Graeme Dennis on Bioanalysis


Graeme Dennis, Commercial Director of Preclinical Pharma at IDBS, explains how The IDBS Bioanalysis Solution can ease an organization’s QC burden, help them manage workflows more efficiently and increases productivity.


My name is Graeme Dennis, and I’m the commercial director of preclinical pharma at IDBS. This role requires me to approach pharma informatics from the domain perspective, versus looking at our products vertically – how we can thread a relevant story for pharma-scientists, whether they’re in bioanalysis, bioprocess, formulation, or even discovery, through our suite, in a meaningful way that gets them excited about the tools.

IDBS is a long-standing, leading contender in the scientific informatics space. At the center of that is our flagship product, E-Workbook, which is a platform environment that spans across inventory, request, advanced spreadsheet data-modelling tools to enable scientists at the bench.

So, we see one overriding trend, and it’s the desire to treat data as an asset in an organization.  As that takes flight, it looks like having a data strategy – a scientific data strategy – for an organization in pharma. What this really comes down to is decoupling the data from a system of acquisition – say the work station or instrument – and the system of use, which can be any of a zillion analytical tools.  This permits us to then leverage the data – not just for the purpose that we gathered it today – but for use in cases in the future, using a tool we may not even anticipate now or a mode of investigation we don’t practice now. You see this in high-content imaging, screening data, even in vivo and in vitro analysis as well – say in bioanalysis and bioprocess. Also, capturing the learnings that we gain in those processes.

Oftentimes – especially in the absence of an ELN – these learnings will just be lost to history. Retaining data, contextualizing it, providing that data provenance so that when I use a piece of data in the future, I know when it was acquired, by whom, using what instrument, under what circumstance, at what site, under what project, and then I have data that truly stands alone as an asset that’s re-usable, that’s re-findable, and so on.

We find a lot of data today is siloed or so-called ‘dark data’. It may live on a SharePoint, it may live in email, quite candidly, or it’s certainly sequestered on paper. So, how can we at IDBS – or honestly, just in our industry – make this data accessible in the long-term, treat it as a free-standing asset, and one of the most important assets we have.

In bioanalysis specifically, there’s an enormous focus on how we can contract the timeline to perform QC and how do we increase the throughput in the laboratory. So, that can be accomplished a few different ways – candidly, it can be approached by a hardware, it can be approached by training, and we can approach it, of course, from the software angle, which is our emphasis.

I do see the same trends making an impact in contract bioanalysis – the pressures are even higher because contract bioanalysis firms are being asked to create more capacity, do this work cheaper, be prepared for greater variability in the kind of test we’re being asked to do, do it and stay in compliance with legal regulations and industry regulations varying across the world and to continue to build their reputation as they do so.

So how do make sure that every implementation is a successful one? This really becomes more of an organizational question than a software one. We have to bring together people, systems and culture to put the significant energy that is required into a successful implementation. This starts to look like coming together to answer questions that might not have been answered within our organization before – what it is we consider a project?  In the discovery space, the notion of a project may be a protein or a disease state. In preclinical, it probably centers around the candidate compound. So how do we answer, organizationally, some of these tough questions that are going to help us form a data strategy that is responsive to these different areas of the business?

This all contributes to how we make our data findable, accessible and fully contextualized. Then once we’ve considered it internal to our organization, how about our collaborations with our external partners. This is the mode of drug discovery now: partnerships within academia, industry, and government. How can we be responsive to those needs around sharing data, which can be a compliance issue itself now, how can we ensure the integrity of our data so that it is reproducible, and it responds to ethical considerations in our industry? And finally, how are we responsive in a compliance frame as well? – whether it is an IND application, a regulatory response to an inquiry we may have received. All of these require the same level of data, accessibility, indexing and consistency that only just recently the industry has really started to achieve.

The greatest gains in productivity we see are in QC, capturing deviations from process.  So, this is where a platform approach can really come into play because we can draw in different elements of a scientific method execution tool/ELN/sample management/method management to bring in something of the right size to the process. For example, in bioanalysis, we can have method execution elements where we can further capture what lots of solution were used, what instrumentation was used, was that instrument clear for use, and then this really tightens up the QC process so that we capture these deviations. As we step through, these can be rolled up in an exception report that what we found working with our clients can take a week or two in a paper process to assemble. It is literally a click in a system like the one we’ve designed.

Incidentally, the same kind of workflow-oriented solution where we have stepped back from the products and just said ‘what do scientists need?’ — we are applying [that] in other areas, because, when we implement scientific tools, configuration and working with the scientists to deploy something that works, is the biggest part of the implementation. So, what we want to do is offer – when someone starts with IDBS – here’s our formulation solution, here’s our bioanalysis solution, and here’s our bioprocess solution — in a way that we can tweak to fit in the specific needs of our customers. But we have the closest possible thing in discovery science and development science to a turnkey solution and that’s an area I’m looking to help IDBS grow.

I mentioned expanding this idea of solutions — versus referring to our product family, which of course is growing –in my role especially, how can we cut across the product family with meaningful solutions. And they answer specific industry needs, so of course my inclination is that IDBS is the best tool to be brought to bear on any of these workflows. So, I want to identify and really clear up what are these clear advantage positions for us and then how we can bring them to market.

So that will involve shows like the one we are at today at AAPS, illustrating the commercial value to these customers whether it’s a CRO, or whether it’s in-house analysis. Maybe a quick additional thought on that, something that we see happening as companies undertake a platform approach, is – especially in the IT and leadership areas – they tend to ask the question ‘why can’t we use the same system in our discovery areas as we do in our highly regulated GxP areas?’. Well, to the scientist that answer is very clear, we can’t have a discovery scientist clicking through six or seven screens to run one sample because that process works best in GxP. So that’s one thing that we are exploring as well is that idea of workflow as a service. If we are talking about analysis – can we bring the right size sample management, the right size method management, and the right size data capture to a process, so that in a platform solution, we can satisfy that early discovery scientist and that late-stage manufacturing QC scientist with the same set of tools.

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