Understanding spray dried intermediates and their

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Understanding spray dried intermediates and their compaction in tablets using PAT, modeling and simulations November 2017 Marcio Temtem1, Katie Kleissas Haynes2, Ligia Bras3, João Henriques1, Kristin Ploeger4

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Hovione – Drug Product Development R&D, Loures, Portugal Merck – MMD Process Analytical Technology, West Point, PA, USA 3 Hovione – Operational Excellence, Loures, Portugal 4 Merck – Pharmaceutical Commercialization Technology, West Point, PA, USA 2

Agenda 1. Spray drying principles a. Initial process scoping: Thermodynamic models b. Scale-independent correlations 2. Compaction simulation a. Downstream impact (compaction simulation) b. Experimental design (DoEs) 3. PAT monitoring 4. Chemometrics / MVDA a. Process monitoring b. Process Understanding 2

Spray Drying Principles



Flash drying



Mild process



Control of SDD attributes (particle engineering)



Scalable



Commercially demonstrated



Solvent based process 3

Spray Drying Principles 1. Thermodynamics

2

• Spray drying conditions determined through heat / mass balance, L-V equilibrium equations • Detect abnormal conditions of operation

2. Atomization

3

• Droplet size estimation as a tool for scale-up • Achieve target particle size distribution

3. Drying Kinetics • Effect on powder properties: e.g. morphology and bulk density.

Fundamental Approach

1, 4

4. Computational Fluid Dynamics • Optimize process performance • Identify pitfalls

4

Spray Drying Principles •

Droplet and particle size are intrinsically connected, however particles can follow different formation pathways depending on drying kinetics and product properties

Fast drying

Twb

High T_out / Low RS_out / High HMT

Tdrying gas

heat

Slow drying Low T_out / High RS_out / Low HMT

dD

drying gas

mass RSdroplet

Inflated Particles

RSdrying gas

Fast Drying

Properties

Slow Drying

Spherical

Morphology

Shriveled

Low

Bulk density

High

Low

Residual solvent

High

Inflated (breakage may occur)

Particle size

Shrunken Particles

Decrease

5

Designing a Robust Spray Drying Process

Time in Development

Process Definition

Initial Process Scale-up • Thermodynamic tools inform space for process investigation

Process Development • DOEs and experimental data to inform process response

Process Commercialization • MVDA (and in-line PAT) monitors process robustness of a defined process over time

Thermodynamic Process Space Spray drying

feed flow rate

outlet temperature

Thermodynamic Space

inlet temperature

7

Thermodynamic Process Space Spray drying High T_in High RS_out

High F_feed

(high solvent content in the powder)

(equipment limitations)

(equipment limitations)

High T_out

feed flow rate

outlet temperature

(impact on powder properties)

Low RS_out (impact to powder properties)

Low F_feed (process is inefficient)

inlet temperature

8

Case Study Spray drying Main goals • Transfer the process from the PSD-2 (pilot scale) to the PSD-4 (commercial scale) • Identify adequate nozzle and spray drying conditions to produce SDD intermediate with predefined properties

20

80

70

From the PSD2…

60

• RS_out (scale dependent) vs.

10 50

Tg, ºC

RS_out acetone, %

15

acetone content • Tg (scale independent) vs.

5 40

acetone content 0

30 1

2

3

4

5

acetone content, %

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Case Study Spray drying BSV vs. RS_out across scales • Good predictive capacity of bulk specific volume (BSV = 1 / bulk density) • There is an offset between spray dryer scales 7 PSD2 scale

BSV_dry, mL/g

6

PSD4 scale

5 4 3 2 1 5

10

15

20

25

RS_out acetone, %

10

Case Study Spray drying BSV vs. HMT across scales • Confirm that Heat to Mass Transfer ratio (HMT) is a good predictor of bulk specific volume (BSV) across scales o Removes some of the offset from scale-densification 6

BSV_dry, mL/g

5 high HMT (PSD2 scale)

4

3 PSD2 scale PSD4 scale

2 0.2 low HMT (PSD2 scale)

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

HMT_acetone, -

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Agenda 1. Spray drying principles a. Initial process scoping: Thermodynamic models b. Scale-independent correlations 2. Compaction simulation a. Downstream impact (compaction simulation) b. Experimental design (DoEs) 3. PAT monitoring 4. Chemometrics / MVDA a. Process monitoring b. Process Understanding 12

Compactability as a Property • Compactability: the formation of material strength in response to an applied force • Powder characterization: – Mimic compaction forces & strain rate on a Huxley Bertram compaction simulator – Fit data empirically; Gompertz provides a good fit w/ 3 parameters – Interested in the slope of the curve (a*k), especially at 20~200 MPa (RC & tableting pressures)

Tensile Strength (MPa)

a

HQ00011.30 Sample A HQ00011.50 Sample B

5

4

slope: a*k

3

2

Gompertz Eqn:

a/e

SDI

A

B

a

4.8

3.4

xc

83

93

k

0.014

0.013

R2adj

0.988

0.996

1

0 0

xc 100

200

300

Compaction Pressure (MPa)

400

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Tableting Process Development • 3 Unit operations impact material microstructure Process Parameters: HMT

Process Parameters: Roll Pressure

Process Parameters: Compression Force

(+ droplet size)

(+ gap, roll speed)

(+ press speed, precompression, feeder speed)

Spray Drying

Roller Compaction

SDD Attributes: BSV, PSD, compactability, porosity, flow

20 um

GRN Attributes: PSD, density, tensile strength, compactability

100 um

Tabletting TAB Attributes: disintegration, dissolution, friability, hardness

100 um

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Tableting Process Development • 3 Unit operations impact material microstructure Process Parameters: HMT

Process Parameters: Roll Pressure

Process Parameters: Compression Force

Spray Drying

Roller Compaction

Tabletting

• Study primary compactability factor in each step strength • Evaluate in a multifactorial DoE

• DoE goal: • Determine how much stress path affects final microstructure (porosity & strength) • Compaction = cohesion, rearrangement, fracture, adhesion, deformation 15

Designing SDI for Compactability • Bulk density vs. compactability – Both are governed by the same process parameters • Tout is the dominant effect – Tcond, gas flow, and liquid feed flow are not significant within a scale

• HMT provides a good prediction of compressibility within a scale Standardized Effects: bulk density Pareto Chart of the Standardized Effects (response is BDdry, Alpha = 0.05)

2.78

Tout Fgas Tcond Ffeed

T_out

Term

F_gas

T_cond

F_feed

0

2

4

6 8 10 Standardized Effect

12

14

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Standardized Effects: a*k Pareto Chart of the Standardized Effects (response is a*k, Alpha = 0.05)

2.776

Tout Tcond Ffeed Fgas

T_out

Term

T_cond

F_feed

F_gas

0

1

2 3 Standardized Effect

4

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Agenda 1. Spray drying principles a. Initial process scoping: Thermodynamic models b. Scale-independent correlations 2. Compaction simulation a. Downstream impact (compaction simulation) b. Experimental design (DoEs) 3. PAT monitoring 4. Chemometrics / MVDA a. Process monitoring b. Process Understanding 17

Process Commercialization • Primary goal of a commercial process: ROBUST! – Intra-batch stability – Inter-batch stability

• Numerous tools, including: – PAT monitoring – Process analytics

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Process robustness PAT applications Solution preparation

Spray drying

Post drying

Process Laser Diffraction Process Mass Spectroscopy Exhaust gas analysis

FBRM Real-time particle size distribution Turbidimetry

Process NIR Drying end-point solid state characterization

Viscosimetry

30 25

Process NIR

20 15 10

Process NIR

5 0 0

10

20

30

40

50

60

70

Real-time physical/ chemical information

Process robustness Process laser diffraction

• Real-time, in-line particle size measurements • Four measurements per second, averaged to one minute • Process robustness and stability can be directly evaluated

Malvern Insitec X 20

Process robustness Process laser diffraction Stabilization

Stationary phase

Transmission

Dv(90) Dv(50)

11.3 50

7.5 3.8

0 1

10

100

Particle Diameter (µm)

0.00 1000

100

15.0 11.3

50

7.5 3.8

0 1

10

100

Volume Frequency (%)

15.0

Cumulative Volume (%)

100

Volume Frequency (%)

Cumulative Volume (%)

Particle size profile

0.00 1000

Particle Diameter (µm)

Process laser diffraction provides real-time particle size distribution dynamics 21

Process Development vs. Commercialization Process laser diffraction DoE runs

Change in conditions

Change in conditions

Transmission Stationary phase

Stationary phase

Stationary phase

Dv90 Dv50 Dv10

Feeding stopped

Feeding started Stationary phases

On-line average particle size distribution of spraydrying DoE runs targeting different particle sizes

Process laser diffraction provides real-time particle size distribution dynamics 22

Agenda 1. Spray drying principles a. Initial process scoping: Thermodynamic models b. Scale-independent correlations 2. Compaction simulation a. Downstream impact (compaction simulation) b. Experimental design (DoEs) 3. PAT monitoring 4. Chemometrics / MVDA a. Process monitoring b. Process Understanding 23

MVDA Background and Benefits

• Powerful, yet simple, fault detection in real time – Benefit: Can detect and possibly correct process faults before the fault causes the batch to be discarded

• Batch-level models offer another tool in which to summarize large amounts of data into easily-understood plots – Benefit: Good tool for a retrospective analysis of a process and process fingerprinting

• Multiple variables and their interactions are continuously evaluated – Benefit: Accurate diagnosis and simplify process visualization

Process Monitoring • Historical batches were used to create a multivariate model during a Spray Drying process, allowing for an exploration into the variables and batches • This plot shows the scores run chart with the typical trajectory of a steady-state process

MVDA Evaluation of the Process on New Equipment • Goal: To evaluate the impact on the process due to a change – Here, a transfer to another “equivalent” spray dryer

• Analysis: Predicted on the batch using the model built from historical batches on the original spray dryer • Conclusion: The process on the new dryer is well within the normal variation of the historical process Score Plot for the Second Spray Dryer Batch

new process

historic expected variation

historic average

Predictive Capability of Process Variables for Determining Material Properties • Goal: To determine if process variables can predict downstream properties • Analysis: Developed an OPLS model to correlate the average process variable values to key material properties • Conclusion: The best correlations were to tap and bulk density, consistent with prior knowledge

Conclusions • Scale-up – First-principle models of the spray drying process can constrain the process space which should be explored during development – Scale-independent correlations can be used to design spray dried powder properties and reduce number of trials for scale-up

• Process space – Process development of tablets containing spray dried intermediate must be aware of interdependency of unit ops – Compactability is another material attribute to consider in development of a spray dried powder

• Process robustness: PAT & MVDA – Process robustness can be monitored by several different approaches – PAT tools provide real-time information of material attributes and process stability – MVDA allows simple visualization of multiple variables for early-fault diagnosis and batch-to-batch comparison 28

Recognition Special thanks to the many people who have supported this work: • Dave Lavrich

• Paula Cordeiro

• Jerry Klinzing

• Susana Campos

• Chris Neu

• Luís Rato

• Aditya Tatavarti • Melissa Curley

and many others…

• Bend Research

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