KNIME as a Platform for Integrated Hit Finding at Novartis. Patrick S. Lee Ph.D. KNIME Fall Summit 2016 San Francisco 9/16/2016

Transcription

1 KIME as a Platform for Integrated Hit Finding at ovartis Patrick S. Lee Ph.D. KIME Fall Summit 2016 San Francisco 9/16/2016

2 Agenda Overview of ovartis Hit finding in drug discovery KIME integration 2

3 A world-leading healthcare company Our strategy is to use sciencebased innovation to deliver better patient outcomes. We aim to lead in growing areas of healthcare. We focus where our skills can help address great medical need: Oncology Cardiovascular Respiratory euroscience Immunology and dermatology Eye care 3

4 Our innovation engine sustains an industry-leading pipeline $8.9B 200+ Invested in research and development R&D projects underway 23,000 People working in research and development worldwide 20 Major regulatory approvals (US, EU and Japan) in

5 ovartis is one of the world s largest companies... Sales By Region 2015 USD Billion Latin America et Sales et Income Asia/Africa/ Australia 21% 9% 33% Europe People (FTEs) 118,700 37% US 5

6 with industry-leading businesses in growing areas of healthcare Innovative medicines* Generics & biosimilars Eye care devices ovartis Oncology ovartis Pharmaceuticals Sandoz Alcon Research Development Manufacturing Business Services * Division structure as of July 1,

7 Global network of more than 6,000 scientists, physicians, and business professionals United States Cambridge, MA East Hanover, J Emeryville, CA La Jolla, CA (GF) Europe Basel/Zurich, Switzerland Asia Shanghai, China Singapore (ITD) 7

8 Each project draws from global core competencies Preclinical Safety, DMPK extgen Sequencing Bioinformatics Chemistry Genetics Project Synthetic Biology Pathways Stem Cells/ Regenerative Medicine Biologics Imaging Translational Medicine 8

9 Drug development is a lengthy, costly and risky undertaking 9

10 Pathway strategy

11 Oncology pathways 11

12 Target validation selection of tool compounds Goal: find small molecule with potent activity against target Utilize internal and external data and select compounds Filter out undesirable compounds Output is list of compounds to be used as tool 12

13 Once target selected, screen to find starting point Select compounds for screening Follow up to obtain finer view of structure-activity relationship 13

14 Hit finding overview Develop hit finding strategy Purchased compounds Internal archive Screen to identify starting point Virtual compounds Assess data Compounds and assay data Go/o-go decision Follow up 14

15 Webportal to expand hits Eric Vangrevelinghe Location 1 dry powder Location 2 10mM solution Location 3 dry powder Location 4 10mM solution Location 5 5mM solution Location 6 10mM solution Location 7 dry powder Location 8 10mM solution - Download quickform - Upload quickform - Complex input form with cell borders - Dynamic input page 15

16 Details from workflow Applied in projects using hitlist from partial High Throughput Screen Expansion with similarity searches against database Filtering using required availability/location criteria Possibility to discard a defined list of compound from the final selection (e.g. already screened deck, problematic compounds) Performance: 150 compounds hitlist is expanded to a list of 1000 compounds takes ~30 minutes. 16

17 Workhorse nodes exemplified in portion of hit expansion workflow O H H RDKit for property calculation with canonicalized SMILES strings Consolidating all data with GroupBy node unique representatives and statistical results 17

18 Spotfire integration - data summary of analogue search Summary of output from query Molecule 1 Molecule 2 Molecule 3 18

19 Substructure search broken down by scaffolds and data availability Molecule 1 O H H *For illustrative purpose only Molecule 3 19

20 Docking compounds with KIME Assess fit of molecule(s) into target of interest Filter compounds to those that likely fit into binding pocket Molecule docked to protein pocket J. Chem. Inf. Model. 2015, 55,

21 Kinase pqsar Deep Learning models as good as HTS cf. 4-pt IC 50 vs 8+-pt IC 50 med. R 2 =0.54 Applied to 60+ projects Model vs. Experiment for novel compounds Eric Martin 728 assays 170 kinases 500,000 cpds 5 million IC 50 s R

22 Conclusion KIME enables drug discovery scientists to process data to enable hit-finding KIME allows for automation across multiple platforms to facilitate hit generation Workflows are easily interchangeable across multiple discipline via web portals 22

23 Acknowledgements Computer-Aided Drug Design (CADD): Eric Vangrevelinghe, Eric Martin, Valery Polyakov, Hanneke Jansen, Fiorella Ruggiu, Mika Lindvall, Rainer Wilcken, Paulette Greenidge, Jose Duca IBR Informatics (X): Mark Duffield, Manuel Schwarze, Timofei Sablin, Bill Shirley, Pradeep Pasupuleti, Daniel Karavakis, Scott Malcolm, Heather Hogg, Christina Lu Developmental Molecular Pathways (DMP): Allen Cornette, Jeremy Jenkins Bacterial Lead Discovery (BLD): Brian Feng, Alun Bermingham, Dita Rasper 23

24 Thank you