Informatics—gaining critical insights through data analysis—plays a major role during research and pre-clinical phases. There’s simply too much data for scientists to process and analyze on their own, or with simpler tools like Excel and handwritten lab notebooks. Scientists are doing everything they can to make the best possible decision during the pre-clinical stage. Taking initiative during these early stages of drug development and discovery allows pharmaceutical companies to produce therapeutics that can increase patient outcomes.
Understanding the data collected in the research and pre-clinical phases is critical drug development. Some of the key pre-clinical activities include capturing data, collaborating with other scientists, integrating and aggregating data, and analyzing that data. In today’s complex laboratory environment, scientists are expected test compounds at a broader scale than ever before. This can ultimately slow down the research process.
Scientists are searching for useful, effective data analysis tools that can automate or streamline certain tasks, allowing personnel to focus on value-added activities. The technology used to analyze data and communicate at a global scale improves processes that inform decision-making during these initial stages of drug discovery.
Informatics fuels the decision-making process
Informatics—gaining critical insights through data analysis—plays a major role during research and pre-clinical phases. There’s simply too much data for scientists to process and analyze on their own, or with simpler tools like Excel and handwritten lab notebooks. Scientists are doing everything they can to make the best possible decision during the pre-clinical stage.
Analyzing, collecting, formatting, cleaning, and organizing data is the busy work that scientists are constantly burdened with at this time, but machines and other specialists can aid in digesting results from tests to help scientists make better-informed decisions.
More compounds means more a robust testing process
During the drug discovery stage, thousands of compounds may be candidates for therapies that make it to the shelves. The amount of different compounds that generally undergo testing depends on the type of drugs and products. Larger-scale companies will perform pharmaceutical tests in the low thousands across the board. If they’re focusing on a smaller subset or more precise project, the range scales down to between 20 to 50 compounds.
Because fewer compounds were tested - and as there were fewer and less complex tests to correlate - in the past it was easier for scientists to understand the data on his or her own. The decision process could occur almost instantly; however, that isn’t the case today. Now, scientists are shifting from small molecule drugs to biologics and only 10% of small molecule projects are considered candidates due to an overarching percentage of failure. Technology allows scientists to make better decisions based on improved IT capabilities and digital platforms.
Today’s research climate fuels a greater need for tech
With more compounds undergoing testing at a broader scale, scientists and R&D organizations may need to consider transforming their approach. Research methods are vastly more complex today, with new methods and techniques constantly making for a more granular process. Globalization is an ongoing trend; research shows that 95% of all countries participate in the clinical research process.
Researchers are generating data from afar in different time zones and sending results by aggregating data through various funnels. Data aggregation is a key component to success, making it critical for organizations to consider integration tools and specialized human resources who can communicate at a global level.
Machine learning and effective IT support allow the lab to view and process data, make better informed decisions, and determine if a drug candidate is viable enough to proceed past pre-clinical.
Easing the path to the decision-making process
With too much time spent trying to find, integrate, and organize data, scientists are constantly further away from the decision-making process. Creating an ecosystem of multiple software packages can be complex; many companies determine there’s an issue and advise changing everything at once. However, breaking the process up into three high-level pieces can ensure a more successful transition.
Effective solutions to reach scientific decisions include:
- Data collection. Data management, from collection to storage, affects every drug development stage. Good data is paramount; it determines what moves to make next. Collect all information about what compounds are being made, and why, to determine the next plan of action.
- Workflow management. Take a look at all of the workflows and processes needed to get the work done and determine their current state. For example, those in charge of testing should have a plan to examine and produce results. Workflow management optimizes the communication efforts. This is an area you can improve incrementally by encouraging the team to focus on data collection as a top priority, streamlining and improving workflows over time.
- Decision support. Once you have all of the data, how do you make the right decision with the tools you have? This is the most critical step; picking the right compounds for clinical trials.
Just like any other step-by-step process, decision support can’t occur until you properly collect and organize data. Capturing and collecting data that leads to better decision making can generate immediate ROI. An organized workflow can be incorporated and maintained over time.
How outsourcing prioritizes informatics in research and pre-clinical process
Laboratories can benefit from reevaluating workflows and focusing on distributing housekeeping tasks elsewhere. The industry is moving toward a largely cloud-based, so scientists should consider the opportunity this presents in terms of task redistribution.
Outsourcing these processes to a skilled specialist saves time, effort, and resources for scientists. This solution-oriented approach introduces modern technology as well as advanced scientific analysis tools. It also increases the focus on capturing data, collaborating with other scientists, and mining and visualizing that data across the organization.
Based on the complexity of experimentation, PerkinElmer devised a system to build a new, comprehensive platform for the lab. It’s designed to be user-friendly and modern, incorporating technology similar to social network platforms with its collaboration and communication capabilities - using modern open source storage and search technology borrowed from social media and online retail giants, providing features similar to social sites like Facebook, LinkedIn and Instagram. It utilizes the same open-source tech as these popular applications, but overlays PerkinElmer’s decades of experience building scientific tools.
The entire solution isn’t in the cloud, as the practicality of cloud-based software sits on a case-by-case basis. However, the industry is starting to incorporate more instruments with cloud capability. For example, screening is often moved to the cloud, as it enables more scalable computation, faster computing of complex screening results. The pharmaceutical industry still has concerns about storing sensitive data in the cloud, though, and many companies are hesitant to make a full transition.
Technology in the lab is constantly facilitating collaboration that makes it easier and more efficient to weed out unnecessary compounds, with the ultimate goal of informing better decision-making. Taking initiative during these early stages of drug development and discovery allows pharmaceutical companies to produce therapeutics that can increase patient outcomes.