1. FDA Regulatory Perspective on Continuous Manufacturing
Celia N. Cruz, Ph.D.
Acting Branch Chief
CDER/OPQ/OPF/DPAII
IFPAC Summer Summit, Carolina, PR
June 09, 2015

2. Outline Introduction to continuous manufacturing (CM)
Description, challenges and opportunities, illustrative example
Considerations for establishing a control strategy for CM process
Process understanding
State of Control
Diversion of non-conforming material and traceability
Batch Definition and RTRt

3. What is continuous manufacturing?
Several descriptions have been proposed:
In a continuous manufacturing process, the material(s) and product are continuously charged into and discharged from the system, throughout the duration of the process1.
From ChE 101: a mode of operation where materials enter and exit the system at the same rate.
Concepts such as mass flow, residence time distribution, time constants, etc.
1. Lee S. et. al. J Pharm Innov DOI /s

4. Why go continuous?
Reduction in processing time per unit dose (minutes vs. days).
Reduction in equipment footprint requirements.
Potential flexibility in duration of manufacturing campaigns based on knowledge of process.
Rapid response to drug shortages, emergencies, patient demand

5. Opportunities for increased product quality assurance and product availability
Implementation of PAT, quality by design, and process controls tools. (systems approach)
Implementation of integrated quality systems that are responsive to process and product observations in real time.
Wealth of process knowledge for trending, decision making, and continuous improvement.
Modernization of manufacturing processes.

6. Illustrative Example: Blending+ =
A + B
C scale
Continuous feeding of
materials at A + B = C rate
Continuous output of
blend at C rate
Batch
Continuous
Parameters: speed, time, fill level
Parameters: feed rate and speed

7. Key elements for developing a CM control strategy
challenges
Process understanding
Impact and Interactions of parameters within a process step
Characterization of process dynamics
State of Control
Process monitoring
Level and integration of controls
Handling of deviations and disturbances in real time
Batch definition
Material traceability and diversion of non-conforming material

8. Process understanding: input parameters
The understanding of process parameters and material attributes impact on product quality.
To establish design space around process steps using of design of experiment to build predictive models and/or using simulation tools (ICH Q8)
To inform alarm and action limits and an approach to process deviations (e.g. adjustments).
To establish criteria for incoming and in process materials.

9. Process understanding: dynamics
Line rate is a variable to be considered
The evaluation measurement of residence time distribution for nominal conditions.
Evaluation of degree of back mixing and dampening of disturbances between points of material entry and extraction.
Typical failure modes or deviations (long term vs. short term). (e.g. feeder variability).
Response to a set point change
Impact of Startup and Shutdown on quality of material.

10. Blending: Process Understanding (e.g.)
E(t)
Continuous feeding of
materials at A + B = C rate
Impact of blender speed, line rate and material properties on variations in assay
Interactions
Blender & line rate adjustments possible based on degree of fill
Average residence time ~ at nominal line rate?
Initial time to reach state of control?
Dampening capacity for a feeder perturbation of up to X% target?
Sampling frequency
Continuous output of
blend at C rate
Parameter limit considerations:
Feed rates
Blender speed
Incoming material specifications

11. State of Control
Establishing a condition in which a set of controls consistently provides assurance of continued process performance and quality. (ICH Q10)
For CM, this can be integration of process parameter limits (set points and alarms), in-process monitoring (PAT), controls (feedback and feed forward), material diversion scheme (real time isolation or rejection), trending, and continuous improvement.

12. State of control will depend on the control strategy implementation
Level 1: Active control system with real time monitoring of process variables and quality attributes
Level 2: Operation within established ranges (multivariate) and confirmed with final testing or surrogate models.
Level 3: Unlikely to be operationally feasible for addressing natural variance in CM without significant end product testing.
Level 3
End product testing + tightly constrained material attributes and process parameters
Level 2
Reduced end product testing + Flexible CMA & CPP within design space
Level 1
Real-time automatic control + Flexible CPPs to respond to variability in CMAs
Control Strategy Implementation Options1
2. Yu, L et. al. AAPS J Vol

13. In-Process Control Requirements
To assure batch uniformity in-process controls shall be established – CFR (a)
In-process controls shall monitor and validate the performance of the manufacturing processes that may cause variability in the drug product
Requires higher frequency measurements for continuous processes compared to batch processes
Valid in-process specifications shall be consistent with the release specification – CFR (b)
Limits shall be derived from acceptable process variability estimates where possible
Rejected in-process materials shall be identified and isolated – CFR (d)
PAT tools can utilized to meet the regulatory requirements for in-process monitoring

14. Approaches for Process Monitoring
Statistical Quality Control (SQC)
Variability in quality attributes of the product are monitored over time
Statistical Process Control (SPC)
The variability in critical process parameters and in-process quality measurements are monitored over time
Monitoring the process variables expected to supply more information (e.g., detection and diagnosis)
May generate a large number of univariate control chart that need to be monitored
Multivariate Statistical Process Control (MSPC)
Takes advantage of correlations between process variables
Reduces the dimensionality of the process into a set of independent variables
May detect abnormal operations not observed by SPC

15. Illustrative Example: Monitoring
Parameters: speed, time, fill level
Parameters: feed rate and speed + =
A + B
C scale
Continuous feeding of
materials at A + B = C rate
Continuous output of
blend at C rate or
Target Content
# of revolutions
Variance
of Blend
n=10
RSD
Mean
Individuals
n = based on sampling frequency (per min)
Process time

16. Multivariate Statistical Process Control
Reduction in dimensionality
Potential to enhance fault detection capabilities
X1 & X2 are highly correlated

17. Types of Controls: e.g.
Open system: no active controls but may trigger external action
Needs clear rules of engagement with the process. (e.g. separation of non-conforming material and/or operator adjustment).
Feedforward: output information is used to automatically trigger downstream action
Need knowledge of process interactions to automatically adjust downstream process in order to compensate for the event. (e.g. run the press differently, if detected granule density was high).
Feedback (closed system): output information is used to automatically trigger upstream (input) action.
Need knowledge of input material relationships and response time to changes.

18. Blending: Controls Layer 1: Blender unit internal feedback controls:
Blender speed (set point)
Feeder feed rates (set points)
Layer 2:
High assay disturbance:
NIR assay reading triggers material rejection down stream, amount based on RTD. (open system)
Change in flow properties:
NIR assay reading and analysis of blend triggers adjustment in compression downstream (feedforward)
Low assay drift:
NIR assay reading analyses triggers adjustment of feeder SP upstream (feedback).
Blending: Controls
Continuous feeding of
materials at A + B = C rate
Continuous output of
blend at C rate
NIR

19. Handling of deviations…
Real time response by operational or working instructions based on process knowledge.
Active controls to address deviations (see previous).
Diversion of non-conformation material scheme based on severity of deviation
Interaction of PAT data analysis and quality decision-making.

20. Diverting non-conforming material and material traceability
The evaluation of overall residence time distribution and the understanding of propagation of a disturbance between extraction points in the system are important to justify the amount of material at risk due to an unexpected even or disturbance.
Ideally, measurement (PAT) and material extraction points should be near where the event can occur, but downstream extraction is possible with understanding of process dynamics.

21. Impact of back-mixing
Continuous manufacturing processes with high buffering capacity or high degree of back-mixing can be robust to process or material disturbances, due to dampening.
However, this can increase the amount of material at risk when the disturbance is expected to have negative impact on quality and can complicate material traceability justifications.
Process experiments at nominal conditions, verification studies, and simulations can be useful.

22. Batch definition
21 CFR defines a batch as “a specific quantity of a drug or other material that is intended to have uniform character and quality, within specified limits and is produced according to a single manufacturing order during the same cycle of manufacture”.
Additionally, a lot is defined as “a batch, or a specific identified portion of a batch, that has uniform character and quality within specified limits; or, in the case of a drug product produced by continuous process, it is a specific identified amount produced in a unit of time or quantity in a manner that assures its having uniform character and quality within specified limits.”

23. Illustrative Example: Blending scale+ =
A + B
C scale
Continuous feeding of
materials at A + B = C rate + =
10x(A + B)
10x C scale
Continuous removal of
blend at C rate
Scale-up options:
Run the blender for 10x longer
Increase rate of A + B proportionally; adjust parameters
Scale-up options:
Blender fill
Blender size

24. Batch characterization
The characterization of the quality of an amount of product manufactured under continuous mode may include analysis of data from process parameter monitoring, in-process material attributes, and final product attributes.
Consideration to the characterization of uniformity of attribute across the batch should be evaluated through justified statistical analysis.
Real time release testing is a natural progression of a robust control strategy for CM.

25. Batch characterization
CQA 1
Process time
Process time
Process time
What would be a sufficient and adequate characterization of the uniformity of CQA1?
What would be an adequate real time release criteria for the CQA1?
Range, an average value, variation, coverage?
If using CQA1 in a model to predict another CQA2, what value should be used?
What out of control events in CQA1 monitoring should trigger quality investigations?

26. Other considerations for CM
Data Challenges
Use of Statistics
Multivariate impact of process parameters on quality attributes
Design of experiments
Analysis of Variance
Development of models
Definition of adjust, isolate/reject limits for process parameter monitoring:
Analysis of process and material attribute variability
Establishment of in-process control limits for material attributes
require in-process specifications ( ) to be set using “previous acceptable process average and variability estimates”.
Analysis of material attribute variability

27. Other Considerations for CM
Challenges
Use of Statistics
Material attribute monitoring
Process Monitoring
Chemometrics
MSPC
SPC
Establishment of real time release testing criteria that ensure uniformity of character of a batch or lot
Justification of sampling frequency
Acceptance criteria’s statistical attributes
Data analysis for Large-N
Process performance monitoring (long term)
CpK
Data trending and analysis

28. Conclusion
Continuous manufacturing is an opportunity for the modernization of pharmaceutical manufacturing and operations.
Process understanding and robustness of the control strategy are the key to CM successfully delivering quality products while enabling flexible operations.
A robust control strategy for a continuous manufacturing process includes a combination of:
real time monitoring of process parameters, alarm system with quality based control limits, real time monitoring of incoming and intermediate material attributes, traceability of final product attribute vs. history of the system, reliable separation of acceptable and non-acceptable materials, feedback and feed forward controls

29. Thank you!