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QbD for inhaler design IPAC RS Device Work Group
presenter: Wilbur de Kruijf Medspray BV, Enschede, the Netherlands
co-authors: Matt Wilby, 3M Phil Swanbury, Vectura Andrew Dundon, GSK
Disclaimer
This lecture reflects the personal experience and opinions of the presenter on this subject, after group discussions with his co-authors. It does not necessarily reflect the
opinion or the current modus operandi of the companies for which the authors are working.
Neither does it represent any official standpoint on behalf of the IPAC-RS consortium.
Note to the reader
This hand-out is not identical to the presentation slide pack, which intentionally contains hardly any text. See also www.presentationzen.com or read Garr Reynolds’ book PresentationZEN for the philosophy behind this.
For the presentation slide pack or any other questions or remarks regarding the presentation, please contact me at
Three parts in this presentation
Part One: explaining QbD - example - baking a cake
Part Two: attempt to apply QbD to an extremely simple medical device: a cough syrup measuring spoon
Part Three: attempt to apply QbD to a multi dose liquid inhaler device. Discussing the necessary differences in approach for simple dosage forms vs complex devices
QbD definitions
• ICH Q8, see the ICH web page. http://www.ich.org/fileadmin/Public_Web_Site/Training/GCG_-_Endorsed_Training_Events/APEC_LSIF_JCCT_workshop_Beijing__China_Dec_08/Day_1/Regulatory_perspective.pdf
• Some more explanation on slideshare.net http://www.slideshare.net/shettyuc/qbd-for-beginners-introduction-13953395?qid=3912d486-7def-47cd-8adf-ecb6bc98836f&v=qf1&b=&from_search=1
QbD step by step
definition phase
risk assessment
experiments
describe design space
control strategy
example: baking a cake
what is a good cake? (QTPP & CQAs) which parameters are in play? (CMAs & CPPs)
this presentation follows the method as described on QBDworks.com for this
baking 5 cakes to create a knowledge space
choosing the design space
choosing the settings for repeatable cake baking results. The end goal of QbD?
0
2
5
7
9
11
0.0 2.3 4.5 6.8 9.0
occu
rren
ce
severity
Risk assessment
flour-sugar-butter balance salt
mix sequence
towel
If you split the risk number in severity and occurrence, different types of risks can be identified. e.g.The cake baking experts indicate that it is unlikely that they mess up the mixing sequence, but if they do, it has a big influence.
Design of Experiments full factorial + center point - 5 cakes
low sugar low salt
low sugar high salt
F B S 1-1-1 nominal salt
high sugar low salt
high sugar high salt
limited this to baking 5 cakes, because 9 did not fit in my schedule ;-)
FBS = Flour - Butter - Sugar
Results (taste panel score) • the experiment is planned for the 2nd week of May
salt level
Flour Butter Sugar Balance 1-1-1 1-1.2-0.8 1-0.8-1.2
low (0.5x)
mid
high (2x) 5 ? 7
? 7 ?
6 ? 6
if you are curious about the real results, email me at [email protected]
Design space
salt level
Flour Butter Sugar Balance 1-1-1 1-1.2-0.8 1-0.8-1.2
low (0.5x)
mid
high (2x) 5 7
7
6 6
the boxes without number are ‘modeled assumptions’ if they end up in your design space, it is wise to verify them
Control Strategy
salt level
Flour Butter Sugar Balance 1-1-1 1-1.2-0.8 1-0.8-1.2
low (0.5x)
mid
high (2x)
control space
design space
Please note that this approach is not good enough to build a design space. That should be more than just the design of experiments results. See also next page.
Baking vs Designing
The example so far describes baking a cake with minor deviations to the given recipe and the available kitchen processes.
If you are ‘designing’ a cake recipe, you may want to explore a much wider variation of ingredients and process settings, see example on next pages.
This also gives you the opportunity to explore a larger ‘knowledge space’, which helps to understand your design space much better.
Des
igni
ng a
cak
e
free range vs industrial
vs margarine
beet vs cane
3 types of
skimmed vs full fat rock vs sea
3 sizes
3 levels 3 levels
3 levels
3 levels
5 flour butter sugar balance levels
Knowledge space
salt level
Flour Butter Sugar Balance 1-1-1 1-1.2-0.8 1-0.9-1.1
low (0.5x)
mid
high (2x) control space
design space
1-0.7-1.3 1-0.8-1.2
further exploring the knowledge space in this example leads to a new design and control space with a higher taste panel score
Simple medical device measuring spoon for cough syrup
larger slides at the end of this slide pack
please note: I added geometric tolerances as critical parameter group
QbD for devices Approach between simple dosage forms (e.g. pills) and drug delivery devices can not be identical
Discussion: is the design space of a drug delivery device mainly defined by the dimensions and tolerances on the technical drawings?
tolerance analysis
+1% +5% +10%
-5% 6 ml +5%
-10% -5% -1%
dim
ensi
on #
22
dimension # 23 nominal low (-0.2 mm) high (+ 0.2 mm)
low (- 0.2 mm)
nominal
high (±0.2 mm)
design space
This example of a simple measuring spoon shows that based on 3D CAD models a quite good description of the design space can be made, without conducting a DoE.
requirement: 6 ml ±15%
Liquid Inhaler Design small movie of the SHL - Medspray ADI device
ADI stands for Aqueous Droplet Inhaler
antibiotics inhaler. a few puffs replace a nebulizer treatment
Drug product requirements
• in vivo lung dose reproducible
• in vivo lung distribution reproducible
• clinical parameters of the antibiotics inhalation
• operating forces acceptable
• inhalation manoeuvre acceptable
• inhaled volume < 1.5 ltr
• air flow resistance acceptable and reproducible
this list is not complete, shortened on purpose to serve as an example in this lecture
Device CQAs • metered dose
• in vitro lung dose
• particle size distribution
• wind up force
• actuation button press force
• air flow resistance
• actuation time
this list is not complete, shortened on purpose to serve as an example in this lecture
for this example we selected ‘alberta throat’ plus NGI
can be measured in vitro !
‘QTPP’ CQA
in vivo lung dose
in vivo lung distribution
clinical endpoints antibiotics
operating forces
inhalation maneuver
metered dose (2) high low low low low
in vitro lung dose (5) high medium high low low
PSD (2.5) medium high low low low
wind-up force (2) low low low high low
press force (2) low low low high low
resistance (6) high high low low high
actuation time (3) medium medium low low high
relate ‘QTPPs’ & CQAs
Risk based approach
FMEAs and fail tree analyses are leading to a list of critical variations, e.g. critical tolerance stacks or variables in the in-use conditions like temperature or humidity or variations in the drug formulation, e.g. potency variations leading to viscosity variations. Also process variations, like injection moulding defects like short shots, flow lines or air bubbles may be critical (e.g. for plastic gears).
VAR CQA
nozzle chip
orifice ø
drug potency variation
in use temp.
power pack
pressure …
user inhalation variations
resistance grid
tolerances
metered dose low high medium low low low
in vitro lung dose (5)
high high medium medium high high
PSD high medium medium medium medium medium
wind-up force low low low medium low low
press force low low low low low low
resistance low low low low low high
actuation time high medium medium high low low
relate ‘variations’ & CQA
Biggest influences specific for this particular antibiotic liquid inhaler
the blue boxes are what we varied in the DoE
the yellow boxes are the outputs
spray time - DoE sp
ray
time
at lo
w te
mp
low
pot
ency
is to
o lo
ng
poss
ible
sol
utio
n: n
arro
w d
own
the
pote
ncy
spec
s an
d/or
the
tole
ranc
es o
n th
e sp
ray
pore
s
Discussion The QbD approach for simple dosage forms seems inadequate for devices.
However, a QbD approach may add robustness to the design of drug delivery devices. Therefore it deserves more attention.
Examples on how to apply QbD to device development are rare. Furthermore, devices are very different, so from the existing examples it is difficult to extract learnings for your own device development.
Combining ISO 20072 (guidance for design verification of aerosol drug delivery devices) with a QbD approach could be considered, as both have a risk based approach.
One more thing
knowledge space
design space
control space
explored in clinic
Discussion: can a QbD approach to device design add robustness to clinical data?
QTPP CQA (weight)
cake looks good (0.2)
cake smells good (0.2)
cake tastes good (0.6)
density (3.8) high low medium
crust hardness (2.6) medium low medium
tasting panel score (7.8) medium high high
visual panel score(2.6) high low low
relate QTPP & CQA
….CMA CQA
flour brand
butter brand
sugar brand
egg type
flour sugar butter
balance
salt amount
density medium medium medium low high low
crust medium medium high medium high low
tasting panel medium high medium high high high
visual panel low high low low medium low
relate CMA & CQA
…….CPP CQA
mixing sequence
mixer speed
oven temp.
oven position
resting under towel
density high high medium low high
crust high medium high high high
tasting panel high medium medium high high
visual panel medium medium high low medium
relate CPP & CQA
rate occurrence of deviations
CMA
flour brand
butter brand
sugar brand
egg type
flour sugar butter
balance
salt amount
CPP
mixing sequence
mixer speed
oven temp.
oven position
resting under cloth
how often? 1 1 3 1 1
Risk assessment
0
20
40
60
80
flour type butter type sugar type egg type balance salt mixing seq mixer speed oven temp position towel
treshold
QTPP CQA
apply correct amount
volume metered by spoon high
relate QTPP & CQA
risk assessment and ranking method taken from qbdworks.com
…….CPP CQA
mould temp
extruder speed
extruder temp
holding pressure
holding time
volume low low low medium medium
relate CPP & CQA
deviations from the selected pressure and time are unlikely
….CMA CQA
different suppliers of PS
volume low
relate CMA & CQA
…….CPP CQA
dimension #22 tolerance
dimension#23 tolerance
dimension 35 tolerance
volume metered medium high low
relate Geometry & CQAs ‘geometry’ is a parameter group which is essential for mechanical design and engineering, which is normally not accounted for in the classic QbD approach.
dimension #22 tolerance
dimension#23 tolerance
occurrence 1 1
occurrence
deviations outside the tolerances on the technical drawings are unlikely.
deviations within the tolerances will not have influence on the CQAs