BATCH RELEASE - The Business Case for Reinventing Batch Release

INTRODUCTION

The headlines have been hard to miss: Regulators stepping in to force a manufacturer and its production partner to suspend operations at a troubled Covid-19 vaccine production plant for several months and discard several million vaccine doses that would have contributed to fighting the pandemic, all due to a contamination issue; and, in a separate instance, another manufacturer and its production and distribution partners having to discard hundreds of thousands of Covid-19 vaccine doses, also due to batch contamination.

While these two cases occurred months apart on separate continents and involved different biopharmaceutical companies and completely different production and distribution partners, both raise serious questions about the ongoing viability of the traditional batch release processes, practices, and systems the pharmaceutical industry has long relied on throughout the stages of commercializing a product, whether that product is being fast tracked during a crisis, as has been the case during the pandemic, or if it is being developed under a more conventional timeline.

From manufacturing issues, chemical contamination, the presence of impurities, and cGMP deviations to failed content uniformities, lack of appropriate approvals and beyond, the potential health hazards to patients that can prompt a batch recall are myriad. The sheer number of product recalls (more than 60 logged by the FDA for 2021 as of mid-October), the astronomical cost these recalls exact on companies and their brand reputations, and the hard-to-quantify but sometimes tragic toll that a flawed batch can take on a human or animal life, can be irrevocable and unforgiving.

TODAY’S REALITIES SHAPE A VISION FOR THE FUTURE

To safeguard lives, ensure the final drug product adheres to predefined objectives, and ensure product and process control, pharma companies have long followed scientific risk-based holistic and proactive approaches like quality-by-design. From early stage manufacturing through the commercial release, numerous critical quality parameters are identified and tested to ensure safety and efficacy of the drug product. Batch release is one of those processes. It deserves not only closer scrutiny, but an end-to-end re-engineering to do away with the cumbersome, inefficient manual processes, siloed information, and lack of standardization involved in the pooling of information from satellite systems.

For pharmaceutical companies — and pharma supply chains — to bring safe, commercial-ready products to market quickly and profitably, and for them to meet growing demand for highly personalized, batch-size-one types of products, it’s critical that they take steps to de-risk and speed up processes like batch release, and do so without compromising quality, safety or the bottom line.

Realizing the vision for a smoother, efficient, and less costly batch release is predicated on companies embracing certain Industry 4.0 digital capabilities, many of which are already proven or have begun to find their way to the pharmaceutical manufacturing business. In that vision, batch release would be:

• Agile, with a single “cockpit” to view and manage the entire quality release process.
• Faster, incorporating a review-by-exception (RBE) approach so quality managers (QMs) and qualified persons (QPs) can quickly pinpoint the root causes of exceptions without the process grinding to a halt, leading to shorter batch review cycle times.
• Less error-prone as data siloes and manual data entry processes are banished, which produces a substantially better right-the-first-time rate.
• Transparent, with all stakeholders working from the same set of data and common, accepted quality standards/parameters.
• Compliant, with the ability to readily adapt to changing regulatory requirements as well as reporting requirements that differ from country to country.
• Heavily automated, with intelligent digital tools to quickly comb through and draw insight from huge amounts of data, evaluate exceptions, then make automated decisions accordingly.
• Reproducible, a result of greater automation and less reliance on changeable human factors.
• Readily scalable, due to standardized processes across internal operations and supplier operations.
• Harmonized across the operation and the supply chain.
• Ready to support the “batch size of one” associated with emerging cell and gene therapies.
• Profitable, by contributing to the bottom line instead of sapping costs from it.

RELEASE & REWARD

Batch release as a profit center rather than a cost center? As farfetched as that might sound, based on calculations from SAP’s work with its pharma customers, companies that put this vision for a harmonized, automated batch release into practice stand to reap a range of benefits in the following areas:

Speed: Digital batch handling can significantly reduce the order preparation time and can bring down the overall batch review time by more than half, thereby increasing the number of batches that can be reviewed, in a particular timeframe, by manifold. The operational efficiency resulting from accelerating the quality batch release business processes can be quite impactful and will significantly contribute to the productivity of the manufacturing process, in turn increasing the out-of-the-door shipments.

Cost Savings: The massive efficiency boost that results from the reduction of time spent by QM reviewing batches by exception can significantly reduce operating expenses, thereby improving the cost of quality. Even a mere 20% reduction in the cost of quality for a firm whose operating expenses per therapeutic area in the range of 80-100 million can translate into an average cost saving of $20 million per study.

Compliance: Adopting a single monitor for the Identification of an erroneous batch and tracking the source of error will help close CAPAs (Corrective and Preventive Action) at a quicker rate, thereby reducing the number of non-conformities open at a single time, leading to higher quality compliance. Assignment, review, resolution, justification, approvals, and documentation of CAPA, all tracked through one instance, can help hasten quality reviews and aid in preventing their recurrence.

Non-Linear Growth: This level of batch release efficiency means that companies can add products without necessarily adding people, an important consideration given today’s tight labor market, and growing demand for batch-of-one products for cell and gene therapy (CGT) and other applications.

Freeing People for Higher-Value Work: Relieved of the time-consuming manual and redundant data-collection, review and management processes that are part of batch release, QMs, and QPs can focus on other work that adds value to the enterprise, like innovations that improve the overall quality process.

Custodianship: In addition to having fragmented solutions, the traditional batch release process often requires hand-holding by the quality analysis, quality control, and manufacturing teams. Because reviews, identification of discrepancies and deficiencies, investigations, and comments often are conducted offline over company-specific collaboration tools and emails, they can be difficult to retrieve. Having a central monitor to record and report decisions and tie them to the source of truth will ensure tighter custody over batch specific information, overall QA release process, conditional transfers, and recalls.

THREE KEYS TO REINVENTION

Each new recall, each FDA intervention, and each news headline about a suspect batch release practice or negative outcome is a reminder that the gap between this ideal vision for batch release and where many companies stand today remains substantial. Closing it is a matter of focusing on, and dedicating resources to, three key areas.

The first is Operational Efficiency. Too many pharmaceutical companies lean heavily on disparate systems, software, spreadsheets, and manual, paper-based processes to maintain their batch records. The result: cumbersome data-management, inaccurate and/or missing information, wasted resources, delayed releases, and at times, unfavorable outcomes that may or may not grab the attention of regulators and the public.

The answer? A single, integrated “control tower” or “cockpit” that provides visibility into and control over the huge volume of data involved in batch release, internally from a manufacturer’s own operations as well as externally from raw material suppliers to CMOs, CROs, and so on. It gives QMs in-the-moment access to all the data they need to rapidly analyze exceptions and make decisions about flagging and quarantining a batch for a potential contamination issue, moving a batch from one phase of development to the next, etc. Meanwhile, batch record data and updates, and other documentation, flow between quality teams in real time as processes and tasks are completed.

For this “eye in the sky” construct to be viable, companies will need to adopt Internet of Things-enabled Industry 4.0 practices in their own factories, with the ability to analyze data to identify bottlenecks, quality issues, and problems with factory assets in real time. It also requires a willingness for supply chain partners to use a common digital platform and share their data. In the end, this sharing of data fosters collaboration and trust among partners, and aligns their interests.

Standardization is the second key to better batch release. The goal: implement and adhere to a set of common practices, processes, systems, and functions that reinforce thresholds, parameters, workflows, etc, throughout the entire batch release. A lack of standardization inside and across quality teams and departments, and along the supply chain (with CMOs and the like) is one of the biggest bottlenecks and risk factors in batch release today.

Perhaps the most important step toward reducing risk and eliminating bottlenecks is moving from a review-by-default to a review-by-exception approach during the production review process, whereby quality teams, enabled by digital tools, focus on identifying and analyzing process deviations as they occur, then making decisions about next steps based on what the data (and analytics) suggest.

Using RBE opens the door to greater automation of processes, including automatic collection and reconciliation of data, automatic release of batches that are deemed ready for market, and the ability to use automated, intelligent tools to identify then address root causes and process trends that may impact the quality and safety of a product. While it’s impossible to say if an RBE approach could have prevented the Covid-19 vaccination recalls, what is clear is that it can enable companies to expedite the safe auto-release of a significant share of batches and shrink the timeline on batch release from a month or more, to perhaps 6 or 8 hours.

The third key to reinventing batch release is Intelligent Technology. With the ability to apply predictive analytics and other artificial intelligence- or machine learning-driven tools to batch release data (and RBE in particular), and to entire batch management and manufacturing processes, companies gain speed, accuracy, and optimization, not only in their batch releases, but also on the factory floor.

In the quality departments, where workflows still heavily rely on manual and paper-based processes, adoption of RPA (robotic process automation) can be a game-changer by eliminating routine data-entry tasks and improving the agility of the process.

With manufactures and regulatory bodies advocating the adoption of sensors and PAT (process analytical technology), the pharma industry is bound to see a huge increase in data points. Applying automation to chartered decisions can help speed the time-to market of new batches.

Adoption of AI (artificial intelligence) and ML (machine learning) technologies can aid in understanding the critical quality parameters of the “golden batch”, starting from the quality and quantity of raw material used, ingredients added at various stages, the calibration of the equipment used throughout the process, the temperature variability and sensitivity of the atmosphere and can help replicate the perfect process resulting in consistent high yielding quality batches.

Intelligent digital technology, we know, is essential to unlocking the operational efficiency and standardization that are so important to faster, more flexible and ultimately safer batch release. But the true catalyst to all this is a mindset shift, whereby a pharma company’s decision-makers choose to abandon outmoded, risk-laden manual batch release approaches and shift to digital, data-driven and exception-based approaches, realizing that doing so can have a profoundly positive impact on the company’s ability to compete in a business where speed to market and product safety are paramount, during a pandemic and otherwise.

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Aparna Seksaria is Industry Solutions Manager for SAP’s life sciences business group, where she leads customer co-innovation solutions and acts as a trusted advisor, providing clear guidance, roadmaps, and industry best practices that enable companies to transform into intelligent enterprises.