New laboratory informatics system aims to improve R&D efforts

DuPont invests more than $2 billion into research and development (R&D). Dow Chemical invests about $1.7 billion a year in research across all its industry segments. Last year, BASF increased its R&D budget to $2.3 billion.

R&D is clearly a value to companies. However, about 56% of companies classify their efforts to turn R&D ideas into projects as only marginally effective, according to a whitepaper from IDC Manufacturing. Only 25% of R&D projects result in a product that reaches the market. 

"The laboratory environment that exists today, without exception, is very fragmented," Accelrys Senior Director Ted Pawela told PlasticsToday. "Customers can have anywhere from 5 to 15 databases of scientific data, but not one system is allowing them to look at the information at one time, which means no single scientist knows all the systems of body of corporate knowledge. What ends up happening, is that they repeat experiments that they have ran before."

Pawela said that there are three types of experimentation: blue sky, which is "trying out crazy ideas in the lab"; optimization, which means small, incremental changes progressing toward a better product (this consists of 99% of product development); and confirmative, which "make sure we are making the same stuff."

Challenges that arise from the experimentation process include R&D personnel that are isolated by geographical and organizational boundaries, which can prevent collaboration. Pawela said that many requests from experiments are "lost in translation" between the people requesting work and those actually doing it.

The market for "traditional" laboratory informatics systems such as LIMS (laboratory information management systems) and ELNs (electronic laboratory notebooks) is evolving, particularly in R&D, according to Accelrys.

As a way to answer R&D woes, Accelrys, who works with companies like BASF and Dow among other corporate giants, has launched what it calls an "industry-first," the Accelrys Experiment Knowledge Base (EKB). The EKB is designed to raise the bar for experimentation management by enabling organizations to transform mass amounts of scientific data into knowledge essential for faster, more efficient new product innovation.

Designed specifically for R&D, the EKB offers scientists the ability to search and mine experimentation data from almost any source. The system also provides integration and interoperability with existing lab equipment and applications, as well as features for improving experimentation management and collaboration.

"The intent is to allow people to manage planning and execution of experimens but, most importantly, to analyze all that information with the experiments," Pawela said. "It can manage future experiments and get access to scientific data no matter what sort of data base it fits in, and to analyze it by understanding trends and get meaning from it."

One way to improve efficiency in laboratories is to eliminate the repetition of experiments. Pawela said EKB does this by helping to leverage investments already made to get more value from them, which can help companies innovate faster and more efficiently.

EKB offers scientists a single system for the searching, analysis and mining of data from any source based on capability to extract, transform and load (ETL).  Pawela said that the unique "data mart" approach allows end users to consider new ways to query data every day, without the necessity to re-architect the system or its database.

EKB leverages four key capabilities in a modular architecture to increase innovation velocity and consistency by reducing or eliminating repeat experiments, enhancing laboratory efficiency, improving experimentation consistency and extracting knowledge from data.

Capabilities include the planning and design of experiments and campaigns; step-by-step execution of experiments; capture of experiments and sample data; and search and analysis of experiments and sample data by properties/descriptors.

By leveraging the Accelrys Enterprise Platform, EKB also complements document-centric systems such as ELNs and downstream systems focused on scale-up, manufacturing and compliance to support an end-to-end, integrated scientific innovation lifecycle management approach. Integration with ELNs also streamlines project documentation while improving global collaboration and intellectual property protection.

Packaging example

An example of R&D solving an issue is the transition of Heinz ketchup from glass to squeezable PET bottles. Initially, the bottle suffered from leaking issues and after hundreds of scientific experiments the designers were able to create a one-way valve system that ensured no product leakage.

Pawela said if they wanted to design the next generation of the ketchup bottle, they would need to access a body of knowledge such as who worked on the last project and what methods did they already try.

"But what if they aren't able to access it or make sense of the data?" Pawela said. "If they can instead look back at what they had already done and narrow it down, they can get to a point where they innovate much faster and much more effectively. We see all of that as a major opportunity to gain a competitive advantage with more consistent and faster innovation to develop not only a new product, but the packaging of a new product."