IDBS Data Management Blog

I read with interest yesterday’s report in the Lancet highlighting the spiralling global costs of cancer treatment, with 12 million people diagnosed with cancer worldwide costing £185bn ($295bn) per year. The report goes on to say that most developed countries are spending between 4% and 7% of their healthcare budgets just on treating cancer.

The complexity of the market is partially to blame for this growing expense – there were 35 approved cancer drugs in 1970, now there are nearly 100, not to mention the additional imaging and biomarker options that are increasingly available to better diagnose disease and monitor treatment efficacy.

We all know that successful, affordable treatment of cancer requires analysis of patient data to compare treatments, side effects and outcomes. What this takes is access to high quality data about a patient’s medical history, treatments and lab results. This is a significant data integration challenge requiring data extraction from multiple systems and clinically orientated integration of that data. Importantly appropriate pseudonymisation of patient records is also vital in using such information for research purposes.

This is still a major problem for Healthcare organisations, governments, payers and providers. Here’s a typical question we need to be able to answer today:

“How many patients with Triple Negative Breast Cancer were treated with Cytoxan and Taxol and what are their outcomes, ethnicity, exercise, drinking, smoking, and dietary profiles? And then let’s look at their genomic profiles.” 

Access to this type of data and analytics, such as Kaplan Meier survival curves, is essential to improved outcomes for cancer patients.

Too often though this is just too hard, because it is a task of epic proportions, pulling data together from files and basic spreadsheets.

We can all celebrate the success of HER2 tests for Herceptin and KRAS for Vectibix, in fact the report mentions a Japanese KRAS study that showed a £32m ($51m) per year saving for treatment of colorectal cancer using this test. However, Personalised Medicine is still a long way off, and while IDBS is helping advance these capabilities, we are also able to support more immediate needs to improve treatment selection and outcomes.

Here at IDBS we are actively supporting the integration and analysis of patient data with many of the leaders in this field of study, including King’s Health Partners in the UK and Windber Medical Center in the USA. Our systems enable clinicians and clinical researchers to quickly select groups (or cohorts) of patients and see a timeline of their diagnosis, treatment, risks and outcomes. This is enabling technology for clinicians worldwide and more importantly for today’s and tomorrow’s cancer patients.

NGS (next gen sequencing) can help in the delivery of personalised medicine and the understanding of disease – no doubt in my mind. But like all the things that have gone before – HTS (high throughput screening), Combinatorial Chemistry, molecular modelling – it is not a silver bullet – but a tool that can be used to aid the cause. Alone it cannot solve the problem.

We have seen it over and over again – a new tech / method / thingamabob that will solve all problems. In the Wild West they called it snake oil and we know what that means!

To properly leverage technologies such as NGS – they need to be “tamed”. By that I mean used effectively and in a manner that will provide consistent, reproducible context-rich results. To do this the results have to be generated in a fashion similar to all other “analytical methods”, with good control of all the variables that can affect results – thus enabling comparison and use of the results in decision-making.

The goal for NGS is obviously to get into the clinical diagnostics area – and this will be a huge step forward for the provision of personalised medicines. However, to do that, the technologies must be used in conjunction with informatics that can provide this extra layer of control and context of the results obtained … I am obviously talking about ELNs and good scientific data management.

There are also many similarities between the front end of the lab process in all these “analytical areas” and the requirements for lab asset handling, traceability of information and “exception reporting” which suggests that NGS labs will benefit from the same tools and informatics systems: i.e. ELNs, LIMS and data management.

Of course I may be a little biased, but I think our data solutions are light years ahead of the rest.

Perhaps it’s time to have a look at why they are causing such a buzz in the market?

IDBS’ Enterprise Customers range from global enterprises to academic research institutions.  When we work with our customers to document their implementation experiences we hear a similar story, regardless of the industry sector or size of the organization. They all expected IDBS to be a cost effective, robust and secure R&D data management solution, but they also cite the following as additional benefits accruing to their decision to select IDBS over other software providers:

• Significant time savings and increased staff productivity
• Simplification of workflows
• Enhanced tools for collaboration both internally and externally
• Flexible framework & rich functionality
• Process improvements across diverse disciplines
• Domain expertise of the IDBS Support and Professional Services staff

At recent IDBS Roadshows and User Group meetings we have heard from a range of organizations using IDBS, including Solae, Amgen and Abbott Laboratories. Sharing these experiences (the highs and lows, best practices, lessons learnt and customizations) are invaluable for the IDBS community, and also for those organizations that are just starting to work on their own projects using IDBS. A selection of IDBS customer announcements published recently includes:

• PharmaLegacy Laboratories – achieve a 10x improvement in validated reporting time
• Ablynx – deploy IDBS to speed antibody research and discovery
• BASF – use IDBS as its enterprise research data management platform
• Lonza – adopt IDBS to optimize bioprocess execution and knowledge management
• AnaptysBio – deploy E-WorkBook to manage data
• University of Nottingham – extends E-WorkBook Suite across global network of campuses

A full list of case studies can be found on our web site for you to browse. If you want to share your own experiences of using IDBS we’d like to hear from you.

President Obama signed the America Invents Act into Law last Friday. So it’s all change at the USPTO and with a handy 15% increase in fees too. But what does it mean to researchers, R&D organizations and their potential blockbuster data?

The days of simply time-stamping the constituent parts of potentially valuable IP and piecing it all together, at some point down the line, are gone. You need to combine the disparate pieces of your invention together, qualify and internally examine it …fast!

Many Electronic Laboratory Notebooks or ELN systems – particularly in the field of medicinal chemistry for pharmaceutical companies – were initially justified (and designed) as solutions for establishing IP priority on a first-to-invent basis. Getting ‘fast-to-pdf’ was the goal.

The actual filing could happen at some later time once all the other information needed to establish the benefit of the chemical compound was generated and itself established as a time-stamped document. With the new legislation, this functional model now breaks. I agree with analysts, such as Michael Elliot of Atrium Research & Consulting LLP, that this will lead to a partial extinction event in this informatics sector.

The legacy ‘fast-to-pdf’ approach also misses the real potential of ELN systems. Patent generation, like R&D, is a multidisciplinary information integration activity that needs to be slick and quick –particularly under first-to-file and therein lies the basis for the long term value of data-centric ELN.

Apart from the obvious benefits of reduced wastage, improved digital security and sharing of best practice, data-centric ELN systems are proven to increase the pace of research, through personal productivity gains, task-flow management and better collaborative decision-making.

This favors ‘first-to-file’, with the surety that the data are substantiated, down to the raw data level. These data can be scrutinized by examiners from multiple jurisdictions and validated easily, despite the years of elapsed time between filing and examination.

Also, and all-important in today’s IP and licensing environment – where ownership of the ‘object’ is far less important than the information about what it does – the data needed for filing must be multidisciplinary. As a result, so must be the ELN system used to secure and exploit valuable IP.

I’ve really enjoyed seeing the Cancer Research UK Stratified Medicine Programme getting so much coverage around the globe. This initiative will blaze the trail for the wider adoption of genetic testing to support diagnosis and treatment of various cancers including breast, colorectal, lung, prostate, ovarian and skin cancer.

The 2 year program will see 9,000 samples and associated clinical data systematically captured and genetically tested for known cancer variants with a view to building a comprehensive warehouse of cancer data. This will then form a research resource to better understand the genetic basis for diagnosis and disease treatment, and in the future support clinical decision-making.

The Cancer Research UK project is part of a larger Stratified Medicines Innovation Platform funded by the Technology Strategy Board and other bodies to advance personalised medicine in the UK.

We at IDBS are proud to be leading one of these projects, designed to support industry and academic collaboration in stratified medicines based on high quality, longitudinal patient information and associated genetics. The Acropolis project will focus on the secure infrastructure to analyse and share this data, leading to improved disease understanding and patient outcomes.

We wish the project team well because this is by no means a trivial undertaking and highlights many of the difficulties faced by academic medical centres around the world in bringing together critical data to support translational medicine research.

The project team face many challenges in bringing these disparate data sets together, not least of which is capturing patient data from the clinical systems it is stored in. This information is frequently distributed across electronic medical records, laboratory systems, PACs and cancer information systems requiring sophisticated informatics to extract the source data and format it consistently. Information governance and protected health information compliance are also key areas to address as is integration with biobanks and incorporation of genetic tests results from standardised procedures.

We will be cheering our sister project on and continuing to provide support to the Cancer Research UK team as we begin the journey towards personalised medicine in the UK.