The biotechnology industry has changed as a result of the pandemic, and expectations of the sector are higher than ever. The recent development of Covid-19 vaccines at pace has shown what science is capable of, and future scientific research must match this level of innovation. As we usher in a new era of biotechnology, staying at the cutting edge will depend on three critical components: faster innovation, digital collaboration, and speed-to-market.
These can be difficult to achieve, and there are a number of barriers to getting there. Identifying these obstacles requires taking a deeper look into the full R&D lifecycle, and understanding how teams work together — or don’t. There is a close connection between research and development teams in biotechnology, but there can be 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:
Modernise the building blocks of R&D
The foundations and principal methods of biotechnology research and development have not changed significantly over time, which is a huge barrier to accelerating innovation. A decade or so ago, digitising processes was the first step towards faster, more efficient (and successful) drug development, and supported major scientific breakthroughs. Analog data slowly became digitised as researchers moved from paper to inputting data into Excel, and then shifted towards electronic lab notebooks (ELN), laboratory information management systems (LIMS) and laboratory execution systems (LES). But 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 also often spend vast amounts of resources developing custom in-house software systems — which can hinder the ability to drive R&D forward within the business. These factors together result in siloes existing within an organisation, and exacerbate the challenge of R&D teams collaborating effectively.
R&D must adapt to today’s digital processes and be able to work for new processes in the future. To make that happen, the first step is to review how R&D processes can be adapted to improve and encourage greater collaboration. For example, by streamlining workflows, automating time-intensive tasks, and identifying tools that actually enable a digital transformation of R&D teams.
Changing the way data is documented
Greater collaboration between scientists and researchers can also be achieved by standardising workflows and the recording of data. New scientific techniques including emerging modalities and CRISPR are powering innovative products, treatments and processes being developed across industries including biopharma, agriculture, and chemicals. In line with this, exponentially more R&D data is being generated from experiments, which is helping to find new insights that can unlock innovations like personalised medicines.
However, this data is only useful if it is standardised and easily accessible, but often this kind of information exists in disparate systems and databases and makes the process of sharing data difficult and inefficient. This problem is exacerbated when teams are spread out around the world, and was particularly evident during the pandemic as teams were forced to work from home.
In order to draw conclusions from data, scientists need to share it between R&D teams. This often requires manual manipulation of data, and 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 data for other teams, while also providing more process context. Meanwhile, development teams need a unique mix of control and flexibility in their process. Scientists spend a lot of time reconciling data between various systems, rather than being able to dedicate time to 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.
This way of documenting insights 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.
A top-down approach to collaboration
Work between R&D teams often lacks seamless collaboration, and the pandemic has highlighted factors that have halted innovation. For example, the Covid-19 vaccine development showed that data accessibility is critical for bringing new drugs and treatments to market at pace.
Implementing real change in the industry requires R&D business leaders to consolidate workflows and develop a culture of collaboration. Removing silos takes time, and 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 does the future hold for R&D teams?
We are at the beginning of a revolution in biotechnology, with new innovation in areas like cell therapy, gene therapy, antibodies, proteins, and other new drug modalities. Now is the time to design a new foundation for biotechnology R&D, where research and development teams can share insights and learnings to complement their work. Consistent leaps in biotechnology R&D, like the ones we’ve seen during the pandemic, will only be possible when R&D teams are freed from the constraints of legacy solutions.
Ashu Singhal is the president and Co-Founder of Benchling
