62
A batch size can also be defined as a range. For example, a batch size range can be established by 63
defining a minimum and maximum run time.
64
3. SCIENTIFIC APPROACHES 65
3.1. Control Strategy 66
The development of a successful control strategy for CM is enabled by a holistic approach, 67
considering aspects specific to CM (discussed below) and the principles described in ICH Q8–
68
performance and product quality. The condition may vary, depending on the mode of CM and the 72
specific process steps. For example, a state of control can be demonstrated for some CM processes 73
when a set of parameters (e.g., process parameters, quality attributes) are within specified ranges, 74
but the processes are not necessarily in a steady state condition. Elements of the control strategy 75
monitor a state of control and, when necessary, take appropriate actions to maintain control of the 76
process. It is important to have mechanisms in place to evaluate the consistency of operation and 77
to identify situations in which parameters are within the specified range yet outside historical 78
operating ranges, or they are showing drifts or trends. The latter situation may indicate that the 79
process is at risk of operating outside the specified operating range and warrants evaluation and, 80
when necessary, corrective action.
81
3.1.2. Process Dynamics 82
Knowledge of process dynamics is important to maintaining state of control in CM. Specifically, 83
understanding how transient events propagate helps to identify risks to product quality and to 84
3
develop an appropriate control strategy (see Section 3.1.5 for process monitoring and control 85
considerations). Transient events that occur during CM operation may be planned (e.g., process 86
start-up, shutdown and pause) or unplanned (e.g., disturbances).
87 88
Characterisation of the residence time distribution (RTD) can be used to help understand process 89
dynamics. RTD characterises the time available for material transport and transformation, and it 90
is specific to the process, composition/formulation, material properties, equipment design and 91
configuration, etc. Understanding process dynamics (e.g., through the RTD) enables the tracking 92
of material and supports the development of sampling and diversion strategies, where applicable.
93
In addition, such understanding is of importance from a process performance perspective. For 94
example, process dynamics may impact process characteristics, such as selectivity in the 95
manufacture of chemical entity drug substances and viral safety in the manufacture of therapeutic 96
protein drug substances.
97 98
Process dynamics should be characterised over the planned operating ranges and anticipated input 99
material variability using scientifically justified approaches. Appropriate methodologies (e.g., 100
RTD studies, in silico modeling with experimental confirmation) should be used to understand the 101
impact of process dynamics and its variation on material transport and transformation. These 102
methodologies should not interfere with the process dynamics of the system, and the 103
characterisation should be relevant to the commercial process. For example, when conducting 104
RTD studies, the tracer used to replace a constituent of the solid or liquid stream should have 105
highly similar flow properties as those of the constituent replaced. A tracer should also be inert to 106
the other components of the process and should not alter how processed materials interact with 107
equipment surfaces. Step testing by making small changes to the quantitative composition of the 108
process stream (e.g., small increments of a constituent) is another useful technique to determine 109
the RTD and avoid the addition of an external tracer to the process. Other approaches can be used;
110
the approach taken should be justified.
111
3.1.3. Material Characterisation and Control 112
Material attributes can impact various aspects of CM operation and performance, such as material 113
feeding, process dynamics, and output material quality. Understanding the impact of material 114
attributes and their variability on process performance and product quality is important for the 115
development of the control strategy. Input materials may require evaluation and control of 116
attributes beyond those typically considered for a material specification used in batch 117
manufacturing. For example:
118 119
For a solid dosage form process, particle size, cohesiveness, hygroscopicity, or specific 120
surface area of drug substances and excipients may impact the feeding of powders and 121
material flow through the system.
122 123
For a chemically synthesised drug substance process, viscosity, concentration, or the 124
For a therapeutic protein (e.g., monoclonal antibodies) process, the higher variability of 128
feed stocks such as metal salts, vitamins, and other trace components may adversely impact 129
4
cell culture performance. Prolonged run times may require different lots of media, buffers, 130
or other starting materials for the downstream CM process, potentially introducing more 131
variabilities to the process.
132
3.1.4. Equipment Design and System Integration 133
The design of equipment and their integration to form a CM system impacts process dynamics, 134
material transport and transformation, output material quality, etc. When developing a CM process 135
and its control strategy, it is important to consider the characteristics of individual equipment as 136
well as those of the integrated system that can affect process performance. These include the 137
system’s ability to maintain a continuous flow of input and output materials, manage potential 138
disruption to CM operations (e.g., filter changes), and complete the intended transformation of the 139
material stream within the respective planned operational ranges of the equipment. Examples of 140
design considerations are given below:
141 142
Design and configuration of equipment (e.g., compatibility and integrity of equipment 143
components for the maximum run time or cycles; geometry of constituent parts to promote 144
the desired transformation; spatial arrangement of equipment to facilitate material flow and 145
avoid build-up or fouling) 146
147
Connections between equipment (e.g., use of a surge tank between two unit operations to 148
mitigate differences in mass flow rates) 149
150
Locations of material diversion and sampling points (e.g., selection of locations for a 151
diverter valve and sampling probe without interrupting material flow and transformation) 152
153
Furthermore, appropriate design or selection of equipment for a CM process may enable process 154
simplification, facilitate process monitoring and material diversion, and improve process 155
capability and performance. For example, in a drug substance process, reactor design can 156
effectively reduce formation and build-up of impurities, resulting in fewer purification steps.
157
Similarly, for therapeutic protein drug substance manufacturing, system design can enable process 158
intensification and reduce cycle times.
159
3.1.5. Process Monitoring and Control 160
Process monitoring and control support the maintenance of a state of control during production 161
and allow real-time evaluation of system performance. Common approaches to process monitoring 162
and control—including establishment of target setpoints and control limits, design space, and 163
specifications for attributes being measured—are applicable to CM.
164 165
Process analytical technology (PAT) (ICH Q8) is well-suited for CM. Example applications 166
include in-line UV flow cells to monitor therapeutic protein concentration information, in-line 167
near-infrared spectroscopy to assess blend uniformity, and in-line particle size analysis to monitor 168
the output of a crystalliser. The use of PAT enables disturbances to be detected in real time.
169
Therefore, CM is readily amenable to automated process control strategies based on, for example, 170
active control such as feedforward or feedback control. Principles of control strategy as described 171
in ICH Q8 and ICH Q11 can be applied to CM processes.
172 173
5
An appropriate sampling strategy is an important aspect of process monitoring and control. The 174
variables monitored, monitoring method and frequency, amount of material sampled (either 175
physical sampling or data sampling using in-line measurement), sampling location, statistical 176
method, and acceptance criteria depend on the intended use of the data (e.g., detection of rapid 177
changes such as disturbances, assessment of quality of a batch when real-time release testing 178
(RTRT) (ICH Q8) is used, analysis of process trends or drifts) and process dynamics. Another 179
important consideration is the avoidance of measurement interference with the process.
180
Assessment of risks associated with data gaps (e.g., PAT recalibration, refill of a feeding system, 181
failure of system components) should inform whether contingency methods are required.
182
3.1.6. Material Traceability and Diversion 183
CM processes may include periods when non-conforming materials are produced, for example, 184
during system start-up and shutdown and when disturbances are not appropriately managed and 185
mitigated. The ability to divert potential non-conforming material from the product stream during 186
production is an important characteristic of CM and should be considered in developing the control 187
strategy.
188 189
Understanding the process dynamics of individual unit operations and integrated systems over 190
planned operating conditions enables tracking of the distribution of materials over time. This 191
allows input materials to be traced throughout production. Material traceability, understanding 192
how upstream disturbances affect downstream material quality, and the use of appropriate 193
measurements (e.g., PAT) allow for real-time determination of when to start and stop material 194
collection or diversion. The amount of material diverted can be influenced by several factors, such 195
as process dynamics, control strategy, severity (e.g., magnitude, duration, frequency) of the 196
disturbances, and location of the sampling and diversion points. Additionally, it is important that 197
the diversion strategy accounts for the impact on material flow and process dynamics when 198
material is diverted. Criteria should be established to trigger the start and end of the diversion 199
period and restart of product collection.
200
3.1.7. Process Models 201
Process models can be used for development of a CM process or as part of a control strategy for 202
commercial production, including the diversion strategy. Process models may also be used to 203
predict quality attributes in real time, enabling timely process adjustments to maintain a state of 204
control. During development, process models can support the establishment of a design space by 205
explaining how inputs (e.g., process parameters, material attributes) and outputs (e.g., product 206
quality attributes) are related. Through use of in silico experimentation, process models also 207
enhance process understanding and can reduce the number of experimental studies.
208 209
For general considerations regarding models (including implications of model impact to validation 210
requirements), refer to Points to Consider: ICH-Endorsed Guide for ICH Q8/Q9/Q10 211
Implementation. For CM applications, additional considerations are discussed below.
212
Model development requires an understanding of the underlying model assumptions (e.g., 217
plug flow versus mixed flow systems) and when these assumptions remain valid. Risk 218
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assessments, sound scientific rationales, and relevant data are needed to select model inputs 219
and model-governing equations. It is important to determine the relevant inputs that affect 220
the model performance, based on appropriate approaches such as sensitivity analysis.
221 222
Model performance depends on factors such as mathematical constructs and the quality of 223
model inputs (e.g., noise, variability of data). When setting acceptance criteria for model 224
performance, the model’s intended use and the statistical approaches that account for 225
uncertainty in the experimental measurement and model prediction should be considered.
226 227
Model validation assesses the fitness of the model for its intended use based on 228
predetermined acceptance criteria. Model validation activities are primarily concerned with 229
demonstrating the appropriateness of the underlying model assumptions and the degree to 230
which sensitivity and uncertainty of the model and the reference methods are understood.
231 232
Monitoring of model performance should occur on a routine ongoing basis and when a 233
process change (e.g., input material, process parameter change) is implemented. A risk-234
based approach to assess the impact of a model change (e.g., optimisation of model 235
performance, change of the model’s intended use, change of underlying model 236
assumptions), scope of model development, and model validation criteria enables effective 237
and efficient lifecycle management of models. Depending on the extent of a change and its 238
impact on model performance, a model may need to be redeveloped and validated.
239
3.2. Changes in Production Output 240
Several considerations associated with some common approaches to production changes are 241
changes may require process modification and process validation.
245 246
Change in run time with no change to mass flow rates and equipment: Issues not 247
observed over shorter run times may become visible as run time increases. Additional risks 248
and constraints should be considered and may include, for example, process drift, increased 249
heat, material build-up, exceeding the performance limit of components (e.g., validated in 250
vitro cell age, resin cycle number, measurement system calibration status), material 251
degradation, membrane or sensor fouling, and microbial contamination. Decreasing 252
production output (below the longest run time previously validated) should not imply 253
additional risks, given the same equipment, process and control strategy are used.
254 255
Increase mass flow rates with no change to overall run time and equipment: The risks 256
associated with this approach may impact output material quality and are related to changes 257
in process dynamics and system capability to handle increased mass flow rates. Therefore, 258
this approach may require re-evaluation and modification of the control strategy, including 259
process parameters and controls, material traceability, RTD, sampling, and diversion 260
strategies.
261 262
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Increase output through duplication of equipment (i.e., scale-out): Considerations for 263
two commonly used scale-out approaches are provided below.
264 265
o Replication of production lines (like-for-like): Replicating the integrated CM 266
production line (i.e., same equipment and setup as the original CM system) can be 267
used to increase production output. The replicate production lines follow the same 268
control across parallel unit operations. Aspects to consider are maintenance of 273
uniform flow distribution among the parallel operations, re-integration of parallel 274
flow streams, changes to process dynamics, and material traceability.
275 276
Scale up by increasing equipment size/capacity: Depending on the process and 277
equipment design, increasing production by increasing equipment size may be possible.
278
General principles of equipment scale-up as in the case of batch manufacturing apply. As 279
elements such as RTD, process dynamics, and system integration may change, various 280
aspects of the control strategy may be impacted. The applicability of the original control 281
strategy should be assessed at each scale and modified where needed.
282
3.3. Continuous Process Verification 283
In CM, frequent process monitoring and control can be achieved through use of PAT tools, such 284
as in-line/online/at-line monitoring and control, soft sensors and models. These tools allow real-285
time data collection for parameters relevant to process dynamics and material quality, and hence 286
ensure the state of control for every batch. Additionally, since CM can facilitate changes to 287
production output without increasing equipment size, there is an opportunity to generate 288
development knowledge at the same scale intended for commercial manufacturing. These tools, 289
together with the system design and the control strategy, facilitate early execution of process 290
validation activities and the adoption of continuous process verification (ICH Q8) as an alternative 291
to traditional process validation.
292
4. REGULATORY CONSIDERATIONS