How today’s next generation ELNs can help drive drug discovery

NGP. What trends are emerging in terms of ELN features for the biotechnology/pharmaceutical industry?

One major trend involves recognizing the importance of late stage biological data to an R&D organization. An ELN that captures and manages data from this much more complex area of biology can achieve significant ROI - typically 15-25% increases in efficiency. Based on feedback from our customers, this is around four times greater than that achievable when compared to chemistry-focused ELNs. Estimates show that ELNs in the medicinal chemistry area typically give a 0-5% efficiency gain, most of which can be lost in the cost of integration to internal systems.

The value of a data point or decision during a study in later stage discovery and preclinical research is orders of magnitude more than that in the earlier discovery stages. For example, the direct costs of a compound synthesis are significantly less than a PK (Pharmacokinetics) study, but the value of making the correct decision from the output of results from a PK study and reducing the time taken to reach the decision, is significantly higher. Pharmaceutical organizations now want to ensure that this very detailed fact and context data from later stages of drug development is integrated into corporate knowledge systems.

A second trend is around how to ‘exchange’ data and ensure that it is valid, so digital signatures and user authentication are becoming more and more important in the ‘pharmaceutical knowledge market’.

One further trend appears to be one of consolidation – to manage the risk and reduce the cost of the upkeep and maintenance of a large number of software tools and their integration. ELNs are being asked to deliver very specific tools for each of the different areas of research across many disciplines at the same time – features such as extension points, open architecture and open format standards are therefore critical.

NGP. How are companies using ELNs today? Is that changing?

The industry started using ELNs as just a simple replacement for a paper lab notebook, by capturing text, images and simple static data. This is changing rapidly as companies demand a lot more from their ELNs. Typical examples include:

• Complex biological data management
• A ‘portal’ for data amalgamation that allows scientists to search and query any data source
• A single point for users to conduct all their work – not just capture simple lab notebook type data (pictures and text)
• A tool for increasing efficiency around the labs – such as task management
• A tool for facilitating consolidation of data from many different scientific disciplines
• A method to get more research-centric data (context and fact) into the corporate systems.

NGP. How can ELNs be used more effectively?

Integration, integration, integration! Pharmaceutical companies want to make the whole process more efficient and now see that effectiveness can be achieved by integrating their process, data and knowledge. A combination of different technologies, an understanding of multiple scientific disciplines and a firm grasp of the drug discovery process is needed.

NGP. Do you have evidence to support claims of greater productivity?

One example quoted from a user who uses IDBS’ BioBook was: “It now takes me 15 minutes to do my data analysis, when it used to take me 5-6 weeks.” That’s real ROI, and more importantly delivers direct business value by speeding up the process of bringing a new drug to market.

A number of customers have told us they “do not see a large enough ROI for chemistry ELNs, so the ROI is not an overwhelming reason to buy - unlike biological ELNs”, and that the main benefit is consolidation.

Example Case Study: Benefits of an integrated environment
Before:

• Measuring weights and recording in a paper notebook
• Take information to statistician in another building
  • Wait for available statistician
• Group
• Check variance
• Re-group
• Check variance
• Repeat until variance minimized
• Requires several hours…

After:

• Measurements recorded directly into solution
• Use specialized randomization tool
  • Wizard-based
  • Pre-validated by statistician
  • Groups animals and automatically provides variance report
• Now takes less than one minute…

Plus…

• Reduction in data transcription errors/time
• Reduced software overheads/maintenance costs
• Removes costs associated with maintaining paper-based system

Indirect time savings:

• 21 CFR part 11 and SAFE compliance
• Re-use of validated studies
• Implementation of global data dictionary across organization
• Minimizes inadvertent duplication of work
• Improved capture, monitoring and compliance of procedures to secure intellectual property
• Decreased time to market, due to faster study completion

With just one of the many features in our solution, time savings of at least 2-6 hours are achieved at the start of every study for each scientist.

NGP. Are there any other benefits?

Previously there have been no real solutions that cater for the complex biological market place, and this very expensive data and knowledge has just been ‘left in spreadsheet files’ and documents. One solution that could provide both records management (context data) and data management (fact data) appeared intangible. IDBS’ BioBook, however, is such a solution, allowing the very rich data in the late discovery and preclinical areas to be accessed, shared and used by the rest of the organization.

One point where this will have a large impact is in predictive ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicology). Good quality data in this area can benefit research decisions at different levels:

• Scientists: Increase bench and library time by reducing data analysis and lab notebook write up burden
• Team Leaders: Reduce time spent preparing reports. Strengthen decision-making support. Facilitate report creation
• IT: Reduce infrastructure cost and complexity
• Business: Improve security, integrity and organization of data. Provide a data store of knowledge of compounds as a resource to facilitate decision making. Dramatically reduce the total cost of ownership around a project by streamlining process and investments, as well as associated on-going maintenance and support costs.

NGP. Are there any downsides?

The cost of integration is usually massively under-estimated and the ongoing cost of custom integration is also rarely considered in the total cost of ownership analysis of a project. If integration is done, it should be in a manner that limits the need for massive ‘repeat’ work. Customization is very expensive and eats into the ROI of any system, so configuration should be used wherever possible. IDBS has made this one of the key strategic directions for their solutions – using web services, data marts and open architectures.

One comment from a large pharmaceutical company revealed that what was initially considered an off-the-shelf purchase for an ELN POC project quickly became a large integration project.

There are some ‘social and behavioural’ downsides that most people recognize, such as taking the time to accept a new way of working, so the ROI benefits are not seen as quickly as some may expect.

ELNs may also increase the focus on software performance and put a greater demand on infrastructure such as networks and the power of the desktop PC environment.

NGP. What sets your products apart from the competition?

We have started by catering for the most difficult areas of biology, which has ensured we have the capabilities to capture and manage very complex multi-dimensional data. Biology also requires some chemistry, so it means that it has been, relatively speaking, easy to add chemistry to our ELN platform.

IDBS is an established scientific data management company. We provide a solution to capture, manage, analyze, report and mine both structured and unstructured data across the whole R&D organization. The solution also integrates with existing systems via web services, to ensure data consistency and availability of data to the rest of the originations systems. Our experience has allowed us to meet the demands of the industry - true electronic experiment data management across the whole R&D organization – not just a tool for replacing paper lab notebooks.

NGP. What do companies tend to overlook in terms of implementing ELN policy?

A strong data management strategy is paramount. Not many organizations think through scenarios such as the extensive use of a tool by, for example, 2000 scientists simultaneously. Again, integration is a potential issue. Many organizations may have stopped using an ELN after the pilot stage because the burden of a full-scale roll out on IT teams and systems becomes enormously apparent.

NGP. Are there opportunities using ELNs that weren't available with paper notebooks?

One example is the automation of tedious tasks – data analysis, report generation, record sign-offs, simplified processes and ‘real time’ process re-engineering (BPM).

Other advantages include:

• Reduced replication of work
• Easier publication of data, opening up information to the rest of the organization and allowing results and information to be combined to form new hypotheses
• Greater management awareness of how many experiments are open, under review and closed at a given time in any project
• Protection of investment in corporate IP, in both the legal sense (patentability) and in the corporate knowledge sense (know how).