IDBS Data Management Blog

Part two of our ‘smart lab’ blog series around ‘getting the data right’ looks at managing external partner relationships and standardization challenges, based on the infographic released by Smart Lab Exchange (#SLABx).

Managing Relationships with External Partners

Scientific relationships, like social ones, are really all about communication. People used to ‘talk’ through the crafted written word, now largely replaced by instant communication through high context voice and video. Scientific communication is about and through data, but we need to break out from the idea that document or file-sharing is today’s best answer. We need to make sure scientists are able to securely access each other’s high context data at the right time. This actively stimulates quality discussion. Dropboxes and file shares do not. Using granular security systems can host multiple collaborating parties and allow them to secure high quality data in a consistent way. They can then choose to share some or all of it within the collaboration (#collaboration). This can mirror exactly the collaboration agreement between the parties.

The smart data principals of context and connectivity have enabled telecoms communities to create networks with massive value. R&D communities should learn from this success and build the quality systems, datasets and collaboration tools that will enable their Big Data (#bigdata) to deliver Big Collaboration for Big Science.

Lack of Standardization

John Reynders, Vice President, R&D Information at AstraZeneca, reminded the JP Morgan Big Data audience in January ‘The future is federated’. This reflects the need to deliver connected R&D against a background of distributed data. Most sane CIOs recognize that creating ‘Death Star’ mega warehouses to drive standardization is impractical and if it involves data such as patient records, also potentially unethical. So how do we initiate standardization in a federated world?

Ontologies have a massive role to play in driving simplification – whether they are internally or externally curated – those which plug into process applications are key. They drive how data is captured and contextualized, a smart approach which enables high value data assets to become interoperable and comparable.

Lon Cardon, SVP at GSK said at the same meeting: “we just need the right approach to noisy datasets.”We believe that this is missing the opportunity to learn from other sectors such as the telecoms industry, where effort and investment is focused on reducing the noise and the gaps, not simply accepting them and filtering them out.

No more excuses

R&D data exists across multiple systems, disciplines and locations. So long as this data can be linked, through context and provenance, it can be made use of. Building a strong foundation of quality, contextualized data is the key.

In today’s cloud-enabled world of extendable bandwidth, the old limitations of scale no longer apply. Gartner Inc. recently highlighted the availability of global R&D knowledge management systems that support multidiscipline collaboration. Where enterprise class systems like E-WorkBook exist there is no reason why high quality data capture, contextualization, ontology and security should remain long term strategies. Putting these in place NOW in ’SmartLabs’ around the world will enable truly smart R&D enterprises.

So much gets written about the woes of R&D. It’s time to stop. Think. Act. That’s our message at this year’s IQPC SmartLab’s conference in Munich (#SLABx) this week. Getting the data right inside today’s ‘SmartLab’ enables smarter enterprise R&D tomorrow and in the future.This first of two blogs kicks off today with integrating legacy systems and managing secure data flow, based on the infographic released by Smart Lab Exchange.

R&D (#R&D) creates and uses data assets. Like any manufacturer it needs to understand where its assets are, how good they are and how to put them together to make a quality product.The advantage of data as a product is that it can be used, reused and repurposed again and again.

The smart principles of R&D data are straightforward:

• Capture data with context
• Make sure you understand the provenance of the data
• Structure it so that it can be combined and consumed by decision-makers in their own way

Straightforward to say but to achieve this data manufacturers need to think and act for the long term, not just immediate ROI.

Integrating Legacy Systems

Some legacy systems,in-house or COTS, form parts of a fixed process that does not need to change. These systems act as feeders for a foundation of quality data and should be retained. They can be driven by other systems such as process ELNs (#ELN), and their data harvested for use elsewhere. Integration through RESTful web service APIs is the most flexible approach but where the technology can’t be applied, bespoke integrations can still be of value to source the right data and context.

However, where a legacy system – particularly one of the many legacy in-house solutions – is standing in the way of progress,the smart approach is to retire them fast and get their function replaced by a cross-domain, multi-process application, such as E-WorkBook. There is no future in making a data compromise for expediency if the net result is poor quality data.

Managing Secure Data Flow

In a today’s multiparty R&D environment it is vital that data, process and context are stored securely. But what does that mean? Firstly, it’s about the individual: their profile, group and role should define what they can do, and what they can see.  Secondly, it’s about data provenance: all data should have audit trails from capture, through any modification, all the way to consumption.

This approach is standard practice in regulated environments but the principle applies everywhere, even if you do not need the GxP rigour at the bench. Thinking about security and audit right at the start is smart because it is too hard to retrofit down the line. If you get the security and audit right at the early stages, then whatever workflow and orchestration tools you have can be used to traffic data to the right place and in front of the right person.

My next blog will look at managing relationships with external partners, the lack of standardization and how data quality is key, because contextualized data underpins everything.

Are too many square pegs and round holes getting in the way?

With the increasing globalization of bioanalytical (BA) operations, there can’t be many labs anywhere in the world that aren’t looking for ways to improve their productivity and reduce their cost basis. But with few, if any, meaningful gains possible on the operational side, one needs to look elsewhere in the process for these advances.

I’d like to propose that better data management and streamlined reporting are potential candidates for the next quantum leap. Improved ways of capturing and organizing both structured and unstructured data; enhanced workflow and process management; automatically enforced QA and superior reporting capabilities – that’s the holy grail we are all seeking. You only have to look at forums debating bioanalytical issues, such as on LinkedIn, to realize the capabilities that people crave.

A LIMS can, and does, play its part in the BA lab. But for added flexibility, new generation ELNs have much more to offer – provided they operate as needed in this highly specialized environment.

Many are the times that I have seen an enterprise attempting to utilize the instrument-bundled capabilities of an ELN, but it appears they are trying to fit a square peg ELN into a lab system with a round hole. Hammer it home hard enough and you will make it fit … but it cannot deliver all the benefits required to be truly beneficial. What you need is a platform neutral ELN solution that gives total access to your data and integrates intelligently with your existing processes.

Call me biased if you like, but there is already an ELN that delivers all that and more – the highly scalable E-WorkBook suite from IDBS. It improves to let you create your own unique methods, enforce compliance with them, and flag any deviations. But it’s not just me saying it. Ronald Shoup, from AIT Bioscience recently commented, “We adopted E-WorkBook to provide bioanalytical preclinical and clinical study data at a faster pace, with higher quality and at higher efficiency.”

Perhaps the most critical aspect of a bioanalytical study is its reporting. Every lab that submits bioanalytical data to a regulatory agency, in this case the FDA, has to comply with identical guidelines. Yet all labs works differently, which can make the reporting process hugely resource intensive. LIMS can help reduce the burden in key aspects of lab operations and is widely used. However, it has deficits in areas normally associated with an ELN – lab functionality as well as process improvement capabilities without the rigidity you expect from a LIMS. To put it simply, choosing the right ELN solution can help you collate your data across multiple runs into a single report, in an easy to use utility (say, Microsoft^® Word), resulting in time saving. Some labs that have deployed E-WorkBook Suite have estimated savings of up to four weeks. If you’re a CRO imagine the difference that could make to your productivity and your bottom line.

If ELNs are the shape of things to come to improve productivity in bioanalysis – and I believe they are – it makes sense to choose a solution that won’t fall apart regardless of the operation you’re trying to improve.

To find out more about the IDBS E-WorkBook Suite click here.

Amongst the companies providing fodder for booth trophy hunters, national dancers, alphorns and buckets of beer, what was one of the big buzzes at BIO 2012? Bioprocessing. Last year this area was just a zone, but it has fermented into a healthy biomass of hundreds of large molecule therapeutics, antibody and vaccine CMOs. Not just the Lonza, DSM and Xcellerons but many, many more providers of bespoke biologics manufacturing were there. This was no Pittcon but the amount of stainless steel and bio manufacturing expertize was the largest ever.

So, how come this growth-based industry is growing so fast? The FDA has fed the growth by its Quality by Design (QbD) requirements, backed up by some recent high profile fines for major pharma batch failures. Coupled with the rise in large molecule therapeutics, vaccines and biosimilars the momentum is unstoppable.

But there is still a big problem brewing in fermenters around the world….data overload. Bioprocess development and QA produce mountains of process and scientific data. This data is vital in building recipes that provide consistency of production and to compare today’s yield against historical data, to take decisions and share them with others. Amazingly many of these processes are still paper driven and laborious, so the impact for a low margin CMO, seeking to attract business and keep the FDA happy,  is clear. Go digital and get efficient. This is not a transactional ‘BI-like’ aggregation of dispersed data but an integrated, managed data approach.

When developing a process and undertaking experimental design, each step needs to be captured, compared to historical data, and integrated with other data to secure IP and provide process insight.  Into this data-rich environment step GMP-compliant process ELNs such as IDBS’ E-WorkBook which is both a research ELN and Process Execution System, able to cross the boundary of R&D. Coupled with Design of Experiments systems such as uMetrics, and internal historian systems, these platforms are able to capture, compute, compare and secure process data; then integrate upstream to Manufacturing Execution Systems (MES) and Enterprise Requirements Planning Systems such as SAP.

The bioprocess development process has been very document-driven and this is hugely inefficient. Documents should be generated just in time rather than generating data from multiple documents. This is where data management takes over from legacy document/paper management. Fast-moving Bioprocess organizations’ process engineers  now inhabit a flexible, cheaper data-driven world with documents generated on demand, rather than living in a document heavy environment.

There was plenty more at BIO 2012 but for sheer biomass at BIO in Mass the Bioprocess team wins. With improved data management and associated process insight these companies will expand further to become major league players providing cheaper, better therapeutics in the decades to come.

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.