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TRANSCRIPT
Art credit: Close-up of the inhibitor binding site of the colony-stimulating
factor-1 receptor kinase domain.Jacqueline MAXIMILIEN, PhD
Senior Scientist
Small Molecule Pharmaceutical Development | Janssen Research & Development, LLC
Control Strategy Approaches in Continuous Manufacturing of Oral Solid Dosage Forms
Drug Delivery Formulation SummitBerlin, March 13th, 2019
Small Molecule Pharmaceutical Development
Why the journey?
Quality:• Increased process control and understanding• Increased assurance of product quality in real time• Advanced control strategies for each unit op• Process analytical technology (PAT)
Regulatory buy-in:• Enabler of Quality by Design (QbD) • Increased quality and safety
Innovation:• Innovative analytical technologies• Advanced models and control systems• Real Time Release
Reduced Costs:• Reduction in processing time and waste• Reduction in footprint requirements• Reduction in API needed for tech
transfer
Flexibility:• Shorter development timelines• Agility and flexibility of manufacturing• Rapid response to drug shortages and emergencies
CM has the potential to increase the efficiency, flexibility, agility, androbustness of pharmaceutical manufacturing
Small Molecule Pharmaceutical Development
Financial and operation benefits for Prezista® 600mg
In-process tests & release tests:
Cycle time - 30 daysIn-Line/At-line
Cycle time - 5 days
7 rooms, 6 discrete pieces of equipment
3%
13 days /1000 kg
Ca. 10 out of 265,000 tablets
BATCH PROCESS
Testing
CONTINUOUS MANUFACTURING
Footprint
Waste
Mfg. Cycle Time
Quality Control
50% reduction
TBD(lower cost of goods; sustainability)
1.1 days /1000 kg(approx. 70% reduction in man hour)
On/at line measurement
Is the journey worth the effort?
Small Molecule Pharmaceutical Development
Continuous Manufacturing at J&J – our strategy
- Commercial: Janssen initiated implementation of CM in two manufacturing site byswitching production from batch manufacturing
- Development: CM is selected to be the preferred manufacturing technology forproduct development
Small Molecule Pharmaceutical Development
Our Journey to continuous – key stops along the way
Criticality
Analysis and
Risk Evaluation
Process Design
Confirmation and
Automation
Control Strategies
Process Performance
Qualification and Continued
Process Verification
Small Molecule Pharmaceutical Development
Our Journey to continuous – key stops along the way
Criticality
Analysis and
Risk Evaluation
Process Design
Confirmation and
Automation
Control Strategies
Process Performance
Qualification and Continued
Process Verification
Small Molecule Pharmaceutical Development
General Principles
Designed to decrease the probability of CQA failure by increasing the detectability and control of CPP/pCPP or CMA/pCMA.
➢ Specificities of the continuous line
➢ Ingoing Materials
➢ Process Monitoring and Control
➢ Diversion Point(s)?
➢ Real Time Release Testing
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http://pqri.org/wp-content/uploads/2017/02/1-Lee-PQRI-for-CM-2017.pdf
Small Molecule Pharmaceutical Development
Risks:
- Material variability
- API
- Excipients
- Feeding disturbance
- Improper blending (time)
- Start-up & shut-down (period)
Risk assessment(identify CPPs,
CMAs)
Establish knowledge & control space
Develop control strategy
So… we’ve changed the synthesis…..
J&J API Batch #1
J&J API Batch #2
J&J API Batch #3
Small Molecule Pharmaceutical Development
Material Characterisation by PCA
9
▪ Multivariate characterisation of excipients and APImaterial properties (e.g flow, specific surface area, particle size distribution etc.)
▪ Facilitate in silico predictive models to describe material behaviour
▪ Elucidate the relationship between various material properties using principal component analysis.
Van Snick, et al. Int. J. Pharm .549 (2018) 415-435
Small Molecule Pharmaceutical Development
Process Monitoring
- Determination of (Critical) Process parameters
- Feeding
- Blending
- Tableting
- Evaluation of equipment performance
- Demonstrate ongoing state of control*
*“A condition in whichthe set of controlsconsistently provideassurance of continuedprocess performanceand product quality”
Small Molecule Pharmaceutical Development
Process Parameters Consideration Examples
11
➢ Type of feeder
➢ Gravimetic
➢ Volumetric
➢ Feeder Weight
➢ Screw configuration
➢ Screw speed
➢ Mass Flow
➢ Blender screw configuration
➢ Transport and mixing elements
Courtesy GEA
Small Molecule Pharmaceutical Development
Feeding and Blending
Feed rate deviations of API (K-Tron LIW feeder)
LIW Feeding is not perfect! Accuracy is affected by:• Material properties (density, etc.)• Feed rate required• Vibrations• Refills
Small Molecule Pharmaceutical Development
Residence Time Distribution (RTD)
RTD: the time (mean and distribution) required for a particle to transit through the
system/unit
PFRIN Out
time
c
IN
Out
c
time
CSTR
Plug Flow Reactor (PFR) –No back mixing
PFRIN Out
time
c
IN
Out
c
time
CSTR
T im e
Co
nc
en
tra
tio
n (
%)
In le t C o n c . P F R
O u t le t C o n c . P F R
O u tle t C o n c . C S T R 1
O u tle t C o n c . C S T R 2
Continuous Stirred Tank Reactor (CSTR) - Perfect mixing
Non-Ideal mixing system
Small Molecule Pharmaceutical Development 14
Residence Time Distribution
Blender 1 Blender 2 Feed tube
Individual feeder mass flows
Excipient mass flow
API concentration outlet blender 1
API concentration outlet blender 2
API conc. tabletAPI conc. inlet
RTD process model for API concentration prediction - Signal flow
Press
RTD based alarm limits
• RTD simulations to identify how long mass flow can deviate above threshold without product quality impact
• Controller actionso Process critical alarm -> STOPo Reject until X% of material left line (e.g. ToR99)
Small Molecule Pharmaceutical Development 15
0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0 3 0 0 0 3 5 0 0 4 0 0 0 4 5 0 0 5 0 0 0
6 0
7 0
8 0
9 0
1 0 0
1 1 0
E la p s e d t im e (s )
La
be
l c
laim
(%
)
H ig h P S D L o w P S DT a rg e t P S D M e an , & rC UL o w e r C I , & rC U U p p e r C I , & rC U
• Verification step changes performed using independent API or excipient
o Target & boundary PSD batches
• Verification limits set by uncertainty and limitations of RTD process model
o Lower and upper CI: θ, τ and rCU
• Samples collected across step change should meet verification limits
RTD Model Verification
Small Molecule Pharmaceutical Development
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Control Strategy – RTD and material diversionA
PI c
on
c.
(%
)
Time
Feeder spike
Start tablet diversion
End tablet diversion
Conservative tablet diversion
API conc. based on feeder mass flow
RTD model prediction – API conc. in tablets
Tablet action limit
Tablet rejection limit for model prediction
RTD model predicts tablet assay, provides material tracking and enables
diversion of potentially non-confirming material
Reference: Ren 2019
Small Molecule Pharmaceutical Development
RTD
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Pros
➢ Development of a model based potency prediction
➢ Concentration based
➢ Micromixing and micromixing error can be estimated
Cons
➢ Unble to describe uniformity on microscopic scale
➢ Needs to be repeated to establish impact of material variability (PSD, SSA etc), hence can consumes large quantities of API
Small Molecule Pharmaceutical Development
Process Analytical Technology (PAT)
▪ Process Analytical Technology tools may be integrated in the continuous manfacturing line to monitor the process in real time.
▪ Examples of PAT tools:
– Raman: Raw material handling
– NIR moisture analyser: LOD
– NIR: blend uniformity and/or content
uniformity
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Small Molecule Pharmaceutical Development
Considerations
▪ PAT tool placement in the CM line
– Correct presentation of medium
– Should not impede process (powder flow etc.)
▪ Development and validation of PAT applications is critical
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Small Molecule Pharmaceutical Development
Feeding and Blending
Each gravimetric feederhas a control loop thatmaintain the desired flowrate by changing the speedat which the screws arerotating
CO
NTIN
UO
US
FLO
W
Small Molecule Pharmaceutical Development 21
Control Strategy – PAT – Initial findings
0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0
0 .0
0 .5
1 .0
C u m u la t iv e re s id e n c e t im e in p re s s
E la p s e d t im e (s )
F(t
)
T ab le t N IR
T a b le t LC
F e e d fra m e N IR
• Tablet press feed frame RTD estimation as part of RTD process model calibration for tablet potency prediction
• Simultaneous blend characterization using PAT tool in top plate of feed frame and tablets elucidated extra time lag and mixing in between probe and compression
• To use PAT tool for for identification and rejection of non-conforming material, one should consider RTD between both locations
Small Molecule Pharmaceutical Development 22
Control Strategy – PAT – Feed frame – Initial findings
• Challengeo Model accuracy sensitive towards upstream process and material variation
Root cause Mitigation: update PAT model
Blending condition Runs included with representative conditions
Feed frame settings DOE runs included that vary in paddle wheel and turret speed
API & excipients lot Include boundary batches – Lot picking – Sourcing - QbD
... ...
• Advantageo Requires significantly less material for calibration & validation than RTD process modelo No probe fouling observed during DOEs
Small Molecule Pharmaceutical Development
Process Design and Control
Feed rate deviations of API (K-Tron LIW feeder)
Feed rate variations filtered by the blender
Based on RTD studies and using a MATLAB simulation script, it was proven that the feed rate can deviate from the tolerance limits for a maximum of 90 seconds without compromising product quality, and before the line needs to be stopped.
Small Molecule Pharmaceutical Development
Control Strategy – Diversion Points
In-line NIR (BU)
At-line NIR (CU)
•After an OOS is detected by the NIR interface,the diverter valve is switched to “reject”position.
•Following restoration of the feeders to target,the diverter valve is automatically switched to“accept” position
Small Molecule Pharmaceutical Development
Sampling based decision making
Max. allowed deviation is USP 905 L2 deviation (OOS)
Sampling interval high enough to not to reject good material and low enough to have feasible sampling interval (OOT).
Natural variation reduced the allowed deviation (OOL)
X2X1
X2-X1
X1
Small Molecule Pharmaceutical Development
Control Strategy – quality comes at a price!
Hundreds/thousands of samples needed to build a robust control strategy
Small Molecule Pharmaceutical Development
Summary
▪ A science-based approach should be adopted to define and control the process and product quality
▪ A combination of RTD and PAT based approach is ideal to bridge transition from development to commercial manufacturing.
Small Molecule Pharmaceutical Development
Acknowledgements – Collaboration is Key to success
▪ Oral Solid Drug Product
Development
– Giustino Di Pretoro
– Bernd Van Snick
– Ashish Kumar
▪ API Small Molecule
▪ Analytical Development
▪ Pharm. Sciences
▪ Engineering
▪ Quality Assurance
▪ Janssen Supply chain
▪ Clinical Supply Chain
28
Art credit: Close-up of the inhibitor binding site of the colony-stimulating
factor-1 receptor kinase domain.Jacqueline Maximilien, PhD
Thank you
Small Molecule Pharmaceutical Development 30
Control Strategy – PAT – Feed frame – Initial findings
• Mitigation for traceability between probe and diversion point
• Cumulative residence time distribution used for determining stat and end of rejection:
• Fast rejection required
• Duration depends on % fraction to be cleared
Probe to tablet
Small Molecule Pharmaceutical Development
𝜃, 𝜏1, 𝜏2
Feeder Alarm
Low levels for other feeder deviations
High level for a feeder deviation
150%
Small Molecule Pharmaceutical Development
15 shifts
4 shifts3 shifts
Equipment Identification Poka-Yoke
Our Journey to continuous – what is the dark side?
• Start-up losses: • Feeders stabilization, blend
homogenization, NIR communication start up, data processing
• Shut down losses: • Feeder and blender design, Blender
loading level
• Unplanned stops: • System failures, communication
errors
Process Yield
Key drivers for yield in CM
Small Molecule Pharmaceutical Development
Process Analytical Tools
- Gravimetric feeding
- Alarms
- Continuous blending
- Model based potency prediction (RTD)
- PAT-based potency estimation
- Tableting
- Model based potency prediction
- PAT-based potency estimation
–Automated accept/reject limits of tablets
- Sampling frequency
Small Molecule Pharmaceutical Development
RTD model based potency prediction and decision making after tablet compression
Variance due to micro-mixing (Stratified CU)
Variance in model prediction (parameters CI)
70
90
110
130
0 1000 2000 3000 4000 5000
Lab
el c
laim
(%
)
Time (s)
USP limit
60
70
80
90
100
110
0 1000 2000 3000 4000 5000
Lab
el c
laim
(%
)
Time (s)
Low PSD
Small Molecule Pharmaceutical Development
Process Design and Control – feeding-blending
Blender 2 Feed Tube Tablet Press
The residence time distribution in each unit operation can be reproduced by a series of idealreactor models
𝐸 𝑡 =
0, 𝑡 < 𝜃
𝑒𝜃−𝑡𝜏1 − 𝑒
𝜃−𝑡𝜏2
𝜏1 − 𝜏2, 𝑡 ≥ 𝜃
o Where 𝜃, 𝜏1 and 𝜏2 represent the mean residence time in PFR, CSTR1 and CSTR2, respectively
PFR
CSTR1 CSTR2 The Residence Time Distribution, E(t), of each reactor is described by the equations
RTD in Continuous Direct Compression process: 4 lag times & 8 time constants
Blender 2