K1 Competence Center - Initiated by the Federal Ministry of Transport, Innovation and Technology (BMVIT)

and the Federal Ministry of Digital and Economic Affairs (BMDW).

Funded by the Austrian Research Promotion Agency (FFG), Land Steiermark and the Styrian Business

Promotion Agency (SFG).

Digital Design of Pharmaceutical Products and

Processes – State of the Art and Challenges

Prof. Johannes Khinast - DDF Summit 2019 - Berlin

Graz University of Technology & Research Center Pharmaceutical Engineering

Austria

Advanced Pharmaceutical Products

Nano-structured Drugs

Duncan R., Gaspar R., Molecular Pharmaceutics, 2011

Non-biological Complex Drugs (NBCD)

Crommelin, de Vlieger (Eds.), Non-Biological Complex Drugs, AAPS/Springer (2015)

Borchard et al., Nanoparticle iron medicinal products, Regul Toxicol Pharmacol.

2012

Biologics & VaccinesComplex Solid Dosage Forms

ASDs

Inhalted DF

Osmotic

tablets

Need for Advanced Pharmaceutical Manufacturing (APM)

Produce more complex products

Reduce process variability and thus..

…improve quality

Shorten supply chains

Individualize manufacturing

Standardize development effort (platforms with early intergration

in development)

Improve robustness

Example: Printing

• Possibility for personalized

production

• Flexible combination of multiple

APIs

• Ideal for high-potent APIS

(HPAPIs)

• Use in clinical supplies up to

commercial manufacturing

• Use in translational medicine

• Possibility to print solutions,

nano-suspensions, suspensions,

melts

• Integration of on-line analytics

Printed orodispersible film (ODF)

Example: Continuous Pharmaceutical Manufacturing• Smaller “foot-print” facility for

development AND production

• Shortens supply chain

• Integrates quality assurance in process

• Provides flexibility and agility

• Allows decentralized production

• RCPE leads European Consortium on

Continuous Pharmaceutical

Manufacturing (ECCPM) -

www.eccpm.com

In silico (Digital) Design

TODAY

FAR FUTURE

Full digital design of

products and processes

Digital design of in vivo

performance

In silico drug discovery

and optimization

In silico clinical studies

Efficacy and toxicity

predictions

• Unit operations

• Flow-sheet modeling

• Advanced control

• Parametrized PBPK

• Basic formulation design

TOMORROW

• High-fidelity modeling of

processes, i.e., CMAs

CPPs CQAs

• Designed performance,

e.g., “Stability-by-design“

• In silico CMC-regulatory

packages (NDA or early

phases IMPD/IND)

• Cloud-based solutions

• Deep learning, AI

Complex Multiphase Flow

CFD (Computational Fluid Dynamics)

Large-scale Granular Flows

DEM (Discrete Element Method)

Spray drying Blender/MixerCoater

Complex Fluids

SPH (Smoothed Particle Hydrodynamics)

Fluidized BedCohesive Materials

Process Modeling

Molecular Modeling

Software: gFORMULATE

GROMACS, CP2K, ORCA

Data Science

Deep learning Data scienceBig data

visualization

Our Platforms

Bio reactors

Extruders

DEM – RCPE’s High Performance Code XPS

GPU-based

Currently < 100 mio non-

spherical particles

Complex geometries

CFD coupling (momentum and

heat exchange)

Complex cohesion models

(e.g., MEPA model)

Dedicated graphics viewer for

real-time visualization

Coupling to gFORMULATE

Siegmann, E; Jajcevic, D; Radeke, C: Strube, D: Friedrich, K; Khinast, J: Efficient Discrete Element Method Simulation Strategy for Analyzing Large-Scale Agitated Powder Mixers, Chem.Ing. Techn 89 (8), 2017.Jajcevic D., Radeke C., Khinast J.G. (2013), Large-scale CFD-DEM Simulations of Fluidized Granular Systems, Chemical Engineering Science 98, p. 298-310

DigiMAT– From Sample to Large-scale Prediction

FORMULATE

1-50 kg (/h)

Example 1 – Wurster Coating Process (CFD-DEM)

• Wurster process forspray coating

• Modelling: CFD-DEM

• CFD (computational

fluid dynamics): airflow

• DEM (discrete element

method): particles

• Spray modelling

• Mass transfer through

evaporation

Source: Böhling et. al, AIChE 2016, (736d)

Source: Glatt, Youtube

Detailed CFD-DEM Simulationstimescale: seconds

Task B: Long-Term Coating Predictiontimescale: hours

outputs:• residence times• cycle times• velocities

PSD Change coating thicknessover time

output+

validation

ModelReduction

Reduced Order ModelCFD-DEM

Time scale: seconds

Fast, Reduced Model

Time scale: hours

Output

Time scale: hours

PSD

thickness over time

CoV(potency) over time

?

model reduction

output:• residence time• cycle time• mass hold up

Compartment ModelCFD-DEM

Time scale: seconds

Compartment Model

Time scale: hours

model reduction

output:• residence time• cycle time• mass hold up

PSD

thickness over time

CoV(potency) over time

Output

Time scale: hours

Coupling Capabilities gFORMULATE - XPS - AVL FIRETM

Fully coupled, integrated simulation platform:

• Momentum coupling (2-way)

• Heat & mass transfer, including evaporation

• Experienced user required

Reduced order modelling:

• User friendly GUI with integration of

various processes

• Short simulation times

• Predictive capability

Compartment Model in gFORMULATE

Virtual Design of Experiments

• CFD-DEM simulations have been

performed for each case

• Experimental validation with DoE#1

• Process parameters influence the

particle movement

• Changes in particle behavior are

reflected in the probability matrices

DoE#Fluidization

AirflowAtomization

AirflowWursterGap Size

1 Fl_2 At_1 Gap_2

2 Fl_1 At_1 Gap_2

3 Fl_3 At_1 Gap_2

4 Fl_2 At_2 Gap_2

5 Fl_2 At_3 Gap_2

6 Fl_2 At_1 Gap_3

7 Fl_2 At_1 Gap_1

8 Fl_2 At_1 Gap_4

Virtual Design of Experiments: Influence on Coating Thickness

0

5

10

15

20

25

30

35

40

45

50

0 6 12 18

me

an c

oat

ing

thic

kne

ss [

µm

]

time [h]

base : Fl_2 At_1 Gap_2

case 2: Fl_1 At_1 Gap_2

case 3: Fl_3 At_1 Gap_2

case 4: Fl_2 At_2 Gap_2

case 5: Fl_2 At_3 Gap_2

case 6: Fl_2 At_1 Gap_3

case 7: Fl_2 At_1 Gap_1

case 8: Fl_2 At_1 Gap_4

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

2

0 6 12 18

std

de

v co

atin

g th

ickn

ess

[µ

m]

time [h]

no change in mean coating thickness

process parameters influence coating thickness variation

Coating Monitoring via Process Analytical Technology

PAT enables advanced manufacturing by

detecting early on if a process deviates from

set point and by controlling process to meet

acceptance limits

Novel sensors as solution to old challenges,

e.g., impurities, coatings, powder flow rates

Flow NMR for monitoring process

chemistries and impurities (a few 100 ppm)

Flow GC and LC

OCT for monitoring coating quality

Advanced spectroscopic methods for online

analysis (e.g., spectroscopic imaging)

Optical Coherence Tomography

Mean coating thickness (average within 60 seconds) and RSD of mean coating thickness (inter-tablet coating variability)

Replication experiments show similar results regarding:

Mean coating thickness

Inter- and intra-tablet coating variability

Process Monitoring: Optical Coherence Tomography for Coating

Comparison of 3 batches

Commercial OCT System - GMP / ATEX / CE Certified The systems includes

1D imaging probe (CE and ATEX conform)

3D imaging probe available mid 2016

GMP conforming documentation/Software available mid 2016

System can be purchased as of March 1, 2016 from Phyllon GmbH (Graz, Austria)

Example 2: Direct Compaction Line

A - In silico Feeder Performance Prediction

DEMModel Calibration

Powder (e.g., FT4

measurements)

Desired process

conditions

Input:

Output:

Discharge rate for

different filling levels

Expected fluctuations

Pressure

Density

Torque

Feeder Simulation

Different rpms

Simulation of

total discharge

process

Feeder and Screw Geometry

1. Hopper

2. Screws

3. Screen

1 Coarse Screw Pitch

Fine Screw Pitch

2

3

Feeder Modeling

Total Discharge Simulation Simulation of discharge process in

parallel (Pressure boundary condition)

Example: 20M particles (300µm)

~10% fill level (impeller)

Process time: >400 seconds (full discharge)

0

2

4

6

8

10

12

0 40 80 120 160 200 240

Mas

s fl

ow

[g

/s]

Time [s]

Feeders

Twin screw loss-in-weight micro-feeder MTS-Hyg

(Brabender Technologie).

Twin screw loss-in-weight feeder KT20 (Coperion K-Tron).

Twin screw loss-in-weight feeder xxCF0500xx (GEA). [1]

[1] Compact FeederTM Instruction Manual, GEA.[2] Gericke feeder layout, Questionnaire Orion.[3] Three-Tec Product Specification Sheet, Flat tray feeders.

Twin screw loss-in-weight feeder DIW PE GZD200.22 and

DIW PE GZD100.12 (Gericke). [2]

Twin screw loss-in-weight feeder ZD 9 FB and ZD 12 FB (ThreeTec). [3]

RCPE’s micro- feeder 1-100g/h [4]

[4] Besenhard, M: Fathollahi, S; Siegmann, E; Slama, E; Faulhammer, E; Khinast, J: Micro-feeding and dosing of powders via a small-scale powder pump, International Journal of Pharmaceutics 519 (1-2), p.314-322, 2017.

Experimental Feeder Testing Rig

Scale on rubber

with dampener ring

to stabilize

collection bowl

Camera for video

collection at outlet

Camera for video

collection inside

hopper

PC for collection of

feeder, scale and

video data

Fig.: Feeder testing setup in fume hood in basement.

Stone weighing

table to dampen

vibration

disturbances

Example video frame

B - Continuous Mixer Analysis CMT

Understanding continuous mixing

important for prediction of process

performance.

Understanding the RTD of blender

Understanding impact of design and

material properties on mixer

performance

Optimization of performance

Control

Variability

36

DEM Simulation: Tracer Impulse

white/transparent:older and newer material

200rpm255g6 k/h

red: tracerfrom t = 2s – 3s

Results - Contour Plot of n CSTR n ~ 1 for large portions of the

operating space

ideal CSTR behavior between200 – 475 rpm

avoid combination ofhigh rpm and low hold-up mass

Mas

s h

old

up

Rotation Rate

Example 3: Bioreactor Modeling and DesignBioreactors come in many different sizes and configurations.

Laboratory system (Chemostat)

Wave reactor

Large-scale fermenterPilot scale

several hundred m3

Single-use

aiBAT – Advanced in silico Bioreactor Analysis Tool

GPU-based large-scale simulations possible

LBM coupled with Lagrangian particle tracking

Multicomponent species (O2, CO2, pH, nutrients,

metabolites) tracking

Energy balance

Bubble breakup/coalescence

kla & power input prediction

Shear impact on cells

Second liquid phase

Stirrer optimization and scaleup

C Witz, D Treffer, T Hardiman, J Khinast - Local gas holdup simulation and validation of industrial-scale aerated bioreactors, Chemical Engineering Science, 2016

Example 4: Hot-Melt Extruders – An Unsolved Challenge

Design of extruders still mostly empirical

Modeling of the screw’s impact on RTD, temperature

trajectory and material degradation unclear

Extruders available:

Leistritz 27mm

Coperion 18 mm

ThermoFischer 16mm

Threetec 8mm

Various down-stream equipment available (hot-die

cutting, strand cutting, etc.)

HME- Typical Screw Design

conveying elements

kneading blocks

specific mixingelements

left handed elements

Modular screw design allows individual configuration:

Cross section profile … leads to self cleaning screws:

A. Eitzlmayr, J. Khinast, Co-Rotating Twin-Screw Extruders: Detailed Analysis based on Smoothed Particle Hydrodynamics. Part 2: Mixing, Chemical Engineering Science 2015

Extruder Modeling Strategy

input parameters

1D-Simulation for Process Control

Experiments

1D Screw Parameters

Material Properties

profiles of process variables

along the screw axis

characterization of

screw elements

for model

validation

and screw

characterization

3D-Simulation (SPH)

Novel Simulation Method: SPH

Challenges:

Geometry: complex & rotating.

Tight gaps (resolution challenge)

Partially filled (free-surface flow)

Complex material behavior

High viscosity dissipation

Highly nonuniform temperature

Melting, Devaporization.

But: no turbulent flow.

SPH snapshot:

SPH = Smoothed Particle Hydrodynamics

Introduction to SPH

1

0

aa BP

h

h

xxWW ba

ab ,

aba

b

baba Wvvm

dt

d

Continuity equation*:

Momentum equation*:

ab

b a

ab

abba

bababa

b a

a

b

bb

a vr

W

r

mW

PPm

dt

vd

122

ab

b

b

b

baS Wm

AA

,

hqW ,

h

xxq ba

Summation interpolant*:

a

Kernel function*interaction radius –smoothing length* h

b

Equation of state*:(weakly compressible)

* J.J. Monaghan, Smoothed particle hydrodynamics, Rep. Prog. Phys. 68 (2005) 1703 – 1759.** J.P. Morris, P.J. Fox, Y. Zhu, Modeling Low Reynolds Number Incompressible Flows Using SPH, J. Comp. Phys. 136 (1997) 214 – 226.

Morris model** (laminar flow)

Validation Case

FVM(Bierdel*)

SPH

Axial velocity(case: pressure drop = 0)

*

* Michael Bierdel. In: Kohlgrüber K, Wiedmann W. Co-Rotating Twin-Screw Extruders. Munich: Carl Hanser Verlag, 2008.

Pressure characteristic

(Variation of parameters &

geometry)

(equal geometry)

SPH FVM*

dim.-less flow rate

dim.-less pressure drop

A. Eitzlmayr, J. Khinast, Co-Rotating Twin-Screw Extruders: Detailed Analysis based on Smoothed Particle Hydrodynamics. Part 1: Hydrodynamics, Chemical Engineering Science 2015

Validation Case

*

A. Eitzlmayr, J. Khinast, Co-Rotating Twin-Screw Extruders: Detailed Analysis based on Smoothed Particle Hydrodynamics. Part 1: Hydrodynamics, Chemical Engineering Science 2015

* Michael Bierdel. In: Kohlgrüber K, Wiedmann W. Co-Rotating Twin-Screw Extruders. Munich: Carl Hanser Verlag, 2008.

Power characteristic

(Variation of parameters &

geometry)

(equal geometry)

SPH

FVM(Bierdel*)

SPH

Axial velocity(case: pressure drop = 0)

FVM*

dim.-less flow rate

dim.-less driving power

Different Screw Elements

C30 C15 M15

K90K30

Conveying Elements Mixing Element

Kneading Elements

Stagger angle 30° Stagger angle 90°

Pitch 30 mm 15 mm

A. Eitzlmayr, J. Khinast, Co-Rotating Twin-Screw Extruders: Detailed Analysis based on Smoothed Particle Hydrodynamics. Part 2: Mixing, Chemical Engineering Science 2015

1D-Reduced Model

Eitzlmayr, G. Koscher, G. Reynolds, Z. Huang, J. Booth, P. Shering, and J. Khinast, “Mechanistic Modeling of Modular Co-Rotating Twin-Screw

Extruders,” Int. J. Pharm., vol. 474, pp. 157–176, 2014.

Consideration of a discretized

screw:

fcrShear KfAtnm

x

p

K

Dm

P

essure

4

Pr

dt

dfVmmf i

iioutini :1

0:1 outini mmf

Mass balance:

dt

df i

...,,,..., 11 iii ppp

Mass balances for a 1D descretized screw lead to

conditions for filling ratio f and pressure p.

Pressure driven & shear driven flow rates:

Thermal energy:

m

iT

(adopted from Choulak et. al [1])

Considered heat exchange phenomena: Convection, Melt

↔ barrel, Melt ↔ screw, Enironm. cooling, Conduction

barrel, Conduction screw, Barrel heating, Viscous heating

Viscous heating power:

222

0

0

2

12

1

x

pH

H

Udy

y

u

HQ

H

y

Diss

Solution for shear flow between 2 plates:

Energy balancing analogous to mass balancing:

H

h

gap flow

channel flow

bt/nCh

D DC

Analogous consideration

of screw regions:

Power and Temperature

kneading

mixingdevaporization

output section

Barrel

Heating 1

2

3

4

RDT Distribution Modeling vs. Experiments

Eitzlmayr, J. Khinast, G. Hörl, G. Koscher, G. Reynolds, Z. Huang, J. Booth, and P. Shering, AIChE J., vol. 59, no. 11, pp. 4440–4450, 2013.

Eitzlmayr, G. Koscher, G. Reynolds, Z. Huang, J. Booth, P. Shering, and J. Khinast, “Mechanistic Modeling of Modular Co-Rotating Twin-Screw Extruders,” Int. J. Pharm., vol. 474, pp. 157–176, 2014.

max. tracer

Tracer

Camera

Strands

RTmax MRT(=50% of area)

0.1*peak

RTstart

peak

0.5*peak

RTraise

RTFWHM

Experimental

Computed

Other Models

Fluid bed granulation

Coating

Compaction Capsule filling

Blending

Current Work: Embedding XPS in Cloud

• Cloud-based solutions allow wide-spread use of algorithms

• Example:

Rescale.com

• Goal is to have

a cloud-based

solution online

end 2019

• Can be used

by companies,

equipment

makers, FDA

Intended Structure

Pharma

Equipment

RCPE Test Labs

FDA / EMAet al.

Academia

SoftwareCLOUD

Custom Workshop

Technology Testbed

Feasibility Trails

Material Science

In silico material properties and impacton processing - DigiMat

Virtualization

Full digital twin of ConsiGma, DC & HME, Cloud-based

Novel & soft sensors and RTRT

AdvancedPAT and

RTR

Regulatory

Creating packages ready for NDA or early phases IMPD/IND

ConsiGma 25 installation at RCPE

Overall Vision or Our Work

OUR CONSIGMA LINE

OUR TEAM

A qualified ConsiGma CTL 25 line

(API blend - WG-tablet)

An extension of the GEA Pharma Solids Center

A training platform and technology testbed

A process/product development support keeping you steps ahead in regulatory filing

Area Leads Scientific Operation

Novel GPU Implementation for Tetrahedra

And Another Major Step Forward – Coupling with SPH

66

Granules in a Shear Box

Shear box test:

100 agglomerates of 50 components

P = 10 kPa

vshear = 0.1 m/s

Roadmap Granulation

69

LABORATORYTESTS

TSWG -PSD

Simple sphere model calibration

Simple feeding modelExtrapolated 3D fields

Shear boxAgglomerate calibration

Current Status – Open Issues

Process and product modeling & simulation are far advanced, beyond what is typically

believed by industry

Strongly emphasize the value of mechanistic modeling: use deep learning and AI only

if there is no other way forward

White spots on the map exist:

Wet granulation

Materials structure prediction

Material aging, curing, morphology transformations

Etc.

Future work must address these white spots

Create a holistic process and product prediction framework

t

Inffeldgasse 13, A-8010 Graz

Prof. Dr. Johannes G. Khinast

CEO / Director – Sciencee-mail: johannes.khinast@rcpe.atphone: +43/316/873/30400

Massimo Bresciani

Executive Director – Scientific Operationse-mail: massimo.bresciani@rcpe.atphone: +43/316/873/30915

Thank You - Acknowledgements

Contributors Academia and Industry:

Dr. Dalibor Jajcevic, Dr. Peter Toson, Peter Böhling, Dr. Christian Witz, Phillip Eibl, Josip Matic, Luca

Orefice, Dr. Patrizia Ghiotti, Martin Warman, Dave Doughty, Dr. Wen-Kai Hsiao, Massimo Bresciani,

Dr. Martin Maus, Dr. Finn Bauer, Dr. Markus Krumme, Dr. Sean Bermingham, and many more!