Webinar 'Could you transform the way you do R&D in just 5 years?' - May 2014
•
1 like•894 views
The document discusses transforming R&D through linking data, people, and collaborations. It identifies key business drivers like accelerated innovation and improved margins. Effective solutions link data to data, people, and collaborations through searchable knowledge bases, security, and reporting platforms. Case studies show implementing flexible solutions in stages from basic modeling to full process integration and alignment across organizations.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Webinar 'Could you transform the way you do R&D in just 5 years?' - May 2014
Similar to Webinar 'Could you transform the way you do R&D in just 5 years?' - May 2014 (20)
Recently uploaded
Recently uploaded (20)
Webinar 'Could you transform the way you do R&D in just 5 years?' - May 2014
- 1. ©2014 IDBS © 2014 ID Business Solutions. All Rights Reserved Transforming R&D Dr Paul Denny-Gouldson VP Strategic Solutions, IDBS
- 2. ©2014 IDBS 2
- 3. ©2014 IDBS Game of two halves Common business drivers, observations, challenges and approaches we see Case studies and examples of what we have done and our approach 3
- 4. ©2014 IDBS©2014 IDBS Key business drivers exist in R&D… Accelerated innovation Products to market faster Improved margins
- 5. ©2014 IDBS©2014 IDBS What can help Link data to data Link data to people Link people to people 1010 0101
- 6. ©2014 IDBS What delivers against the drivers Searchable & actionable knowledge base Increased data security & IP protection / utilisation Collaboration and Reporting platforms 6
- 7. ©2014 IDBS©2014 IDBS Cumulative Survival Rates © Nestlé Feb 2013 (Taken from ‘Knowledge Based Capital in a Company’, Stefan Dobrev)
- 8. ©2014 IDBS©2014 IDBS Change is at the foundation of these principals 8 An R&D Data Value Chain Knowledge of what has gone before is very helpful in making decisions about where to go Treat “collaboration” as a requirement but do not differentiate outside and inside There are issues with the outside elements and the inside elements !
- 9. ©2014 IDBS Solid Data Foundations
- 10. ©2014 IDBS©2014 IDBS But what is “collaboration” R&D and what drives it?
- 12. ©2014 IDBS R&D (change!) Challenges Security Normalisation Networks Context Ease of Use Language Ontologies Semantics Quality Storage Domain Resilience Openness Exchange Catalog Performance Users IP Compliance Culture Aggregation Provence Privacy Trust
- 13. ©2014 IDBS Inside-outside outside-inside “Collaboration is not just about outside the fire wall” 13
- 14. ©2014 IDBS But when it is outside The work practices of scientists are changing “Our scientists expect to have access to their work and data while off site at a collaborators site” CIO Large Pharma This brings its own problems wrt security of data and access of systems 14
- 15. ©2014 IDBS©2014 IDBS Some of the essentials of change 15 An defined set rules that allows flexibility Can not restrict everything – it gets in the way of business • Defined rules on data type and “level of security” Management of security needs to be divested otherwise it gets to complex & big • Centralised security rules and biz object definition & management Partner engagement process and delivered software tools • Hosted / managed services with rapid scale up and down
- 16. ©2014 IDBS User Expectations are changing
- 17. ©2014 IDBS Web web web Conduit for collaboration Remote and mobile access OS independence Social networking + tagging Links to gadgets But with: Provenance, Context, Fact, Security and (Trust)
- 18. ©2014 IDBS Big data – Small data Big data gives you ability to look for new stuff But when you find it you have to distil it and put it somewhere
- 19. ©2014 IDBS©2014 IDBS 19 Technical aspects that help SAAS and hosted environments • Hosted and managed service platforms that have integration capabilities Simple to deploy software that requires ZERO training • Web solutions reduce the complexity of roll out – but there are still gotchas ! Unified security and data model • Standards based security implementation and flexible object model
- 20. ©2014 IDBS Collaboration Models we have seen Collaborations come in many flavours both domain centric and business centric Often the data management requirements are similar in shape and size data access/security is always key!
- 21. ©2014 IDBS Intra organisation, domain centric, collaborations we have seen
- 22. ©2014 IDBS Pre Clinical Development Process Chemistry & Isotope Production Formulation (GLP) Analytical & Stability Testing (GLP) BioAnalysis (GLP) Metabolite ID Material Sciences Pharmacology & Safety Pharmacokinetics Drug Metabolism
- 23. ©2014 IDBS Process Development - Biologics Analytical Sciences & QC Cell Line Development Cell Culture & Fermentation Purification Formulations & Stability Central Services (Media & Buffer Preparation, Equipment Management)
- 24. ©2014 IDBS External, business centric, collaborations we have seen
- 25. ©2014 IDBS Acquisition – Numerous partners On-boarding new employees easy Data loading templates used to load historical data Now using the E-WorkBook Suite for the data analysis where Excel was previously used Considered hierarchy setup & historic documentation gradually added Enabled software consolidation Principal Product Company CRO Academia Very simple exchange of formatted data !
- 26. ©2014 IDBS Outsourcing – Virtual R&D Accessed from Europe and US All hardware needs fully externalized Faster turn around of data than previously seen Reduced reworking of data -Don’t need to wait for ‘Bob’ to come back from holiday to access the data Principal business can see everything! Ability to further analyze data from CROs if needed Principal CRO 1 CRO 2 CMO End-2-end visibility of data and process
- 27. ©2014 IDBS GSTT Members South London Members KCL Members Research Sources Clinical Sources Guy’s and St. Thomas’ NHS Research Sources Clinical Sources South London & Maudsley NHS KCH Members Research Sources Clinical Sources Kings College London University Research Source s Clinical Sources King’s College Hospital NHS Research Sources Clinical Sources Thames Cancer Registry (KCL) Facilitator R&D Services Late Stage Dev Services Providers Manufacturing Services Industry Early Dev Service Providers Complex Collaborations Single cross-organizational Research & Development Knowledge platform Searchable integrated clinical, diagnostic, pathological, sample, research and genomics data from multiple sources Highly secure information and procedures to protect data confidentiality Powerful visualization of research data and an evaluation of data quality Fully hosted and managed collaboration infastructure
- 28. ©2014 IDBS A proven approach to change
- 29. ©2014 IDBS©2014 IDBS Low to higher risk & costs / ROI minutes to years Approach – options to support a journey Simple data modeling Capturing unstructured data Simple structured data Structured data & process Cross collaboration Enterprise analysis & insight
- 30. ©2014 IDBS©2014 IDBS Low to higher risk & costs / ROI minutes to years Gartner Pace model Simple data modeling Capturing unstructured data Simple structured data Structured data & process Cross collaboration Enterprise analysis & insight System of Record Biz Differentiation Biz Transformation
- 31. ©2014 IDBS©2014 IDBS VISION – evolved over time Flexible, ambitious vision Large scale project Increased data quality Reduced collation time International process harmonization IP standardization Streamlined reporting Want to do more Lab throughput increased by 50% time to report reduced 5 fold
- 32. ©2014 IDBS This really does sum it up nicely
- 33. ©2014 IDBS©2014 IDBS VISION – full process integration & alignment Two stage, ambitious vision Large scale project across many groups Process harmonization Real time process reporting Less errors & time looking for them Holistic view of development process Foundations of QBD Duration: 1 year Radical changes in business process and measurably improved compliance
- 34. ©2014 IDBS©2014 IDBS VISION – notebook replacement & sample management Medium scale project >200 users (EU) Data security – IP protection Paper removal – time saving Sample management Reduction of repeated experiments Each scientist is saving around >4hrs per week Duration: <6 months for step1 Ongoing for step 2 “Best adopted IT system in the company’s history” - Library Services
- 35. ©2014 IDBS©2014 IDBS Key business drivers exist in R&D… Accelerated innovation Products to market faster Improved margins
- 36. ©2014 IDBS©2014 IDBS ENGINEERING ENERGY FOOD & BEVERAGE CHEMICALS AGRICULTURAL SCIENCES CONSUMER GOODS PHARMA & BIOTECH HEALTHCARE Research Development Research Development Research Development Research Development Research Development Research Development Research Development Research Development JCI John Deere Sealy Total Cargill Danone Driscoll Foods BASF DuPont Bayer Syngenta L’Oréal Pfizer Daiichi Sankyo Genentech GSK Pfizer Barts Jewish General Biological sciences Translational sciences Pharmacology & preclinical sciences High volume testing Chemical sciences Analytical sciences Data & financial modeling IDBS is a leader in the field
Editor's Notes
- This is a great slide from Nestle showing their goals and objectives based on product attrition It shows that there is benefits to be had all across the R&D cycle, and better use of data, leveraging knowledge will help the atrition rates as you move from R to D and then once in D it is about being efficient
- Not here to talk about IDBS in the regular way – size, locations etc. This presentation is all about discussing what are your core drivers and where you want your R&D to go and how you are thinking to get there.
- Have to explain the maturity model and how businesses get benefits from our software in different ways. Can use the extended deck here to walk through the slides – you must introduce it though as it helps build the value of what we have for the future. Always remember that you have options. It isn’t always a simple race to the top. Choose the journey that best suits your individual circumstances, business drivers and need for ROI. Maybe you don’t have a grand vision but you can see a vision of a paperless laboratory – our experience says that you will achieve that and then want to do more as you learn, and your business adapts, to the change. This is where we are unique – we will not take you down a cul-de-sac or dead-end technology – it will adapt to meet your changing requirements
- Always remember that you have options. It isn’t always a simple race to the top. Choose the journey that best suits your individual circumstances, business drivers and need for ROI. Maybe you don’t have a grand vision but you can see a vision of a paperless laboratory – our experience says that you will achieve that and then want to do more as you learn, and your business adapts, to the change. This is where we are unique – we will not take you down a cul-de-sac or dead-end technology – it will adapt to meet your changing requirements
- Use case 2 – Pharmaceuticals & Biotechnology Vision – evolved over time Type 1, 2 & 3 Project: Initially to reduce data collation time, then to increase data quality and streamline reporting. Some reports took 4-6 weeks to complete after the lab work had finished. Analysis showed process improvements and structured data capture could reduce this to 1 day. This moved deployment to Type 3. The customer (Abbott) started the project in a small group but it quickly gained traction with other groups. Project completed over various phases over the past 6 years starting with limited roll out to less than 250 Type 1 and 2 users. Hard benefits: The success of this project attracted other groups, which shifted the focus to cross-domain daisy chaining. A costly, protracted pilot was avoided. Focused week-long workshops drove the choice of the system and eliminated unnecessary questions such as ‘Can I use it?’ and ‘What’s the value?’ The soft benefits: Process harmonization across different geographies The system was rapidly adopted and used to drive Type 3 and 4 user adoption. Now have 2,000+ users across multiple business groups and domains, ranging from pharmacology, DMPK, bioanalysis, formulations, analytical and QC labs.
- Use case 4 – Pharmaceuticals & Biotechnology Vision – full process integration and alignment Type 3 & 4 Project: Move to structured data and processes before entering a new age of cross-domain data collaboration. This biologics development customer (Novartis) defined the project as Type 3 moving to Type 4 in a short timeframe. Hard benefits: Rolling out the new systems had cumulative benefits across the organization: Process harmonization Reduction of errors and time spent looking for errors Real time process reporting These were all used to define the business benefits of the project. Removal of paper and IP capture were also included. Soft benefits: Lonza’s investment in process and informatics enables them to differentiate themselves against their competitors Daisy chaining groups enabled downstream groups could see previous projects and make better decisions Time and money no longer wasted on repeating prior work due to lack of accessible information New system to track everything, make it reportable and give a holistic view of the entire development process - the foundation for Quality By Design Lonza need to understand variables with complex relationships with each other and how they affect key properties of what they are making. This will allow them to understand their processes and predict likely outcomes. This is still in its infancy, but the key for any simulation and prediction is good input data and process harmonisation, which is what the system now offers. The project was completed in 1 year and is now employed by 400 users. This shows that you can drive a business value case even when starting further up the project types.
- Use case – Danone Vision – Lab IP capture and protection then workflow automation as experience grows Type 1 Project Hard benefits Paper removal (no more cutting out, sticky tape and printing) saved between 2 and 4 hours per week per scientist. Soft benefits Better IP security and access for legal teams to proactively patent – first to file changes. IP capture at source and centralization protects investment and makes knowledge easily accessible Avoided a protracted pilot through a short Phase 0 implementation, which validated expected time-savings Potential to reduce repeated experiments to save further time and money Project completed in a very short timescale. Rolled out to 200+ users within 6 months.