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K1 Competence Center - Initiated by the Federal Ministry of Transport, Innovation and Technology (BMVIT)
and the Federal Ministry of Science, Research and Economy (BMWFW).
Funded by the Austrian Research Promotion Agency (FFG), Land Steiermark and the Styrian Business
Promotion Agency (SFG).
Hier “rcpe ppt header Large 01.emf” platzieren
Model informed development of dry powder inhaler (DPI) formulations and processes
Drug Delivery to the Lungs Conference (DDL)
6th - 8th Dec 2017, Edinburgh
*Ass. Prof., Graz University of
Technology
Scientific leader , RCPE
Amrit Paudel*, Sarah Zellnitz, Sumit Arora, Benedict Benque, Michael Brunsteiner,
Eva Faulhammer, Peter Loidolt, Johannes G. Khinast
Content
DDL 20171/29/2018 Slide 2
Introduction and context
Physchem properties prediction of inhaled particles
Mechanistic modeling of DPI manufacturing process
Particles-capsule-device interaction
Predictive inhalation biopharmaceutics
Concluding remark
Introduction and context
Drug Delivery to the Lungs Conference (DDL)
6th - 8th Dec 2017, Edinburgh
About RCPE GmbH
1/29/2018 DDL 2017Slide 4
Independent Research Center for pharmaceutical process
and product development, located in Graz, Austria
Leveraging predictive tools for drug product/process dev.
1/29/2018 DDL 2017Slide 5
Advanced Digital Design of
Pharmaceutical Therapeutics
• Developing a multi-scale modelling
framework and toolkit
Oral biopharmaceutics tool
• Providing innovative and validated
oral biopharmaceutics toolkit that
integrated predictive in vitro and in
silico approaches
CPPDComputational Product and Process Design
• Harnessing computational tools including molecular
structure and material properties in silico to
complement rational manufacturing
• Promoting realistic computer simulations of particle
aerosolization, delivery and deposition,
• Promoting patient-tailored inhaled medicines,
• Promoting integration of device and formulation design
• ….
Emerging strides towards applying “predictive science” to demonstrate actual benefit of QbD
approach of process and product dev.
DDL 20171/29/2018 Slide 6
A realm of multi-physics, multi-scale modelling for inhaled pdt.
Multi- length/ time scale models and hybrids, integrated with experiments for rational design,
development and optimization
Minimize trial & error (thus minimize/ flag the risk & uncertainty)
Physchem properties prediction of inhaled particles
Drug Delivery to the Lungs Conference (DDL)
6th - 8th Dec 2017, Edinburgh
1/29/2018 Slide 8 DDL 2017
Design particles
• Carrier-based (Lactose-based)
• Carrier-free (Soft Agglomerates)
• Composites (PulmoSol™)
• Porous (LPP, PulmospheresTM)
Measure/ compute key design attributes
(API, excipient, intermediates)
Performance
Stability
Processability
• Micromeritics• Surface• Solid-state• Mechanical• ….
Dispersivity
Detachment
Emitted dose
Fine particle fraction
Stability...
Perfect/ right/ ideal inhaled particles
Consistent properties, easy to handle, easy to (or no) post-process
Balanced properties inter-particle interactions for processing, product & patient
DPI Materials: Properties to process/ product
Factors governing particle-particle interactions and powder flow
1/29/2018 Slide 9 DDL 2017
Surface disordering, amorphization Elastic properties Attachment energies mobility/diffusion
Energy levels (IP) Work functions Shape/ morphology,
exposed atomic surfaces
Mobility/diffusion Surface water interactions,
wettability
VdW/polar interactions, Surface complementarity Dispersive energy
Material descriptors computable from atomic resolution models
1/29/2018 Slide 10
Elastic properties
Energy differencesMobility/diffusion local/roto-vibrational mobility
attachment energies VdW/polar interactionsSurface complementarity
Energy levelsWork functions
surface water interactions, wettability
DDL 2017
DDL 20171/29/2018 Slide 11
Case study: Tribo-electrification, In Silico and from First Principles
Trehalose (TRE)Mannitol (MAN)
Acetyl salicylic acid (ASA)Ibuprofen (IBU)
Lactose monohydrate (LAC)
Pearson correlation
Experimental charge density (GranuCharge) Charge density v/s ionization potential
Higher level of DFT theory improves
predicitvity of tribo-charging
Further opportunities in extending the
prediction via estimation of dynamics/
charge dissipation from first principles
Case study: Van der Waals/polar interactions
DDL 20171/29/2018 Slide 12
1. randomly move API crystal to position with
different orientation and distance
2. perform a brief energy minimization
3. calculate interaction energy carrier ↔ API
4. Go to 1 (~10000x)
Salbutamol sulfate (SAL): stronger/more adhesive interactions with
the carrier (LAC) than the other APIs
This is confirmed (indirectly) through exptl data (IGC, contact angles)
Complementary information to support/interpret AFM/CAB
interaction energies
v/s inter-particle
distance
Beclomethasone DP (BEC)Carvedilol (CAR)Salbutamol base (SAB)Salbutamol Sulfate (SAL)Lactose monohydrate (LAC)
Mechanistic modeling of DPI manufacturing process
Drug Delivery to the Lungs Conference (DDL)
6th - 8th Dec 2017, Edinburgh
DPI manuf.: Interplay of material attributes, particle engineering and processing
1/29/2018 DDL 2017Slide 14
Powder process (modelling) challenges
Complex physics → Model complexity
Particle load (Carrier : Fine = 50 :1)
Huge no. of particles (eg. 1 mg, 100 µm ~ 103 v/s 1 µm ~ 109)
Spray tech:
Spray-drying (SD)
Spray freeze drying (SFD)
Spray congealing (SC)
Fluid bed drying (FB)
Mechanochem
Milling
Mechanofusion
Mixing
Other..
Supercritical fluid
Solvent
crystallization
Freeze drying
Particle
Engineering
API-coated-carrier particles:
• Lactose, mannitol based
• FCA (Mg-stearate, leucin, silica!)
Carrier-free particles:
• Spray dried, Milled API
• Passified particles
• Co-crystals
Composite/ matrix:
• Solid dispersion
(dextran)
• Ternary systems
• Porous particles
Formulations
Capsule, device filling
Capsule:
Physical properties
Mechanical properties
Machinability
Process:
Filling mechanism
Scale
Machine type
Dispersivity
Detachment
Emitted dose
Fine particle fraction
Stability...
Performance
q Flowq Seggregation
q Phase transitions
q Homogeneity
DEM-CFD Modeling of powder flow, mixing, transport @RCPE
1/29/2018 DDL 2017Slide 15
DEM modeling for particle flow is based on in-house DEM code, XPS@ (eXtended Particle
System).
CFD modeling for gas flow is based on commercial code, AVL FIRE™.
DEM-CFD coupling is developed by RCPE with special demands for industrial applications.
The advantages of using the new, coupled software can be summarized as follows:
XPS@ is based on the highly efficient GPU computational technique.
Multigrid Technique enables high feasibility of parallelizing capability.
Computational expense for large amounts of particles (up to 25 Mio.) is reasonable.
Complex geometries can be addressed for various industries.
DEM process modelling work flow
Model calibration:• Contact model selection
• Calibration of DEM contact model parameters
based on standard experiments (shear testing,
compressibility, wall friction, angle of repose etc)
describe flow behavior of a certain powder
Process modelling:• Geometry based on the unit operation
• DEM model properties based on the model calibration
describe in silico the processing of a certain powder in the
simulated unit operation set up
BlendingFeeding Conveying Capsule filling
1/29/2018 DDL 2017Slide 16
Real experiment
Numerical experiment
Yield locus
1/29/2018 DDL 2017Slide 17
Stage I Stage II Stage III
Bog=0
d=200 µm
Bog=10000
d=200 µm
Free flowing
powder
Cohesive
powder
Bond no.The powder mass and the pressure
inside the nozzle are recorded while
moving through the powder bed
Periodic
boundary
conditions
0 mm
8 mm
Case study: DEM process modelling of the dosator cps filling process
1/29/2018 DDL 2017Slide 18
free flowing
very cohesive
Initial rel. density
Evolution of dosed mass inside the nozzle
Optimal
operation
range for
powder with
� � =0.507
! " ,$ %& = ! ( ,$ %& ∗ * *+
! " ,$ %& = ! ( ,$ %& ∗ **+
Max. main principal stress to limit
the strength (disperseabilty)
Min. main principal stress to
obtain the minimum strength
Determination of plug stability
𝝈𝒄,𝒎𝒊𝒏 =𝒓𝝆𝒃𝒖𝒍𝒌𝒈
𝒔𝒊𝒏𝟐𝝋𝒘
Minimum compression strength
required for dosing (stable plug)
𝝈𝟏~𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆 𝒊𝒏𝒔𝒊𝒅𝒆 𝒕𝒉𝒆 𝒏𝒐𝒛𝒛𝒍𝒆
Loidolt et al. Int. J. Pharm (2017)
Case study: DEM process modelling of the dosator cps filling process
1/29/2018 DDL 2017Slide 19
Fast mixing in x direction (front-to-back)
Mixing in z direction (left-to-right) slower, but
increases over time
Formation of two segregated cores of fine
particles
Reduced at 62 rpm
Most carrier-API interactions expected in
these regions
Lacey index over time / #revolutions
Qualitative DEM visualization
Case study: DEM process modelling of placebo blending process
Particles-capsule-device interaction
Drug Delivery to the Lungs Conference (DDL)
6th - 8th Dec 2017, Edinburgh
State-of- the art: Particles-capsule-device interaction
1/29/2018 DDL 2017Slide 21
Inhaler + CFD
100 items
Inhaler + DEM
14 items
Deagglomeration + DEM
273 itemsISI Web of ScienceTM
Predominant focus on separately dealing airflow trajectories in DPI devices and ex-situ agglomerating
The emphasis is missing on how properties of formulations and of capsules propagate to
aerodynamics and dispersion
1/29/2018 DDL 2017Slide 22
0
10
20
30
40
50
60
70
80
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Flo
w r
ate
(L/m
in)
Inhalation time (s)
Input inhalation profiles (IPs)
Use case: 60L/min
IP-1
0
20
40
60
80
100
0 0.2 0.4 0.6 0.8 1Perc
enta
ge o
f part
icle
s r
ele
ased (
%)
Time (s)
Simulated amount of released particles
Use case: 60 L/min
IP-1
More realistic inhalation profiles (IPs) can be used for evaluating inhaler performance.
Good performance is obtained with a varying IP, i.e. being applicable to different patients.
Delvadia et al. (2016)
Utilization of DEM-CFD: Evaluating inhaler performance
DEM simulation of carrier particles ejecting the rotating capsule
Coupling with CFD of air flow in inhaler
Sliding mesh for capsule movement
Particle entrainment
Depiction of particle-particle and particle-wall collisions
(relevant for API detachment)
1/29/2018 DDL 2017Slide 23
Utilization of DEM-CFD: powder dynamics in capsule inside inhaler
Capsule properties and hole morphology: work in progress
Study influence of capsule properties and RH on hole size, position and
shape (smooth or ragged, flap attached)
Implementation of hole geometries in DEM of capsule rotating in swirl
chamber
Varying cohesion to reproduce effect of RH
Rougher surface due to low lubricant content reflected by higher friction
and cohesion at capsule wall
1/29/2018 DDL 2017Slide 24
Cui Y., Zellnitz S. et al. Int J Pharm (2014)1/29/2018 DDL 2017Slide 25
Limited possible size difference in DEM
Only carrier particles simulated
API detachment and flow in post-processing
API detaches from carrier in wall collisions (primarily swirl chamber wall) and
in fluid flow
Comparison of collision force with limiting force for each detachment
mechanism (Lift-off, Rolling, Sliding)
Limiting force: Adhesion force measurements (AFM) or molecular modeling
API detachment due to air flow as a function of Reynolds number and
position angle
API assumed to move with air flow (low Stokes number)
Capsule properties and hole morphology: work in progress
Predictive inhalation biopharmaceutics
Drug Delivery to the Lungs Conference (DDL)
6th - 8th Dec 2017, Edinburgh
Predictive inhalation biopharmaceutics: what is out there?
1/29/2018
Building blocks of Inhaled PBPK modeling
Slide 27 DDL 2017
• Influence of particle size
• Effect of charge and humidity
• Influence of ASPD
• ICRP 66
• MPPD
• ARLA online calculator
• Mimetikos PreludiumTM
• LungSim
• CFD
• ….
• Dissolution modelling
• Clearance modelling
• Particle uptake by
macrophages
Pulmonary PBPK modeling
Pre-deposition
modeling Deposition
modeling Post-deposition
modeling
0
10
20
30
40
Extra-Thoracic(%) Pulmonary(%) Exhaled (%) C/P
Capsule - GP
Capsule - MPPD
Prediction from regional drug deposition from MPPD model was better able to capture the
Cmax in the plasma concentration time profile
1/29/2018 Slide 28 DDL 2017
Predicted Regional Lung Deposition Predicted v/s in vivo Plasma Conc.
Case Study: Early Phase Inhaled PBPK– Capsule based DPI of compound X
0
10
20
30
40
50
Extra-Thoracic(%) Pulmonary(%) Exhaled (%) C/P
% D
rug
De
po
sit
ion
Reservoir - GP Reservoir - MPPD
• Prediction from MPPD and Gp provided the expected exposure range of Compound X when
administered through reservoir DPI formulations1/29/2018 Slide 29 DDL 2017
Case Study: Early Phase Inhaled PBPK– Reservoir based DPI of compound X
DDL 20171/29/2018 Slide 30
Validated predictions of lung deposition pattern
Models for pulmonary dissolution – regional variability
Regional variation in epithelial permeability
Validation of pulmonary concentrations
Predictive inhalation biopharmaceutics: what are we missing?
Concluding remarks
Drug Delivery to the Lungs Conference (DDL)
6th - 8th Dec 2017, Edinburgh
1/29/2018 Slide 32 DDL 2017
General Inference
• Computational material science holds potentials to provide formulation and process
developers (models) the descriptors that can be difficult to measure reliably
• Emerging mechanistic modelling approaches of oral drug product manufacturing process
can now be gradually applied to DPI manufacturing process
• With growing trends in PBPK modelling for DPI, there are still opportunities to improve
deposition models, establish SLF, in vitro dissolution media/set up as inputs for models
• The rational combination of experimental & theoretical approaches requires a sound
knowledge of the strengths and limitations of the used methods and algorithms
DDL 20171/29/2018 Slide 33
Inhalation research @ RCPE: Figures and facts
• Particle engineering, drug product
profiling and prediction, advanced mfg
science & processing, PBPK modelling
• Pharm science, clinical science,
modelling science, and IP
• Capacity to run up to 9 project per year
in the inhalation field
• Network with universities
Keywords
Multi year/multi partner collaborations (2015 – 2020)
Particle engineering for high-strength DPI development
DPI research consortium: formulation, capsule shell, process and device
Optimization of Biopharmaceutical Toolbox for Inhaled products
Lipid microparticles for advanced and safe inhalable formulations
Recent projects (2015 – 2017)
• Particle engineering and characterization of lactose and mannitol
• Analysis of powder properties and segregation/ carrier detachment
• Combined Gastroplus-MPPD deposition modeling of the effects of
different formulations on the predicted in vivo performance of DPIs
• In vitro solubility, dissolution, pulmonary permeability and PBPK of DPI
• Evaluation of the alveolar clearance of PG fatty acid esters
DDL 20171/29/2018 Slide 34
Inhalation research @ RCPE: Publications
Littringer et al. Spray Drying of Mannitol as a Drug Carrier—The Impact of Process Parameters on Product Properties. Drying Technology, 2012, 30(1), 114–124
Karner & Urbanetz Triboelectric characteristics of mannitol based formulations for the application in dry powder inhalers, Powder Technology. 2013, 253, 349–358
Zellnitz et al. Preparation and characterization of physically modified glass beads used as model carriers in dry powder inhalers. International Journal of Pharmaceutics, 2013, 447, 132-138
Littringer et al. Spray drying of aqueous salbutamol sulfate solutions using the Nano Spray Dryer B-90 – The impact of process parameters on particle size. Drying Technology, 2013, 31,
1346–1353
Zellnitz et al. Surface modified glass beads as model carriers in dry powder inhalers—Influence of drug load on the fine particle fraction. Powder Technology, 2014, 268, 377-386
Faulhammer et al. Low-dose capsule filling of inhalation products: Critical material attributes and process parameters. International Journal of Pharmaceutics, 2014, 473, 617-626
Zellnitz et al. Influence of surface characteristics of modified glass beads as model carriers in dry powder inhalers (DPIs) on the aerosolization performance. Drug Development and Industrial
Pharmacy, 2015; 41(10),1-8
Faulhammer et al. Carrier-based Dry Powder Inhalation: Impact of Carrier Modification on Capsule Filling Processability and in vitro Aerodynamic Performance. International Journal of
Pharmaceutics, 2015, 491, 231–242
Faulhammer et al. Multi-methodological investigation of the variability of the microstructure of HPMC hard capsules. International Journal of Pharmaceutics, 2016, 511, 840-854.
Wu et al. An in vitro and in silico study of the impact of engineered surface modifications on drug detachment from model carriers. International Journal of Pharmaceutics, 2016, 513, 109-
117.
Stranzinger et al. The effect of material attributes and process parameters on the powder bed uniformity during a low-dose dosator capsule filling process. International Journal of
Pharmaceutics, 2017, 516, 9-20.
Pinto et al. How does secondary processing affect the physicochemical properties of inhalable salbutamol sulphate particles? A temporal investigation. International Journal of
Pharmaceutics, 2017, 528, 416-428.
Loidolt et al. Mechanistic modeling of a capsule filling process. International Journal of Pharmaceutics, 2017, 532, 47-54.
Bäckman et al. Advances in experimental and mechanistic computational models to understand pulmonary exposure to inhaled drugs. European Journal of Pharmaceutical Sciences 2017
Salar-Behzadi et al. Effect of the pulmonary deposition and in vitro permeability on the prediction of plasma levels of inhaled budesonide formulation. International Journal of Pharmaceutics,
2017, 532, 337-344.
1/29/2018 Slide 35 DDL 2017
Finally…
Inhaler-by-chance Inhaler-by-design
1/29/2018 Slide 36 DDL 2017
Massimo Bresciani, ppa.Executive DirectorScientific [email protected]
Ass. Prof Dr. Amrit PaudelScientific Leader, Advanced product & [email protected]
Univ.-Prof. Dr. Johannes KhinastScientific Director / [email protected]
Q & A