The value of efficient data management in digital drug development era
By Naveed Panjwani, Clinical R&D Consulting Lead (Europe) and Elliot Stamp, Clinical R&D AI & Data Lead (Europe)
In the ever-evolving landscape of pharmaceutical (pharma) R&D, the pivotal role of data management is becoming increasingly important. As the industry grapples with soaring complexities and escalating costs associated with bringing new drugs to market, harnessing and analysing data has emerged as a crucial catalyst for progress. In this landscape, where precision and efficiency are paramount, data stands as the linchpin that distinguishes visionary pharma companies from their peers. In this blog, we explore the business value of harnessing data to enhance digitalisation of R&D activities, the challenges associated, and the transformation required to increase productivity and achieve cost-efficient cycle times in pharma R&D.
The business value
Being able to harness vast and diverse datasets effectively can set companies apart by providing invaluable insights that guide strategic decision-making and drive operational excellence. In an environment marked by a relentless pursuit of ground-breaking discoveries, the ability to navigate and extract meaningful information from the wealth of available data becomes a competitive advantage. Companies that prioritise the use of robust data management systems can gain a distinct edge in streamlining their R&D processes, from accelerating drug discovery to optimising clinical trials.
Moreover, the role of patient data in shaping personalised medicine solidifies its significance as a business differentiator further still. Pharma companies that are effective in leveraging patient-specific data for the development of targeted therapies and personalised treatment plans can not only enhance patient outcomes but also position themselves as pioneers in the era of precision medicine.
Data also plays a pivotal role in regulatory compliance and risk mitigation, a critical aspect in an industry governed by stringent regulations. Companies that excel in data management not only ensure compliance with industry standards but also demonstrate a commitment to patient safety and ethical practices, thereby establishing a reputation for reliability and trustworthiness.
Understanding the productivity of R&D
Source: Measuring the return from pharmaceutical innovation: Unleash AI’s potential
Deloitte’s latest report in their annual series on ‘Measuring the return from pharmaceutical innovation’, Unleash AI’s potential, analyses the predicted returns from investments in R&D for a cohort of 20 leading pharma companies.1 The analysis provides insights into biopharma productivity. Deloitte’s analysis over the past 14 years has shown a steady decline in productivity between 2010 and 2019, a short-lived improvement due to the impact of the COVID-19 assets in 2020 and 2021, followed by a dip in 2022, and in the 2023 cycle, the industry is beginning to see signs of some improvement.
A key strategic response to continue to improve productivity is to lower costs through digitalisation of R&D activities and investing in better data management strategies to boost R&D productivity and reduce cycle times.
The value of data management in clinical trials
A rapid convergence of technologies in clinical trials is necessitating faster R&D processes and enhanced interoperability. The integration of AI, wearable technologies, and advanced data analytics is accelerating the pace of innovation. This trend demands that R&D processes become more agile and adaptable, with a strong emphasis on technology interoperability to manage and analyse the increasing volume and complexity of data generated.
Clinical trials are increasingly looking to leverage a diverse array of data types, including genetic, biomarker, and real-time patient monitoring data from wearables and implanted biosensors. The evolution in data collection techniques and the emergence of new data modalities are reshaping how data is used in drug development. This trend highlights the growing importance of sophisticated data analysis techniques and the central role of data scientists in driving R&D innovations. However, there is a need to take proactive measures to ensure high data quality, uphold transparency and mitigate potential bias. Figure 1 demonstrates the value of employing better data management strategies across life science organisations to improve clinical trial cycle times and increase the pace of drug development.
Figure 1. Opportunities across clinical development for better data management
Source Deloitte analysis, 2024
Deloitte estimates that pharma companies have an opportunity to unlock $5-7 billion dollars in value from the use of AI, with R&D representing the top value opportunity at 30-45 per cent.2 Currently the hard evidence of a positive impact of AI at scale in pharma is still in its infancy with major barriers to broader adoption of AI in drug discovery and development pertaining to key factors such as demystifying complex disease biology, overcoming challenges with the quality of data, addressing potential bias in historical datasets used in AI algorithms, the need for investment in data infrastructure and AI capabilities and tackling scientist’s anxiety and resistance to use AI.3
The value of data management in improving regulatory and patient safety insights
Organisations need to stay on top of evolving regulatory landscapes by enhancing their regulatory intelligence capabilities to simplify the synthesis and understanding of complex, diverse and unique rules, interpretations, and expectations of biopharma companies. Regulatory intelligence data capturing the regulatory requirements across key markets can better inform the development processes to enable faster regulatory submission.
The obligation for biopharma companies to report adverse events (AEs) that arise from the unique patient insights and increased data being collected means many biopharma companies are adopting automation and advanced analytics to improve transparency across reporting methods and build trust in their pharmacovigilance (PV) systems. This includes creating next-generation digital learning systems that can increase the efficiency, quality, completeness and cost-effectiveness of generating richer insights on product quality and patient safety.4
The challenges in transforming data management
Unfortunately, organisations are not able to simply flick a switch and instantly have high quality data all underpinned with robust data management practices. This is the destination, and embarking upon a data transformation requires recognition of the challenges currently faced across the R&D value chain. Organisations therefore need to overcome fundamental business and data challenges to become more data-driven and unlock the full potential of their data.
Based on Deloitte’s experience and numerous published insight pieces we have identified a number of business and technical challenges.
The business challenges
- Time to market: complex business processes and high manual intervention leads to non-compliance, extended cycle times and restatement of development milestones.
- Inefficient data management: siloed data management and storage, with no governance, leads to inefficient and duplicate manual work.
- Optimising patient recruitment and retention: inability to leverage data and digital tools to use novel techniques like synthetic control arms, RWD, telehealth, devices, etc. to optimise patient recruitment and visits.
- New data modalities: complexity in seamless integration and use of new data modalities to drive business innovation and competitive advantage.
The technical challenges
- Data architecture rigidity: existing capabilities lack the agility to support new clinical data points resulting in siloed solutions that are not interoperable.
- Speed vs governance: IT executives are often caught between business demand for new capabilities and their mandate to carefully govern current data and analytics.
- Legacy architecture: processes are driven by legacy architectures which are resistant to change making it difficult to take advantage of new platform and analytical capabilities.
- No design patterns: inconsistent design patterns for various data domains leads to redundancies and inefficiencies resulting in duplicate effort and technical debt.
If left unaddressed, business and technology data and analytics challenges impede productivity and output of individual functional areas across the R&D value chain.
A transformation of data management is needed to improve R&D productivity and cost efficiency
By addressing these business and technical challenges, management of clinical development can achieve more seamless and efficient data management, enabled by cloud-based data technologies. To do so, life sciences R&D organisations will need to embark on a transformation journey. We believe that this transformation should be guided by strategic data pillars, as shown in figure two, where each pillar builds a core set of data capabilities with targeted benefits and outcomes.
Figure 2. The strategic pillars of R&D data transformation
Source: Deloitte Clinical R&D data practice
Conclusion
Pharma companies who take pragmatic steps to establish a data management ecosystem fit for harnessing a vast variety of data sets will gain the competitive edge and enable biopharma to capitalise on AI’s potential and improve the rate of return on investment in biopharma innovation.
Acknowledgements
With thanks to Stephanie Delanbanque, Girish Dharmoji, Andrew Garrood, Hitesh Amin, Devon Johnston, Karla Feghali, Raveen Sharma, Seshamalini Srinivasan and Alok Soni for their contributions to the research and drafting of this blog.
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2 Unleash AI’s potential - Measuring the return from pharmaceutical innovation – 14th edition
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