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Efficient Engineering Simulation to Inform
and Optimise Capsule Inhaler Design
DDL Conference 2018
Stuart Abercrombie
13th December 2018
Senior Consultant, Team Consulting
2Confidential © Team Consulting Limited 2018
SummaryEfficient engineering simulation to inform and optimise capsule inhaler design
Fine particle delivery is a key attribute for cDPI
effectiveness
The action of fine particle generation is highly
complex and difficult to model
A balanced approach to simulation complexity can
be effective for informing cDPI design
This approach can be used to successfully increase
the fine particle delivery from a cDPI
cDPI = Capsule dry powder inhaler
3Confidential © Team Consulting Limited 2018
SummaryEfficient engineering simulation to inform and optimise capsule inhaler design
The science of predicting fluid flows (including
liquids, gases and powder aerosols) using
computational techniques
CFD: Computational
Fluid Dynamics
Confidential © Team Consulting Limited 2018 4
An introduction to cDPIs
5Team Consulting Limited 2018
Capsule Dry Powder InhalersAdvantages of cDPIs
✓Mature platform
✓ Straightforward to fill
✓ Plenty of space
✓ Ready to go for clinic
6Team Consulting Limited 2018
Capsule Dry Powder InhalersDisadvantages of cDPIs
✗ Hard to handle
✗ Difficult to use
✗ Pricey piercers
✗ Risk of variable performance in clinic
7Team Consulting Limited 2018
Capsule Dry Powder InhalersTAE concept prototype Gen1
TAE Gen1
8Team Consulting Limited 2018
Capsule Dry Powder Inhalers
Dispersion
Deagglomeration
Dispersion
Deagglomeration
Flow straightening
Flow straightening
TAE concept prototype Gen1
0m/s
30m/s
0m/s
30m/s
Team Consulting Limited 2018 9
What type of engineering simulation did
we use to iterate the cDPI design?
10Team Consulting Limited 2018
Meaningful engineering simulations for early-stage development were intended to improve understanding of
how the device works and increase the efficiency of design iterations.
Engineering Simulation ApproachComplexity level
RSM = Reynolds stress model, DEM = Discrete element model, LES = Large eddy simulation
11Team Consulting Limited 2018
Inhaler airflow resistance was measured with physical prototypes for the validation of CFD simulations.
Engineering Simulation ApproachValidation
12Team Consulting Limited 2018
Engineering Simulation ApproachValidation
CT scan of a TAE
Gen1 prototype
(animation)
13Team Consulting Limited 2018
Inhaler airflow resistance was measured with physical prototypes for the validation of CFD simulations.
• Metrology of prototype parts was very important to investigate discrepancies between CFD and testing.
Engineering Simulation ApproachValidation
Team Consulting Limited 2018 14
What kind of insight can simulation
provide for cDPI design?
15Team Consulting Limited 2018
Engineering Simulation ApproachPerformance metrics to inform cDPI design
Airflow
resistance
Pressure
budget
Capsule
flow rate
Capsule wall
shear stress
Flow stagnation
regions
Fine particle
impact regions
Airflow
velocities
Airflow turbulence
intensity
Carrier particle
impulse
Carrier particle
impact velocities
Fine particle
exit swirl
Capsule
velocities
Operating pointDose dispersion
Dose depositionDose deagglomeration
16Team Consulting Limited 2018
Cumulative Impulse, J (Ns) [1]
Represents a time-averaged force acting on carrier
particles and the duration for which that force is applied.
• Inertial effects
• Aerodynamic drag (including fluctuations from
turbulence)
• Wall impacts forces
Peak Normal Impact Velocity, Vn (m/s) [2]
Represents the maximum force applied to a carrier particle
during wall impacts.
Engineering Simulation ApproachPerformance metrics to inform cDPI design
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1. Harris D, Nandgaonkar A, Sangaiah G, Kane P: A Mathematical Model to Optimise the Airway for a Dry Powder Inhaler, Respiratory Drug Delivery 2008; 2: pp451-456.
2. Shur J, Lee S, Adams W, Lionberger R, Tibbatts J, Price R: Effect of Device Design on the In Vitro Performance and Comparability for Capsule-Based Dry Powder
Inhalers, AAPS J 2012; 14: pp667-676.
Vy1
Vz1 Vx1
Vy2
Vz2 Vx2 Vn
Vt
Team Consulting Limited 2018 17
How did we use simulation results to
inform cDPI design changes?
18Team Consulting Limited 2018
Design iterations for TAE Gen2 increased the proportion of the pressure budget expended across the main
body of the deagglomeration chamber.
• Intended to increase swirl-based deagglomeration due to more highly swirling flow.
Simulation ResultsDesign changes for TAE Gen2
-4000Pa
0Pa
-4000Pa
0Pa
TAE Gen1 TAE Gen2
25% of 4kPa
expended here 65% of 4kPa
expended here
19Team Consulting Limited 2018
TAE Gen2 produces more highly swirling flow that continually accelerates as the chamber converges towards
the outlet.
Simulation ResultsDesign changes for TAE Gen2
0m/s
70m/s
0m/s
70m/s Decelerating
swirling flowAccelerating
swirling flow
TAE Gen1 TAE Gen2
20Team Consulting Limited 2018
Design iterations for TAE Gen2 increased expected levels of powder deagglomeration compared to Gen1.
• Cumulative impulse, J (Ns): Large increase for Gen2
• Peak normal impact velocity, Vn (m/s): Small decrease for Gen2
Simulation ResultsPerformance metrics
Team Consulting Limited 2018 21
What was the impact of design
changes on in-vitro performance?
22Team Consulting Limited 2018
Impactor testing was carried out using Copley NGI apparatus.
The analytical method was provided and carried out by Intertek
Melbourn using HPLC.
Commercial sample Asthalin Rotacaps (Cipla) were used.
• 15mg carrier-based formulation with labelled dose of 200µg
salbutamol sulphate.
• 5 capsules per NGI, 3 repetitions of each NGI test point.
A commercial sample HandiHaler (Boehringer Ingelheim) was
included as an example of an existing benchmark high
resistance, high performance cDPI.
In-Vitro TestingApproach
NGI = Next generation impactor
HPLC = High-performance liquid chromatography
23Team Consulting Limited 2018
TAE Gen2 achieved a significant improvement in fine particle delivery compared to Gen1.
In-Vitro TestingParticle size distribution results at 4kPa
FPD = 29%
FPF = 46%
FPD = 40%
FPF = 49%
FPD = 40%
FPF = 56%
MMAD = 2.2µm
MMAD = 2.8µm
MMAD = 2.2µm
FPD = Fine particle dose (<5µm), FPF = Fine particle fraction (<5µm), MMAD = Mass median aerodynamic diameter
24Team Consulting Limited 2018
TAE Gen2 prototype exhibited a good level of flowrate independence across the range 2 to 6 kPa with FPD
varying between 39 – 40% and FPF varying between 52 – 57%.
In-Vitro TestingParticle size distribution results at 2 to 6kPa
FPD = Fine particle dose (<5µm), FPF = Fine particle fraction (<5µm), MMAD = Mass median aerodynamic diameter
Team Consulting Limited 2018 25
Conclusions
26Team Consulting Limited 2018
Increasing the CFD metric of cumulative impulse for carriers correlated with increasing fine particle delivery.
• Exposing carrier particles to significant forces over an increased residence time within the cDPI acts to
increase the proportion of drug particles that are separated from carriers.
ConclusionsSimulation performance metrics correlated with in vitro measurements
27Team Consulting Limited 2018
Increasing to the CFD metric of peak normal impact velocity for carriers correlated with decreasing MMAD.
• Exposing carrier particles to greater magnitude impact forces acts to decrease the average size of drug
agglomerates released during impacts.
ConclusionsSimulation performance metrics correlated with in vitro measurements
28Team Consulting Limited 2018
SummaryEfficient engineering simulation to inform and optimise capsule inhaler design
This study demonstrates a balanced approach to
simulation complexity to inform early-stage design
iterations for a cDPI.
The approach was shown to achieve a significant
improvement for in vitro fine particle performance:
• Increased proportion of the dose reaching a
patient’s lungs
• Reduced side effects associated with mouth and
throat deposition
• Improved consistency of therapy across variable
inhalation efforts
29Team Consulting Limited 2018
To the team at Intertek Melbourn for support with in vitro
impactor testing.
To the team at Carl Zeiss Ltd for support with CT scans and
metrology of prototypes.
To the inventors of the TAE concept prototype and contributors to its continued development.
• David Harris, Jamie Greenwood, Oliver Harvey and Philip Canner
AcknowledgementsThank you!
Team Consulting Limited 2018 30
Appendix
31Team Consulting Limited 2018
Engineering Simulation Approach
Momentum 1st order 2nd order 2nd order 2nd order 2nd order
Turbulence Model k-ε 1st order k-ε 2nd order k-ε Re 2nd order RSM 1st order RSM 1st order
Dynamics Steady Steady Steady Steady Unsteady
Turbulence modelling
Contours of tangential
velocity for TAE Gen2
geometry at 4kPa
(-25 to +65m/s)
The approach taken to modelling flow turbulence was key to ensuring the accuracy of CFD simulations.
2 eqns
1 hrs
2 eqns
2 hrs
2 eqns
3 hrs
7 eqns
6 hrs
7 eqns
8 hrs
No. turbulence
equationsSolve time