Control Strategy Approaches in Continuous Manufacturing of ...(OOT). Natural variation reduced the...

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


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?


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


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


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

7

http://pqri.org/wp-content/uploads/2017/02/1-Lee-PQRI-for-CM-2017.pdf


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


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


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”


Process Parameters Consideration Examples

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➢ Type of feeder

➢ Gravimetic

➢ Volumetric

➢ Feeder Weight

➢ Screw configuration

➢ Screw speed

➢ Mass Flow

➢ Blender screw configuration

➢ Transport and mixing elements

Courtesy GEA


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


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)


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


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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


RTD

17

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


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

18


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

19


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


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


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


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.


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


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


Control Strategy – quality comes at a price!

Hundreds/thousands of samples needed to build a robust control strategy


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.


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

[email protected]

Thank you

mailto:[email protected]


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


𝜃, 𝜏1, 𝜏2

Feeder Alarm

Low levels for other feeder deviations

High level for a feeder deviation

150%


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


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


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


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

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