the synergy of science & statistics - statcon.de · the synergy of science & statistics...

© 2012 PRISM Training & Consultancy Ltd

The Synergy of Science & Statistics Helping Science Reach Its Full Potential

4th European DoE User Meeting 27th June 2012

Dr. Paul Nelson Technical Director, PRISM TC

http://www.prismtc.co.uk/


Aim

Voice of the Customer

Project Manager

Analytical Scientist

Engineer

Operator

Scientist

Statistician Quality Engineer



Outline

Superheroes & Solvents

Quality beer Design (QbD)

Rumsfeld & Bloom’s Taxonomy



Partnership

“These are the right data,

but not necessarily in the right order !”

& Lots of Data ≠ Information

Design & Data

HP Team, Technology & Process



Animal A - Replicate B - Antagonist Saline Drug

1 1 0 1.6 2.5

2 1 2 1.4 2.3

3 1 7 1.3 1.9

4 1 12 1.4 1.6

5 1 17 1.6 1.5

6 2 0 1.9 3.2

7 2 2 1.6 2.4

8 2 7 1.2 1.8

9 2 12 1.4 1.4

10 2 17 1.5 2.2

11 3 0 1.7 2.7

12 3 2 1.5 2.1

13 3 7 1.6 2.3

14 3 12 1.3 1.6

15 3 17 1.7 1.6

16 4 0 1.8 2.8

17 4 2 1.6 2.3

18 4 7 1.5 2.1

19 4 12 1.6 2

20 4 17 1.5 1.7

21 5 0 1.4 2.3

22 5 2 1.6 2.6

23 5 7 1.5 1.9

24 5 12 1.5 1.7

25 5 17 1.4 1.6

Between Animals Within Animals

C - Agonist

A Picture is Worth…

Data is not Information

What information can you extract from the data table?

The best analysis is the simplest analysis, which gives the needed insight



Superhero Soccer



Spiderman

Storm

Dr Octopus

Green Goblin

Venom

Mysterio

The Beast

Professor X

Mr Fantastic

The Thing

The Human Torch

Dr Doom

Silver Surfer

Dr Strange

Electra

Invisible Woman

Blade

Captain America

Iron Man

Thor

Hawkeye

Black Panther

Black Widow

Ghost Rider

Juggernaut

Nick Fury

Iceman

Magneto

Kingpin

Apocalypse

Superhero Soccer ‘Superhero HPT’



Data is not Information:

What information can you extract from the data table?

Convert Tables to Plots Human pattern recognition can now identify patterns

‘Superhero HPT’

Superhero Soccer

Superhero Map Navigational Aid



Obsnum Enviro Solvent Type mp bp dielectric dipole refind ET30 density logp watersol

01 Hi 1-Butanol Alcohol -88.6 117.7 17.51 5.8 1.3993 0.6 0.8098 0.88 0.125

02 Hi 1-Propanol Alcohol -126.2 97.15 20.45 5.5 1.3856 0.62 0.804 0.25 1.126

03 Hi 2-Propanol Alcohol -88 82.2 19.92 5.5 1.3772 0.55 0.786 0.05 1.117

04 Hi 2-Butanol Alcohol -114.7 99.5 16.56 5.5 1.3971 0.51 0.808 0.61 0.176

05 Un 2-Methoxyethanol Alcohol -85.1 124.6 16.93 6.8 1.4021 0.67 0.965 -0.77 1.103

06 Un 2-Pentanol Alcohol -73 119 13.71 5.5 1.4064 0.49 0.81 1.19 -0.612

07 Un 3-Methyl-1-butanol Alcohol -117.2 130.5 15.19 6.1 1.4072 0.57 0.8092 1.22 -0.518

08 Un Benzyl alcohol Alcohol -15.3 205.45 13.1 5.5 1.5404 0.61 1.042 1.1 -0.992

09 Un Cyclohexanol Alcohol 25.25 161.1 15 6.2 1.4648 0.5 0.9642 1.23 -0.755

10 Hi Diethylene glycol Alcohol -7.8 245.7 31.69 7.7 1.4475 0.71 1.109 -1.51

11 Hi Ethanol Alcohol -114.5 78.3 24.55 5.8 1.3614 0.65 0.785 -0.31 1.231

12 Hi Methanol Alcohol -97.7 64.5 32.66 5.5 1.3284 0.76 0.7914 -0.77 1.393

13 Un Triethylene glycol Alcohol -4.3 288 23.69 10 1.4558 0.7 1.125 -1.33 0.852

14 Un Water Alcohol 0 100 78.3 5.9 1.333 1 0.9282 -1.38 1.774

15 Lo Cyclohexane Alkanes 6.7 80.7 2.02 0 1.4262 0.01 0.778 3.44 -3.069

16 Lo Hexane Alkanes -95.3 68.7 1.88 0 1.3749 0.01 0.66 3.98 -3.359

17 Un n -Pentane Alkanes -129.7 36.1 1.84 0 1.3575 0.01 0.6262 3.39 -3.129

18 Me Toluene Aromatic -95 110.6 2.38 1 1.4969 0.1 0.867 2.73 -1.747

19 Un Fluorobenzene Aromatic -42.2 84.7 5.42 4.9 1.4684 0.19 1.023 2.27 -1.797

20 Un Piperidine Basics -10.5 106.7 5.8 4 1.4525 0.15 0.861 0.85

21 Me Pyridine Basics -41.55 115.25 12.91 7.9 1.5102 0.3 0.982 0.65 1.094

22 Un Quinoline Basics -14.85 237.1 8.95 7.3 1.6273 0.27 1.093 2.03

23 Un Tributylamine Basics -70 214 2.6 1.4291 0.04 0.7771 4.56 -3.116

24 Me Triethylamine Basics -114.7 88.9 2.42 2.9 1.401 0.04 0.7275 1.45 0.224

25 Un 1,2-Dichloroethane Chlorinated -35.7 83.5 10.7 6.1 1.4448 0.33 1.235 1.48 -1.092

26 Un Chlorobenzene Chlorinated -45.6 131.7 5.62 5.4 1.5248 0.19 1.106 2.84 -2.45

27 Un Chloroform Chlorinated -63.15 61.2 4.81 3.8 1.4459 0.26 1.48 1.97 -1.246

28 Hi Dichloromethane Chlorinated -94.9 39.6 8.93 5.2 1.4242 0.31 1.33 1.25 -0.699

29 Un cis -Decalin Chlorinated -43 195.8 2.2 0 1.481 0.02 0.8965 5.08 -5.192

30 Me Ethyl acetate Esters -83.55 77.1 6.02 6.1 1.3724 0.23 0.9 0.73 -0.041

Solvent Selection: PCA As with the Superheroes dataset, a Principal Components Analysis can be used to reduce the dimensionality of a Solvents dataset to 3 variables:

PC1 PC2 PC3

0.1223 -0.5478 -0.6731

0.1827 -0.8059 -0.5273

0.1851 -0.7619 -0.5236

0.0581 -0.6724 -0.4907

0.3826 -0.5166 -0.3058

0.0007 -0.4303 -0.6485

0.0307 -0.5392 -0.677

0.1896 0.3877 -0.5335

0.1832 0.1877 -0.5797

0.7403 0.1097 -0.7094

0.2561 -0.9099 -0.5457

0.364 -1 -0.6148

0.7405 0.2679 -0.8199

0.7641 -0.607 -0.7912

-0.786 -0.037 -0.4575

-0.9843 -0.5399 -0.6614

-1 -0.8077 -0.5534

-0.6333 -0.1214 -0.1017

-0.2796 -0.0101 0.1906

-0.1993 -0.0826 -0.2721

0.2088 -0.0589 0.0965

0.1088 0.8027 -0.2521

-0.7581 0.0722 -1

-0.488 -0.6761 -0.2877

0.0259 -0.0138 0.4528

-0.2875 0.3084 0.1775

-0.1808 -0.0036 1

-0.0476 -0.3065 0.9004

-0.9387 0.4213 -0.7062

-0.1067 -0.5805 -0.0167

PCA

45 Solvents: 9 characteristics:

• mp – melting point

• bp – boiling point

• dielec – dieletric constant

• dipole – dipole moment

• refind – refractive index

• ET30 – empirical polarity parameter

• density

• logp – log octanol-water partition coefficient (lipophilicity)

• watersol – water solubility



Solvent Selection: Create Candidate List The Historical Data tool can be used to load the Principal Component scores into Design Expert.

Once the PC scores have been pasted into the software, a Candidate List can easily be created:



Solvent Selection: Optimal Design Design Expert’s Optimal design tool can be used to fit the required model.

To ensure the design only uses our available solvents, we use the Candidate List we created earlier.

For example, we might want to investigate Yield, Impurity and Starting Material. We’ll choose to run 15 reactions to fit a quadratic model.



Solvent Selection: Review Design We allow the software to select the best solvents to optimally cover our design space:

Full dataset (45 Solvents)

Optimal selection of solvents



Solvent Selection: Analyse Responses After running the reactions, we use Design Expert to analyse the responses and find predicted optimal PC scores.

Overlay of Design Points, colour-coded by Yield



Solvent Selection: Find Optimal Solvents We then use our own PCA 3D Visualiser tool to find the closest solvents to the predicted optimal PC scores.

The PCA 3D Visualiser is preloaded with the Solvents and Superheroes datasets, but you can also import and visualise any PCA dataset of your choice! It is available free of charge here


http://www.prismtc.co.uk/pca-3d-visualiser/



Pharmaceutical cGMPs for the 21st Century -- A Risk-Based Approach

PAT -- A Framework for Innovative Pharmaceutical Development, Manufacturing & QA

ICH Q8, Q9 & Q10 -- Pharmaceutical Development, Quality Risk Management &

Quality Pharmaceutical Systems

A View from the Regulator

Rolf Carlson

Johan Carlson

Design Expert DoE Tipsheets are available on our website


http://www.prismtc.co.uk/tipsheets/


Experimental Space Understanding

Screen Optimise Scope

Temp Sto

ichio

metr

y

Select

Solvent

Substr

ate

Reaction Space

Robust/Verify

Quality beer Design (QbD) Design Space Confidence

Fit for Purpose work packages



6 ºC ± 0.5 (SD = 1/6)

Quality Risk Assessment via simulation in case further adjustments needed to the Control Strategy

Addition Rates

Reaction Time 2.1 eq ±0.1 (SD = 0.2/6)


6.65 vol ±0.2 (SD = 0.4/6)

2.9 vol ± 0.1 (SD = 0.2/6)



Quality beer Design (QbD) Quality Risk Assessment via simulation in case further adjustments needed to the Control Strategy

60

HighLow

Z.Bench = 6.17

100.3599.9099.4599.0098.5598.1097.65

LSL

Actual (overall) capability is what the customer experiences.

spec limits.

percentage of parts from the process that are outside the

-- The defect rate is 0.00%, which estimates the

Conclusions

Upper Spec *

Target *

Lower Spec 97.5

Customer Requirements

Mean 99.487

Standard deviation 0.32195

Actual (overall) capability

Pp *

Ppk 2.06

Z.Bench 6.17

% Out of spec 0.00

PPM (DPMO) 0

Process Characterization

Actual (overall) Capability

Are the data above the limit?

Comments

Capability Analysis for Product Yield %

Summary Report

How capable is the process?

60

HighLow

Z.Bench = 6.17

100.3599.9099.4599.0098.5598.1097.65

LSL

Actual (overall) capability is what the customer experiences.

spec limits.

percentage of parts from the process that are outside the

-- The defect rate is 0.00%, which estimates the

Conclusions

Upper Spec *

Target *

Lower Spec 97.5

Customer Requirements

Mean 99.487

Standard deviation 0.32195

Actual (overall) capability

Pp *

Ppk 2.06

Z.Bench 6.17

% Out of spec 0.00

PPM (DPMO) 0

Process Characterization

Actual (overall) Capability

Are the data above the limit?

Comments

Capability Analysis for Product Yield %

Summary Report

How capable is the process?



Fit for Purpose?

Define

Measure & Analyse

Control

Improve

Quality beer Design (QbD) Biphasic alcohol-mediated gaseous

entrapment system



Method

Pour beer (330ml), into a

500ml plastic container

Dispense Time 30sec

Glass Angle 45 degrees

Pour Height 60mm

Temperature Ambient

Operator Novice

Beer Supplier Stella

Result

Head of foam = 34mm

Target

Head of foam = 15-25mm

Quality beer Design (QbD) No alcohol was consumed during the execution of these experiments




Professional

Glass Angle: 45º – 90º

Pour Height: 0 – 60 mm

Irregularly Shaped

Region

(45, 10)

(75, 60)

(90, 30)

Two vertices which simply won’t work!



Rumsfeld & Bloom’s Taxonomy

“There are known knowns; there are things we know that we know.

There are known unknowns; that is to say there are things that, we now know we don't know.

But there are also unknown unknowns – there are things we do not know, we don't know.” United States Secretary of Defence, Donald Rumsfeld February 12, 2002



There are Known Knowns...

One Way Mean 1, 1

Treatment 10, 9

(Error) 50, 40



There are Known Unknowns...

Two Way Mean 1, 1

Treatment 10, 9

(Box) 5, 4

(Error)=Tmt x Box 50, 36



Three Way Mean 1, 1

(Box) 5, 4

Antag. 5, 4

Agonist 2, 1

(Error)=V x F x B 50, 16

(B x Ag) 10, 4

(B x An) 25, 16

An x Ag 10, 4

But there are also Unknown Knowns...



Split Plot Mean 1, 1

(Box) 5, 4

Antag. 5, 4

Agonist 2, 1


(B x Ag) 10, 4

(B x An) 25, 16

An x Ag 10, 4

But there are also Unknown Unknowns...



Split Plot

Between Animal ANOVA

Mean 1, 1

(Box) 5, 4

Antag. 5, 4

Agonist 2, 1


(B x Ag) 10, 4

(B x An) 25, 16

An x Ag 10, 4




Split Plot

Within Animal ANOVA

Mean 1, 1

(Box) 5, 4

Antag. 5, 4

Agonist 2, 1


(B x Ag) 10, 4

(B x An) 25, 16

An x Ag 10, 4




Unconscious

Conscious

Incompetent Competent

Consciousness

The 4 stages of competence

Bloom’s Taxonomy



• Our scientific colleagues without whom our work and this presentation would not be possible

• The organisers for the invitation to attend and participate at this forum

Our thanks to...

For further information please visit our website: www.prismtc.co.uk Or please feel free to contact us:

Paul Nelson Technical Director

Iain Wilson Managing Director

Andrew Macpherson Consultant

Stuart Wilson Consultant



mailto:[email protected]




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