Efficient Engineering Simulation to Inform and Optimise ... · the outlet. Simulation Results Design changes for TAE Gen2 0m/s 70m/s 0m/s 70m/s Decelerating swirling flow Accelerating

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


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


Capsule Dry Powder InhalersDisadvantages of cDPIs

✗ Hard to handle

✗ Difficult to use

✗ Pricey piercers

✗ Risk of variable performance in clinic


Capsule Dry Powder InhalersTAE concept prototype Gen1

TAE Gen1


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?


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


Inhaler airflow resistance was measured with physical prototypes for the validation of CFD simulations.

Engineering Simulation ApproachValidation



CT scan of a TAE

Gen1 prototype

(animation)


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.



What kind of insight can simulation

provide for cDPI design?


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


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


How did we use simulation results to

inform cDPI design changes?


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


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


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


What was the impact of design

changes on in-vitro performance?


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


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


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


Conclusions


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


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



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


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!


Appendix


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

Documents

Efficient Engineering Simulation to Inform and Optimise ... · the outlet. Simulation Results Design changes for TAE Gen2 0m/s 70m/s 0m/s 70m/s Decelerating swirling flow Accelerating