Industry Insight: Biology and data capture in the life sciences industry

Benefiting the life sciences industry

Put simply, we help our customers extract maximum value from their research data so they can bring more effective and safer new medicines to market.

To IDBS, biology has always presented a more interesting challenge than chemistry.

If you think about it, chemistry is in general sequential, empirical and follows a set process. Biology, on the other hand, is more subjective, fluid and requires interpretation. We also know a lot less about biology right now than we do about chemistry. This presents challenges from a data management perspective because biology requires you to maintain significant context around the data in order to have any chance of understanding what a particular biological result is saying. An analogy I like to use is this: you have a round table and you ask a chemist to measure the diameter, and they give you the answer of between 72 and 74 centimetres. Ask a biologist to do the same and they will say between 50 and 100, but they need 4 more tables before they can commit to an answer. This may be a whimsical view but it demonstrates that there is a difference in understanding and expectations between chemistry and biology.

Finding new medicines is all about the integration of chemistry and biology. And it is our role as a software solutions provider to the life sciences industry to cater for the differences, yet ensure that the underlying data in each camp can be shared and that enough context is retained to facilitate understanding.

ActivityBase was the first commercial solution available for the capture and management of biological data. Our aim was to provide a scientist friendly tool that would allow biologists to collect experimental data, share it, analyse it, and use it as a basis for decisions on what to do next. As a product, ActivityBase delivered great value to the industry by improving the overall productivity and efficiency of biologists. ActivityBase also gave project leaders a higher quality and fuller complement of available data on which to make decisions.

We are now repeating that concept with our biology ELN product, BioBook.

BioBook takes the principles of data management – data capture, analysis and reporting – and allies this with the record keeping, audit control and patent protection offered by ELNs to provide a unique and complete solution to managing late-stage research biological data.

IDBS and Silico Discovery

The collaboration has provided us with more options. It is actually quite straightforward. The K3 federation technology allows us to access data from many different and diverse sources. Put into context, by combining E-WorkBook with K3, we enable our customers to access all the internal and/or external data they require from one product platform. And we achieve this without complex integration and by making data access seamless to the end user, who doesn’t have to know where to look for the data, but finds it regardless. This is especially applicable to scientists working in such areas as target identification, validation, biomarkers and translational medicine.

Future developments

We are doing a lot of work at the moment to ensure that our entire product range continues to be an integrated framework. We view functionality within our products as components of a bigger picture, which means that we have the ability to expose and share functionality across our products quickly and easily. It also allows us to join up functionality, including visualisation, statistics and prediction, in different ways to provide specific tasks within products – making this functionality part of a workflow.

In addition, we are seeing many customers moving to a Service Oriented Architecture (SOA). Componentisation helps this and we are increasingly using services to integrate our products within our customers’ architecture. Our BioBook product effectively demonstrates the advantages of this approach, by using web services that allow us to integrate the solution into our customers’ infrastructure seamlessly and quickly.

Challenges and evolutions within the Drug Discovery marketplace

I have always had an issue with the term Drug Discovery because it suggests an acceptance that we will simply fall over new medicines as we walk (often blindly) down a path of scientific research – rather than building a path to where we want to go. Someone once said: “If Boeing were to produce new planes like pharmaceutical companies produce new drugs, they would build thirty versions of a plane and the one that flew would be the one that they would market.” If you suggested to Boeing that this is good way for them to get best value from their research dollars, they would laugh at you. The pharmaceutical industry can’t survive using this business model anymore.

Am I oversimplifying things and being overcritical of the pharmaceutical industry? Maybe, but I really believe that we have to work harder to remove serendipity from the process – by designing drugs rather than discovering them. If we accept this as our higher aim, this will drive a number of key changes in terms of how we do things moving forward.

A practical example of where I see big changes in how scientists are now working together is in the area of translational medicine, which is already having significant impact on drug research. Traditional lines of interaction between scientists are definitely becoming blurred, in other words the division between ‘omics, screening, pharmacology, preclinical and clinical is becoming less clear. I think this is a good thing. Yes, from a people point of view maybe these departments are still separate and will probably continue to be so in the future, but from a data perspective, it is a completely different picture.

Organisations are starting to take a more holistic view of the whole process of drug research and with this comes challenges. Not only is there more data and more different types of data to deal with, but the complexity in how both structured data and unstructured data can be related is also getting more complex. What I hope will change as the more holistic view develops is a move away from the traditional route of pulling data together, doing some analysis or visualisation and prediction, and coming to a conclusion as to how to proceed, while only recording the inputs and outputs of this process. To me, what is really interesting is how the decisions are being made and what assumptions are being taken, not necessarily the decision itself. If you can trap this and make it visible, then you are disseminating incredibly valuable information that organisations can share and learn from. I feel that few companies are doing this at the moment and don’t realise that they are sitting on a mountain of untapped value in the data they already have!

This is where I believe IDBS can play a key role in moving forward. By supporting this holistic approach to drug research and facilitating the concepts of translational medicine, especially in terms of bringing data together and presenting it back to scientists in the most usable and useful format, IDBS preserves the correct context to make sense of data.

Current technological trends in Drug Discovery

The industry has never had a shortage of new technology, normally arriving with a fanfare and associated hype that promises to radically change or improve our ability to produce new drugs in some way. The problem is that despite what the marketers said about HTS, uHTS, combi-chem, HCS and genomics, and the advances these technologies would deliver, the time and cost to develop new drugs has increased, not decreased. It’s not that these technologies, or their use, was flawed in any way – most have provided some reasonable level of value and are now considered part of the mainstream. But we cannot ignore the fact that it is just harder to get a drug on to the market these days.

New technology, whatever it is, should always add value to the process, so we should continue to bring it onboard as quickly and efficiently as we can. But technology on its own will not be enough. Again, we have to look to change how we do things rather than simply doing the same things in a different way, which involves more than just technology.

www.idbs.com

Neil Kipling, Chairman and CEO, IDBS

Neil founded IDBS in 1989 and has since developed what was a small niche data management consultancy into a leading provider of integrated software solutions for the life sciences industry.

Prior to founding IDBS, Neil worked as a technology consultant, primarily with financial institutions in London. Neil’s career began at Lloyds of London where he worked as a business systems analyst after graduating from Leeds University in 1984 with a B.Sc. in Microbiology.