The extensive amount of data available within drug development pipelines poses a significant challenge for scientists trying to derive insights. The range of different techniques being used generates data in many different formats and sizes – creating difficulties when structuring, sharing, and analysing the research. It has been estimated that two thirds of researchers’ time is spent on processing data – valuable time that could be used for higher value scientific analysis and the complex tasks that researchers do best.
Historically, the strategic and scientific advantage of data technologies have been overlooked, limiting their value in research. However, as new types of data and tools become available, a unique opportunity is emerging for smarter and more effective discovery, development, and commercialisation of innovative biopharmaceutical drugs.[i]
During the COVID-19 pandemic, research facilities have been working at a reduced capacity, or refocusing on the virus, and with widespread home working, new technologies are being relied on more heavily than ever. As things start to get back up and running, the need for these technologies will not diminish, and we are starting to see how valuable they really can be.
For example, AI is being adopted in the field of bioinformatics, where the Ribonucleic Acid (RNA) sequence of SARS-CoV-2 can be thoroughly analysed to develop the right antiviral drugs. In order to better manage the impact of the virus, new drug discovery, development, and testing processes are being set up. AI tools have the potential to accelerate the speed of drug discovery, development, and testing, allowing pharmaceutical companies and healthcare authorities to tackle this and future pandemics.[ii]
Adapting to the ‘new normal’
As we begin to ease out of lockdown, we believe now is the right time for life science organisations to implement a digital refresh strategy. We now have a real motivation for digital first – enabling and enabled by AI/ML. This is underpinned by technologies that already exist such as cloud and automation technologies that enable people to work from home, from the lab, or wherever they want.
People have been talking about artificial intelligence and machine learning in life science for a long time, but now, more than ever, digital first has to be the one central strategy. The potential pitfalls are still the same – data harmonisation, data connection and availability of good quality data. A typical lab may have 20-100 different data formats or sources, creating challenges in structuring and integrating this data together. Furthermore, as research processes evolve, additional data-streams often are added. This is a real issue for scientists and organisations as a lot of the time, researchers cannot find the data that they need, which leads to wasted effort and work being repeated.
Typical tools are also compromising data integrity. From the data that is coming off a machine, the processing, the analysis and then the subsequent output – each of those steps are prone to manual errors and processes are often uncontrolled, so data integrity is compromised.
The end goal is to iterate faster and find the hidden treasures in existing data. Whilst there is lots of real-world data and health records where you can pick out trends, the downfall is they are hard to work with, so finding the needle in the haystack can require a lot of work and valuable time. The key challenges are the harmonisation of internal and public sources and different multi-omics data types. You need high quality data and specialist knowledge to be able to leverage from these data types. The Aigenpulse Platform is an enabling technology for companies to be able to do this themselves.
The Aigenpulse Platform
Built specifically for the R&D enterprise, the Aigenpulse Platform is a next generation life sciences SAS platform that enables high-quality outputs to be generated at multiple stages of the R&D life cycle. Our platform is built specifically to be connected with other enterprise systems and software solutions, with our no-code multi-omics data platform enabling machine learning technology to be democratised throughout organisations.
With compliance at the core of everything we do, we have developed an automated system to collect, template and store the evidence required for any configuration of the Aigenpulse Platform. We can ensure your data is secured and your privacy is preserved – enabling customers to deploy the Aigenpulse Platform in the regulated environments such as cell and gene therapy manufacturing sites or regulated clinical laboratories.
To find out more about the Aigenpulse Platform, visit our website here, or contact us to discuss your data challenges.
[i] ResearchGate, “The Role of Big Data and Advanced Analytics in Drug Discovery, Development, and Commercialization” (2014)
[ii] ABIresearch, “Cloud-based AI in a post-covid-19 world”, (2020)