The Role Of Data In Drug Discovery

We spoke to IDBS Biologics Development Solution Owner, Unjulie Bhanot to discuss the pivotal role of data in drug discovery, the biggest challenges scientists face when developing new drugs, and how IDBS aids R&D teams and scientists with biologics development and drug discovery.

How does IDBS assist firms involved in drug discovery and development?

IDBS is a key member of the Danaher Life Sciences platform, part of a group of companies that are powering enterprise and R&D organisations in drug discovery and development. The IDBS platform allows organisations to bring drug development into the digital age, transform their operations and realise the full benefit of their current and future investments in new and innovative process technologies.

After more than a decade of working with world class drug discovery and development organisations, IDBS recognise the pain points and needs of the hour to enable these organisations to be successful in their endeavours. From removing the need for manual transcription to increasing adherence to data integrity principles, or breaking down barriers between different teams to provide continuity and traceability in processes; to ensuring smoother and seamless transactions between the activities a scientist encounters in their day-to-day life, IDBS provides one digital platform to connect process and product data.

Put simply, we provide our customers with a cutting-edge foundation to drive efficiency and enable better insight and process understanding.

What are the biggest challenges scientists face across the drug development lifecycle?

The biggest challenge of them all is delivering high-quality therapeutics to patients faster and more cost effectively. While business executives are faced with increasing operational costs such as the rising price of land, resources and managing supply chains, they also come up against increasing regulatory scrutiny from agencies such as the FDA, MHRA etc.

Scientists are often under pressure to keep up with the ever-changing pace of biological and technological innovation in drug development; from developing new treatments such as cell and gene therapies, to learning and adjusting to technologies such as high throughput systems and AI tools.

Essentially, these translate to functional challenges in trying to manage highly complex development operations with an outdated foundation of paper, Excel and manual processes, compounded further by a complex landscape of different analytical and informatics tools. As a result, scientists can suffer from data overload in a multiplicity of formats. Sifting through too much data that isn’t easily accessible, query-able and filterable is equally tiresome and detracts scientists from being able to focus on science.

What are the consequences of data errors during drug discovery?

The consequences of data errors and inadequate data integrity in drug discovery can range from anything such as having to repeat the preparation of a buffer, to repeating a whole study, to the failure of an entire batch. Imagine a scenario in which the concentration of a drug product has been incorrectly transcribed between systems and remains unverified, and a whole series of experiments and unit operations are performed according to the value; the scale of this mistake could prove disastrous for an organisation.

These mistakes can trigger a number of outcomes. To begin with, internally this can cause the loss of productivity of teams or the wastage of material. However, as this issue escalates, organisations can face compliance queries from customers and auditors, which can in turn impact credibility and market reputation. Ultimately this can cause a delay in getting the drug to market, and critically impact revenue recognition.

With significantly large investments made by organisations in this space, to be ahead in the race against time for developing drugs, this is not a situation they would wish to find themselves in.

What should labs be doing to improve data integrity?

Introducing data integrity principles in such a way that does not create additional tasks for scientists is fundamental to an organisation’s success. Centring a data management strategy around the principles of ALCOA+ would involve deploying a digital solution that allows data to be captured contemporaneously, in an attributable fashion and ensuring that the data acquired is managed consistently and remains available, for example for reference.

While developing such a strategy, it is important to consider the holistic product, data and software ecosystem; for example determining the scientific process steps that contribute to drug development, the existence of legacy and modern instrumentation (and how they capture data), points of human intervention, and data collection and inter-departmental or inter-company data transfer junctures.

Key capabilities to consider when deploying a digital solution as part of a data management strategy, include; the ability to streamline result data acquisition efficiently while maintaining data integrity through direct integrations, establishing relationships between experiment metadata and experimental outcomes, and enforcing linkages automatically between consumed and generated materials in experiments. This list isn’t exhaustive, but it does highlight how the correct software platform can remove the burden of having to manage data integrity on top of ground-breaking science.

What technologies, tools and practices should labs implement to increase the throughput of life-saving drugs?

Instrumentation technology has reached new heights; instruments built on the principles of High-throughput screening such as multi-plate readers, and multi-parallel microbioreactors facilitate significant efficiency gains in the quest to develop novel therapeutics. Additionally, technologies such as Single-Use Systems, Process Analytical Tools and Continuous Processing Solutions aim to reduce operational costs, measure product quality and process performance real-time and aim to reduce delays synonymous with historical batch manufacturing technologies (e.g. cleaning of a stainless-steel bioreactor).

Though the intention of these technologies is to increase the throughput of therapeutics, organisations are inevitably faced with the conundrum of huge volumes and variety of data, at increasing velocities. Data will be generated in a multiplicity of formats, and often requires additional effort to clean, structure and standardise in order to determine which data to trust and draw conclusions from; it is clear to see this demands enormous effort.

Therefore, it is imperative for organisations to look to implement digitally mature data management strategies that lay the foundations of harmonised data capture to ensure well-constructed and contextualised data, aligned to data integrity principles. This will ensure data integrity concerns and roadblocks are removed, and organisations can harness the potential of their data.

What trends do you see emerging in the drug discovery space?

Looking to the future, therapy wise I see the industry transitioning to cell and gene therapies, novel proteins with new delivery methods, and perhaps a rise in in-vitro companion devices in combination with drug development, to gauge the patient cohort most likely to benefit from the therapeutic in development.

From an informatics standpoint, I believe there will be growth in the space of AI and Machine Learning, and a rise in the prevalence and importance of bioinformatics and data science.

As a result of these streams, instrumentation technology and data management providers will undoubtedly look to keep up – by implementing cloud based strategies to leverage key benefits such as improved software integration, updates to latest and greatest software versions, and reduce the total cost of ownership to ultimately to enhance efficiencies and data integrity and reduce the time it takes to take a drug to market.

Unjulie Bhanot is the IDBS Solution Owner for Biologics Development, in the Strategy team based out of IDBS’ Guildford office in the UK and has worked in the Biologics R&D Informatics space for over 6 years.

She joined IDBS in 2016 and spent over 2 years as part of the global professional services team, where she was responsible for designing and deploying solutions using the IDBS product stack into Biologics-based organisations within Europe. In early 2018, Unjulie transitioned into the global solutions consulting team where she focussed on presenting the business and technical value of IDBS Solutions to customers as well as supporting the professional services team in their implementation of these Solutions. Over the last 2 years, Unjulie has taken on a leading role in the development of the IDBS BioProcess Solution; a responsibility that is now an integral part of her role as the Biologics Development Solution Owner.

Prior to joining IDBS Unjulie worked as an R&D scientist at both Lonza Biologics and UCB and later went on to manage the deployment IDBS’ E-WorkBook Suite within the Analytical Services department at Lonza Biologics, UK.