Implementation of QbD based Control Strategy during Drug Product Continuous Manufacturing

Frantz Elbaz

DDF summit Berlin, March 12th, 2018

TRD

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Overall QbD strategy

Define Target Product Quality (QTPP with list of CQA)

Identify Knowledge Baseline

Develop Product/Process Understanding

Define Design Space (for CPPs and CMAs)

Control Strategy (for CPPs and CMAs)

Quality Monitoring

ImprovementContinual

The Matterhorn, CH

2

QTPP and CQAs

Practical definitions :– QTPP : list of key characteristics that could impact the DP in-vivo

performance

– CQA : a quantifiable property of the DP that is considered as critical for achieving the DP intended in vivo performance (purity, efficacy and safety).

For a solid oral dosage form :➢ Aspect

➢ Assay

➢ Content Uniformity

➢ Degradation/Stability

➢ Dissolution rate , ...

Only the CQAs that could be impacted by formulation and/or process variables are considered in the RA.

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Product/Process understanding

Developed based on the outcome of the first RA– Identification of the pCPPs and pCMAs

– Use of screening DoE and standalone trials to confirm (or not) criticality of

identified pCPPs and pCMAs

– Use of factorial DoE to define the Design Space for selected CPPs and CMAs

where regulatory flexibility is targeted

– Elaboration of specific control strategies for identified CPPs and CMAs (could

be : double check in BR, process control systems, PAT tools, regulation

loops, ...etc)

Specifically for CM, this understanding could be

conducted at “full” scale (commercial equipment train)

and then avoid any scale-up.

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Continuous Manufacturing DP processing at Novartis

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1. blending

A B

2. granulating 3. drying 4. sieving 5. tabletting 6. coating

Model compound formulated (25% DL) with common excipients

PAT instruments plugged at several locations to monitor and

maintain the process in a state of control (BU, LOD, PSD, CU) via

regulation loops to be implemented

First step was to focus our efforts on granules LOD after drying

Product/Process understanding

Example of the screening DoE that was conducted

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7 Factors selected based on Fishbone analysis and Criticality Matrix of process

# Parameter to investigate Abbr. unit

1 solid feed rate SFR kg/h

2 liquid feed rate LFR kg/h

3 screw speed TSG SST rpm

4 barrel temp TSG BTT °C

5 dryer rotation speed FBD DRF rph

6 drying temperature FBD DTF °C

7 drying air volume FBD DAF m3/h

8* Room Temperature RT °C

9* drying inlet humidity FBD (=room humidity) DIH g/kg

10* pre-blend LOD PBL %

11* Product filter pressure at start of experiment (=influenced by runtime) PFS Pa

Screening-DoE:

Fractional Factorial

Design with 27-3 = 16

experiments and 3

center points

(Resolution IV)

And keep some pCPPs and pCMAs constant (screw design, DS characteristics,

Excipients characteristics....)

* "uncontrolled" DoE-parameter will be monitored during trials

Example : LOD of «dry» granules

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Why focus was on LOD? Because granules LOD after drying impacts several CQAs

One full rotation sampled, each sample measured 3

times (error bars = variation within sample)

Starting point : fixed parameters used

for wet granulation and drying modules

Conclusions :

➢ LOD (measured by sampling and halogen

moisture analzer) equilibrated after 120

minutes

➢ LOD in acceptance range (LOD of blend ±

0.5%) after 30 minutes

➢ Need to shorten both the « wasting » and

« equilibration » durations.

DoEs conclusions

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LOD

Liquid Feed Rate

Dryer Rotation Speed

Solid Feed Rate

Drying Temp

Drying Air Volume

SFR*LFR

SDoE: identification and

quantification of main

effects (CPPs)

ODoE: process models

for control of responses

established (e.g. LOD)

LOD = 1.225+0.072*DRS

Model verification

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Adapting manually the dryer rotation speed (DRS identified as CPP), based on DoE-based

statistical descriptive model

Model based control demonstrated, accuracy is limited by models confidence interval and the common

variability in the process

Overcompensation could be avoided by combining model-based control with direct PAT-feedback

DoE ressources consumption

Comparison between CM and batch processes :– 16 trials

– Wet granulation / drying process + sieving + tableting for CM

– Wet granulation / drying process + sieving + external phase addition + final

blending (lubrication) + tableting for batch mode

– 40% DL

– CM allows a significant decrease of DS and FTE needs.

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DoE CM DoE batch mode

Batch size 2 hours run (6 kg of blend) Given by the wet

granulator bowl size, dryer

bowl size, container size :

16 kg of blend

Total duration 2 weeks 6 weeks

DS consumption 38.4 kg 102.4 kg

Conclusions and next steps

• Conclusion on one model compound

– Example of effective QbD implementation in CM

– Demonstration of feasibility and verification of the model for one responseimpacting at least one DP CQA

• Further application possibilities

➢ Using this model compound :

– Generate other offline and online models for other responses (BU, granules PSD, tablets characteristics including CU)

– Implement control actions in the automation system to ensure that the process is maintained in its state of control

➢Extend this approach to other compounds (NCE and marketed products)

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Acknowledgements• Victoria Pauli

• Markus Krumme

• Peter Kleinebudde

• Simon Ensslin

• Florence Desvignes

• Maud Duchesne

• Samuel Fischer

• Ahmad Mohamad

• Julien Taillemite

• Nhat Quang Nguyen Trung

• Laurent Pellegatti

• Yves Roggo

• Philipp Heger

• Hervé Notter

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Abbreviations used• BR : batch record

• BU : Blend Uniformity

• CM : Continuous Manufacturing

• CMA : Critical Material Attribute

• CPP : Critical Process Parameter

• CQA : Critical Quality Attribute

• CU : Content Uniformity

• DL : Drug Load

• DoE : Design of Experiments

• DP : Drug Product

• FBD : Fluid Bed Dryer

• LOD : Loss On Drying

• PAT : Process Analytical Technology

• pCMA : potential Critical Material Attribute

• pCPP : potential Critical Process Parameter

• PSD : Particle Size Distribution

• QTPP : Quality Target Product Profile

• RA : Risk Assessment

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