PHARMA SPECIAL ....Digitization, automation, and online testing: The future of pharma quality control PART II

PHARMA SPECIAL Digitization, automation, and online testing: The future of pharma quality control

PART II

For example, Biogen plans to use this distributed QC method of real-time release and review by exception in its new manufacturing facility near Solothurn, Switzerland. When production starts up in 2019, the Solothurn facility will achieve raw-material control through screening and genealogy, with minimal testing using rapid identification and electronic data exchange. Bioreactor processes controlled through in-line instruments will eliminate the need for process control sampling. The new facility will have adaptive process control levers, lab execution by recipe, and automated data transcription from all equipment, all based on a deep understanding of raw materials, processes, and product characteristics. The integrated control system allows employees to see data and react in real time.³

As pharma companies start exploring ways to build distributed QC facilities, they may be able to pull in relevant technologies from adjacent spaces. For example, the PharmaMV platform from Perceptive Engineering and the Sipat platform from Siemens could provide the advanced process control necessary to enable parametric release. Meanwhile, AI systems from companies such as Arago and IBM could allow pharma companies to automate tasks that historically have been performed by highly trained expert employees. (For a view on one company’s efforts, see sidebar, “On the path to the future: How one company is exploring and adopting new quality-control technologies.”)

Typical implementation pitfalls hampering successful transformation and value capture

As pharma labs evolve, they face significant costs associated with implementing IT and automation solutions. Even expensive solutions can deliver a strong positive return on investment (ROI), but many companies, unfortunately, struggle to capture value from these digital upgrades. These companies typically encounter one or more of the following pitfalls:

1. Not having a clear vision of what evolution horizon is the right target for a specific lab. While most labs can make a solid business case for the digitally enabled horizon, not all labs have sufficient volumes and operational setup to justify automation and distributed QC. For example, it could be hard to justify an investment in automating a smaller lab where the potential cost savings might be less than $200,000 a year, whereas the same investment could quickly generate positive ROI for a large sterile facility with significant environmental-monitoring volumes.

2. Not having a compelling business case for the transformation. Many companies start implementation of costly IT systems without a clear understanding of the full benefits such solutions can generate. This often results in delays in implementation and the rollout of partial solutions. For instance, labs might move to paperless systems on individual modules but still need significant manual efforts to move data from one system to another. This can lead to situations where analysts must record test results into a paper log before manually entering the data into a laboratory information-management system (LIMS). This manual-entry step prevents them from capturing the full savings they should get from automating documentation.

3. Targeting a fully tested end-to-end future-state prototype rather than testing and rapidly scaling up high-value solutions to capture quick wins. For example, schedule automation and optimization can be implemented quickly and start generating significant value even if a lab is not yet mostly paperless and fully digitized.

4. Lacking proper planning or management for rollout of new systems and technologies. In extreme cases, it can take pharma companies several years and more than $100 million to implement a LIMS. Given such a lengthy time frame and the fast pace of technological change, some of the LIMS capabilities are liable to become obsolete before they get rolled out across the entire network. Pharma companies need skilled resources to accelerate the rollout and should avoid the temptation to engage in excessive customization at each site. A poor rollout can cost five to ten times more and take three to five times longer than a properly planned investment executed with good long-term planning.

5. Not having a full understanding of the capabilities of the systems they acquire. Pharma companies may purchase a system such as LIMS to comply with data-integrity regulations without truly understanding or considering the system’s potential to generate improvements in productivity.

6. Pursuing automation rather than optimization. Scheduling automation can deliver 2 to 3 percent of the QC cost savings, but automation plusdynamic scheduling optimization can yield three to four times more value.

7. Self-imposed constraints from a perceived need to validate all systems and technologies. Many of the high-impact changes, such as optimized scheduling and data-enabled deviation analysis, do not require validation and refiling.

8. Missing the skill set to extract full value from their data. Most typical pharma labs do not have the advanced analytical capabilities needed to get the maximum value from data sources. As a result, the labs collect data, but the data does not get used properly to generate insights that could prevent problems or reduce testing volumes.

9. Spending too little time and effort on developing a robust change-management program. Digital transformation requires radical changes in mind-set and has major implications for the organization and individual employees who must develop new skills and competencies. To succeed, companies must make up-front investments in changing the culture, winning buy-in across the business, and forging strong links between business and IT functions.

How to get started?

The good news is that most of the technologies needed to attain any of the three horizons of Industry 4.0 QC labs already exist today. Many of the technologies mentioned are already being deployed in pharma environments, with some successful pilot projects already completed and others in the approval stage.

To successfully implement Industry 4.0 technologies, pharma companies need to set the right aspirations and move quickly. Here are five things they can do to get started today:

1. Test several use cases and technologies quickly to find the best ones for each lab type.

2. Create lighthouse QC labs to showcase the potential benefits of amalgamating these innovative technologies.

3. Find out which innovative tools can have the greatest immediate impact, then roll them out quickly across multiple sites. Don’t get bogged down trying to set up a fully functioning lab with every possible desirable technology. Many use cases, such as scheduling optimization, can be implemented before other elements (for example, paperless labs) are in place.

4. Establish a clear target state and business case for each lab early on. Track the value capture along the way, and reinvest the savings toward the next technological upgrades. It is important to make an assessment separately for chemical labs and microlabs, because the baseline cost and the impact of improvements may differ significantly.

5. Aim for the highest-value horizon justified by the business case when planning and building new labs to preempt the need for digital-transformation upgrades right after the lab opens its doors.

6. Start building the needed talent base and skills early on. Clearly understand future capability needs, invest in training high-potential employees, and invest in hiring employees with the new required skill sets (for instance, advanced data analytics) during early stages to enable faster scale-up.

Modern technologies can make QC faster, more agile and reliable, more compliant, and more efficient. By setting appropriate goals, choosing the right technologies, and scaling up quickly, pharma companies can become QC leaders and reap the rewards in the form of speed, compliance, cost savings, and productivity improvements.

By Yan Han, Evgeniya Makarova, Matthias Ringel, and Vanya Telpis

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/digitization-automation-and-online-testing-the-future-of-pharma-quality-control?cid=other-eml-alt-mip-mck&hlkid=0b415bdaf0a542fca1d46170163f2211&hctky=1627601&hdpid=139c1776-fae0-464e-9208-f47c40d547ca