Biomanufacturing flexibility

whitepaper
Biomanufacturing
Flexibility
Future-proofing facilities for multiple
product lines and higher titers
© Bioproduction Group. All Rights Reserved.

BIOMANUFACTURING FLEXIBILITY
Biomanufacturing Flexibility
Future-proofing facilities for multiple product lines and higher titers
INTR ODUCTION
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.
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1998 Narrow
Range of Titers
2010
Wide Range
of Titers
© Bioproduction Group. All Rights Reserved. 1
“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
“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
“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
0
1985 1990 1995 2000 2005 2010
TITER (mg/L)
YEAR

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.
BATCH 1 BATCH 2 BATCH 3
CEX /AEX
chromato-graphy
Viral
Filtration
FIGURE 2:
Downstream processing unit
operations for existing and
new proposed processes
FIGURE 3:
Maximum number of batches
in the facility
EXISTING
PROCESS
NEW
PROCESS
Unit
Operation 1
Unit
Operation 2
Unit
Operation 3
Unit
Operation 4
Unit
Pool Operation 5
Filter /
Freeze
Protein A
chromato-graphy
CEX /AEX
chromato-graphy
Viral
Filtration
Filter/
Freeze
UFDF
AEX /CEX
chromato-graphy
CEX /AEX
chromato-graphy
Viral
Filtration
Filter/
Freeze
UFDF
AEX /CEX
chromato-graphy
pH
Viral
Inacti-vation
Heat Viral
Inactivation
EXISTING
PROCESS
NEW
PROCESS
Unit
Operation 1
Unit
Operation 2
Unit
Operation 3
Unit
Operation 4
Unit
Pool Operation 5
Filter /
Freeze
Protein A
chromato-graphy
BATCH 1 BATCH 2
Filter/
Freeze
UFDF
AEX /CEX
chromato-graphy
CEX /AEX
chromato-graphy
Viral
Filtration
Filter/
Freeze
UFDF
AEX /CEX
chromato-graphy
pH
Viral
Inacti-vation
Heat
Viral
Inacti-vation
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.

60
50
40
30
20
7.0
6.5
6.0
5.5
5.0
FIGURE 5:
Processing time for a
Chromatography step.
‘Organizational Learning’
exhibited for a chromatography
unit operation
4.5
TIME (MONTHS)
PROCESSING TIME (HOURS)
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
THROUGHPUT (kg/week)
0-2
2-4
4-6
6-8
8-10
10-12
12-14
14-16
16-18
18-20
20-22
22-24
24-26
10
LIKELIHOOD (%)
New Process
Existing Process

© 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%
Scheduling
Labour
Non-wet time (prep)
Unscheduled Maintenance
MAIN UNIT OPERATIONS 60%
Prep and processing
Wet times
Buffer prep and
hold CIP/SIP

120
100
80
60
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.
40
20
0
Fermentor SIP
Fermentor and
Transfer Lines CIP
Inoculation of
next scale
Fermentation
Inoculation
Media HTST/Transfer
to Fermentor
Preparation
TOTAL TIME FOR ONE BATCH (HOURS)
BIOREACTOR SCALE
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
THROUGHPUT (RUNS/WEEK)
BOTTLENECK AREA
SCALEUP
PRODUCTION
FERMENTOR
CENTRIFUGATION
PROTEIN-A
CHROM STEP
CHROM STEP
VF
UFDF
FILTER FREEZE

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.
FEEDBA CK
Please provide your feedback at http://www.
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FURTHER READING
Johnston, Zhang (2009). Garbage In,
Garbage Out: The Case for More Accurate
Process Modeling in Manufacturing
Economics, Biopharm International, 22:8
MORE INFORMATI ON
BIOPRODUCTION GROUP
CONTACT@BIO-G.COM
WWW .BIO-G.COM

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