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

wilbur@medspray.com

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?

Risk assessment & ranking larger slides at the end of this slide pack

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 wilbur@medspray.com

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

in vitro lung dose - DoE in

vitr

o lu

ng d

ose

: NG

I with

Alb

erta

thro

at

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?

Thanks to my co-authors

• Matt Wilby, 3M

• Phil Swanbury, Vectura

• Andrew Dundon, GSK

Cake baking example

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

cough syrup spoon

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

risk assessment

0

20

40

60

80

100

PS suppliers mould temp extruder speed extruder temp holding pressure holding time dim 22 dim 23 dim 35

treshold

apparently this medical device is quite simple and safe