SCALING UP QBD

IFPAC 2014

Tamar Ben-Avi Head of Pharmaceutical Technology

Content

Introduction

Method

Case study 1 – High Shear Granulation

Case study 2 – Blending

Case study 3 – Tablet Compression

Lesson Learnt

From the Continuous Process Validation approach perspective, the scale-up is a stage of development in the product life cycle. Its success is measured by robustness of the resulting process and product.

Introduction

Guide references (not many)

….Risk associated with scale-up should be considered in control strategy development to maximize the probability of effectiveness at scale. The design and need for scale-up studies can depend on the development approach used and knowledge available.

A risk-based approach can be applied to determine the design of any appropriate studies for assessment of the suitability of a design space across different scales. Prior knowledge and first principles, including simulation models and equipment scale-up factors, can be used to predict scale-independent parameters. Experimental studies could help verify these predictions.

Design space does not have to be re-established at commercial scale, yet, it should be initially verified as suitable prior to commercial manufacturing.

Design space verification includes monitoring or testing of CQAs that are influenced by scale-dependent parameters.

ICH Quality IWG: Points to Consider for ICH Q8/Q9/Q10 Implementation, 6 December 2011

Method

What to do? Or – How to do it?

When?

Who is on board?

Why? Well, this one is easy….

Prior knowledge gained from the pilot batches: QTTP, CQA’s, pilot scale CPPs, design space

Scale up consideration: batch size, limitations, operational issues, safety

Perform risk assessment at the system level

Perform risk assessment at the unit of operation level

Method – What?

Identify CPP’s and operational process parameters (OPP’s)

Define control strategy (PAT, Equipment controls, PSD, etc.)

Use a model to translate the pilot scale design space to a commercial scale design space

Evaluate initial scale up through trials. Use the controls you have identified

Risk assessment

Establish commercial scale design space

Use NVA control loop

NVA – Non Valuable Activities

Activity/process that does not add value to your product

“Clean” process = less undesired process variability

“This really is an Innovative approach, but I’m afraid we can’t consider it. It’s never been done before.”

Method – When?

“The commercial manufacturing sites should be involved in interactions with the development team throughout the mid to late stages of the development….” ISPE, PQLI from concept to continual improvement part 2

Launch Filing Selection Development Exhibit Clinical FDA review

Lab Scale Formula

development Pilot scale

Good place

to start

start of

PV2 & PV3

Method – Who?

When technology transfer is not an handover - but an invitation to join the party.

Joint mission.

Same measures (key performance indicators ).

Glatt: I saw that you added the

binder by dissolving it in 90C.

Can’t it go into the powder?

Einstein: According to

literature it might affect the

bioavailability

Glatt: Well, We use cold loop and

heating to 90C …there might be

some safety issues.

Einstein: We can go cold

but it will take you time.

Glatt: maybe use an

homogenizer to speed up

things?

Einstein: it might work. In 2

weeks we’ll manufacture the

pilot-bio batch. Let us check it

prior the bio

ICH Quality IWG: Points to Consider for ICH Q8/Q9/Q10 Implementation, 6 December 2011

Scale Up Models

Scale up and optimizations models are usually quantitative empirical or semi empirical models

The Purposes of a Model

Describe the effects of input parameters on output responses Facilitate faster process optimization

Provide a simplified description of a complex system

Understand process robustness

Make process predictions

Case Study 1 – High Shear Granulation

Weighing

Fluidized Bed dryer

Wet milling Granulation High shear

Dry milling Blending

Compression Packaging

IR tablets.

Four strengths.

The strengths are proportional and differ by color only. Color is in the blend.

42% API in the final blend. API BCS class II.

compression

Blending

Dry milling

Fluidized Bed dryer

Wet milling

Granulation

High Low High Medium Medium High Physical attributes

Low Low Low Low Low Low Assay

Low High Medium Medium Low Medium Content uniformity

High Low High Medium Low High Dissolution

High Medium Medium Medium Low High disintegration

Overall Risk Assessment

High shear granulation – CPP’s

Input Scale up Initial risk assessment

Justification and initial strategy

High shear mixer dimensions

High Use a scale up model. (chopper ratio vs bowl not similar, effect need to be evaluated through trials)

High shear mixer load

High Keep constant from pilot scale trials

Dose amount Medium Keep the PW/API ratio similar. Optimization might be needed.

Dosing rate Low Pilot rate X 12

Mixer speed High Use a scale up model.

Densification time

High Use a scale up model.

Chopper speed Medium Scale up depended. Verify through trials

High Shear Granulation – Scale Up Model

Froude Numbers were suggested as a criterion for dynamic similarity and a scale-up parameter in wet granulation. The mechanics of the phenomenon was described as interplay of the centrifugal force (pushing the particles against the mixer wall) and the centripetal force produced by the wall, creating a “compaction zone”. Froude number. Relates the inertial stress to the gravitational force per unit area acting on the material. It is a ratio of the centrifugal force to the gravitational force.

Fr = n2 d / g n = Impeller speed d = Impeller diameter

Scale Up Model – Cont.

Commercial scale Pilot scale

1.25 0.51 Impeller diameter (m) [d]

Impeller speed [N ] x

60

Impeller speed [N ] x

60 Impeller speed (rpm)

Calculated from Fr Calculated from Fr Impeller speed (s-1) [N ]

Constant )0.325-0.65) Froude number [Fr]

For information only Impeller tip speed (ms-1) = Impeller speed (s-1) x x d

Quantity x 12 Quantity Dry powder weight (kg)

Keep the same percentage Loading level in bowl (%)

Keep the same percentage. Water content (% of dry powder

weight)

Rate x 12 Rate Spray rate (g/min)

Keep same time for END POINT Wet massing time (s)

Commercial scale 100 rpm, 110 kg

Pilot scale 150 rpm, 9 kg

Pilot scale 170 rpm, 9 kg

Commercial Pilot

100 150 RPM

108 9 PW KG

dv10 dv50 dv90

Gran. 81 159 310

Dens. 119 288 647

dv10 dv50 dv90

Gran. 80 166 366

Dens. 83 201 396

dv10 dv50 dv90

Gran. 79 155 360

Dens. 111 257 503

Scale up initial results

PSD is similar.

Compression parameters are similar.

Lower dissolution results in pilot scale are attributed to API loss due to small batch size.

Recommendation – continue to commercial scale.

Pattern Mixer

Speed

Dose

Amount

GMX

Solution

Flow Rate

KW

granulatio

n (KWg)

KW

densificati

on (KWd)

KW delta

(KWd-

KWg)

D50

granulation

D50

densification

300 mic 180 mic 150 mic 75 mic Dissolution

+++ 105 110 11

+−− 105 100 9

−−− 95 100 9

++− 105 110 9

−−+ 95 100 11

−+− 95 110 9

−++ 95 110 11

+−+ 105 100 11

Lower 14 15

Upper 17 18Specifications

DOE Study (commercial scale)

Purpose: to model the effect of Mixer Speed, Dose Amount and GMX Solution Flow Rate on KW granulation (KWg), KW densification (KWd), KW delta (KWd-KWg), D50 granulation, D50 densification, Dry milled granulate sieve test results.

Parsum results (In- line particle sizing probe)

With particle growth the particle rate decreases.

Granulation stages are well identified.

KW granulation (KWg) KW densification (KWd)

KW delta (KWd - KWg)

D50 Granulation D50 granulation – Mixer Speed*Dose Amount

D50 Densification D50 Densification – Mixer Speed*Dose Amount

Dry milled granulate [%] MT 300 Micron Dry milled granulate [%] MT 180 Micron

Dry milled granulate [%] MT 150 Micron 300 Micron– Mixer Speed*Dose Amount

DOE Study – Initial Conclusions

For KW granulation (KWg) and KW densification (KWd) data, the factors Mixer Speed and Dose Amount were found significant

For D50 granulation and D50 densification data, the factor Dose Amount was

found significant. P-value for Mixer Speed*Dose Amount interaction was found to be higher than 0.05 but lower than 0.1, therefore the interaction effect is borderline

For 300 mic data, the factor Dose Amount was found significant. P-value for Mixer Speed*Dose Amount interaction was found to be higher than 0.05 and slightly higher than 0.1, therefore the interaction effect is borderline

For 180 mic and 150 mic data, the factor Dose Amount was found significant. For 75 mic data, no factors were found significant

It is recommended to explore more data for examination of the

interaction effect and the model

The real attribute (1)

Dry milled granulate properties are significantly affected by the granulation process parameters.

The effect of granulation parameters on Dissolution/Disintegration can be

explored.

No significant effect of granulation parameters was found on dissolution in the

explored range.

The real attribute (2) -The Synergy of the model & PAT combined approach

Case Study 2 – Blending

• IR tablets • Four strengths • The strengths are proportional and differ by color only • Extragranular is 25% of the Formula

Weighing

Fluidized Bed dryer

Wet milling Granulation High shear

Dry milling

Compression Packaging

Blending

Blending– Scale Up Model

Froude Number – unfortunately not applicable in this case

The combined approach: Thumb rules + Prior knowledge + Trial and error

Commercial scale Pilot scale

0.36 0.34 Capacity

8-15 RPM (we chose 8) 25 (constant) RPM

375 47 min???? 375 (15 min) Number of revolution

Blend uniformity study results

Variance is mainly due to differences between batches.

Real attribute (3) - Optimize the experiment design in order to answer hypothesis.

All blend batches, MBSD values, consecutive reading (no air)

MgSt addition

Waste of time….again

Blend uniformity – NIR Results

Weighing

Fluidized Bed dryer

Wet milling Granulation High shear

Dry milling Blending

Compression Packaging

Case study 3 – Tablet Compression

Method

Step I : Starting values of major parameters

Step II: Achieving target tablet weight

Step III: Searching for optimal compression conditions

Step IV: Search for compression problems (sticking, capping etc.)

Step V: Search for maximal possible speed

Procedure

Sub

step Parameter

Step I : Starting values of major parameters

1) Partially optimized process- use 36 punches New/significantly modified formula/process- use 4-8 punches

2) Verify that all lower punches being installed protrude throughout their circumference at the ejection station

3) 1 Tableting speed, tablets/h x1000

In case sticking is not expected=180 (small tablets 200) In case sticking is expected or no data=100

4) 3 Numeric value for Fillomat speed spec = 50% of Tablets/h spec value

5) 6 Tablet filling depth mm=- 2x tablet's target thickness

6) 18 Tabl. cyl. ht. mainpr. mm (cylinder height mainpress) = gap between U & L punches)= 30% of Tablet filling depth

19 Tabl. cyl. ht. prepr. mm (cylinder height prepress)

7) In case sticking is not expected=

40% above Tabl. cyl. ht. mainpr. mm

In case sticking is expected=

10% above Tablet filling depth. mm

20, 21 Penetr. prepress. mm+ Penetr. mainpress mm (penetration depth at prepress and mainpress stations)

8) In case capping is not expected, and blend includes fines-

2.00

In case capping is expected and blend doesn't includes fines-

1.00

9) 32 Control circuit (Adjust on=0)

Arbitrary

value

applicable

for most

products

Density NLT

0.5

Assumption: the powder will compress 70% or more

Start with high

pre comp force

Pre comp force 1/3 from Main comp force

Compression optimization methodology, Amnon Blumenthal, PhD.

0

5

10

15

20

25

30

0.8 1.3 1.8 2.3 2.8 3.3 3.8

kP

/kN

mm

Hardness vs. distance between punches in pre-compression station (18 fixed 0.91)

hardness ave. hardness stdev

parameter 5 parameter 13

0

5

10

15

20

25

30

0.8 1 1.2 1.4 1.6

kP

/kN

mm

Hardness vs. distance between punches in main compression station

ave. without pre stdev without pre

parameter 5 witout pre

Compression study – conclusions Filling depth: 4.85-4.8 values of parameter 6 results stable weight.

Fill – O – Matic speed: 120 tab/hr. Main comp deviation 2.5-4.6% .

Hardness increases when parameter 18 decreases.

No sticking or capping were observed (180-220 tab/hr ).

Tablet width is reaching the lower spec when reaching high hardness.

0

5

10

15

20

25

30

0.8 1.3 1.8 2.3 2.8 3.3 3.8

kP

/kN

mm

Hardness vs. distance between punches in pre-compression station (18 fixed 0.91)

hardness ave. hardness stdev

parameter 5 parameter 13

0

5

10

15

20

25

30

0.8 1 1.2 1.4 1.6

kP

/kN

mm

Hardness vs. distance between punches in main compression station

ave. without pre stdev without pre

parameter 5 witout pre

Real attribute 4 - compression parameters are well defined and are used as a control tool for the operator. Variation from the process can be easily detected and the nature of it can be investigated.

This product was launched 5 years ago.

Taro purchased the Parsum system 2 years ago.

Taro purchased the first NIR system 5 years ago.

The original pilot scale parameters were not scalable.

The process was developed using the scale up methodology which was presented . Only sieve data was available.

Since launch 409 batches were manufactured, 2 were rejected. None due to process related issues.

Process capability for all CQA is greater then 1.3.

Few more facts, we are nearly there…..

So, Why we conducted this study?

Filing Selection Development Exhibit Clinical FDA review

Lab scale Formula

development Pilot scale

Good place to start

but it never ends!!!

PV 2 PV 3

Process Validation: Lifecycle Approach

Scale up should be treated as any other development stage.

Technology transfer means joining forces for a common goal.

The synergetic effect of combined PAT & models approach:

– Process Knowledge

– Quality

– Cost effective

Never stop learning your products. During PV3 stage initiate studies. Don't just collect data cause you were guided to.

Question your research methods all the time. Eventually, it will have the greatest impact.

Lesson Learnt

Not - although we are generics….

But - because we are generics!

Acknowledgments Marta Lichtig, Senior Scientist Doron Yariv, PAT Expert Ella Katzir, Senior Technologist Sharon Levi, Statistician Rami Zajicek, site manager, for believing from the start

We lead with honesty & integrity

Our API is people

We think about what's best for

Taro

We live Quality &

Excellence

1950 Established

in Israel

1984 Topical product

operations in Canada

1988 Entered U.S. market

2010 Controlling shares

transferred to Sun Pharma

1961 Chemical synthesis

in Israel