Process Analytical Technology (PAT) & Six Sigma - Powerful Allies in Variance Reduction

Process Analytical Technology (PAT) & Six Sigma:

Powerful Allies in Variance Reduction

Presented by: Murray Adams, February 16, 2005

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Today’s Discussion

• Process Analytical Technology (PAT)– What are PATs?– Benefits

• Powerful Allies: Aligned principles of PAT & Six Sigma– How do PAT & Six Sigma work together to reduce variation

• Case Study: Evaluation of Thermal Effusivity as a PAT– What is Thermal Effusivity and how does it work– Pharmaceutical applications and results– Application of Six Sigma tools and techniques to develop

process understanding & control– Endpoint identification – some options

• Conclusions

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What’s PAT?

• FDA has been calling upon industry to improve the methods by which they “build” or “design” quality into their products.

• Sept. ‘04, FDA finalized a guidance to facilitate this improvement– PAT – A Framework for Innovative Pharmaceutical Development,

Manufacturing, and Quality Assurance

• Some of the key messages in this guidance document include:

– Intended to support innovation and efficiency and alleviate industry concerns regarding innovation leading to regulatory impasse

– Founded on product/process understanding and risk-based approach which will result in development and production of safe, effective and affordable medicines of high quality

– Promotes continuous real time quality assurance (at, in, or on-line) with appropriate controls to prevent or mitigate the risk of producing a poor quality product

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• Sampling and conventional analytical testing provides “snapshots” of quality attributes at various points in time

• Usually test the finished product at the end of the process

• PATs can provide real-time, or near real-time, sequence of pictures showing the evolution of the process not just isolated “frames”

• Continuous, real time, quality assurance

Snapshots vs. a Movie

+

Beginning Middle

+

End

Beginning EndContinuous Monitoring

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“A process is well understood when:– all critical sources of variability are identified and explained– variability is managed by the process [controls to mitigate risk]– product quality attributes can be accurately and reliably predicted”

PAT: An integrated approach to understanding

* From: Pharmaceutical Quality by Design: Improving Emphasis on Manufacturing Science in the 21 st Century, Ajaz S. Hussain, Ph.D. , Deputy Director, Office of Pharmaceutical Science, CDER FDA, August 5, 2004

PAT• Raw Materials

• In-process Materials• Processes

Six SigmaData

Knowledge & Understanding

Microbiological

Chemical

Risk Analysis

Mathematical

Physical

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It’s all about data driven decisions & knowledge…..

• If what we know of our processes cannot be expressed in numbers, we do not know very much about them……

• If we do not know very much about them, we cannot control them…..

• If we can’t control them, we can’t compete!

Interpretation by Motorola University

“When you can measure what you are speaking about and express it in numbers, you know something about it, but when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind;”

Lord Kelvin (1821-1907)

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Benefits of PAT

Current Reality

• Heavily loaded labs = delayed test results

• Finished product testing; can’t “test the quality in”

• Parameters and specifications often set before all CTQs are understood

• Not always sufficient process knowledge to identify root cause and prevent recurrence

• Generates data, but how much information/knowledge?

PAT Vision

• Immediate real-time monitoring and feedback

• Continuous quality assurance with potential real-time release

• Improved process knowledge allows us to “build or design the quality in”

• On/in/at-line monitoring allows management of variability and historical data analysis

• Six Sigma tools convert data into knowledge

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Scenario 1: “Typical” Variation in a Non-PAT Process

• The variation in the process inputs is “typical” of what has been experienced historically and is adequately tolerated by the process.

• Finished product is tested at end of process and meets spec.

INPUTS OUTPUTSPROCESS

LSL USL

LSL USL

“Typical” Variation of Inputs

LSL USL

LSL USL

Pass

Blend for X min.(Fixed Process

Endpoint)

Typical Outputs

Unknown CTQ

+ =

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Scenario 2: “Atypical” Variation in a Non-PAT Process

• Variation of one or more inputs changes beyond historical range.

• Finished product is tested at end of process and fails to meet spec.

FAILURE

Blend for X min.(Fixed Process

Endpoint)


“Atypical” Variation of Inputs

LSL USL

LSL USLLSL USL

LSL USL

“Atypical” Outputs

Unknown CTQ

+ =

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Scenario 3: “Atypical” Variation in a PAT Process

• Variation of one or more inputs changes beyond historical range.

• PAT process endpoint is not fixed, it is controlled by a performance measure which is monitored and endpoint is adjusted accordingly.


“Atypical” Variation of Inputs

Process Monitored(“Adjustable” Process

Endpoint)

LSL USL

LSL USLLSL USL

Pass

Controlled Outputs

Unknown CTQ

LSL USL

+ =

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Case Study

Thermal Effusivity as a PAT:

An Evaluation Using Six Sigma Tools

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Evaluation of Thermal Effusivity as a PAT

• Feb ‘04 Patheon began an evaluation of Mathis Instruments’ BT-04 static Thermal Effusivity unit

• March ‘04, preliminary evaluations considered a broad range of applications:– Fluid bed drying endpoint detection (% moisture content)– Lubrication of granulation (Mg Stearate)– Uniformity of powder blends: high shear, V-blender, totes

• Aug ’04, in-depth evaluation of ability to monitor uniformity of powder blends in “tumble style” blenders

• October ’04, beta-testing of new “wireless” effusivity unit allowing monitoring of dynamic systems

*Tables, graphs and data reproduced with permission from Patheon Inc. and Mathis Instruments

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What is Thermal Effusivity?

Wood feels warm

Metal feels cold

Heat always flows from a hot object to a cold object.

Wood is not a good conductor of heat, so it is slow to absorb the heat.

Metal has higher “thermal effusivity” so the heat from your hand flows into the metal quickly - creating the sensation of it being cold.

Like your hands, Mathis sensors produce a small amount heat. They can also detect the rate of heat flow away from the sensor. This provides an accurate measure of the thermal effusivity of the materials in immediate contact with the sensor.

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Proto-type Wireless Thermal Effusivity Unit

How Does Thermal Effusivity Work?

Materials inside the blender have different thermal properties at the onset of blending

Heat is generatedby the sensor. SomeHeat penetrates thematerials, but some is retained.

The amount of heat retained at the surface of the sensor causes a temperature increase. The rate of increase is compared and used to determine theuniformity.

As blending proceeds, the effusivity detected by each sensor approaches a predicted value which is a weighted average of the contribution of each of the individual components in the blend. As the blend approaches uniformity, the differences between the effusivity values at each sensor decreases.

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Results of Preliminary Evaluations

• Lactose and Avicel in a 325L tote

• Uniformity achieved in 9 minutes with 0.7% RSD

200

220

240

260

280

300

320

340

360

380

400

0 3 6 9 12 15 18 21 24 27 30

Blend Time (minutes)

Th

erm

al E

ffu

sivi

ty

(Ws1

/2/m

2 K)

Sensor 1

Sensor 3

Sensor 4

Powder Blend Uniformity

250

270

290

310

330

350

370

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85


Th

erm

al E

ffu

sivi

ty

(Ws

1/2/m

2 K)

Lubrication of Blend

• Addition of Mg Stearate changes effusivity properties of granulation

• Possible application for lubing end-point detection or compression controls??


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Results of Preliminary Evaluations

• Initial conditions: impeller low, chopper off produced RSD of 3.8 – 5.8%

• Increased impeller to high and turned chopper on to low – RSD of 1.0 – 1.9%

High Shear Dry Blend

LOD vs. Effusivity

• Comparison of effusivity to % Loss on Drying (%LOD)

• Correlation of initial data produced an R2 = 0.94

Uniformity in a High Shear Granulator - Bowl 2

210

220

230

240

250

260

270

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5


Eff

us

ivit

y (

Ws

½/m

2K

)

SENSOR 1 SENSOR 2 SENSOR 3 SENSOR 4

1.061.521.901.880.951.904.405.793.80%RSD

4.54.03.53.02.52.01.51.00.5Blend Times

Blue – Impeller LOW/Chopper OFF Green – Impeller High/Chopper LOW

Uniformity in a High Shear Granulator - Bowl 2

210

220

230

240

250

260

270

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5


Eff

us

ivit

y (

Ws

½/m

2K

)

SENSOR 1 SENSOR 2 SENSOR 3 SENSOR 4

1.061.521.901.880.951.904.405.793.80%RSD

4.54.03.53.02.52.01.51.00.5Blend Times

1.061.521.901.880.951.904.405.793.80%RSD

4.54.03.53.02.52.01.51.00.5Blend Times

Blue – Impeller LOW/Chopper OFF Green – Impeller High/Chopper LOW Scatter Chart (%LOD vs Effusivity)

y = 5.5862x + 256.19

R2 = 0.9446

0

100

200

300

400

500

600

0 5 10 15 20 25 30 35 40 45 50

% LOD

Eff

usi

vity

(W

s½

/m2 K

)


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Detailed Evaluation of Blend Uniformity Monitoring

• Blend uniformity was chosen for a more detailed evaluation….lots of questions to be answered

“Knowledge is the ability to answer profound questions” – W. E. Deming

What’s the impact of vessel size and % nominal load?

What’s the effect of PSD and shape?

What’s the effect of variations in powder density?

How sensitive is effusivity?

What’s the correlation between effusivity and conventional chemical analysis?

How do we define the blend end-point?

….and many more questions!

Where should we locate the sensors? What’s the effect of blender

speed?

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Where to start?

• What are the sources of variation in our process?

• What is the relationship between inputs and outputs?

• Which inputs are critical and must be controlled? How?

• SHOW ME THE DATA!Measurement Method Machine

Manpower Materials Environment

Variability of Effusivity Results

(X) Sensor Error

(X) Timing of Reading

(C) Software Parameters

(C) Time Btween

(C) % Nominal Load

(C) Blender Speed

(C) Parameter Controls

(C) Vessel Shape

(C) Vessel Size

(C) Number of Sensors

(N) Operator Experience

(C) Operator Training

(C) Particle Size Dist. (C) Density

(C) Morphology

(C) Heat Capacity

(C) Compound

(C) Flowability

(C) Blend Uniformity

(C) % Formulation

(N) Particle Shape

(N) Moisture Content

(N) RF Interferance

(N) Voltage Fluctuations

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Measurement System Analysis (MSA)

+ =True Product

VariationVariation of

Measurement System “Observed” Variation

Repeatability Reproducibility

Product

Measure

+

Total=

Repeatability - the variation within one sensor

Reproducibility - the variation between multiple sensors

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BT-04 Static MSA Results

• Knowing source(s) of variation directs efforts to reduce error and further improve sensitivity

• Quantifying measurement error will allow setting a meaningful limit for endpoint detection

• 5 materials tested individually to ensure content of blender was continuously “homogenous”

• Effusivity measured by 4 sensors every 2 minutes over a period of 20 minutes

Source Variance Standard DeviationTotal Measurement (Gage) 70.0711533 8.37085141 Repeatability 23.7522722 4.873630292 Reproducibility 46.3188811 6.80579761


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Sensor Timing

• Introduction of the wireless design allowed measurement while the blender is in motion.

• Dynamic powder bed added challenge to achieving reproducible sensor measurements.

• Designed Experiments (DOE) were conducted to identify the optimal measurement point(s) in the blender rotation.

• Low R2 values indicated that a critical factor(s) was missing.

• Identified slippage in blender drive train as a source of variation.

1.4

1.5

1.6

1.7

1.8

2.0

2.1

2.2

2.3

2.4

1.2

1.4

1.5

1.7

1.9

2.0

2.2

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

10.0

Res

po

nse

Va

lue

B25

B19


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Which picture is more typical of your processes?

An observation of validation:

200

220

240

260

280

300

320

340

360

380

400

0 3 6 9 12 15 18 21 24 27 30


Th

erm

al E

ffu

sivi

ty

(Ws

1/2 /m

2 K)

Sensor 1

Sensor 3

Sensor 4

“Textbook Results”

Thermal Effusivity vs Blend Time

340

350

360

370

380

390

400

410

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Time (Minutes)

Eff

usi

vity

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

%R

SD

(A

cro

ss S

enso

rs)

%RSD Sensor B23 Sensor B18 Sensor B25 Mean Overall Mean

“Less Than Ideal”

How much do we really know about what’s going on inside the blender?

“....a well-rehearsed demonstration that a manufacturing formula can work three successive times.”

Harwood and Molnar Pharm. Dev. Tech. 1998


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Endpoint Identification: Option 1

• Process knowledge can help to determine specifically defined criteria to demark endpoints and ensure consistency .

• Range control chart demonstrates “good” special cause variation…. one possible means of end-point detection.

Xbar Chart - Sub-groups Across 3 Sensors

UCL=393.06

LCL=367.068

CEN=380.064

350

355

360

365

370

375

380

385

390

395

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

R Chart - Sub-group Ranges

UCL=32.699

LCL=0.0

CEN=12.704

-5

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36


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0

0.5

1

1.5

2

2.5

3

3.5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Test Point (n=15)

SD

Rat

io

Rolling Standard Deviation (Relative to Initial SD)

95% Confidence

2.718


• e-test: When the ratio of the initial std dev relative to the final std dev is greater than 2.718, we can be at least 95% confident that there has been a significant change (reduction) in the variation.


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• The ultimate goal is to have sufficient understanding of our equipment, processes and formulations to develop a product which blends to the point of uniformity and is stable.

• When the variation of the effusivity decreases below the level of +/-3 Sigmameasure from the mean, the blend will have reached the optimal uniformity which can be reliably measured.

Blend Time

Eff

usiv

ity

12

+3σmeas.

-3σmeas.

Uniformity

Sensor #1Sensor #2

Sensor #3Sensor #4

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Conclusions

• Thermal Effusivity has been demonstrated to provide useful data regarding the uniformity of powder blends and other pharmaceutical attributes of interest.

• PAT, statistics and data are not the “ends” but only the “means” by which to achieve the ultimate objective which is process understanding and control.

• PAT provides the “Framework” for innovation, Six Sigma provides the structure and tools to provide understanding.

• Better understanding of our processes will result in improved quality, added value, reduced costs, greater flexibility and is an overall competitive advantage.

“Knowledge is power.” – Sir Francis Bacon

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Q & A

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Process Analytical Technology (PAT) & Six Sigma - Powerful Allies in Variance Reduction