The arm of a researcher in full PPE uses a mouse at a computer station in the lab in this hero-sized image.

How ELNs Can Help to Drive Innovation in Life Sciences

Electronic lab notebooks have the potential to significantly improve data management issues in biopharmaceutical development

Research and development is considered to be the backbone of the life sciences. The success of biopharma companies depends on the discovery and development of new medicines. Yet, despite all the resources that go into R&D—around $200 billion USD per year—up to 85 percent of all research is wasted due to a lack of data standards and mismanagement of laboratory data.

Electronic lab notebooks (ELNs) have the potential to significantly improve this issue by implementing standardized ontologies so that laboratory data is searchable and reusable from inception. ELNs are much more than a paper notebook replacement—recent research by The Pistoia Alliance found that more than half (57%) of companies surveyed said they use an ELN to enable better data management, inventory management, and scientist collaboration. The pandemic has proved that digital technologies and collaboration are essential to driving innovation. ELNs will support the ongoing movement toward a more digitalized life sciences industry, as well as improving the workflow of the scientist.

Where are we with ELNs today?

Although digitalization has touched all other aspects of our lives, such as banking and shopping, some technologies have failed to reach the laboratory to the same extent. While most researchers do use an ELN, many still rely on pen and paper or use poorly maintained, unwieldy spreadsheets to record lab data. Aside from being inefficient for the scientist, not using an ELN makes finding and analyzing data extremely difficult, leading to duplicated efforts and wasted resources. 

Moreover, where ELNs are used, they are not able to reach their full potential because they are not designed with the scientist in mind. Many ELNs do not integrate well with other software, which complicates exporting data for analysis. Practicality is another issue, with ELNs requiring the use of a computer or screen that may not be permitted in certain controlled environments, such as those involving radiation. 

Finally, there is the issue of vast volumes of data and experiment context becoming “trapped” in ELNs. This is caused by the lack of universal standards for recording data, and the fact that formats differ greatly between ELN vendors. Research is of little use if other scientists and regulatory bodies cannot access it. For example, at the point of drug submission, a huge amount of time is spent manually sifting through multiple data formats in drug submissions, which causes direct delays to new drugs being brought to market. Here are three ways that ELNs can be improved to overcome these challenges and help to drive innovation. 

1. Semantic enrichment for re-use of data

Semantic web technologies have the potential to unlock data that are stored in free-text formats in ELNs. This includes crucial context of experiments—or metadata—such as lab conditions, chemical concentrations, or any other information that might otherwise be lost in the “notes” section of ELN entries. Making these notes machine-readable increases the chances that data can be searched and reused, leading to fewer duplicated experiments. 

Enriching text relies on cross-industry collaboration to build new standards for ontologies and to map their relationships. For example, if an organization sponsoring a trial refers to “paracetamol” in their ELN, but the contract research organization (CRO) it works with refers to the same drug as “acetaminophen,” it is important to map the relationship between these two terms so that the ELN can recognize them as the same drug in future searches. Creating these standards relies on companies collaborating. The Pistoia Alliance SEED project is an example of this, pulling together the resources of several pharma companies to produce computer-readable standard data that is aligned with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles.

2. The future of capturing lab data 

ELNs and data input technologies must be built with the researcher’s workflow in mind to be widely adopted. COVID-19 has reiterated the importance of remote working, the virtual lab, and touchless interaction in our lives. ELNs must now be adapted to fit into these new working practices, whether that means being able to access the ELN via a mobile phone app away from the lab or enabling experiments to be simulated virtually. 

Alternatively, voice assistance could be useful for many scientists whether they are working with contagious pathogens, radioactive materials, or when simply trying to maintain a sterile lab environment. Voice assistance technology already exists in the form of home assistants such as Alexa or Siri, but now is the time for lab software and hardware vendors to develop them further to recognize chemical names, methodologies, and lab equipment. Finally, like free-text enrichment, it is essential that these tools are purpose-built to recognize scientific terminology in order to record data accurately and in a way that can be searched for future use.

3. Integrated safety warnings 

Information about dangerous reactions and potential chemical hazards can also be contained in an ELN—such as data recorded in the open-source Chemical Safety Library, originally developed by the Pistoia Alliance and now in partnership with CAS, a division of the American Chemical Society. The CSL aims to reduce the risk of hazardous incidents in the lab by sharing safety information with the whole community to prevent repeat accidents. This public information could be integrated into an ELN. Furthermore, by harnessing AI, software developers could take this one step further by integrating an interactive, smart system that can alert scientists in real time. For example, if the reagents they are planning to react could cause an accident, or if specialist handling protocols are required for a chemical.

Augmenting the scientist in this way would be particularly useful to support those just entering the field, like graduates. Pioneering technology that makes labs safer, as well as more efficient and intuitive, will also encourage the younger and more tech-savvy demographic of researchers to join the field.

Aware of the laboratory space

For the industry to realize the full potential of ELNs, solutions must be designed with the laboratory in mind. This means making them adaptable to scientists’ working needs, such as hands-free or dark room compatible, and being able to understand scientific terminology. Additionally, the importance of standards cannot be underestimated—they are the only way to bring down costs and drive innovation. Collaboration between ELN vendors, data scientists, and bench scientists on creating new standards will maximize the benefits of ELNs and continue accelerating digital transformation across the industry.