ul><li>Depression   </li></ul></ul><ul><ul><li>Alzheimer's disease   </li></ul></ul><ul><ul><li>Parkinson's Disease   </li></ul></ul><ul><ul><li>Multiple sclerosis (MS)     </li></ul></ul><ul><li>Cardiovascular Disease </li></ul><ul><ul><li>Cardiovascular event prediction   </li></ul></ul><ul><ul><li>Chronic renal failure   </li></ul></ul><ul><ul><li>Coronary artery disease   </li></ul></ul><ul><ul><li>Obesity </li></ul></ul>3 February 2011 Skolkovo Vision SomaLogic
    39. 39. Not all pathologies will be equally stratifiable 3 February 2011 Skolkovo Vision Trusheim, et al., Nat Rev Drug Discov 6 , 287-293, 2007. Biology Economics
    40. 40. Recommend large scale longitudinal bio-banking effort <ul><li>‘ Framingham Study’ </li></ul><ul><li>10’s of thousands of individuals </li></ul><ul><li>Multiple locations </li></ul><ul><li>Many decades </li></ul><ul><li>What is normal? </li></ul><ul><li>Aging? </li></ul><ul><li>Disease progression? </li></ul><ul><li>Therapeutic response? </li></ul><ul><li>Adverse events? </li></ul><ul><li>Multiple omics </li></ul>3 February 2011 Skolkovo Vision Watson, Kay and Smith Nature Reviews Cancer September 2010 . 10 , p646
    41. 41. Summary 3 February 2011 Skolkovo Vision
    42. 42. Summary, cont. <ul><li>Goals achievable using validated methods </li></ul><ul><li>Translational medicine - cell biology to clinical impact </li></ul><ul><li>Paradigm shift – focus on phenotype (proteomics) as discovery approach </li></ul><ul><li>Generate targets, therapy and clinically validated diagnostics in short time frame </li></ul><ul><li>Discoveries readily commercializable </li></ul>3 February 2011 Skolkovo Vision
    43. 43. Thanks <ul><li>Forest White </li></ul><ul><li>Ernest Fraenkel </li></ul><ul><li>Steven Tannenbaum </li></ul><ul><li>Doug Lauffenburger </li></ul><ul><li>Nick Saccomano </li></ul><ul><li>David Harrison </li></ul><ul><li>Dana Faratian </li></ul><ul><li>Igor Goryanin </li></ul><ul><li>Gordon Mills </li></ul><ul><li>Brian Hennessy </li></ul><ul><li>Alex Polinsky </li></ul>3 February 2011 Skolkovo Vision
    44. 44. Individual breast cancer tumors have cells with distinct lineages 3 February 2011 Skolkovo Vision Nicholas Navin, et al., Genome Res. 2010 20 : 68-80 Human breast cancers display mono- and polyclonal evolution
    45. 45. Response of metastatic breast cancer to single-agent systemic therapy 3 February 2011 Skolkovo Vision Gonzalez-Angulo, et al ., Adv. Exper. Med. and Biol. 608, 1-22, 2007 Drug Response Rate, % Capecitabine 20 – 36 Docetaxel 18 – 68 Doxorubicin 25 – 40 Gemcitabine 14 – 37 Paclitaxel 17 – 54 Vinorelbine 25 – 47 Tamoxifen 21 – 41 Aromatase inhibitors 10 – 20 Trastuzumab (Herceptin) 12 - 34
    46. 46. Pathway analysis approaches 3 February 2011 Skolkovo Vision Dana Faratian, et al ., Cancer Res 2009; 69: (16). August 15, 2009 Kinetic (or dynamic) computational models offer the opportunity to cheaply and efficiently test the efficacy of targeted therapies in silico (i.e., computationally), as part of the preclinical testing process
    47. 47. Biomarkers for immunofluorescence analysis from reverse phase protein arrays - RPPAs 3 February 2011 Skolkovo Vision RPPA protein identification HistoRx AQUA quantification Dana Faratian
    48. 48. Spectrum of Computational Tools To Link Observations 3 February 2011 Skolkovo Vision
    49. 49. Reason: humans are biologically heterogeneous <ul><li>Phenotype is a function of a complex mixture of </li></ul><ul><li>Genetics </li></ul><ul><li>Environment </li></ul><ul><li>Life choices </li></ul><ul><li>Underlying biology </li></ul><ul><li>Extreme example - genetically identical humans are phenotypically non-identical </li></ul>3 February 2011 Skolkovo Vision Lee Hood, Institute for Systems Biology Left Index Fingerprints from Identical Twins Twins 1 Twins 2 Tim Spector, King’s College London, www.TwinsUK.ac.uk
    50. 50. No measured trait correlates 100% between identical twins <ul><li>Freckles 90% </li></ul><ul><li>Myopia 90% </li></ul><ul><li>Acne 80% </li></ul><ul><li>Height 80% </li></ul><ul><li>Osteoporosis 75% </li></ul><ul><li>Diabetes 70% </li></ul><ul><li>Obesity 70% </li></ul><ul><li>Blood clotting 70% </li></ul><ul><li>Back pain 65% </li></ul><ul><li>IQ 65% </li></ul><ul><li>Asthma , allergy 60% </li></ul><ul><li>Arthritis (OA) 60% </li></ul><ul><li>Cataracts 60% </li></ul><ul><li>Motion sickness 60% </li></ul><ul><li>Naevus count 55% </li></ul><ul><li>Pain threshold 55% </li></ul><ul><li>Migraine 50 % </li></ul><ul><li>Varicose veins 50% </li></ul><ul><li>Menopause 50% </li></ul><ul><li>Blood pressure 50% </li></ul><ul><li>Menarche 40% </li></ul>3 February 2011 Skolkovo Vision Tim Spector, King’s College London, www.TwinsUK.ac.uk
    51. 51. Similarly with protein blood levels – correlation is not 100% <ul><li>CRP, IL-6 – 40% </li></ul><ul><li>Vitamin D – 40% </li></ul><ul><li>Factor V, VIII, XIII - 70% </li></ul><ul><li>Collagen cross links - 50% </li></ul><ul><li>Alkaline phosphatase –75% </li></ul><ul><li>Creatinine - 50% </li></ul><ul><li>Platelet count - 65% </li></ul><ul><li>CD4/CD8 ratios - 60% </li></ul><ul><li>White Cell apoptosis rates - 65% </li></ul><ul><li>Platelet counts - 50% </li></ul><ul><li>Leptin - 70% </li></ul><ul><li>Glucose - 51% </li></ul><ul><li>Insulin - 60% </li></ul><ul><li>HOMA - 57% </li></ul><ul><li>HbA1c - 60% </li></ul><ul><li>T4, T3 - 63% </li></ul><ul><li>Telomere length - 40% </li></ul><ul><li>HbF – 50% </li></ul><ul><li>WCC- 55% </li></ul><ul><li>Response to folate - 55% </li></ul>3 February 2011 Skolkovo Vision Tim Spector, King’s College London, www.TwinsUK.ac.uk
    52. 52. Conclusions <ul><li>The linkage between genotype and phenotype is complex and indirect </li></ul><ul><li>The linkage will not easily be deconvoluted </li></ul><ul><li>It is modulated by environment and life choices </li></ul>3 February 2011 Skolkovo Vision
    53. 53. New opportunities for value creation <ul><li>As a gatekeeper to patients , the diagnostic becomes a portal through which subsequent therapies must pass </li></ul><ul><li>It can promote initial adoption of a particular therapy, potentially expanding the value of the market for the therapy </li></ul>3 February 2011 Skolkovo Vision Trusheim, et al., Nat Rev Drug Discov 6 , 287-293, 2007.
    54. 54. Diagnostics can take many forms <ul><li>gene mutations </li></ul><ul><li>gene expression patterns </li></ul><ul><li>proteins </li></ul><ul><li>proteomic patterns </li></ul><ul><li>metabolomics </li></ul><ul><li>histology </li></ul><ul><li>imaging </li></ul><ul><li>physician’s clinical observations </li></ul><ul><li>self-reported patient surveys </li></ul>3 February 2011 Skolkovo Vision
    55. 55. General Mechanisms of Resistance to Systemic Therapy 3 February 2011 Skolkovo Vision Gonzalez-Angulo, et al ., Adv. Exper. Med. and Biol. 608, 1-22, 2007
    56. 56. ResponseNet algorithm for identifying response networks 3 February 2011 Skolkovo Vision Ernest Fraenkel, Nat Genet. 2009 March, 41(3) : 316–323 Assay Genomic Data Proteomic Data Probabilistic interactome Computational tools
    57. 57. What does this study show? <ul><li>Phenotype-based, functional approaches lead directly to novel drug targets </li></ul><ul><li>Novel targets may already have chemical matter </li></ul><ul><li>Proteomic signature may become mechanism-based biomarkers </li></ul><ul><li>Biomarkers may become diagnostics </li></ul>3 February 2011 Skolkovo Vision
    58. 58. Competitive advantage, especially with multiple potential resistance mechanisms 3 February 2011 Skolkovo Vision Standard Approach Enabled Approach <ul><li>Come to market with new chemo-therapeutic </li></ul><ul><li>Know likely resistance mechanisms </li></ul><ul><li>These define </li></ul><ul><li>companion therapies </li></ul><ul><li>companion diagnostics </li></ul>Rx Rx
    59. 59. Mechanism-based Biomarker and Dx discovery 3 February 2011 Skolkovo Vision
    60. 60. Applications for biomarker technology 3 February 2011 Skolkovo Vision SomaLogic
    61. 61. Cell biology to clinic 3 February 2011 Skolkovo Vision Complex Biology Phenotype-driven Drug Discovery Continue
    62. 62. AQUA® technology brings standardization and reproducibility to immunofluorescence analysis 3 February 2011 Skolkovo Vision http://www.historx.com
    63. 63. AQUA technology advantages http://www.historx.com 3 February 2011 Skolkovo Vision
    64. 64. Supporting technology: Reverse phase protein arrays 3 February 2011 Skolkovo Vision 100 unique proteins Dana Faratian
    65. 65. Protein predictor for breast cancer recurrance validated using HistoRx AQUA Technology <ul><li>Aim: To develop a protein-based Recurrence Score for ER+, node-negative, Tamoxifen-treated breast cancer patients </li></ul><ul><li>Discovery phase using 86 target RPPA panel </li></ul><ul><li>Three protein predictor validated using HistoRx AQUA Technology (over 1500 patients) </li></ul><ul><li>Algorithm stratifies 75% of patients into good prognosis group – might not require adjuvant chemotherapy </li></ul>3 February 2011 Skolkovo Vision Dana Faratian
    66. 66. Patient stratification using pathway analysis approaches 3 February 2011 Skolkovo Vision Dana Faratian, et al ., Cancer Res 2009; 69: (16). August 15, 2009 Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico In cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy Other targets also identified A similar result was found in a cohort of 122 breast cancers treated with Trastuzumab
    67. 67. Expanded tissue analysis platform: AQUA® brings standardization and reproducibility 3 February 2011 Skolkovo Vision http://www.historx.com
    68. 68. Advantages <ul><li>High-throughput </li></ul><ul><li>High-sensitivity </li></ul><ul><li>Highly quantitative </li></ul><ul><li>Low CVs </li></ul><ul><li>Replicates </li></ul><ul><li>Wide dynamic range </li></ul><ul><li>Small sample volumes spot 100s slides </li></ul><ul><li>Multiplex phospho:total on same slide (Odyssey) </li></ul>3 February 2011 Skolkovo Vision
    69. 69. SOMAmers Detected by Hybridization to Probes Printed on a Highly Multiplexed Array 3 February 2011 Skolkovo Vision SomaLogic
    70. 70. Fluorescent aptamers may bring increased resolution to immunofluorescence/IHC analyses 3 February 2011 Skolkovo Vision Bryan P. Schneider, et al., Clin Cancer Res 2008;14(24)
    71. 71. Data sharing 3 February 2011 Skolkovo Vision
    72. 72. Data Analysis, Visualization and Sharing Capabilities Giles Day 3 February 2011 Skolkovo Vision Infrastructure and Business Systems Scientific Systems and Methods Collaboration 1 2 3 Value, Complexity & Resources 3 2 1  Lab automation, sample logistics  Experimental data capture, processing and analysis  Decision support  Data mining and visualization  Compound design & analysis methods  Knowledge management  New technologies and methods aligned with BBC scientific goals  Including academic collaborations  Infrastructure to enable a global community of scientists to organize, share and mine scientific findings across Skolkovo  Desktops, servers, networks, storage, communications  Email, finance, HR, portfolio, Digital Library, intranet…  Site integrations, future acquisitions  collaborations (includes monitoring portfolio & progress against goals)
    73. 73. Commercial progression 3 February 2011 Skolkovo Vision This Project RVC Seed Investments Fund RVC venture funds (Maxwell, Bioprocess) ROSNANO RVC – Russian Venture Company New Targets Rx, Dx Discovery External Biotech Drug Develop-ment Market
    74. 74. Russian scientists involvement <ul><li>Russian Academy of Science, Universities, </li></ul><ul><li>Russian Academy of Medical Sciences, </li></ul><ul><li>Target validation, in vitro assays, pharmacology, in vivo models </li></ul><ul><li>Medicinal, computational, peptide, protein, nucleic acids chemistries </li></ul><ul><li>ChemRar, ChemBridge, new companies </li></ul><ul><li>HTS, high content screens, secondary screens </li></ul><ul><li>ADME, early safety </li></ul><ul><li>Animal preclinical safety and efficacy models </li></ul><ul><li>Medical Academies and Universities </li></ul><ul><li>Phase I-III clinical trials </li></ul>3 February 2011 Skolkovo Vision
    75. 75. Impact for Russia <ul><li>Seed and develop innovative Russian Pharma industry </li></ul><ul><li>Contributes to the implementation of Pharma 2020 strategy </li></ul><ul><li>Substitution of imported drugs with own drugs </li></ul><ul><li>Connect Russian academic scientists in the fields of advanced molecular biology, biophysics and biochemistry with cutting edge technologies from US and Europe </li></ul><ul><li>Development of Russian advanced technologies that can compete worldwide </li></ul><ul><li>Learning to apply modern technologies for innovative drug discovery and development </li></ul><ul><li>Address major Russian health needs – such as lung cancer, breast cancer, heart disease, diabetes – with innovative drugs developed in Russia </li></ul>3 February 2011 Skolkovo Vision
    76. 76. Impact to economy in different industries <ul><li>Pharmaceutical manufacturing under GMP </li></ul><ul><li>GMP manufacturing will attract major pharma to RU </li></ul><ul><li>Develop GMP CRO industry </li></ul><ul><li>GLP preclinical drug testing </li></ul><ul><li>Develop GLP CRO industry </li></ul><ul><li>Diagnostics development </li></ul><ul><li>Attract electronic, IT support technologies </li></ul><ul><li>Attract device manufacturing and informatics technologies </li></ul>3 February 2011 Skolkovo Vision
    77. 77. Establish Skolkovo Enterprise Center <ul><li>Skolkovo, as a center for the discovery and commercialization of new medicines </li></ul><ul><li>Provide resources to enable students and faculty to design and launch successful commercial ventures based on medical innovations developed at Skolkovo </li></ul><ul><li>Expand broadly to any technology </li></ul><ul><li>See http://entrepreneurship.mit.edu/MITER/video.html </li></ul>3 February 2011 Skolkovo Vision
    78. 78. Back up slides 3 February 2011 Skolkovo Vision
    79. 79. Genetic approaches - GWAS Impact not fully realized <ul><li>904 Associations </li></ul><ul><li>165 traits </li></ul><ul><li>~3 M SNPs </li></ul><ul><li>Crohn’s disease , </li></ul><ul><ul><li>71 associated </li></ul></ul><ul><ul><li>markers account for less than ~20% of genetic variance </li></ul></ul><ul><li>Height - 44 associated markers account for ~5% of genetic variance </li></ul>3 February 2011 Skolkovo Vision www.genome.gov/gwastudies .
    80. 80. Overlap of genetic risk factor loci for common diseases 3 February 2011 Skolkovo Vision Complex diseases are complex Kelly A. Frazer, et al., NATURE REVIEWS | GENETICS 2009 |
    81. 81. Multi-gene Oncotype DX expression panel – prognostic tool for breast cancer recurrence <ul><li>Helps physicians provide more personalized treatment decision for their patients. </li></ul><ul><li>Predicts magnitude of chemotherapy benefit </li></ul><ul><li>Quantifies the likelihood of breast cancer recurrence </li></ul><ul><li>Is the only multi-gene expression assay included in ASCO and NCCN guidelines </li></ul><ul><li>Is scientifically validated, extensively ordered and widely reimbursed </li></ul>3 February 2011 Skolkovo Vision genomic health – www.oncotypedx.com
    82. 82. Mutations in KRAS abrogate EGFR inhibition by mAb colorectal cancer therapies 3 February 2011 Skolkovo Vision Arch Pathol Lab Med—Vol 133, October 2009 KRAS Testing in Metastatic Colorectal Cancer—Monzon et al
    83. 83. Only a fraction of the genetic components of common diseases and traits have been identified 3 February 2011 Skolkovo Vision *AMD – Age related macular degeneration Wei Chen, et al. PNAS 2010 (AMD); Andre Franke, et al., Nature Genetics 2010 (Crohn’s disease); Benjamin F. Voight et al., Nature Genetics 2010 (type 2 diabetes); Hana Lango Allen et al., Nature Genetics 1020 (height) Disease/ Trait Overall Heritability, % Overall Heritability Explained, % Gene Locations Identified AMD* 45 - 70 50 12 Crohn’s Disease 50 - 60 23 71 Type 2 Diabetes 30 - 70 10 38 Height 60 - 80 15 180
    84. 84. A proteomics approach looks at phenotype - Move directly to Rx and Dx 3 February 2011 Skolkovo Vision Rx cRx 1 cDx 1 Phospho- pattern Pattern is the Companion Diagnostic Companion Rx, Restores Sensitivity Resistance Mechanisms POC demonstrated in breast cancer – Forest White, MIT Resistant phenotype Tumor cell - Sensitive phenotype
    85. 85. Drug response is heterogeneous because the disease is heterogeneous 3 February 2011 Skolkovo Vision Bryan P. Schneider, et al., Clin Cancer Res 2008;14(24)
    86. 86. The disease is heterogeneous 3 February 2011 Skolkovo Vision Bryan P. Schneider, et al., Clin Cancer Res 2008;14(24) Genetic heterogeneity Phenotypic heterogeneity
    87. 87. Reverse phase protein arrays 3 February 2011 Skolkovo Vision
    88. 88. Global Unbiased Discovery Biology Global Biomarker Discovery Global Unbiased Discovery Toxicology Academic Pharma Partner <ul><li>New targets </li></ul><ul><li>New chemical matter </li></ul><ul><li>Safety and efficacy biomarkers </li></ul><ul><li>Mechanism based diagnostics </li></ul>Academic Commercial Large Scale Longitudinal Bio-banking Commercial Academic Computational Infrastructure Commercial Academic
    89. 89. Drug response is heterogeneous because the disease is heterogeneous 3 February 2011 Skolkovo Vision Bryan P. Schneider, et al., Clin Cancer Res 2008;14(24) David Botstein, et al., Molecular Interventions 2(2) 101-109 (2002)
    90. 90. Use biomarkers to relate tumor phenotype to clinical outcome <ul><li>Validate candidate biomarkers identified in cell-based studies </li></ul><ul><ul><li>Phosphoproteomics </li></ul></ul><ul><ul><li>Pathway analysis approaches </li></ul></ul><ul><ul><li>ResponseNet and network reconstruction </li></ul></ul><ul><ul><li>Reverse phase protein analysis – RPPA </li></ul></ul><ul><ul><li>Multiplexed aptamer approaches </li></ul></ul><ul><li>Validate candidate biomarkers identified in tumor-based studies </li></ul><ul><ul><li>Reverse phase protein analysis – RPPA </li></ul></ul><ul><ul><li>Multiplexed aptamer approaches </li></ul></ul>3 February 2011 Skolkovo Vision
    91. 91. Skolkovo Technopolis 3 February 2011 Skolkovo Vision
    92. 92. Classification of breast cancer subtypes using to IHC stratification tools 3 February 2011 Skolkovo Vision David Huntsman, et al ., PLoS Medicine May 2010 | Volume 7 | Issue 5 | 78% 92% 8% 22% 6% 16% 58% 42%
    93. 93. This project: Two pronged focus 3 February 2011 Skolkovo Vision Short Term Goal Long Term Goal Breast Cancer
    94. 94. Cancer has complex genetic origins: alterations in 24 pancreatic cancers 3 February 2011 Skolkovo Vision Siân Jones, Vogelstein, et al., Science 321, 1801 (2008)
    95. 95. <ul><li>‘ Rather than seeking agents that target specific mutated genes, agents that broadly target downstream mediators or key nodal points may be preferable.’ </li></ul><ul><li>- Bert Vogelstein </li></ul><ul><li>But … how will those nodes be identified ? </li></ul>Gene alterations map to 12 pathways and processes 3 February 2011 Skolkovo Vision Siân Jones, Vogelstein, et al., Science 321, 1801 (2008)
    96. 96. Insights into the genetic basis of type 2 diabetes (T2D): GWAS 3 February 2011 Skolkovo Vision Adapted from Kelly A. Frazer, et al., NATURE REVIEWS | GENETICS 2009 | and Benjamin F. Voight et al., Nature Genetics 2010. 38 genomic intervals confer increased risk to T2D in Caucasians They explain less than 10% of the total variance Predicted that >85 associations are required to fully explain variance
    97. 97. Genetic components of common diseases identified so far 3 February 2011 Skolkovo Vision *AMD – Age related macular degeneration Wei Chen, et al. PNAS 2010 (AMD); Andre Franke, et al., Nature Genetics 2010 (Crohn’s disease); Benjamin F. Voight et al., Nature Genetics 2010 (type 2 diabetes); Hana Lango Allen et al., Nature Genetics 1020 (height)
    98. 98. Genomic Dx Success: KRAS Dx stratifies EGFR mAb responders 3 February 2011 Skolkovo Vision KRAS mutant Wt KRAS No response to panitumab Response to panitumab Salvatore Siena , et al., Review | JNCI Vol. 101, Issue 19 | October 7, 2009
    99. 99. Genomic Dx Success: Cancer stratification using BCR-ABL gene fusion 3 February 2011 Skolkovo Vision Nicholas Lydon , volume 15 | number 10 | october 2009 nature medicine Gleevec The BCR-ABL gene fusion is causative for CML and ALL. Imatinib (Gleevec) is specific for the TK domain in abl, c-kit and PDGF-R Severe congestive cardiac failure is an uncommon but recognized side effect of imatinib Imatinib (Gleevec) has passed through Phase III trials for CML, and has been shown to be more effective than the previous standard treatment of α-interferon and cytarabine.
    100. 100. Proteomic tools: Cell biology to clinic 3 February 2011 Skolkovo Vision
    101. 101. Proteomic tools: Cell biology to clinic 3 February 2011 Skolkovo Vision Targets and leads Biomarkers Patient stratification
    102. 102. Breast cancer: A multi-stage stratification process 3 February 2011 Skolkovo Vision From www.oncotypedx.com <ul><li>Screening </li></ul><ul><li>Mammogram </li></ul><ul><li>Breast self exam </li></ul><ul><li>Diagnosis </li></ul><ul><li>Biopsy </li></ul><ul><li>Tissue imaging </li></ul><ul><li>Surgery </li></ul><ul><li>Lumpectomy </li></ul><ul><li>Mastectomy </li></ul><ul><li>Definitive diagnosis </li></ul><ul><li>Tumor size </li></ul><ul><li>Nodal status </li></ul><ul><li>ER,PR, HER2 status </li></ul><ul><li>Histology </li></ul>Oncotype DX testing 21 Gene expression signature <ul><li>Adjuvant treatment decision </li></ul><ul><li>Hormonal therapy </li></ul><ul><li>Chemotherapy </li></ul>
    103. 103. The disease is genetically and phenotypically heterogeneous 3 February 2011 Skolkovo Vision Joel Gray, January, 2007 Science@Berkeley Lab http://www.medble.com/breast-cancer-information.htm
    104. 104. New phosphorylation pattern seen with Tamoxifen resistance in breast cancer <ul><li>Many novel tyrosine phosphorylation events seen </li></ul><ul><li>Direct visualization of new cell biology </li></ul>3 February 2011 Skolkovo Vision Forest White, MIT MCF7–HER2 is a Tamoxifen resistant cell line, 4-OHT is a Tamoxifen analog
    105. 105. Aptamer-based serum proteomics 3 February 2011 Skolkovo Vision SomaLogic

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