Fragmented data has become a strategic barrier. Here's how disconnected information contributes to duplicated work, inefficient decision-making and lost scientific knowledge
Modern drug discovery generates enormous volumes of data, yet much of that knowledge remains isolated. Scientists spend valuable time searching for historical results, recreating experiments and manually assembling information from multiple systems, adding unnecessary cost, duplication and delay to research programmes.
Fragmented data has become a strategic business issue. When specific scientific information becomes disconnected, it leads to duplicated work, inefficient decision making and lost scientific knowledge.
There are multimodal data platforms available that can support the digitalisation required to remain competitive. By expediting the change management process with a platform that brings AI workloads and compliance processes into one centralised location, pharmaceutical firms can go a long way in mitigating the financial impact of poor data management across research and development.
Drug discovery: a high risk of failure
While sectors like manufacturing have made digitalisation inroads in recent years, pharmaceutical R&D worldwide continues to struggle to maintain complete organisation of complex data sources and information. A recent report says that 90% of new drugs fail before they reach the patient [https://pmc.ncbi.nlm.nih.gov/articles/PMC9293739/].
“It's taking over 10 years or more to bring a medicine to market, a molecule to market, or a new therapeutic to market,” said Jennifer Petrosky, Life Sciences, Global Industry Development Lead at Siemens Digital Industries Software.
“This is only rising; it's taking more and more resources to commercialise new drugs as complexity increases. We all want more specific therapeutics to address our unique needs, which requires more scientists, more tests, and more experiments.”
Pharma company data is frequently trapped in disparate formats across different teams – scientists, regulatory, process development, including physical notebooks, Excel files, and specific instrument environments - even when the data estate is all being managed on the same premises. This isolates information and slows down the discovery of vital context, while preventing global research teams in different locations from effectively collaborating.
Trial and error can prove labour-intensive in the long run. But there is infrastructure available that can boost collaboration capabilities and provide a sandbox-like environment for researchers to map out their next move before rolling out developments.
Financial impacts of silos
Forte Group research reveals that delayed trial timelines, in the process of finding new drug molecules, bring an average daily cost totalling between $40,000 and $55,000. Meanwhile, the same study calculates that disconnected data has been found to cause the matching of patients in 80% of clinical trials to be delayed [https://www.fortegrp.com/insights/quantifying-the-financial-impact-of-fragmented-data-in-pharma-rd].
Further financial impacts that may not be immediately obvious to pharmaceutical companies include replication of processes and assets, along with an inability to build on existing knowledge.
“A team in one country may be unable to access insights collated by their colleagues in another country, meaning they will not see where they’ve already failed,” said Petrosky. “One part of the company may find that the current pipeline will not provide value to the patient, but recreation of those processes may occur elsewhere due to that lack of access.”
Meanwhile, historical information based on past projects becomes inaccessible without a completely unified repository, compounding resource waste.
Petrosky added: "With the trend moving towards combination products, you now have medical device companies working with pharma companies to understand what's the best delivery mechanism for a particular biologic medication.
“This could range from a pre-filled syringe to a transdermal patch to a pill form. When finding the right formulation, having access to these other parts of the organisation would help speed up the delivery of these developments.” For multimodal approaches, such blind spots lead to avoidable flaws that are discovered late in the development process, resulting in missteps, delays and financial losses."
Compliance and regulatory risks
Not only does manual data management cause search and outreach delays for pharma firms, but it also exposes data to regulatory compliance issues. With sensitive timelines and various quality attributes involved, and without the right data infrastructure in place, high possibilities remain of regulatory approval and more importantly patient access delays.
Manual data practices introduce substantial compliance vulnerabilities regarding traceability during drug development. Adoption of a centralised platform is necessary to ensure end-to-end data integrity.
“If you're not protecting that data from a GLP or GXP perspective, you open your organisation up to downstream risks,” said Petrosky.
“When it’s time for commercial manufacturing, regulatory agencies will want to see how the drug was tested and the relevant processes and tests were conducted to prove that the drug is of high quality and efficacious.”
FAIR principles, underpinned by cloud-based traceability capabilities, ensure the right accessibility controls.
Streamlining research processes
Rather than starting with AI, pharmaceutical companies focusing on R&D need a data foundation that is fully backed by contextual processes and workflows, along with intuitive project management capabilities. Without the right context in place, the roadmap for delivering new molecules efficiently and on-time risks going off course.
Completely modernising pharmaceutical R&D infrastructure, with the help of a platform like Luma [https://www.dotmatics.com/luma], can increase the control that data stewards and bench scientists alike have over evolving processes. Leveraging advanced scientific applications like Protein Metrics and Bioglyph within Luma empower research teams to perform multiple analyses and tests in-silico before ever going to the bench.
“The beauty of Luma is in building all those AI capabilities into the platform itself,” said Petrosky. “It provides the scientists with creative freedom to continue to do the physical tests that are required but focus on the molecule that has the best chance of improving patient outcomes.”
Scientists can drive simulation-driven decision-making and AI-powered scientific intelligence. Luma enables this multimodal research and discovery. From here, they can conduct detailed design of experiments and data analysis and translate experimental workflows into AI-ready knowledge while staying aligned with complex project requirements.
Petrosky added: “The platform enables integrated cross-discipline collaboration to unify experimental computational and analytical workflows, to drive AI-powered discovery and knowledge management. This will ultimately lead to multiscale and in-silico optimisation.”
This is the first article in a four-part series exploring the importance of data continuity to drug discovery. With connected scientific data becoming the foundation of modern drug discovery innovation, investing in a platform that fits and enriches your current research workflows can allow pharmaceutical firms to stay ahead of evolving patient and organisational requirements. You can find more information below:
https://www.siemens.com/en-gb/solutions/material-research-design/
