Biomanufacturing flexibility

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whitepaper

Biomanufacturing flexiBilityFuture-proofing facilities for multiple

product lines and higher titers

© Bioproduction Group. All Rights Reserved. 1

BIOMANUFACTURING FLEXIBILITY

iNtrODUCtiON

As biomanufacturing evolves and product portfolios become broader, bulk production facilities must be ready to meet challenges they haven’t faced before. Traditional facility design and operation is often based on a single product with a fixed titer, in largely stainless steel vessels. This creates issues as new products need to be produced in the same facility, often alongside their older counterparts.

This challenge becomes even more significant with consolidation of facilities, and the need to produce material at significantly lower operating costs. Factors such as the need to be competitive as a trusted partner or CMO, or to compete with upcoming biogenerics and biosimilars are also factors driving production optimization. In this white paper, we outline ways of creating highly efficient biomanufacturing facilities that can be used to support multiple products in a single facility, at a range of titers.

Biomanufacturing flexiBility

Future-proofing facilities for multiple product lines and higher titers

“Our plant was designed for 1 gram per liter, so processing higher titers has been a challenge for us. at the moment we look at only a few variables, and that’s led to problems. we need to model the plant properly – wFi, labor, piping segments – to truly understand whether our plant will be flexible enough to accommodate new products.”Biomanufacturing Engineer, Modeling Head

“as a result of the merger, we are trying to maximize utilization of plants… we have excess capacity, so efficient operations in the one plant is critical.”Senior Director, Strategic Planning, Large Biopharmaceutical Manufacturer

“LiCeNse aND Leave”

Biomanufacturing facilities have historically been built on the premise of ‘license and leave’: build a facility and then change as little as possible once in operation. High levels of industry regulation have prompted designers to custom-build facilities for one product (or a small number of products), license the facility for that specific product, and then make minimal changes to the facility – ‘leaving it’ to produce the product without improvements. Even in cases where a facility was designed with ‘flexibility’ in mind, it is often far from clear how that flexibility allows for the processing of new products, which may have substantially different technology platforms and volume requirements.

FIGURE 1: evOLUtiON OF titer FOr COmmerCiaL prODUCts, 1985-2010

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2© Bioproduction Group. All Rights Reserved.


Figure 1 shows titers for new commercially released products by year and their evolution over time. As seen above, titers follow an ‘exponential growth’ pattern where product volumes per liter are expected to double every two years or so. The most important element of the graph, however, is to note the increasing range (or variability) of titers that are in commercial production: currently between 1 and 4+ grams per liter. This high range of titers brings with it a number of challenges for drug substance manufacturing facilities. If designed for low titers, downstream processing steps and tank volumes may be insufficient to process high titer material in a single batch. Conversely, if designed for higher titers, there are significant implications for raw materials costs in wasted resins and, in some cases, in volumes being too low to operate certain downstream tanks. In either case, it is clear that manufacturing facilities are limited in the range of titers they can efficiently process.

UNDerstaNDiNg FLexibiLity

Biomanufacturers are often attracted to the concept of flexibility, but find it difficult to justify to the accountants since the metrics to measure the value of flexibility are unclear. Ostensibly, ‘flexible facilities’ are able to accommodate a range of downstream processing steps (since there is currently no standardized platform process across the industry) and also able to produce multiple products with relatively short changeovers. However, there are very few examples in the industry of multi-product facilities to draw from, and little evidence yet that where investments have been made in ‘flexible facilities’, there has been lower capital investment or operating costs. It may be some time before the current glut of industry capacity shows the true value of such ‘flexibile facilities’ in practice.

For leading biomanufacturing companies, the concept of flexibility today is premised on the idea of being able to quickly perform technology transfer of a new product to an existing facility, ensuring that it fits within the facility, and determining a maximum sustainable run-rate (for costing purposes). Transitioning from a single-product to multi-product facility can be difficult, depending both on the culture of the facility as well as the overall scope of modifications required in changeover. Secondary considerations include utilities requirements, labor profiles, raw materials costs, and waste processing. We outline the concept of flexibility with a case study of a new process implemented in an existing manufacturing facility.

“Ultimately we’d want to have more than one product manufactured in our plant. but without a platform process, that’s difficult. Downstream processing especially isn’t standardized.”Director, Manufacturing Sciences

Case stUDy: retrOFittiNg a biOmaNUFaCtUriNg FaCiLity with a New prOCess

A large biomanufacturing organization was considering bringing a new product into an existing facility through a trusted partner / contract manufacturing arrangement. The existing product in the facility was a relatively low titer, 1.5 g/L process with three chromatography steps in the downstream operation, using Protein-A for initial capture followed by a number of CEX (cation exchange) and AEX (anion exchange) chromatography steps as well as viral filtration and UFDF (ultra-filtration / diafiltration). A summary of the processing steps for the existing process is shown in Figure 2 (’existing process’).

The new process to be introduced to the facility had a largely similar upstream and had a similar titer to the existing process, at 1.4 g/L. However, clinical studies had been conducted using a different processing methodology with a smaller number of processing steps and a heat-based viral inactivation in the pool step after the first unit operation. A summary of the processing steps for the new process is shown in Figure 2 (’new process’).



Bio-G’s analysis showed that the use of CEX technology rather than Protein-A for the first processing step created a high volume of product in the pool step. Since heat-based viral inactivation was used in this processing step, the material had to be slowly heated, held at a constant temperature for a fixed duration, then cooled. This heating process created a significant delay which was not accounted for in the original design, and could not be engineered out by fitting a larger heat exchanger. As such, the introduction of this apparently benign process created significant delays in the process and adversely affected overall throughput. Additionally, since the facility did not have pool tanks (or buffer hold tanks) of sufficient volume, a lower amount of product than originally planned had to be processed per batch.

In Figure 3, we show another issue detected by Bio-G’s analysis, created by the removal of one of the unit operations from the process. In this diagram, boxes indicate the maximum number of simultaneous batches that could be processed by the facility (allowing for batch-to-batch segregation rules). The smaller number of unit operations means that fewer batches can be in the downstream, lowering the run-rate in the facility.

FIGURE 3: maximUm NUmber OF batChes iN the FaCiLity

FIGURE 2: DOwNstream prOCessiNg UNit OperatiONs FOr existiNg aND New prOpOseD prOCesses

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A histogram showing the maximum output of the facility for existing and new products is shown in Figure 4. Despite both products having similar titers, the issues listed above had a significant impact on throughput. The average weekly throughput for the existing product was 21 kg / week, while the new product was only 5.5 kg / week. The resulting cost of goods was four times higher than expected, making the opportunity significantly less valuable for the company.



FIGURE 5: prOCessiNg time FOr a ChrOmatOgraphy step. ‘OrgaNizatiONaL LearNiNg’ exhibiteD FOr a ChrOmatOgraphy UNit OperatiON

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FIGURE 4: maximUm thrOUghpUt OF the FaCiLity FOr New vs. existiNg prODUCts

maximiziNg eFFiCieNCy thrOUgh ‘LiviNg Data’ mODeLs

The case study above highlights the issues associated with bringing new process platforms into an existing facility. Small changes in process design (such as switching order of unit operations, titer changes, additional product treatment steps etc.) can have a massive impact on throughput and facility efficiency. In the quest for a truly ‘flexible’ facility, planners need to predict ahead of time what possible processing technologies may be coming in the future, map these against the product portfolio inside and outside the company, and understand how titer and a host of other factors may impact the result. Performing this task is difficult and getting any one of these predictions wrong can have a dramatic impact on how ‘flexible’ the facility is for a particular product introduction.

Instead, the focus for leading biomanufacturing companies has been to establish robust processes for increasing the velocity of the technology transfer to existing facilities, and increasing efficiency at those facilities to ensure they produce material at the lowest possible cost. The challenge for engineers is that transforming a single-product facility into a multi-product, short-changeover facility necessitates a completely different approach to facility design. Areas for column packing, for example, must be optimized for speed; skids must be movable between campaigns; piping segments and transfer panels and the complex relationships between vessels must be accounted for.

In addition to all these factors, ‘process learning’ must be considered. Process learning means that the first batch of product will have a longer cycle time than the second batch, and that over time operational efficiencies may have a dramatic impact on overall performance. Figure 5 shows an example of this ‘process learning’ as seen in processing times for a chromatography step, pulled directly from automation systems using Bio-G’s Crosswalk tool. Over the 18 month period shown, the average time to perform this operation decreased by nearly 1 hour (15%).

“we don’t want to have idle plants, so we’re moving more and more into multi-campaign… lots of products in one facility, and quick tech transfer. the big question: what products are going to be in the facility? without that knowledge, or some kind of product platform, it’s very difficult to plan accurately.”Technology Transfer Specialist, Top 10 Biopharmaceutical Manufacturer

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© Bioproduction Group. All Rights Reserved.


5

Understanding the effect of ’process learning’ is critical when trying to improve process efficiency. Best-in-class biomanufacturing organizations are increasingly tracking the effect of this year-on-year learning on key metrics like cycle times and overall throughput. Collecting data for a single period (say, the last few batches) doesn’t accomplish this task and can lead to overly optimistic assessments of facility run-rate when a product is first introduced. Seeing the pattern of behavior over months and years as it evolves allows planners to understand initial run-rates 1, 2 and 3 years into the future as the facility ‘leans out’ the production process.

OperatiONaL FaCiLity Fit

Best-in-class biomanufacturing organizations are not just using this data at an operational level, but pushing this information further and further up the process development supply chain. Rather than having Process Development create new processes in isolation, sharing operating cycle time data and critical bottlenecks in existing processes gives these groups the tools to create highly efficient, multi-product plants that tradeoff ‘platform processes’ with the increased efficiency of product-specific processing techniques. This requires assembling data not just from ‘wet time’ operations – bind, elute etc – but taking a holistic approach to modeling that includes both setup and teardown times. Figure 6 shows the percentage of time spent on main operations in a typical facility compared to other (non-main) operations. Clearly, if 40% of the time in a facility is spent on other operations, a model focusing only on the main unit operations will be inaccurate, and will typically fail to understand the complex interdependencies between unit operations. Instead, a comprehensive approach using simulation modeling, live data collection, and integrated data updating is required. We discuss this approach using a case study below.

FIGURE 6: OperatiONaL FaCiLity Fit mODeLs aCCOUNt FOr UNit OperatiONs aND aLsO OperatiONaL CONsiDeratiONs

OTHER OPERATIONS 40%

SchedulingLabourNon-wet time (prep)Unscheduled Maintenance

MAIN UNIT OPERATIONS 60%

Prep and processingWet timesBuffer prep and hold CIP/SIP



FIGURE 7: CyCLe times FOr staiNLess-steeL biOreaCtOrs assUmiNg a 3-Day FermeNtatiON time

Case stUDy: impLemeNtatiON OF DispOsabLe biOreaCtOrs

In this case study, we examine a biomanufacturer looking to implement disposables-based technology to manage the move to a multi-product facility, while minimizing possible contamination risk and lowering turn-around and changeover times. Disposable bioreactors have potential benefits in their ability to avoid CIP and SIP operations, shortening the overall preparation time required for a bioreactor, and maximizing its utilization. In Figure 7 below, we show the time required to prepare a stainless steel bioreactor at different scales (note the use of automated CIP and SIP systems). This creates potential time savings benefits for disposable bioreactors for a scale-up fermentation process of around 10-15 hours, or around 10% of the total time.

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In order to assess the impact of this 10-15 hour saving, a sensitivity analysis was performed with an existing model at the facility using Bio-G’s Simulation System. The model looked at the effect of these potential time savings in fermentation, as well as in other areas of the facility. The results of this analysis are shown in Figure 7, with the key activities on the x-axis and the relative importance of those activities to throughput shown on the y-axis. This Figure shows the effect of reducing the processing times for each activity in the facility in turn: if the change has no impact, the activity is unlikely to be a bottleneck. Conversely, peaks in the chart indicate areas where a reduction in processing times would have the most impact on throughput. These activities are likely to be the bottlenecks in the facility.

“we thought the new process would have a higher run-rate than it did. we totally missed the downstream bottlenecks, meaning major retrofit projects and nearly three months of downtime.”Senior Director, Engineering

FIGURE 8: Key bOttLeNeCKs iN the FaCiLity

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As can be seen from the graph, there are no areas in fermentation that show benefit from cycle time reduction. The critical bottlenecks for the facility, at the titers considered, are in the downstream Protein-A column and in the final UFDF step. Much of the time spent in these two processing steps were non-wet times, and not predicted by chemical and mass balance toolsets used by Process Development. The result was a process that was badly mismatched between upstream and downstream, and had a significantly lower throughput than previously communicated to management.

This case study clearly shows the importance of improving efficiency and flexibility in a facility through identifying the correct areas to focus engineering resources. Models that do not have real data, do not consider the ‘learning curve’ associated with tech transfer, and only look at chemical and mass balance are often dangerously optimistic. The challenges for biomanufacturing professionals are understanding the dynamics of their plant, how flexible it is for a specific product, and how that product will really be processed, using real data from the facility. With such a framework, biomanufacturing organizations can create truly flexible facilities that minimize the impact of consolidation and improve manufacturing efficiency.

CONCLUsiONs

Biomanufacturers face the prospect of facility consolidations, biogenerics and biosimilars, lower margins, and a burgeoning set of new products (and product versions) with widely varying titers and processing steps. The industry has a glut of manufacturing capacity, much of which was designed for one or a small number of products at fixed titer. In such an environment, it is clear that understanding and implementing flexibility is essential to maximize these existing (and future) assets’ potential and to create robust processes that allow tech transfer to become a routine business.

There are many obstacles to such an effort: automation systems are highly rigid, current facility designs are specific to a single or small number of products, and changeovers are unfamiliar to operators. The largest obstacle, however, is an understanding of how flexibility can be created in such a facility, and how modeling and actual production data can be used to support this effort.

The industry critically needs to move beyond a ‘back of the envelope’ approach and even from a focus based solely on chemical engineering considerations. A systems-based approach calls for a way of seeing those chemical- and mass-balance considerations in the context of manufacturing operations, and incorporates effects such as process learning and facility evolution.

Bioproduction Group’s Crosswalk and Simulation toolsets provide such a modeling platform. By integrating with supply chain and production data feeds while still remaining flexible enough to integrate with Excel and other planning tools, they provide a way of moving biotech to a truly flexible and robust platform for analysis and optimization.

FeeDbaCK

Please provide your feedback at http://www.zoomerang.com/Survey/WEB22AYVM3VLFC

FUrther reaDiNg

Johnston, Zhang (2009). Garbage In, Garbage Out: The Case for More Accurate Process Modeling in Manufacturing Economics, Biopharm International, 22:8

mOre iNFOrmatiON

biOprODUCtiON grOUp [email protected] www.biO-g.COm

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Biomanufacturing flexibility