Continuous Process and Life Cycle Management

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Continuous Process and Life Cycle Management The Factory of the Future

Dr Frank Roche

Innovation Centre of ExcellenceGlobal Manufacturing and Supply

12th

June 2009



1

1899



2

2005



1903



Standard Slide Pack & Logos

Line of Text

–

Line of text

2005

First flight



1875



….we’re actually better than that !

we’ve innovated with our molecules and products

–

ground breaking NCEs

–

creative product line extensions and branding

–

innovative drug delivery systems

however………………

we’ve ignored our manufacturing processes

–

gross profit margins high cf. CoGs

so why bother !

–

validation burden and perceived regulatory hurdles

–

fear of change

–

historically limited skills sets for process design

–

haven’t appreciated the strategic value of the “voice of the process”



Socio -

economic pressures

–government pricing

–cost vs

benefit on drug performance

–global recession –global sustainability/green agenda

–access to medicines

Regulatory pressures –PAT framework for industry (ICH Q8,9,10)

–reluctance to approve PLEs

Pipeline pressure –industry inability to replace expiring assets

–block busters few and far between

–lower volume, niche products

Declining WW manufacturing volumes

–gross overcapacity

–large depreciation burdens

Do we need to change ?



Invention vs Innovation

"Invention is the first occurrence of an idea for a new product or process,

while innovation is the first attempt to carry it out into practice"

(Fagerberg, 2004: 4)

“We’ve got a few problems going fromlab scale to full scale commercial”

Ray Scherzer 2005



Manufacturing costs represent 25-40% of total product costs,much more than R&D

Reduce UKP £4 Bn annual spend in manufacturing

Manufacturing costs are divided into: –

50% Materials

–

25% Personnel Costs

–

25% Operating and Depreciation

Pressures facing manufacturing



Some of the current manufacturing problems

Large inefficient batch equipment

Low utilization 30 -

40 % on average

Low product yieldsExcessive amounts of product non-conformances

Long lead-times due to stage and final product testing

Capital and labour intensiveHigh operating costs

High inventories and excessive warehouse capacity

Cycle time improvement perceived to be limited by regulatory constraints

1% yield improvement ~ $40M in savings



12

80% of all pharmaceutical products

are solid dosage forms

Many of these products differ only

in terms of dosage level

Perhaps more significantly, many

of these products differ only in

terms of drug identity

Focus still very much on Oral Solid Dosage Forms



13

Gran/ Drying Blending Compression Coating Packing

F a i l

F a i l

F a i l

Testing

PASS

or

•End Product Testing alone is insufficient to understandthe complex interaction of product and process

•We must proactively understand and learn from•the starting materials

•each unit operation

•the testing itself

Formulation

Processing

Monitoring

Current situation – secondary OSD manufacturing



14

Granulation/ Drying

Actual processing time + release + packing = 2 days

Result is high inventory including “work in progress”, long lead times,disconnected processes, long changeovers, high process losses off line analysis, low

utilisation etc

…… but Total Elapsed Time is 30but Total Elapsed Time is 30 -- 60 days60 days

DelayDelay DelayDelay DelayDelay DelayDelay

Blending Compression Coating Packing

Where are we going wrong ?



15

V Blenders: lab and pilot plant scale

Lab scale Pilot Plant Scale

Current Technology – scale up approach



16

Commercial scale !



17

Key Measure Typical

Material waste (mass balance) 5% upwards

Manufacturing lead time 6 weeks

Manufacturing stock (including work in progress) 2 months

Manufacturing complexity & potential for errors high

Overall equipment effectiveness 30%

Total transit distance miles

Labour utilisation on added value tasks 15%

Current situation – key measures

#1 &



18

Process Design

Potential Answer #1: Process Understanding & Data DrivenManufacturing



19

Liquid coverage and spray flux: Powder Technology 134(3), 223 (2003)

Nucleation and spray flux: Chemical Engineering Science 60(14), 3751 (2005)

Transformation Diagrams

dvw2

Q3Ψa

⋅⋅⋅

⋅=

pτ

dvw2

Q3Ψa

⋅⋅⋅

⋅=

mechanical

dispersion

drop

controlled

regime

intermediate

regime

0

0.1

1 100.1

1

10

n

ar r ow er s i z e

d i s t r i b u t i on

Hapgood map

v tip= f(N,D)

<v>surface, powder =

f(B,N,D,d,ρ,H)

x

y

z τrecirculation = f(D, <v>surface + powder )

v tip=f(N,D)

<v>bulk =f(B,N,D,d,ρ,H)

shear ∝

N

<v>radial, powder =

f(B,N,D,d,ρ,H)

4Ψsingle ef −

=

spray nozzle tip

shape = FAN50

moving

powder bed

liquid spray,

Q rate, m3/s

d droplet, μm

v

velocity

powder

[m/s]

w width [m]

h

height

[m]18 Nm3/h

15 Nm3/h

33 Nm3/h

vtip

~ 0/5/10 m/scounter clockwise

N

~ 1 5 0 0 / 3 0 0 0 r p m

c l o c k w i s e

fill(min): 1/3

fill(max): 2/3

v

QΨalt

⋅=A

Process Understanding and Mathematical Modelling

Sander van den Ban

Computational Fluid Dynamics



20

Sander van den Ban

Univariate Data Trending – simple sensing



21

Sander van den Ban

Univariate Data Trending – simple sensing



22

Paul Frake

Advanced Sensing for Controlling Critical Quality Attributes

-4

-3

-2

-1

0

1

2

3

4

9:36:00 10:04:48 10:33:36 11:02:24 11:31:12 12:00:00 12:28:48 12:57:36

Time

S

c a l e d

c o

r d

c o

u

n

t

1-50

46-251

233-50

Spraying

Drying

Cord length

Attrition during drying ?

Granule growth



23

Multivariate Statistical Process Control

Batch adjustedback in line with others

Pre-compressionforce was adjusted to

bring batch closerto average

Compression profileCompression profile -- meaningful use of large complex data setsmeaningful use of large complex data sets

Nicky Fletcher

3 batches lowerthan others

http://www.pulpandpaper-technology.com/projects/imatra/index.html#imatra1

http://www.pulpandpaper-technology.com/projects/imatra/index.html#imatra1



24

Potential Answer #2: Integrated Manufacturing

High Process Velocity

–

Rapid batch or continuous manufacture

–

Drive to reduce variability, 6 sigma

–

High yield, increase productivity

–

Lower operating, inventory and capital costs

…………..increase competitiveness

Process Intensification –

Small, fully enclosed processes

–

High level of automation, reduced manual intervention

Advanced Process Control –

identify and explain sources of variability

–

manage variability via the process

–

predictable output for range of raw materials,

process and environmental conditions

Level 4

Level 3

Level 2

Level 1

Level 0

Level 5

Plant

Control

Operations

BusinessPlanning

Enterprise

Planning

Manufacturing Resource

Planning Systems

Instrumentation

Quality

Systems

New Product Supply

SystemsSupply Chain Systems

Supporting

Systems

Manufacturing Operations

& Control (MOC) Systems

Control Systems

Instruments



25

Compression

Coating

Granulation &Drying

Blending and Dispensary

50m

3 0 m

W a s h B a y s

Large Facilities

Expensive HVAC

and Finishes

75-300 Kg Bx

Scale

High Labour Reqt.

Large WIP

Off-Line QC Testing

Cycle time 60days

Typical OSD Factory Layout



26

Conceptual Layout – Rapid Batch

Key Differences:•

Small scale, close coupled process designed for flow

•

Same scale transfer from R&D to GMS

• Joint R&D/GMS operating model•

Cycle time reduced from 60 days to < 24hours

•

Flexible for other platform technologies



27

The OSD Factory Of The Future Could Look Like:

12m

5 m

2 lines would match conventional footprint capacity

R&D would develop using suitable OSD compounds by first intent

No break in containment, 99%+ yield efficiency, very little WIP

Radically less labour Significantly more environmentally friendly

Product released for use as it is made



28

Soft Sensor Showing Moisture (as LOD!)

Steady state operation makes generation of

process descriptive data easy

Why Now - Continuous Processing of Allopurinol – Wellcome



29

Why Now Continuous Processing of Allopurinol WellcomeItalia 1991

Maturing control technology,

both for individual unit operations

such as gravimetric powder feed,

advanced instrumentation and

supervisory control technology,makes continuous tablet making

an increasingly attractive

technology option.



30

Conclusions

We can get significant improvement out of our current manufacturing technology

–

better process understanding of unit processes and materials

–

data driven decision making

If we want a step change improvement in manufacturing efficiency we need to look atIntegrated & continuous manufacturing

Step change improvements in manufacturing could offer emerging market opportunities

………however

Integrated & continuous manufacturing requires

– Real process understandingadvanced mathematical, engineering and material science skills

advanced control technology

an entrepreneurial funding model to change out existing technology

– A continuous improvement quality mindset –

New thinking from equipment suppliers, product developers, manufacturing teams,quality teams and regulators



31

A Measure of Success

“The measure of success is not whether you have a

tough problem to deal with, but whether it is the same problem you had last year”

John Foster Dulles



Continuous Process and Life Cycle Management The Factory of the Future

Dr Frank Roche

Innovation Centre of Excellence

Global Manufacturing and Supply

12th

June 2009

Continuous Process and Life Cycle Management

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