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Data Science in Drug Discovery

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Presenter: Marina Sirota, UCSF

Recent advances in genome typing and sequencing technologies have enabled quick generation of a vast amount of molecular data at very low cost. The mining and computational analysis of this type of data can help shape new diagnostic and therapeutic strategies in biomedicine. In this talk, I will discuss how such technological advances in combination with data science and integrative analysis can be applied to drug discovery in the context of drug target identification, computational drug repurposing, and population stratification approaches.

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Data Science in Drug Discovery

  1. 1. Data Science in Drug Discovery Marina Sirota, PhD Assistant Professor Institute for Computational Health Sciences October 22, 2015
  2. 2. Data Driven Research
  3. 3. Integrative Personal “Omics” Profiling Genome Transcriptome Epigenome MicrobiomeProteome / Metabolome Antibodyome
  4. 4. Moore’s Law – Biology and Computation Cost Per Genome Cost of Computational Resources
  5. 5. Can we use data integration to… Biomarker Discovery … to find better diagnostic markers? Disease Mechanism … understand disease better? Therapeutics … find new uses for existing drugs?
  6. 6. Motivation • Problem: – Takes roughly 15 years and over $800 million to develop and bring a novel drug to market – 90% of drugs fail in early development • Solution: Drug Repurposing – Lower cost – Reduce risk of failure
  7. 7. Problem Statement Can we use public data to systematically predict relationships between drugs and diseases? Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ. Discovery and Validation of Drug Indications Using Compendia of Public Gene Expression Data. Science Translational Medicine. Aug 2011.
  8. 8. Problem Statement Can we use public data to systematically predict relationships between drugs and diseases? Diseases Drugs Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ. Discovery and Validation of Drug Indications Using Compendia of Public Gene Expression Data. Science Translational Medicine. Aug 2011.
  9. 9. What is Gene Expression Profiling? • Global snapshot of cellular function and activity – Genome sequence – what might be going on – Expression – what is actually going on • 25,000 genes 1,000,000 proteins • We can measure a few thousand proteins, but gene expression is a global proxy How Can We Measure Expression?
  10. 10. Microarrays • Thousands of probes are hybridized to a solid surface • Takes advantage of complementary DNA sequences • Process: – RNA is extracted from the sample – Fluorescent labeling – Hybridization and wash – Scanning and signal processing – Normalization and analysis!
  11. 11. Data Sources • Collection of expression data from cultured human cells • 453 experiments of 164 drugs • Covers broad range of effects – FDA approved drugs – Non drug bioactive small molecules • Publicly available gene expression repository – Platforms – 11,745 – Samples – 961,202 – Series -39,679 • There are numerous experiments dealing with over 200 diseasesBarrett et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009. Lamb et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006.
  12. 12. Disease Gene Expression Data (GEO) Butte AJ, Chen R. AMIA, 2006. Download all GDS Experiments GEO Identify Disease Associated Experiments Identify Normal vs. Disease Experiments 176 datasets, 3113 arrays, 100 diseases Dudley J, Butte AJ. PSB, 2008. Dudley JT, Tibshirani R, Deshpande T, Butte AJ. Disease signatures are robust across tissues and experiments. Mol
  13. 13. Disease Gene Expression Signature Disease Individuals Healthy Controls Disease Gene Expression Signature
  14. 14. Drug Gene Expression Profile Treated Sample Untreated Sample Drug Gene Expression Profile
  15. 15. Up-regulated Down-regulated Hypothesis Gene Expression Profiles Disease Drug BDisease Drug A Disease Drug C Genes Genes Genes Treatment Adverse Reaction ? ???? Lamb et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.
  16. 16. Computational Pipeline Disease Gene Expression Signature Genes Drugs Disease-Drug Scores Drugs Similar to Disease Drugs Opposite to Disease
  17. 17. Drug-Disease Relationships Drugs Diseases Positive Correlation – Adverse Reaction? Negative Correlation - Therapeutic
  18. 18. Similar Diseases Cluster Based on Disease-Drug Similarity
  19. 19. Families of Drugs Cluster Based on Disease-Drug Similarity
  20. 20. Families of Drugs Cluster Based on Disease-Drug Similarity
  21. 21. Drug-Disease Relationships Drugs Diseases Positive Correlation – Adverse Reaction? Negative Correlation - Therapeutic
  22. 22. Crohn’s Disease • An inflammatory disease of the intestines that has an autoimmune component • Affects 500,000 people in North America • No known pharmaceutical cure • Current solutions: – Reduce inflammation with anti- inflammatory drugs and corticosteroids (prednisone) – Bad side effects – Surgical solutions
  23. 23. Therapeutic Predictions for Crohn’s Disease
  24. 24. Therapeutic Predictions for Crohn’s Disease
  25. 25. Topiramate – An Anti-Seizure Drug • Suppresses the rapid and excessive firing of neurons that start a seizure • Enhances GABA-activation • Used to treat epilepsy, bipolar disorder • Antidepressant • Investigated as potential treatment for obesity and type II diabetes
  26. 26. Topiramate and Crohn’s Genes that are up-regulated by the drug are down-regulated in the disease Genes that are down-regulated by the drug ar up-regulated in the disease
  27. 27. Animal Model for Crohn’s • TNBS (trinitrobenzene sulfonic acid) + ethanol induced rats: – Excellent and reproducible experimental model for Inflammatory Bowel Disease (Crohn’s and Ulcerative Colitis) – Toxin-based model Normal TNBS Induced
  28. 28. Pilot Validation Study Design • Pilot Study – 18 rats – Healthy (control) – TNBS-Induced Untreated – TNBS-Induced Treated • 80 mg/kg topiramate, injected daily • Colon tissue macroscopic damage score Reetesh Pai, Mohan Shenoy and Pankaj Jay
  29. 29. Validation Results
  30. 30. Two Follow-up Validation Studies • 48 rats each – 4 groups of 12 rats – Healthy Controls – TNBS + Vehicle – TNBS + Prednisolone – TNBS + Topiramate • 7 days • Clinical Signs, Pathology Score, Histology • Endoscopy Images
  31. 31. Clinical Signs
  32. 32. Pathology Scores B S+Veh B S+Pred B S+Top Vehicle 0 1 2 3 4 5 GrossPathologyScore **** B
  33. 33. Histology
  34. 34. Endoscopy
  35. 35. Drug-Disease Signature
  36. 36. Ongoing work • Extending the drug datasets to use structural data • Incorporating meta-analysis methods • Application to cancer (lung cancer, liver cancer, medulloblastoma) • More focused cell line selection • Looking at dosage response and combination therapy prediction • Leveraging EMR and clinical trial dataChen B, Sirota M, Fan-Minogue H, Hadley D, Butte AJ. Relating Hepatocellular Carcinoma Tumor Samples and Cell Lines Using Gene Expression Data in Translational Research. BMC Medical Genomics, 2015. Wu M, Sirota M, Butte AJ, Chen B. Characteristics of drug combination therapy in oncology by analyzing clinical trial data on clinicaltrials.gov. Pac Symp Biocomput. 2015.
  37. 37. Can we use data integration to… Biomarker Discovery … to find better diagnostic markers? Disease Mechanism … understand disease better? Therapeutics … find new uses for existing drugs?
  38. 38. Precision Medicine responders non-responders test
  39. 39. Acknowledgements Atul Butte Joel Dudley Annie P. Chiang Alex Morgan Pankaj Jay Pasricha Mohan Shenoy Minnie Sarwal Reetesh Pai Julien Sage Silke Roedder Alejandro Sweet- Cordero Bin Chen Hanna Paik Dexter Hadley
  40. 40. Institute for Computational Health Sciences @ UCSF
  41. 41. Thanks! marina.sirota@ucsf.edu

Notizen

  • Good morning my name is Marina Sirota. I’m currently a lead research scientist in the division of systems medicine at Stanford university. Previously I worked at Pfizer under David Cox in a genetics group working on applying next gen sequencing technologies to discover novel drug targets and develop population stratification techniques for clinical trials. Today I will tell you a bit about translational bioinformatics, systems medicine and how it might impact transplantation practice in the near future
  • Since then we have come a looong way. Thousands of people have been sequenced and millions of individuals have been genotyped. These are all resources that have been created and most importantly they are open to the public.
  • People are also starting to use sequencing in creative ways – immunome, metagenome, epigenetics cell-free DNA.
  • The observation made in 1965 by Gordon Moore, co-founder of Intel, that the number of transistors per square inch on integrated circuits had doubled every year since the integrated circuit was invented. Moore predicted that this trend would continue for the foreseeable future.
  • Drug repurposing is finding a novel indication for a known FDA approved drugs

    Computational work can be instrumental in making this feasible

    Examples: Viagra - was initially studied for use in hypertension


  • So far I have focused on the genetics piece or the DNA, I would like to talk a little bit about RNA or the middle piece of the central dogma and ways we can measure it using gene expression profiling
    Expression profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment Used to generate hypothesis, mechanism of action

    Post translational modification, alternative splicing
  • Microarrays are one technology to measure gene expression. They were first developed in 1995, nearly 20 years ago.
  • Have GEO data, but the format doesn’t make it easy to ask relevant questions

    We have built an infrastructure to enable this sort of analysis
  • This approach is especially likely to yield good results for diseases with a strong gene dis-regulation component such as autoimmune disease or cancer
  • If the up-regulated disease genes appear near the top (up-regulated) of the rank-ordered drug gene expression list and the down-regulated disease genes fall near the bottom (down-regulated) of the rank-ordered drug gene expression list we can conclude that the drug and the disease expression profiles are similar

    if the up-regulated disease genes fall near the bottom of the rank-ordered drug gene expression list and the down-regulated disease genes are near the top of the rank-ordered drug gene expression - therapeutic

    Randomization by picking a signature at random and recomputing drug disease scores 100 times FDR
  • 100 diseases 164 drugs
    16000 drug-disease pairs
    53 diseases significant predictions

    Not everything is treatable
  • Hieararchical clustering

    Brain cancers

    Other Cancers

    Lung Injury

    UC and crohn’s
  • histone deacetylase (HDAC) inhibitors (in red)

    Drugs known to affect different parts of the same pathway also cluster together:

    phosphatidylinositol-3-kinase (PI3K) inhibitors LY−294002 and wortmannin (in green)
  • heat shock protein 90 (HSP90) inhibitors (in orange)
  • Chose Crohn’s but have others
  • Known drug
    Two that are better
    One is FDA approved so go for this one
  • Looked for an animal model of Crohn’s and found one
  • Define macroscopic damage score
    Scale 0-6 what they mean
    Define axes
  • Earlier this year President Obama launched a $215 million investment in Precision Medicine Initiative will pioneer a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.
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