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IDBS Blog | 31st July 2023

Mitigating data integrity risks by utilizing cloud-based data management throughout the BioPharma lifecycle

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By Scott Weiss, VP, Product & Strategy, IDBS

The last two years bore witness to the first drugs designed with the help of artificial intelligence (AI) being tested in clinical trials. While AI is still in its infancy in drug discovery, industry experts envision a digital omvandling in BioPharma will take place over the next five years.1 Digital initiatives provide an opportunity to modernize legacy processes, replace labor-intensive and time-consuming manual methods and improve overall data quality through holistic BioPharma Lifecycle Management (BPLM).

Key to attaining that digital transformation is data integrity and integration. While data integrity is critical to building confidence in the supply chain and product quality, many organizations remain highly reliant on spreadsheets, manual data entry, paper records and email. Robert Di Scipio, the founder of Skyland Analytics, now part of IDBS, writes in MedCity Nyheter that this creates numerous opportunities for error and can result in Food & Drug Administration (FDA) warning letters, fines or recalls. He states that data capture may start early in BioPharmaceutical R&D, but too often, a variety of disparate IT systems are installed without a view toward data coherence throughout process development and clinical and commercial manufacturing.

Di Scipio adds that to reduce the risks of delayed, incomplete and inconsistent data, BioPharma companies must establish a solid data management approach early in product development. One way to do this is by building a digital backbone that connects product and process data to support key activities further downstream, such as late-stage process development, scale-up and tech transfer, and manufacturing where quality assurance and compliance become critical. By implementing a cloud-based data backbone, data can be gathered and organized in a central platform maintaining data integrity throughout the BioPharma lifecycle.

Is your BPLM data F.A.I.R.?

Standards and guidelines outline data integrity expectations for BPLM. For instance, manual data transcription workflows require extensive additional quality checks to ensure data integrity and that they meet regulatory requirements, such as 21 Code of Federal Regulations (CFR) Part 11 or good laboratory/manufacturing practices. And the FDA ascribes to ALCOA+ to describe its data integrity expectations and ensure compliance with 21 CFR Part 11. Per ALCOA+, data must be Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring and Available.

A similar concept, the F.A.I.R. Principles, has recently gained favor. F.A.I.R. ensures data are Findable, Accessible, Interoperable and Reusable. More of a design principle than a standard, Scott Weiss, Vice President of Product and Strategy at IDBS, recently wrote in PharmTech magazine that F.A.I.R. recommends or relies on operators to subscribe to one system that enables crosstalk between two or more different systems in a machine-readable format. The output generates data that are comprehensible, reusable and contextualizable.

BPLM FAIR data

Integration leads to integrity in BPLM

Weiss states that contextualizing and aligning the data pulled from different systems presents a challenge as daunting as accessing the source data itself. Recreating the information “chain of custody” typically requires combining partial data sets from different systems, harmonizing terms and identifiers and ensuring data are aligned correctly. This is one of the most tedious and error-prone steps, creating significant data integrity risks, particularly when done manually with a human operator.

Fortunately, technologies on the market are now able to automate the capture, contextualization and integration of data from multiple systems to improve data integrity. The latter – integration – remains a perpetual problem for the industry. While some life science companies have the in-house skills to integrate systems, many find the task resource-intensive and time-consuming. Weiss writes that newer cloud-based software platforms embrace a holistic, integrated approach to BPLM, by combining elements of workflow execution, preconfigured system and hardware integrations, contextualized data curation built on F.A.I.R. principles and integrated analytics to drive business intelligence. Their adoption will be driven by the flexibility to meet current and future BioPharma needs to deliver ongoing business benefits, reduce total cost of ownership and provide a scalable foundation of data lifecycle management to accelerate Industry 4.0 initiatives, he says.

One such technology is IDBS Polar, a flexible cloud-based platform that enables BioPharma companies to transform how they capture, analyze, report and share data throughout BioPharma research and development and on to manufacturing. An embedded integration layer simplifies the curation of a process data backbone and an AI-enabled Insight engine that puts the power of advanced analytics in the hands of scientists and engineers for fast, confident decisions.

The next generation of integration is here

Data volume continues to increase, so it’s more important than ever to integrate the data generated throughout product development into a single, accessible archive. Today’s technology addresses the challenge of integration while also ensuring data integrity. The result is next-generation data integration that takes us on the path toward AI and beyond.

 

Om författaren

Scott Weiss on BPLM

Scott focusses on shaping our product and strategy organization to deliver results today while architecting a compelling future aligned to our vision of advancing human health by accelerating the next generation of therapies. He has held a number of roles over his 19-year tenure at IDBS and has a deep understanding of our products, markets and customers. Most recently as VP, Business Development & Open Innovation, Scott supported our strategy development.

Prior to joining IDBS, Scott spent over 20 years in the pharmaceutical industry, where he led multiple drug-development programs from conceptualization to entry in clinical trials and has authored over 30 peer-reviewed scientific papers and patents.

He obtained his PhD in Psychology from the University of Leeds, specializing in Neuroscience.

 

References

  1. Diaz, N. (2023). How AI can speed up läkemedelsutveckling. Hospital Review. Retrieved from [https://www.hospitalreview.com/article/12345/how-ai-can-speed-up-drug-development]
  2. Weiss, S. (2022). An integrated approach to the data lifecycle in BioPharma. Retrieved from [https://www.pharmtech.com/view/an-integrated-approach-to-the-data-lifecycle-in-biopharma]
  3. Di Scipio, B. (2022). Why and how to achieve effective data management strategies early in the BioPharma lifecycle. MedCity News. Retrieved from [https://medcitynews.com/2022/11/data-management-drug-manufacturing-contract-manufacturing/]

 

Further reading

eBook: Digital strategies to power next-generation BioPharma

Whitepaper: PharmaIQ and IDBS: Improving BioPharma R&D lifecycle management with BioPharma 4.0

Infographic: Aspen Survey: BioPharma development data management

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