Ashu Singhal, President & Co-Founder of Benchling, shares newly heightened expectations for the biotechnology industry following the developments of the last 18 months.
The last 18 months have heightened expectations for the biotechnology industry. Scientists came together to develop vaccines at record speeds and set the tone for future scientific research. In this new era of biotechnology, staying competitive requires three critical components: faster innovation, digital collaboration, and speed-to-market.
But achieving these components isn’t easy. There are barriers, and identifying the problems requires taking a deeper look into the full R&D lifecycle to evaluate how teams work together — or don’t. Despite a close connection between the research and development process in biotechnology, there’s often a disconnect between the two functions when it comes to how they actually work.
With this in mind, here are three key considerations biotechnology R&D teams need to take to accelerate innovation within their organisations.
Update the foundations of R&D
The foundations and principal methods of biotechnology research and development have not changed significantly enough and this is a huge barrier to accelerating innovation. Digitalisation a decade or so ago was the first step towards faster, more efficient (and successful) drug development and uncovering major scientific breakthroughs. Analog data slowly became digitised — with a shift from paper to more data into Excel, gradually moving to electronic lab notebooks (ELN), laboratory information management systems (LIMS) and laboratory execution systems (LES). However, the reality is that even a decade later, R&D teams are still falling back on Excel, email, and even paper notebooks, alongside disparate point solutions for ELN, LIMS, and LES.
Larger organisations are often spending vast amounts of resources to develop their own custom in-house software systems, hindering the ability to drive R&D forward within the business. All of these events culminate in creating silos that exacerbate the challenges of collaboration between R&D teams.
R&D organisations need to suit today’s digital processes and beyond. This begins with a thorough review of its foundational processes to improve and encourage greater collaboration, such as streamlining workflows, automating time-intensive tasks, and identifying tools that actually enable a digital transformation of R&D teams.
Standardising processes and data analysis
Greater collaboration between scientists and researchers can also be achieved by standardising workflows. New scientific techniques including emerging modalities and CRISPR are powering innovative products, treatments and processes being developed across industries including biopharma, agriculture, and chemicals. Companies are generating exponentially more R&D data from their experiments, which is driving critical new insights that can unlock innovations like personalised medicines.
However, this data is only useful if it’s standardised and accessible. It’s common for critical information like this to exist in disparate systems and databases, making the process of sharing data cumbersome and inefficient. This problem is exacerbated when working with globally distributed teams and was particularly evident during the pandemic as teams were forced to work from home.
Data sharing between R&D teams today often requires scientists to manipulate data manually if they want to draw important connections. It stands to reason that adding a new system could mean old data gets scattered, lost, or forgotten, making it difficult to track results, streamline experiment execution, and ensure quality control.
What’s more, research teams are highly specialised and need to simplify complex handoffs with other teams while providing more process context. Meanwhile, development teams need a unique mix of control and flexibility in their process. Scientists are spending a large proportion of their time reconciling data between various systems instead of surfacing and sharing key insights that have the potential to accelerate drug development. In addition, due to the regulated nature of development processes, data standardisation and control is critical for compliance and analysis.
The way insights are documented today needs a major overhaul. Using a unified R&D platform built specifically for the complexity of modern scientific research is one of the best ways to both significantly improve productivity and accelerate timelines for both research and development teams.
Collaboration needs to be encouraged
We have established that work between R&D teams lacks seamless collaboration and the pandemic has highlighted areas that have severely stifled innovation in the industry, such as data accessibility, which was critical in the development of the Covid-19 vaccines.
Implementing real change in the industry requires R&D business leaders to consolidate workflows and foster a culture of collaboration. Silos cannot be removed overnight, teams need to be equipped with the right tools and resources so they are united in pursuit of a common priority: making breakthroughs in scientific research to drive innovations.
What’s next for R&D teams in biotech?
We’re witnessing a true biotechnology revolution with new innovation in fields such as cell therapy, gene therapy, antibodies, proteins, and other new drug modalities. The time has come to design a new foundation for biotechnology R&D, where research and development teams can share insights and learnings to complement their work. Only when R&D teams are freed from the constraints of legacy solutions can the industry make consistent leaps in biotechnology R&D like we’ve seen with the Covid-19 vaccine.
About the author
Ashu Signhal is the co-founder and President at cloud R&D, Benchling, an informatics platform used by scientists to accelerate, measure, and forecast R&D from discovery through bioprocessing. Founded in 2012, Benchling now has over 600 pharmaceutical and biotech customers globally, nearly 100 of which are in Europe.
