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).

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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 Operationsmassimo.bresciani@rcpe.at

Ass. Prof Dr. Amrit PaudelScientific Leader, Advanced product & deliveryjohannes.khinast@rcpe.at

Univ.-Prof. Dr. Johannes KhinastScientific Director / CEOkhinast@tugraz.at

Q & A