IDBS Blog | 31st August 2023

Digitize pharma lifecycle management for faster regulatory filing

By David Brick, Senior Director, Data Science, IDBS

Why is pharma lifecycle management necessary?

Regulatory submissions in pharma lifecycle management are a balancing act for all organizations, especially as regional regulatory demands become more complex and data-reliant.¹ If you are a start-up organization, you must straddle between limited capital and tight timelines as you await drug approval. If you are a mature company, you are on a quest to speed up the time to file the next new drug application (NDA) or biologics license application (BLA). Add in the increasing complexity of new biologic therapeutics and platforms and QC teams are under added pressure to figure out what data is relevant and critical to capture for filing.

In both cases, the savvy BioPharma organization should plan for these challenges early in the drug approval process, advises Ken Forman, Senior Director of Product Strategy, IDBS, in a recent Drug Discovery World article.² And key to this plan is proactive lifecycle management through each stage of drug discovery, development and commercialization – including drug candidate identification, clinical trials, manufacturing and supply chain activities. All pharma and biotech companies already follow this standard cycle to get therapeutics to market. A proactive lifecycle management approach entails actively laying out a plan for how your company will move up the digital AND process maturity curves over time. Without this intentional planning, companies risk always being reactive, getting buried by poor processes that can’t scale and/or having systems that can’t provide insights and answers to business questions.

A recent article in Contract Pharma concurs, stating that “it’s only logical to take regulatory strategies into consideration from the very beginning of a product’s lifecycle journey. Life science companies can reduce the risk of regulatory rejection or unexpected obstacles to regulatory approval by building regulatory lifecycle management into initial product development planning”.¹

For many companies, though, control of these pharmaceutical lifecycle management activities often resides in disparate, siloed parts of an organization. Some may even outsource these activities to contract organizations such as contract research organizations (CROs) and contract development and manufacturing organizations (CDMOs). As a result, all the pharmaceutical lifecycle management data winds up in disparate, siloed systems – often paper-based – resulting in data integrity issues that could put your regulatory filings and ultimate approvals at risk.

Informed life science leaders understand that efficient pharmaceutical lifecycle management defines how systems are interrelated, ensures there is proper documentation on how a product was developed, tracks outputs produced at each stage of development and maintains that the right information is used at the right time.

A digital workflow and data backbone can boost pharma lifecycle efficiency

While the industry recognizes the importance of pharmaceutical lifecycle management in regulatory approval, few have mastered the practice. IDBS finds that many life science organizations are still using paper or Excel at some point in their lifecycle management to control process data. And while some companies may define themselves as digitally enabled because they have a collection of software tools, the journey to achieve a true digital transformation (dubbed BioPharma 4.0) is a slow one. According to research conducted by life science consultancy Axendia on the state of digital transformation in life sciences manufacturing, 43% of pharmaceutical, biotech and medical device companies are currently undergoing digital transformation. Respondents state speed to market, enabling data-driven decisions and improved regulatory compliance as some of the key drivers for this transformation.³

Forman agrees, stating: “deploying a digital workflow with a common data backbone” to a well-defined pharmaceutical lifecycle management plan enhances understanding of data across the product lifecycle and can help clear the way for faster regulatory approval. However, many companies will take what they think is the easiest path to recording pharma lifecycle management data: a spreadsheet. While he admits that manually entered data quickly captures relevant information, it can also lead to errors and does not guarantee that the data is in a context that allows it to be compared to, or analyzed with, data sets in other spreadsheets or digital systems. Additionally, the data will need to be reformatted for regulatory authorities. The more data, the more complex it becomes to piece critical data together coherently, adding time to the filing and risk to the filing’s accuracy.

The better option, says Forman, is to design a digital workflow. This may not be immediately feasible if part of your lifecycle management process is outsourced to a CDMO. But if both you and your contract partner have a shared interest in improving data quality and regulatory speed, then a digital workflow could be an important discussion topic when renegotiating service agreements.

Designing a digital workflow will highlight all pain points in your pharma lifecycle management process. Determine which of these areas are priority items and incorporate digital tools to “improve partner collaboration and streamline data management and sharing,” says Forman. For example, a centralized GxP data backbone can improve data integrity, simplify data storage and minimize error-prone points. This backbone can also enable data sharing among partners.

Enhance your process robustness program with a digital workflow

The Product Quality Research Institute defines robustness as “the ability of a manufacturing process to tolerate the expected variability of raw materials, operating conditions, process equipment, environmental conditions and human factors.”⁴Some companies incorrectly consider this more of a post-approval issue, but, in fact, process robustness should be adopted during process performance qualification and clinical trials manufacturing to help ensure regulatory approval — or even hasten it.

A digital workflow can play a key role in a process robustness program. Such a system can capture specification limit violations and identify missing or inaccurate data. A monitor-by-exception approach can filter out the “noise” in warnings and alerts to allow resources to be focused quickly on critical problems. Often, process robustness requires engineers with strong math and statistical understanding to identify problematic trends. The ultimate objective is to move from reactive process monitoring to proactive process control using real-time data.

Forman says: “Good data is a basic requirement in determining whether processes are performing as expected. Sloppy data is a ‘human factor’ that can decrease robustness.”

Digital workflows can improve data sharing, including tech transfer

Moving data from the lab to manufacturing requires a seamless flow. Without standardized data management and process documentation, data sharing about tech transfer between departments can become challenging. One study of 400 pharma professionals indicated that siloed data hinder effective information sharing across the pharma lifecycle, including during technology transfer and commercial manufacturing.³

Thus, digital workflows can improve tech transfer within pharmaceutical lifecycle management. According to Forman, “With the right digital standards for effective data sharing in place, tech transfer could even support continuous improvement and enable learning gained at different scales and/or sites to focus development and optimization efforts on the areas of greatest impact to product quality and yield.”

Digital standards exist but to make the most of tech transfer, pharma companies need them to be more precise. For example, ISA-88, a standard for batch control, provides a standard structure but not a standard code. Therefore, implementation of commercial ISA-88-compliant systems on the market, such as manufacturing execution systems (MES), can vary, even within the same organization. Additionally, many of these systems are too focused on execution rather than defining processes so they do not easily support a process-centric approach to pharma lifecycle management.

Forman points to more promising ISA-88 compliant approaches, such as Batch Markup Language (BatchML) and Business to Manufacturing Markup Language (B2MML), to make the most of tech transfer and facilitate process data exchanges.

Cloud-based solutions reduce data integrity challenges

Digital workflows should reside in the cloud to ensure efficient and interactive data sharing. While misconceptions surround the safety of cloud-based systems, experts claim that cloud-based solutions actually reduce data integrity challenges and that a cloud data repository can be a “single source of truth with access easily and securely provided to authenticated individuals.”¹

IDBS Polar is a cloud-based pharmaceutical lifecycle management platform that enables efficient process execution and data curation necessary to accelerate time to market. IDBS Polar facilitates end-to-end lifecycle management from early clinical development to tech transfer to regulatory approval, while PIMS by IDBS extends this data backbone with critical manufacturing process and quality data for monitoring, investigations and analytics.

Barriers to digitized pharma lifecycle management are lessening

A possible unintended consequence of the life science industry’s growth through mergers and acquisitions is the myriad of vocabulary and schemas being brought under one roof. These discrepancies are impacting processes and procedures related to lifecycle management. Forman writes that “a lack of common terminology for parameter naming, for example, might lead to confusion among process engineers. However, it can also cause more serious discrepancies between in-process control data supplied from two different sites that use different parameters for quality comparison. This can lead to poor product release decisions and even FDA ‘Form 483’ write-ups around data integrity”.

So, whether you are a start-up company, CDMO or a Big Pharma organization, the drivers for reducing time to regulatory filings are evident. And as you continue to walk that balance beam between managing cash flow, meeting financial expectations and hitting milestones, and developing life-saving therapeutics, consider the benefits of securely sharing data across your lifecycle network of partners to optimize the pathway to regulatory filing.

Forman is acutely aware that a fully harmonized and digitized pharmaceutical lifecycle is a way away, yet he remains optimistic. And he may have reason to feel that way. A 2022 survey found that 35% of respondents cited risk aversion as a barrier toward implementation compared to the 2021 survey in which risk aversion was the number-one barrier.³ So, while a digital transformation may not be easy or fast, life science insiders see change coming in how companies manage their pharma lifecycle on the path to regulatory approval.

About the author

David has more than 30 years of experience in consulting, project management, data management and data warehousing for reporting and analytics applications. He has spent more than 20 of these years focused on pharmaceutical and biotech manufacturing and process development. As part of Skyland Analytics and IDBS, David has been responsible for data management, connectivity and sharing aspects of PIMS as well as technical implementation project delivery and is now driving towards solutions to better share data between process development and manufacturing groups.

Prior to joining Skyland Analytics, David served as Director, Professional Services for Dassault Systèmes BIOVIA (and its predecessors Accelrys and Aegis Analytical) where he had responsibility for all implementation activities for the Discoverant® informatics software for Life Science manufacturers, and for the Nexus data access and aggregation components of the product. His clients included more than 50 process development and manufacturing facilities worldwide.

David has both a BSc in Applied Mathematics with University Honors and a MSc in Statistics from Carnegie Mellon.

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