Over the years, we’ve been involved in a number of LIMS/ELN selection projects. In this new series of blog posts, we’ll take a look at some of the lessons learned from those projects.
It’s a commonly held misconception that ELN/LIMS implementations are a wholly technological solution to a scientific problem. The reality is that every successful informatics implementation is a three-legged stool consisting of people, process and technology. Each leg plays an essential role in a successful outcome of the project, and eliminating any one leg can have disastrous consequences.
Regardless of the size of the organisation, most life science companies have some form of governance structure in place to insure that strategic decision makers have a view of what’s happening within the company, identify challenges and resources, and set direction.
When selecting an eLN/LIMS system, the governance structures within the company help set the priorities, drive the agenda for the effort, and that ensure that key decision makers and internal subject matter experts help advance the selection process. Buy-in at all levels of the company is essential, and alignment with business goals and priorities ensure that the teams use the same filters when reviewing vendors and their solutions.
At a minimum, you’ll want the following types of people involved:
Senior Leadership – these leaders will form the steering committee. Their responsibility is to identify and recruit key opinion leaders in the organization, set the tone, vision and agenda for the efforts, identify strategic priorities, and set budgets.
Key Opinion Leaders/Subject Matter Experts – Their role is to help explain your scientific processes, the tools you use, the data you collect, and the analyses you perform. This information will drive the requirements gathering process. They’ll also help evaluate each of the solutions. Look for people who are typically early adopters of new technologies, who have hands-on experience with lab processes and whose opinions hold sway within the organization.
Research Informatics/IT – Their role will be to evaluate the solution on its technical merits, determine how the solution fits with the existing infrastructure, and to assess the associated costs for each solution.
At a certain level, all drug discovery, diagnostic and medical device companies follow the same industry-standard, stage-gated approaches approved by their industries. But the devil is in the details, and key to a successful systems implementation is to have a common understanding of the process that your company follows to get a product out the door. It’s this process that you’re looking to support with your new system. You’ll want to understand the answers to the following questions:
What are the steps in the process?
Which steps in the process generate data?
What instruments and software are involved?
What types of data files are generated? (Excel, PDF, XML, JSON, proprietary file types?)
How big are the data files?
Are the instrument controllers networked together? Do they automatically deposit files on a shared drive? How frequently is the drive backed up?
Who is responsible for that data? For generating it, for verifying it, for recording it?
When something goes awry, how do you track down the source of the problem? By and large, variations in experimental data tend to come from two sources: operator variability and material variability. Are you collecting the information necessary to track down the source of experimental variation?
One of the consequences of having a rapidly-moving, science-driven company, is that the velocity of change means that scientific groups are often operating in relative isolation, unaware of the processes upstream and downstream of them and the impacts on eachother.
This can result in handoffs between organizations that require additional work. For example, you might be outsourcing part of your process to a CRO, and the data you get back must be “munged” in order to get it in the right format for the software application that you’re using to analyze the results. That data munging process can be time-consuming and error prone, and wherever possible, we want to try and eliminate these types of problems by bringing them to the surface and creating organizational awareness, but also looking to vendors for possible solutions to those problems.
The technologies that drive your business are constantly changing. And having a broad view of the players in the landscape can make all the difference in your selection process.
Here are some of the more recent innovations that we’re seeing:
Although vendors have always had solutions that could be deployed in your own data center, over the past 7 years we’ve seen the movement towards the cloud. This makes it easier and less expensive for startup companies to manage their data and operations without large capital expenditures in infrastructure. It also means that geographically-dispersed organizations can work more effectively together. And with an increasing reliance on partnerships, cloud-based solutions can make it possible for partners to share data in a secure fashion.
Machine Learning and Artificial Intelligence have been hot buzzwords over the past few years, but until recently they’ve been a solution in search of a problem. Lately, that’s changed, with the emergence of Machine Learning applied to screening data. It allows your scientists to discover new trends from very diverse data sets. You can examine screening data for millions of molecules and discover unseen correlations between structural or sequence, and changes in activity in QSAR (Qualitative Structure Activity Relationship) data and BSAR (Biological Sequence Activity Relationship) data.
Science is by nature a collaborative endeavour, and vendors are building collaborative capabilities into their systems. These capabilities come in different forms. Collaborative image annotations allow researchers to ask for help from colleagues, and direct their attention to specific regions of interest. Think of it as Google Docs for images. Event feeds and dashboards help teams keep up-to-date with projects, and communicate more effectively.
IOT in the Lab
Industry standards organizations like the SiLA (Standards in Lab Automation) have been working in on standards to allow instruments and robots to communicate with LIMS software using standard Internet-of-Things (IoT) protocols.
Need help getting started with your ELN or LIMS project? Contact us