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Powering Question-Driven Problem Solving to Improve the Chances of Finding New Medicines


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Making true “molecule”-“mechanism”-“observation” relationship connections is a time consuming, iterative and laborious process. In addition, it is very easy to miss critical information that affects key decisions or helps make plausible scientific connections.

The current practice for deciphering such relationships frequently involves subject matter experts (SMEs) requesting resource from resource-constrained data science departments to refine and redo highly similar ad hoc searches. The result of this is impairment of both the pace and quality of scientific reviews.

In this presentation, I show how semantic integration can be made to ultimately become part of an integrated learning framework for more informed scientific decision making. I will take the audience through our pilot journey and highlight practical learnings that should inform subsequent endeavours.

Published in: Data & Analytics
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Powering Question-Driven Problem Solving to Improve the Chances of Finding New Medicines

  1. 1. Powering question driven problem solving to improve the chances of finding new medicines Samiul Hasan, Data Analytics and Visualization Director, GSK Data & Computational Sciences Connected Data London 4th October 2019
  2. 2. Science (+Business) Most hypotheses are not going to make it
  3. 3. Aspirations of scientific knowledge management 1) Persistence – Efficient organization. – The hypotheses that we validate/invalidate today need to be revisited by the next generation of scientists. 2) Vigilance – Effective organization. – Without access to the right data and prior knowledge at the right time, we risk making very costly, avoidable business decisions. Andrew Witty, GSK CEO 2008-2017
  4. 4. Inconsistent use of language at source = Serious downstream problems SDS – Serine dehydratase – Sodium dodecyl sulfate – Shwachman–Diamond syndrome – Safety data sheet GSK – GlaxoSmithKline – Glycogen synthase kinase
  5. 5. Data & knowledge capture forms: Regulatory in purpose but what about reward in design?
  6. 6. All Pharma motivation: Acquire knowledge to assert confidence in core types of evidence
  7. 7. Self-learning questionnaires: Concept “Auto-suggest” metadata tagging [AUTHOR ACTION] & auto literature evidence searching [AUTHOR REWARD] to improve language consistency1 at source and findability2 of reported evidence [OUTCOME] 1Great for improving search engines 2Great for making scientists effective
  8. 8. Application of state-of-the-art algorithms: What’s possible now? vs What will be possible with expert- curated data over the next few years?
  9. 9. Examples 1. Determine from the context of the sentence whether author meant “GlaxoSmithKline” or “Glycogen Synthase Kinase” when he/she wrote “GSK” 2. Classify and present sentences (+link to documents) with most similar metadata content to expected answers 3. Recommend predominant synonyms being used by individual departments e.g. is a particular department really working on “Glycogen Synthase Kinase” and using the synonym “GSK”? 4. If a significant efficacy or safety event is reported in a “Clinical” questionnaire, automatically alert the author whether the outcome/risk was predicted earlier in a “Pre-clinical” questionnaire. 1Named entity recognition, 2Document classification, 3Reinforcement learning, 4Trigger event detection
  10. 10. Scoring “everything” – does it make sense to do it now or once we actually have enough labelled training sets? e.g. email spam filtering
  11. 11. Pilot work
  12. 12. 1. Found evidence from rare disease clinical trial missed by project team 2. Found mechanistic hypothesis that a program team had not considered 3. Identified plausible mechanism for lab observation Impact
  13. 13. • It’s all about the questions • Technology can help overcome cultural challenges • Persistence and patience key Summary
  14. 14. THANK YOU