How effective data strategies in labs are increasing the throughput of lifesaving drugs

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Pietro Forgione of IDBS explains how effective data strategies in labs are increasing the throughput of lifesaving drugs.

From R&D contract organisations to large pharmaceutical companies, many drug development firms face the same issues when it comes to data: legacy systems and methods of data capture, and inefficient collaboration among teams and organisations. While these incumbrances presented sizable hurdles to establishing workflows before, COVID-19 has exposed them as serious problems putting the brakes on higher throughput of viable products.

The development lifecycle of a drug or therapeutic is a complex journey. It is comprised of painstaking processes and rigorous experimentation, all of which must be conducted within strict regulatory frameworks. It’s not a short or cheap process, taking anywhere between 10 and 15 years for a drug or vaccine to go to market and costing an average of $1bn across the whole development lifecycle. But during a pandemic, we simply don’t have a decade to wait.

Firms involved in drug development must therefore look to implement the changes that will bring about efficiencies, improve collaboration, and reduce time to market. Increasingly, this means establishing new systems, technologies and processes that capitalise on the most important asset – data.

Adapting to the times

With a ban on non-essential travel and social distancing rules in place, working from home has become the new normal. If we were heavily reliant on technology before, it was nothing compared to our needs now. It has seeped into every aspect of our lives – shopping, entertainment, work, and social contact. Today, technology doesn’t just complement daily life; it sustains it. And the life sciences industry is no different.

Research and development (R&D) organisations are racing to find a therapy to fight SARS-CoV-2 until a vaccine can be rolled out to the public. Limited by the current situation, many drug discovery and development companies are rapidly seeing the benefit of technology to keep in touch with colleagues and progress research and development projects.

Scientists, of course, want to get on with science. But when it comes to meeting regulatory requirements, and ensuring experimental data is collated and recorded accurately, the administrative burden can be overwhelming. This is especially true of labs using outdated methods of data capture. Notebooks and study binders, used in conjunction with Excel spreadsheets, for example, do not guarantee accuracy. Even when employing more advanced technologies, such as laboratory information management systems (LIMS), much of the administrative information associated with projects, particularly that which is essential for regulatory compliance, must be captured manually.

Research even indicates that up to 50 days in an R&D scientist’s working year is spent recording data in this manner. This explains why up to 20% of development work must be repeated due to data integrity issues.

Companies that are digitally enabled are generally moving faster than those using legacy systems to capture, organise and store their data. The combination of automation and best industry practice built into software significantly reduces human error, minimising re-work and saving time.

Finding a viable therapy/vaccine faster

US-based biotech company Moderna was one of the first to manufacture a vaccine against the SARS CoV-2 virus causing COVID-19 and started clinical trials on 16 March. Considering the virus’ genetic sequence was only released 63 days before, Moderna’s development speed is unprecedented.

The vaccine is based on relatively new technology – mRNA, or messenger RNA. While SARS-CoV-2 is a novel virus, Moderna has previously collaborated with partners to tackle MERS-COV (Middle Eastern Respiratory Syndrome), another type of coronavirus. The vaccine had only reached the early research phase, but, according to Moderna, the data from the study has been invaluable to the response to SARS-CoV-2.

It provided insights into the mRNA-based vaccine now in clinical trials – significantly shortening the research timeline. Having all the data and experiment information from the MERS outbreak available has made it quick and easy for scientists to re-use the data for the current pandemic. Imagine the potential breakthroughs if this were the norm.

Scientific informatics software to organise and contextualise R&D data could cut weeks and even months off therapy development, avoiding re-work and ensuring patients have access to life-saving medications faster, without compromising on safety or quality. But poor data capture is only one area of lab life that could use improvement. As we’ve seen from biotech companies partnering up to find a solution to this novel disease, collaboration can speed up development of both a vaccine and a potential therapeutic to work alongside it.

Communicating effectively is vital to efficiency in the lab

Drug discovery and development rarely happen in one place. Disparate teams across multiple labs, locations and organisations work collaboratively in a variety of ways and using any number of different systems. Labs may be using the same technologies and instrumentation during daily runs, but without a unified method of collating, recording and sharing data, inefficiencies and errors are likely. Furthermore, just as human error can derail progress when it comes to recording and analysing data, ineffective collaboration across teams and organisations compounds the problem.

And in today’s environment, where meeting colleagues face-to-face is not a possibility, tools to continue to communicate effectively become a necessity. Data can get lost in emails and storing information in spreadsheets that can be altered undermines data integrity. This is why data strategy must be a top-line item when it comes to establishing business objectives for drug development firms.

Efficiencies like this abound, but while collaboration does not necessitate a cloud environment, applications and cloud integrated technology, such as electronic laboratory notebooks (ELNs), are delivering ever-greater efficiencies for labs by reducing the burden on scientists and enabling seamless collaboration and workflows that overcome legacy methods and technologies.

Time is of the essence in the battle against disease, and scientists working in drug development strive daily to drive innovation at pace. But rigorous scientific processes cannot be compromised in the race to deliver viable products. Achieving efficiencies wherever possible is profoundly important, and a combination of effective data tools, technologies and management is a good place to start.


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