Open Science: the revolutionary approach to pharmaceutical research

A step change is occurring in life sciences research. An industry once regarded as a bastion of intellectual property and patents, with research taking place behind the walls of lab facilities, is embracing the open-source revolution. Whether within public entities or large private players in global pharmaceutical research, scientists from around the world have started to put collaboration at the forefront of their work. Such is the impact of this, that Seqera Labs data has shown nearly three quarters of 500 global research scientists polled believe open-source collaboration is now “fundamental” to the work they do. Modern pharmaceutical research is only as good as the software that underpins it, and with a collaborative approach to developing the tools that help us diagnose and understand disease, the life sciences sector is seeing rapid advancements in areas such as genetic diagnostics, cell therapies, and personalised vaccine.

Transforming research infrastructure

When we think of pharmaceutical research, we tend to go straight to the output: pathbreaking drugs and vaccines in the treatment diseases no one thought possible. Yet it is the research infrastructure that is truly at the heart of biomedical science. This once-guarded process now benefits from open-source software where pharmaceutical companies and individual researchers alike can share their data for others to use. This sort of collaboration is made possible by modern software engineering practices including sharing of source code, containerization technology and workflow tools that allow for analyses to be shared and run across different platforms, organisations and continents.

Where reproducibility and repetition were once a significant barrier to research, and cost-constraints the key factor in funding a project, scientists now have the tools to effectively process huge datasets that can be independently verified and replicated. Open science has allowed experts from all around the world to collaborate on the methods and analytics tools which can be deployed securely and efficiently within private cloud environments. Open science principles such as FAIR are so embedded within the research process that they have been integrated as a new core principle of the EU’s Horizon Europe programme to boost international collaboration in research.

What’s more, the UKHSA has announced that investing in genomics and genomics sequencing capacity will make up a key part of its health security strategy. Genomics sequencing was a critical part of the UK’s surveillance systems through the course of the Covid-19 pandemic. The vast mobilisation of the country’s sequencing capacity was made possible due to the use of reproducible models and data pipelines shared by scientists globally. Such have been the advances in sequencing and subsequent analysis that a full human genome can now be performed with 24 to 48 hours.

These developments may have seemed fanciful just a half decade ago, but the collaborative development of the key analysis tools and infrastructure in the cloud has ensured more scientists from around the world can focus on the science and get to a result faster. In terms of the efficiency improvements when running large-scale sequencing programs for pathogenic surveillance, diagnostics or developing precision medicine, this change is key. Open science is powering the genomics revolution.

Improving the affordability equation

The increasing sophistication of medical treatments was brought to the forefront of the public consciousness via the development and application of mRNA technology in Covid-19 vaccines. While this approach may help revolution how we treat infectious diseases, the highly promising advances in CAR T-cell therapies and other personalised treatments bring new challenges to affordability.

BioNTech, recently announced thattrials of its personalised cancer vaccineswill be taking place as early as autumn of this year. These treatments rely on powerful informatics processes, which must analyse terabytes of raw sequence data to providing the right information quickly, reproducibly, and effectively. Scientists are now increasingly sharing the machine learning tools that underpin these analyses for the benefit of the wider scientific community, and in the name of innovation. There is clearly a recognition that improved scalability of research infrastructure is now fundamental for clinical treatments and future novel developments.

BioNTech, recently announced that trials of its personalised cancer vaccines will be taking place as early as autumn of this year. These treatments rely on powerful informatics processes, which must analyse terabytes of raw sequence data to providing the right information quickly, reproducibly, and effectively. Scientists are now increasingly sharing the machine learning tools that underpin these analyses for the benefit of the wider scientific community, and in the name of innovation. There is clearly a recognition that improved scalability of research infrastructure is now fundamental for clinical treatments and future novel developments.

Another example of open-science research principles in action is the pioneering work done by Genomics England. Alongside the NHS, Genomics England is working to deliver whole genome sequencing for patients with rare diseases and cancer. Such work requires extensive data collection and evaluation. By opting to use a centralised cloud-based platform, Nextflow Tower, researchers working on these projects can draw conclusions on patient needs with increasing efficiency, launching, managing and monitoring scalable data pipelines all within a cloud-based system.

As demand for personalisation of medicine grows, being able to build scalable and repeatable data analysis workflows is crucial. This type of research and treatment is hugely expensive, which is why saving both money and time by being able to collaborate on both data generation and analyses will allow for more innovation and better outcomes for patients. We are already seeing how data sharing can facilitate collaboration between Big Pharma companies, researchers and small biotechs, speeding up productivity and efficiency at the earliest levels of research.

The bottom line is that open science, and a culture of collaboration across researchers from around the world, has led to significant improvements in research infrastructure. With the recent advances in machine learning it has become clear that computationally driven research will form backbone of the scientific process over the next decades. Open science will form a central theme in this new chapter of biomedical discovery.