The Use of Physics-Based Modeling to Better Design Drug ...€¦ · Pulmonary drug delivery Inhaler...
Transcript of The Use of Physics-Based Modeling to Better Design Drug ...€¦ · Pulmonary drug delivery Inhaler...
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Yoen-Ju Son, PhD
Merck Research Laboratory, Summit, NJ
The Use of Physics-Based Modeling to Better Design Drug-Device Interface
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Presentation Outline
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Physics-based modeling in pharmaceutical industry
Pulmonary drug delivery
Inhaler design
- Aerosol physics
- Numerical models for inhaler design
- In Vitro-in Vivo correlation
Case studies on optimization of Formulation/Device
interface for Respiratory Products
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Physics Based Modeling
Pharmaceutical formulation - Analyzing process equipment (mixing, heat generation,
drying etc).
- Evaluating product performances (dissolution, spray pattern
etc).
Formulation/Device interface - Optimize drug/device interface
- Device patient interface
Process scale up
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Physics Based Modeling
Formulation/Device interface for Respiratory Products
Optimize drug/device interface
- Capsule based passive DPI (CFD)
- Mist (CFD)
Device patient interface
- MDI vs DPI (CFD)
- MDI with and without holding chamber (CFD)
- Particle growth in the airway (CFD)
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Extrathoracic
Deposition (> 5µm)
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Undetached
Agglomerated
particles
MDI
DPI
Propellant
Formulation Device
Respirable
particles (1-5 µm) Nebulizer
Pulmonary Drug Delivery
Combination products
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Inhaler Design: Simulation
Inhalation
Therapy
Physicochemical
aspects Biological aspects
Performance of
inhaler
Particle deposition in
the lung/airways
ADME
Aerodynamic properties
Patient dependency
Chemical properties
- Solubility
- Lipophilicity (log P)
- Molecular weight
- pka
Physical properties
- Aerodynamic diameter
- Inter-particulate forces
Aerodynamic properties
Particle deposition
in the lung/airways
Physical properties
- Aerodynamic diameter
- Inter-particulate forces
No
Well accepted for upper
airways (MT-TB)
Well accepted for DPIs
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Inhaler Design
System description
Coding and simulation
(Simulation tool)
Experiments
(Device/formulation) 7
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Physics Based Models
Inhalers Applications
DPIs
Airflow with the device
Mousepiece geometry
Particle-wall/ particle-particle collisions
Powder emptying; capsule spinning
Particle de-aggregation mechanism
MDIs
Aerosol plume behavior
Nozzle diameter
Propellant property
Holding chamber
Nebulizers Atomization mechanism; piezoelectric
Followed by drug deposition in the airways !
C.A. Ruzycki et al., Expert opin. Drug Deliv., (2013) 207-323
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Particle Deposition in the Airways
> 5 µm
3 - 5 µm
Alveoli (deep lung)
: 1-5 µm
Impaction
Sedimentation
Weibel human airway model : 0-23 generations
Video of CFD simulations
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Computational Fluid Dynamics (CFD)
Turbulence
Laminar
Re number Vs Airway Generation
Trachea
Large airways
Small airways Acinus
Turbulence is a dominant airflow in a device and large airways which tend to
produce chaotic vortices.
Particle
de-agglomeration
Drug depositional
loss
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Turbulence Simulation (CFD)
Direct numerical simulations (DNS)
Resolve all eddies (meshes)
Large eddy simulation (LES)
Intermediate approach
Reynolds-averaged navier-stokes (RANs)
Model just ensemble statistics
Most common method in DPI simulation
3D Geometry
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Device/Formulation Interface
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Case study I: DPI (CFD)
Y-J. Son et al., Pharm. Res. 30 (2013)
S.R.B. Behara et al., Pharm. Res. 31 (2014)
DPI details
Formulation: spray-dried sub-micrometer particle
agglomerates
Mouse-piece with built in 3D rod array
Optimized for sub-micrometer particle dispersion
Physics-based modeling with in vitro validation
Mouthpiece geometry and airflow
Particle-wall/ particle-particle collisions
Particle de-aggregation mechanism
Powder emptying; capsule spinning, size of the
capsule
Drug deposition in Mouth-Throat (MT) region
Capsule Based Passive DPI
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Case study I: Simulation (MP Design)
Y-J. Son et al., Pharm. Res. 30 (2013) 14
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Case study I: Validation
Y-J. Son et al., Pharm. Res. 30 (2013)
Key Turbulence Factor Simulated Experimental Results
k : turbulence kinetic energy
TI: turbulence intensity
ω: specific dissipation rate
NDSD: non-dimesional specific dissipation rate
Inhaler k TI ω NDSD Rank
1 4.2 13.5 61,9 82.6 6
2 5.6 20.1 111.3 148.4 2
3 12.1 47.2 64,85 86.5 5
4 474.2 50.5 132,87 106.3 3
5 9.5 38.3 72,67 96.9 4
6 19.1 26.3 127,28 169.7 1
MMD (µm) FPF1µm/ED FPF5µm/ED
1.35 18.8 89.5
1.21 26.8 95.8
1.36 19.2 94.5
1.27 26.9 95.9
1.31 20.4 95.5
0.98 38.8 94.6
𝑁𝐷𝑆𝐷 = 𝜔 ∙ 𝑡𝑒𝑥𝑝
𝑡𝑒𝑥𝑝 =𝑉
𝑄
Match rank order
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Case study 2: Respimat (CFD-Visualization)
H. Wachtel et al., RDD (2008).
D. Hochrainer et al., J Aer Med 18(2005).
Respimat Soft Mist (Boehringer Ingelheim)
New liquid aerosol generating device.
Two high-velocity jets created at the uniblock
outlets and generate a slow-moving aerosol
The velocity is approximately 0.8 m/s at 10 cm
distance from the nozzle, compared with 2.0-
8.4 m/s for a conventional pMDI.
Influencing factors (CFD simulaion)
- Jet velocity
- Impaction angle
- Solvent
- MP design (air-flow in the MP)
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Case study 2: Respimat (Impinging Jets)
H. Wachtel et al., RDD (2008).
MP Design: CFD and Flow Visualization
Prototype Respimat
The larger air vents
incorporated in the marketed
Respimat design (b) minimize
the back-flow.
CFD calculations provide
numerical data but require
careful selection of the
mathematical models of
turbulent flow.
The uncertainty surrounding
model selection can be
avoided by utilizing flow
visualization studies using up-
scaled inhaler models.
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Device/Patient Interface Optimization
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Case Study 4: DPI vs. MDI Lung Delivery
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Drug deposition in MT-TB region
Product Device API Aerosolization Correct use
Flovent HFA
(GSK)
MDI Fluticasone (44g) Turbulence and
compressible flow
Slowly Deeply
(SD)
Flovent Diskus
(GSK)
DPI Fluticasone (50g) Turbulence and high
speed jet
Quickly Deeply
(QD)
P.W. Lonest et al., Pharm. Res. 29 (2012)
Tests Flow profile
Correct MDI SD
Correct DPI QD
Incorrect MDI QD
Incorrect DPI SD
CFD and Experimental Validation
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MDI SD (C) DPI QD (C)
MDI QD (IC) DPI SD (IC)
Used a well validate model
With “correct” inhalation, MDI
delivers 2x dose to the upper
TB with ½ the loss in the MT
DPI was not influenced by
inhalation pattern
CFD is a useful tool to
optimize device/patient
interface.
20 P.W. Lonest et al., Pharm. Res. 29 (2012)
DPI MDI
CFD 65.2% 44.3%
Experimental 72.5% 39.0%
Case Study 3: DPI vs. MDI Lung Delivery
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Case Study 4: MDI With/ Without Holding Spacer
Proventil HFA/Holding Spacer Proventil HFA (Merck)
Validation can be done using
published data
Help design medical devices
with improved drug delivery
efficacy.
C. Kleinstreuer et al., J. Aerol. Med. 20 (2007) 21
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Influence of Humidity in the Airways
Case Study 5: Particle Behaviors in the Airways
Y-J Son et al., Ann Biomed Eng. 40 (2012)
Y-J. Son et al., European J Pharm. Sci 49 (2013)
CFD predictions of excipient effect on exiting growth size for an
aerosol.
- Initial size: 0.9 µm
- Albuterol sulfate (AS), AS with citric acid (CA) and AS with NaCl
- Spray dried AS with Mannitol (MN) and L-Leucine (Leu)
AS:MN:Leu
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CFD predictions of growth for insulin : NaCl (50:50 w/w) combination particles for slow and deep inhalation
Case Study 5: Particle Behaviors in the Airways
P.W. Longest, RDD 2012
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Limitations in Computational Simulation
Highly complex nature of aerosol formation and transport
complicates the application of computational methods in inhaler
design.
- Difficult to capture all the relevant physics involved in inhaler operation.
- Difficult to simulate the entire process (eg. MDI spray physics, metering etc.).
- Simplifications should be justified with some degree of validation.
Complexity of variations in respiratory tract geometry is
associated with additional factors such as breathing pattern, age,
disease state and patient-device interaction.
- Drug deposition will be influenced by airways geometry.
- Limitation in simulations in deep lung drug deposition due to the alveolar
regions that contract and expand with time.
- Current in vivo simulation is limited to upper airways.
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Summary
Physics-based modeling can provide an effective tool for
analysis.
Physics based modeling is well established for
- Dry powder inhaler; turbulence mechanism (CFD)
- Drug deposition in upper airways (MT-TB).
CFD is not meant to wholly replace traditional experimental
techniques but rather to provide an additional means for
analysis.
Regulators have yet to approve significant changes to inhalers
using combinations of computational and experimental
methods.
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Backup
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Particle De-agglomeration
Particle de-agglomeration in DPIs
- Need to understand a break-up energy.
- Turbulence is not the only energy source !!
Discrete element modeling (DEM)
- Useful to understand the exact mechanism of aerosolization and
particle de-agglomeration in these systems.
- Coupling CFD methodology to DEM may provide a means to
predict the process as a whole.
K F1
F2
F3
F4
Discrete particle system
Specify
interactions
between two
bodies
Integrate the
equations of
motions for all
particles
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Computational Simulation
Physical equations are solved in 3D geometry
- Realistic geometries need to be constructed
- Simulation process subdivided geometry into small discrete volumes
(mesh or grid).
Concurrent experiments-computational simulation should
be conducted to leverage the strengths of each approach
Experiments
- Provide initial size distribution of the aerosol
- Benchmark deposition within a realistic airway model
- Used to initially validate modeling results
Simulation
- Allows modification and optimization of device performance
- Analyze experimentally difficult systems (entire TB or alveolar airways)
and provide additional resolution of deposition
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Computational Fluid Dynamics (CFD)
Particle Deagglomeation Mechanism in DPIs
Passive Active
• Narrow passage: Easyhaler®, AIR®
• Impaction: Aerolizer®
• Tortuous and helical channel: Turbuhaler®
• Air classifier: Airmax®, Novolizer®
• Cyclone: 3M Conix™
• Battery powered: Spiros®
• Pressured air: Exubera®, Aspirair®
• Piezo-electric: Microdose®
• In-situ micronization: MAGhaler®
• Spring impactor and tape: 3M Taper™
Majority of marketed DPIs adopt a passive mechanism
Compared with pMDIs and nebulizers, CFD has been used to a
larger extent in the analysis of DPIs.
Due to the strong dependence of DPI performance (passive) on
airflow
N. Islam and E. Gladki: IJP. 360 (2008).
Discontinued
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Case study 2: DPI (CFD-DEM Coupling)
Particle trajectories in the device
The powder dispersion in a cyclonic flow was simulated
Model device: Aerolizer® (Novartis)
Z.B Tong et al., Chemical Engineering Journal 164 (2010).
A B C
E D
A B C D E
D50 (µm) 4.1 3.3 3.3 3.3 2.6
Span 0.4 0.4 0.9 1.2 0.4
Diameter (µm) 50.4 51.1 51.1 51.4 52.8
Strength (pa) 416.5 527.1 590.9 622.5 755.5
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Case study 2: DPI (Particle Trajectories)
Particle size
2.6, 3.3, 4.6 µm
Smaller the particles size, lesser
the particle breakup
Polydispersity
0.4, 0.9, 1.2
Smaller the span, better the
particle break up
Z.B Tong et al., Chemical Engineering Journal 164 (2010).
Size is a more clinical factor
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Computational Simulation
Physical equations are solved in 3D geometry
- Realistic geometries need to be constructed
- Simulation process subdivided geometry into small discrete volumes
(mesh or grid).
Concurrent experiments-computational simulation should
be conducted to leverage the strengths of each approach
Experiments
- Provide initial size distribution of the aerosol
- Benchmark deposition within a realistic airway model
- Used to initially validate modeling results
Simulation
- Allows modification and optimization of device performance
- Analyze experimentally difficult systems (entire TB or alveolar airways)
and provide additional resolution of deposition
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