Agile Day Twin Cities - Lightning Talk (Repko)
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This document discusses product agility when developing treatments for diseases like cancer. It describes the complex and iterative process of drug discovery and development at Novartis, involving thousands of experiments, assays, and clinical trials across multiple sites. It emphasizes the need for continuous learning, visualizing complexity through various data sources, forming product teams of different experts, planning experiments to learn key scientific questions, and collaborating to clarify goals and explore new approaches. The overall process involves blending discovery work with clinical evaluation in an agile manner to efficiently gain knowledge and progress potential new treatments.
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- 1. Product Agility When Your Product is Curing Cancer Brian Repko Agile Day Twin Cities – Lightning Talk November 17, 2017 Agile Day Twin Cities
- 2. Agile Days Twin Cities Lupus Hashimoto Thyroiditis Cystic fibrosis Multiple sclerosis Huntington’s disease PSP Alzheimers Parkinsons Epilepsy COPD Lung Cancer Crohns Disease Arthritis Glioblastoma Diabetes Leukemia Alpha 1-antitrypsin Public2
- 3. Agile Days Twin Cities Novartis Institutes for BioMedical Research (NIBR) Public3 ~6,000 Scientists / 7 sites globally ~90 New Molecular Entities ~400 Research projects Source: ClinicalTrials.gov as of December, 2016 >500 Ongoing clinical trials 0 100 200 300 400 500 600 Novartis Roche AZN Merck JNJ BMS Pfizer GSK Lilly AbbVie Bayer Sanofi Boerhinger Phase 3 Phase 2 Phase 1 Drug Discovery and Early Development
- 4. Agile Days Twin Cities Drug Discovery and Development Public4
- 5. Agile Days Twin Cities Drug Discovery Dx Ch Pre- clinical Ex Clinical Op Public5 Targets & Pathways Candidates Assays & Compounds Clinical Readiness Clinical Evaluation
- 6. Agile Days Twin Cities Product Agility Continuous Product Learning • Blending Discovery and Delivery • For TIDVAL – Disease Key Scientific Question Study Experiment • For Dx/Op/Ch – Millions of assays and read outs • For PCS/Clinical – Thousands of assays and read outs • All the while producing product (medicinal chemistry / formulation) • Visualizing and Addressing Complexity • Cells are complex systems! Chemicals are a vast space! • Bioinformatics • Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics • Cheminformatics • in-silico Drug Discovery and Development, PK / PD • Data Visualization is key – tied to Computational Sciences • Complexity always leads to experiments (or whole new KSQ)! • Complexity is also forcing us to leverage deep learning Public6
- 7. Agile Days Twin Cities Product Agility Product Communities • Mapping Teams to Products and Services • ”Teams of Excellence” • Chemist, Biologist, PK/PD Specialist, Pharmacologist, Pathologist, Molecular Biologist, Computational Biologist, Bioinformatician • Each specialty also has their tribe within the organization • Fostering Ubiquitous Leadership • We all know that we don’t know the other person’s expertise • Assign Team Members to Experiments • Conscious decisions to get key people onboard early • Not everyone for everything – just enough process Public7
- 8. Agile Days Twin Cities Product Agility Early Discovery • Selecting “Where?” to Start • Which diseases do we choose? • Based on literature and what is known • We will often do the basic ”wide net” research ourselves • Huge investment – Huge returns • Rare diseases are VERY important • PSP as a way to understand Alzheimers • Collaborating and Clarifying “Why?” • Patient-centered – many presentations start with this • Exploring “How?” • Science moves fast and new technologies allow for fresh takes Public8
- 9. Agile Days Twin Cities Product Agility Planning to Learn • Adding Early Validation • Various decision boards and decision points • ”Set thinking” – not all decisions are binary! • What is the Next Best Investment – or Kill It Early • For knowledge-based discovery work, TEACHING >> LEARNING • Learning Outside the Code • ”Open Source the Framework” • Constantly reviewing what others are doing • Collaboration in the non-competitive space Public9
- 10. Thank you
Editor's Notes
- I used to be in the Oncology team so I’ll mostly speak to my time there…
- Novartis vs NIBR 20% of all profits funds NIBR Drug Discovery and Phase I Clinical Trials What is Phase I, II, III? NIBR Disease Areas NIBR Platforms – ASI, CBT, NBC, GDC, PK, PCS NIBR Clinical Groups – TCO, TM, BMD NIBR Support Organizations - lots! (NIBR Informatics - aka NX) NIBR “product” – NCE (new chemical entity) or NME (new molecular enitty) NX - Service to Disease Areas in creating Products (NX to NME is like OS to software system)
- NIBR is Discovery and Phase I Clinical Trials Phama / Oncology take it from there
- Exploratory – Target ID and Validation (TIDVAL) Discovery – “Hits to Leads” (Lead Discovery) Optimization – “Leads to Candidates” (In-vitro / In-vivo studies) Characterization – sPOC (selected for Proof-of-Concept) Pre-clinical Clinical – for us Phase I only (FIH / FPFV) POC