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Big Data, AI, and Pharma

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I have framed this talk to encourage Pharmacy students to embrace computing in general, and data science and artificial intelligence techniques in particular. The reason is that data-driven science has overtaken traditional lab science; chemistry and biology that underlie pharmacy have become data-driven sciences, and a significant majority of the new jobs in pharma industries demand data analysis skills. Increasingly, traditional bioinformatics approaches are being complemented or replaced by machine learning or deep learning algorithms, especially for cases that have large data sets. I will provide a few examples (e.g., drug discovery, finding adverse drug reactions and broadly pharmacovigilance, and selecting patients for clinical trials) to demonstrate how big data and/or AI are indispensable to pharma research and industry today.

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Big Data, AI, and Pharma

  1. 1. Big Data and Artificial Intelligence (AI) in Pharma Keynote @ College of Pharmacy Graduate Retreat University of South Carolina, Columbia, SC, 11 October 2019 Amit Sheth USC Artificial Intelligence Institute http://ai.sc.edu Icon source used in the entire presentation - https://thenounproject.com Presentationtemplate by SlidesCarnival UGUR Kursuncu UTKARSHANI Jaimini JOEY Yip Thanks for help in preparing this presentation
  2. 2. ABSTRACT I have framed this talk to encourage Pharmacy students to embrace computing in general, and data science and artificial intelligence techniques in particular. The reason is that data-driven science has overtaken traditional lab science; chemistry and biology that underlie pharmacy have become data-driven sciences, and a significant majority of the new jobs in pharma industries demand data analysis skills. Increasingly, traditional bioinformatics approaches are being complemented or replaced by machine learning or deep learning algorithms, especially for cases that have large data sets. I will provide a few examples (e.g., drug discovery, finding adverse drug reactions and broadly pharmacovigilance, and selecting patients for clinical trials) to demonstrate how big data and/or AI are indispensable to pharma research and industry today.
  3. 3. Source: Raconteur, taken from http://rcnt.eu/un8bg
  4. 4. 5% of all Google searches are health-related. Source: https://googleblog.blogspot.com/2015/02/health-info-knowledge- graph.html Healthcare data will experience a compound annual growth rate (CAGR) of 36% through 2025. Source: https://healthitanalytics.com/news/big-data-to-see-explosive-growth- challenging-healthcare-organizations FDA Sets Goals for Big Data, Clinical Trials, Artificial Intelligence. Source: https://healthitanalytics.com/news/fda-sets-goals-for-big- data-clinical-trials-artificial-intelligence
  5. 5. Source: https://healthitanalytics.com/news/how-artificial-intelligence-is-changing-radiology-pathology
  6. 6. Source: https://www.sciencedirect.com/science/article/pii/S0167629616000291?via%3Dihub #fig0015 As of 2019, many of the largest pharmaceutical firms spend nearly 20% on R&D. As of mid 2019, AstraZeneca (AZN) blazed the path by spending 25.63% of revenues on research and development. Source: https://www.investopedia.com/ask/answers/060115/how-much-drug-companys-spending-allocated-research-and-development-average.asp
  7. 7. “Information is cheap. Understanding is expensive. Karl Fast, Professor of UX Design, Kent State University AI is about converting data into knowledge, insights and actions.
  8. 8. Source: https://www.cbinsights.com/research/ai-healthcare-startups-market-map-expert-research/ AI Startups in Drug Discovery
  9. 9. Patient-generated Health Data (PGHD) is becoming the next most important data. Source: https://patientengagementhit.com/news/what-are-the-pros-and-cons-of-patient-generated-health-data https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2015.1362?siteid=healthaff&keytype=ref&ijkey=6C1y7.jaIT7q U&#aff-1 “Real world evidence can help answer questions that are relevant to broader patient populations or treatment settings where information may not be captured through traditional clinical trials” PGHD
  10. 10. kHealth Asthma: A multisensory approach for personalised asthma care in children
  11. 11. Emerging Healthcare Model More interactions, pervasive role of data and analysis Pharmaceutical Research Healthcare Delivery Public Health Precision Medicine Population Health Real World Evidence Combination therapy Outcome Analytics Nudge EconomicsQuantified Self Performance-based Pricing Risk Adjustment Phenotypic Drug Discovery Adverse Event Prediction How can I stay healthy & get better when I do get sick? Source: UI Integrative Data Science Lab (Wild Informatics Colloquium, October 2019)Special Thanks: David Wild, IU
  12. 12. “Data-driven science has overtaken traditional lab science and a significant majority of the new jobs in pharma industries demand data analysis skills. Increasingly, traditional bioinformatics approaches are being complemented or replaced by AI algorithms, especially for cases that have large data sets (Big Data).
  13. 13. • There is an incredibly rich resource of public information relating compounds, targets, genes, pathways, and diseases. • 96 million compounds (PubChem) • 268 million compound bioactivities (PubChem Bioassay) • 10,256 drugs (DrugBank) • 560,000 protein sequences (SwissProt) • 2.3 billion nucleotide sequences (EMBL) • 99 million life science publications • Even more important are the relationships between these entities. • Biological assay with percent inhibition, IC50, etc (e.g. ChEMBL, PubChem) • Crystal structure of ligand/protein complex • Co-occurrence in a paper abstract • Computational experiment (docking, predictive model) • Statistical relationship • System association (e.g. involved in same pathways cellular processes) Big Data in the Public Domain Source: UI Integrative Data Science Lab (Wild Informatics Colloquium, October 2019)
  14. 14. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Knowledge Graph (Ontology) Machine & Deep Learning Natural Language Processing (NLP) Data Science (Statistical Analysis) What is AI?
  15. 15. AI for Big Data Integrate complex, disparate data sources to gain insights Techniques: Semantic technologies, entity mapping, path-based prediction Integrate disparate data, algorithms, and human knowledge to impact big problems Technologies: Deep Learning, Knowledge Graphs, graph analytics, hypothesis testing and generation, visualization Integrate the worlds of data, things & humanity in a way that enables us to thrive as a society Fields: Design thinking, data science, IoT/sensors, networks, ethics, social sciences, computing, culture & society Based in part on: https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new-health/transforming- healthcare.html
  16. 16. Knowledge Graph (Ontology) Source: UI Integrative Data Science Lab (Wild Informatics Colloquium, October 2019) Chem2Bio2RDF Source: BMC Bioinformatics, 2010, 11, 255; chem2bio2rdf.org Comprehensive knowledge extraction from multiple sources -> Knowledge Graph
  17. 17. Source: https://dzone.com/articles/drug-discovery-knowledge-graphs Drug Discovery Knowledge Graph (Representation as structured data)
  18. 18. ● Constrained association search between myocardial infarction and rosiglitazone. ● Showing ranked paths up to three edges in length that (i) contain a gene and (ii) are ranked highly by KL-divergence showing literature support. Source: D.J.. Wild, et al., Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research, Drug Discov Today (2012), doi:10.1016/j.drudis.2011.12.019 Knowledge Graph for Systems Chemical Biology
  19. 19. Source: https://image.slidesharecdn.com/2016-mar-sheth-onto-summit-talk- 160407182258/95/ontologyenabled-healthcare-applications-exploiting-physicalcybersocial-big-data-21- 1024.jpg?cb=1460053681 Natural Language Processing (NLP)
  20. 20. Heterogeneous data obtained from large scale discharge records and hand curated disease-gene associations are used to jointly learn meaningful vector representations of disease and gene concepts in a latent vector space, where interactions of diseases and genes are retrieved and discovered. Source: Gligorijevic, Djordje, et al. "Large-scale discovery of disease-disease and disease-gene associations." Scientific reports 6 (2016): 32404. Machine & Deep Learning
  21. 21. Data Science (Statistical Analysis) Source: https://doi.org/10.1080/17460441.2019.1637414 Big Data and Big Data analytics in drug discovery. (a) Displays various data analysis methods, how they differ in the number and complexity of parameter they can handle, and how this is related to their transparency to human comprehension. (b) Illustrates the explosion of drug discovery-related data in the public domain and how they relate to cancer and ML publications. (c) Illustrates in a pyramid format the differences between data, knowledge and wisdom and how different resources belong to different heights within this pyramid.
  22. 22. Source: https://www.bbc.com/news/technology-45219902 Bridge gaps in pharmaceutical research Drug discovery Pharmacovigilance Adverse drug reactions Drug-drug interactions Personalized healthcare Challenges our whole approach to healthcare The future: bringing together all the capabilities of Informatics Role of AI & Big Data in Pharmacy
  23. 23. AI in Pharmaceuticals DRUG DEVELOPMENT Source: http://www.pharmexec.com/ai-pharmaceuticals
  24. 24. AI in Pharmaceuticals MEDICAL DEVELOPMENT & COMMERCIALIZATION Source: http://www.pharmexec.com/ai-pharmaceuticals
  25. 25. DRUG DISCOVERY SELECTION OF PATIENTS FOR CLINICAL TRIALS AUTOMATION OF PHARMACEUTICAL REPORTING ○ Modelling of different types of cancer cells to work out what conditions allowed the disease to develop ○ Use the information to try and create new treatments ○ AI Matches drugs to larger databases of patients quicker than human annotation ● Using data from clinical trials to generate sections of the CSR report ● Using AI to automate pharma reports ● Frees up medical writers’ time ● Allowing them focus on more high value analysis and adding technical insight to reports.Automate report writing Source: https://pixabay.com/de/illustrations/medizin-pharma-pille-flasche-2801025/, https://www.resources.yseop.com/CSR-use-case AI in Pharmaceuticals PHARMA
  26. 26. AI in Pharmaceuticals DRUG DISCOVERY The application of Big Data analysis and Machine Learning across the drug discovery cycle. In contrast to the traditional linear diagram often used to illustrate drug discovery, iterative clinical Big Data and ML are increasingly used to inform target identification, transforming drug discovery into a more iteratively ‘circular’ endeavour. Source: https://doi.org/10.1080/17460441.2019.1637414
  27. 27. Companies using AI in Drug Discovery ● AbbVie ● Gilead ● Pfizer ● Amgen ● Genentech ● Roche ● Astellas ● GSK ● Sanofi ● AstraZeneca ● Ipsen ● Santen ● BASF ● Janssen ● Servier ● Bayer ● Merck Group ● SK Biopharmaceuticals ● Boehringer Ingelheim ● Mitsubishi Tanabe Pharma ● Sunovion ● Bristol-Myers Squibb (BMS) ● Nestlé ● Sumitomo Dainippon ● Celgene ● Novartis ● Pharma ● Eli Lilly ● Novo Nordisk ● Takeda ● Evotec ● Ono Pharmaceuticals ● Wave Life Sciences Source: https://blog.benchsci.com/pharma-companies-using-artificial-intelligence-in-drug-discovery
  28. 28. AI in Pharmaceuticals PHARMACOVIGILANCE Source: https://journals.sagepub.com/doi/full/10.1177/2042098617736422 Post-marketing safety analysis Decision-relevant evidence Facilitate pharmacoepidemiologic studies conducted across multiple databases Development of large networks of observational databases of Electronic Healthcare Records across North America, Europe and Asia.
  29. 29. Pharmacovigilance: Use of AI in Adverse Event Case Processing ● Adverse Event Case processing comprises four main activities- patient intake, evaluation, follow‐up, and distribution. ● Each of these four main activities is associated with multiple deliverables, and each of these deliverables is composed of multiple decision points. Source: Schmider, J., Kumar, K., LaForest, C., Swankoski, B., Naim, K., & Caubel, P. M. (2019). Innovation in pharmacovigilance: use of artificial intelligence in adverse event case processing. Clinical Pharmacology & Therapeutics, 105(4), 954-961.
  30. 30. AI in Pharmaceuticals ADVERSE DRUG REACTIONS Drug Use/Abuse: Loperamide Discovery ▰ In a Web forum dataset, it was observed that users reported taking the anti-diarrhea treatment drug Loperamide (sold over the counter in Imodium) to self-medicate from withdrawal symptoms. The opioid addictions treatment drugs Buprenorphine and Methadone are commonly prescribed for treatment of withdrawal symptoms. Until now, it was unknown that Loperamide, can be (and is being) used for the same purpose. Which is more, it was observed that users reported the possibility of mild psychoactive (opiated) effects from megadosing - which is the practice of taking severely excessive amounts of a drug. ▰ Three toxicology studies followed citing our work. ▰ FDA Warning in 2016. ▰ More at: http://wiki.knoesis.org/index.php/PREDOSE Source: R. Daniulaityte, R. Carlson, R. Falck, D. Cameron, S. Perera, L. Chen, A. P. Sheth. "I Just Wanted to Tell You That Loperamide WILL WORK": A Web-Based Study of Extra-Medical Use of Loperamide. Journal of Drug and Alcohol Dependence. 130(1-3): 241-244, 2013.
  31. 31. AI in Pharmaceuticals DETECTING DRUG TO DRUG INTERACTION RISKS Applicability of data mining using different sources: applicability showing the importance of Drug Drug Interactions (DDIs) as the cause of Adverse Drug Effects (ADES), in the detection of novel DDIs and in the development of knowledge databases. Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454455/
  32. 32. AI in Pharmaceuticals PERSONALIZED HEALTHCARE Source: https://www.forbes.com/sites/reenitadas/2017/03/08/drug-development-industry-bets-big-on-precision-medicine-5-top-trends-shaping-future-care- delivery/#64f516115d3a ● Patient Monitoring ● Personalized Treatment ● Patient-Generated Health Data (PGHD)
  33. 33. AI in Pharmaceuticals DATA COLLECTION Smarterdata Data Sophistication Smart data should answer: ★ What causes my disease severity? ★ How well am I doing with respect to prescribed care plan? ★ Am I deviating from the care plan? I am following the care plan but my disease is not well controlled. ★ Do I need treatment adjustments? ★ How well controlled is my disease over time? Example of Abstraction
  34. 34. AI and Pharma Conferences Source: https://www.aiinpharma.com/
  35. 35. Members in AI in Pharma Summit Source: https://www.aiinpharma.com/
  36. 36. Big Data and Artificial Intelligence in Pharma 36 CONCLUSION Domain Knowledge Graph BIG Data (Public & PGHD) ARTIFICIAL INTELLIGENCE with Data Science & NLP PERSONALIZED PHARMACY

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