In March this year, the Bill & Melinda Gates Foundation launched an initiative called the COVID-19 Therapeutics Accelerator philanthropically bringing together a consortium of 15 of the world’s leading pharma companies ‘to accelerate the development, manufacture and delivery of vaccines, diagnostics and treatments for COVID-19’. In the first instance, these companies have agreed to share their proprietary libraries of molecular compounds that already have some degree of safety and activity data with the virus.

Data plays a crucial role in the global efforts to combat the pandemic and with access to up-to-date data we can gain vital insights into the epidemiology of the virus, virus detection, mitigation of spread, treatments, potential vaccines, etc. This, in turn, can assist in calibrating countermeasures and accelerating research and development. We see on a daily basis how public policy responses and private sector initiatives are informed by – and reliant on – complex and fast-moving analytics, and an agile approach to decision-making that is based upon the latest feedback.

Pharmaceutical companies have long relied on empirical data in order to drive research, identify patterns, test theories and understand the efficacy of treatments. Applying advanced analytics with teams of data scientists, as well as iterative approaches, has already cut elapsed time for drug trials. A shift towards sharing data and collaboration with others may offer a further step change in lead time to market.

Data analytics seems likely to increase its importance and effectiveness exponentially during this event, and organisations need to be certain that maximum value is being leveraged. New sources of data are springing up to help in the efforts to combat COVID-19, such as the Kings College London app for self-reporting, which is polling citizens for their health status. Having the capabilities available to analyse emerging data, shared from across the country, and even globally, to identify the most important factors that can help slow the spread of the virus and help find long-term solutions to the pandemic is critical.

The absence of a clear deliverable/cost trade-off can see expensive resources being consumed as a matter of faith rather than certainty in the eventual outcome. Conversely, a narrow focus on line-of-business hypotheses may be missing cross-organisational value in the massive data sets held which could be exploited more widely to accelerate innovation on a broader plain. While the urgency will hopefully abate, recognising the value of the data sets available and how to use the information they hold, alongside the changes in approach driven by the necessity of collaboration, and the speed and agility required to get to a solution, may together create transformative changes during this time that should sustain beyond the current emergency.

COVID-19 has demonstrated that ways of working which seemed impossible are becoming the new normal. Changes to regulatory timelines for drug approvals during the pandemic, for example, have seen a dramatic contraction. Both patients and pharma companies alike will now question whether the precautionary balance should be permanently recalibrated.

Being able to process and make sense of wider data sets and apply advanced analytics technology alongside a different mindset towards collaboration creates a great opportunity for scientists and pharmaceutical companies to accelerate drug discovery and efficacy testing while reducing time and cost to market.

Through collaboration, pharmaceutical companies can reap the time and cost benefits of sharing information. While pharma has been notoriously guarded with regards to information sharing in a bid to be the ‘first’ when it comes to drug discovery, for example, companies are now recognising that treating data in siloes is a missed opportunity.

Sharing legacy systems containing disparate data and providing access to historic data in ways not previously possible, including through the use of machine learning to interrogate files or published papers for relevant data, allows companies to consolidate widespread trial or clinical data effectively and rapidly to create common data sets or platforms that they can work from.

Predictive analytics can also help reduce the need for physical trials in order to increase efficiency and time to market by making use of existing data sets, extrapolating the given information and making predictions for future outcomes and trends. Pharmaceutical organisations need to leverage this data to make intelligent decisions, drive operational efficiencies, find patterns and trends in the data and decide how it can be used in the future.

Again, in March, the Innovative Medicines Initiative (IMI) issued a call for Innovation Actions into detections, therapeutics, antivirals and diagnostics (not vaccines). In addition, they have brought together experts from across the industry to establish how manufacturing and logistics channels can be shared across pharma to ensure rapid production and distribution of new products.

We’ve already noted that the industry has started to look at how it can get greater access to more data through collaboration and believe this will prove to be a game changer towards accelerated drug development. It enables researchers to examine huge data sets of previous trials, results and findings. Having such access to such comprehensive data sets could not only enable development of a particular drug, but also reveal other potential uses.

For example, Novartis (the company that chairs the COVID-19 Therapeutics Accelerator initiative) has made available several compounds from its libraries that are considered suitable for in vitro antiviral testing as well as evaluating other existing products to see if any could be used in response to the pandemic, looking beyond their originally approved indications.

Big data can also help pharma companies identify the correct patients for specific drug trials through analysis of demographics, genetic information, patient history and other factors which can speed up and reduce costs of trials. In order to facilitate access to personal data there are ethical and regulatory obstacles such as GDPR compliance or operating to GxP guidelines to address, but the importance of public health needs to be balanced against the privacy of citizens. Happily, that’s not a judgement call on the desk of this industry observer!

Assuming the right safeguards are put in place to permit wider access to this personal information, pharma companies will still need to maintain SOX compliance, which may exacerbate existing data retention and governance challenges. Collaboration may again hold the key, so that freedom to access the data doesn’t become a burden that deters individual pharma companies.

Getting the right strategy, platforms and skills for all of this is going to be key for the most successful companies, much like in every other sector. Data is at the heart of a confluence between tech companies and pharma, and deep partnerships will start to emerge, if they weren’t there already. Collaboration is fundamental in tackling this pandemic, but potentially even more so for the development of drugs and treatments in the future.

John Gorrell is Pharmaceuticals & Healthcare lead at Coeus Consulting