design of experiments for formulation chemists · design of experiments (doe) •doe, statistical...
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Design of Experiments for Formulators
iFormulate Webinar
12th July 2018
Introduces…
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This webinar is being recorded and will be made available
The audience is muted and you may ask questions using the question function in GoToWebinar
This webinar will last around 45 minutes
PROGRAMME
• Introductions
• Design of Experiments for Formulation
• Benefits of using DoE
• Case study supporting QbD
–Blending for formulation
• Training Course in Design of Experiments
• Q&A
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INTRODUCTIONS
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Dr Jim BullockE: [email protected]: +44 (0)7450 436515
Dr David CalvertE: [email protected]: +44 (0)7860 519582
A Little About iFormulate
• A company founded in 2012 by two experienced industry professionals…
• Combining diverse experiences, knowledge and wide range of contacts:
• …polymers, materials science, chemistry, imaging, dyes, pigments, emulsion polymerisation, biocides, anti-counterfeiting, environmental, formulation, consultancy, marketing, business development, strategy, regulatory, training, events, R&D, innovation
• Complementary network of Associates
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Our Services
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Design of Experiments for Formulation Chemists
Dr Paul Murray
Design of Experiments for Formulators
Webinar, July 2018
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Introduction
• Design of Experiments for formulation
• Benefits of using DoE
• Case study supporting QbD• Blending for formulation
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Traditional Approach
• Starts from a route
• Finds a process
• Perhaps struggles to understand it
Route
Process
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DoE for Process Understanding
• Starts from a Route
• Understands the factors that affect the chemistry
• Designs a process on the basis of knowledge
Route
Process Understanding
Process within design
space
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Design of Experiments (DoE)
• DoE, Statistical Experimental Design or FED (Factorial)
• DoE is an efficient, structured way to investigate potentially significant factors and their cause-and-effect relationships on an experimental outcome
• Careful factor selection increases the chances of extracting useful information• which factors to change
• the range of the variation
• DoE provides information about the way the total system works
• Utilises statistical methods to extract and interpret the relationships between the factors
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So… development of a formulation
• Formulation• Tablet, capsule, liquid, ……
• Bulking agent
• Caking agent
• Slipping agent
• For tableting• Speed, pressure ……
• For liquids • Solvents, additives ……
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So… Formulation processes
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Active ingredient
Binder
Slip agent
Bulking agent
Disintegrant
Surfactant
Formulation equipment
Formulation type
Formulant Ratios
Particle size
Active concentration
Compression force
Temperature
Spray pattern/rate
Mixing
Factors Responses
Discrete
Dissolution time
Dissolution profile
Tablet strength
Humidity stability
Active Ingredient
Stability
Continuous
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Formulation processes
• Options for dosage forms: tablets, ointments, capsules, suspensions, gels…• Generally a separate design required for each type
• Granulation• Particle size, amount of binder, mixing, drying…
• Tableting• Compression force, tableting speed, tablet size…
• Tablet coating
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OVAT
One Variable at a Time
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One Variable at a Time (OVAT)
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Co
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ntr
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Temp
• Consider the performance of a
reaction in relation to two
factors – concentration and temp
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One Variable at a Time (OVAT)
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• From an arbitrary chosen starting point one
factor is varied
Co
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ntr
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Temp
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One Variable at a Time (OVAT)
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Temp
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One Variable at a Time (OVAT)
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• An artificial ‘local’ optimum is identified
Co
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Temp
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One Variable at a Time (OVAT)
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• An artificial ‘local’ optimum is identified
Co
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ntr
ati
on
Temp
10% conversion
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One Variable at a Time (OVAT)
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• An artificial ‘local’ optimum is identified
Co
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ntr
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Temp
10% conversion
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One Variable at a Time (OVAT)
• The genuine optimum may be missed• the experimental approach may make it impossible to find!
• Inefficient use of resources• better conditions are available
• 11 experiments carried out
• Limited coverage of chemical space (design space)
• No information of dependency of one parameter on another• interactions
• No measure of inherent variability• experimental error
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DoE: Screening Design
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DoE: Optimisation
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What will DoE do for you?
• A well-performed experiment will provide answers to questions such as:
• What are the key variables/factors in a process?
• At what settings would the process deliver acceptable performance?
• What are the key main and interaction effects in the process?
• What settings would bring about less variation in the output?
• Does the supplier or quality of a material effect the process?
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What will DoE do for you?
• A good experimental design will:• Avoid systematic error
• Be precise
• Allow estimation of error• To provide confidence interval and significance of the results
• Have broad validity
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DoE
Designing Experiments – Improving Answers
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The Experimental Design Process
• The validity of an experiment is directly affected by its construction and execution
• Attention to the design of the experimental is extremely important
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The DoE Process
1. Aim & Objective
2. Factors & Ranges
3. Response
4. Select design
5. Carry out & analyse
6. Check results
7. Model data
8. Validate predictions
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1. Aim & 2. Objective
3. Factors & Ranges
4. Response
5. Select design
6. Carry out &
analyse
7. Check results
8. Model data
9. Validate predictions
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The DoE Process
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Identify Factors
• Consider all steps in the process• Order of addition
• Equipment
• Reagents, additives
• Rates of heating, cooling, mixing ……
• Grades of material© Paul Murray Catalysis Consulting Ltd
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Selecting Responses
• Maximise information from experimental data • enough of the right type of data is available
• Responses should• give accurate and consistent results
• closely replicate actual experimental outcome
• minimise variability between repeats
• measure change as close to the event as possible• even minimal work-up as can lead to additional error
• vary more than the ‘noise’ of the measurement area as a result of the changes
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Fractional design
to investigate all
potential factorsFurther experimental
design(s) focusing on
chemical space of
interest
Quadratic design for
detailed reaction or
process modelling
Confirmation of
understanding
across entire
operating range
Design Selection
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Mixture Designs
• Ideal for formulation
• Look at factors as a fraction of whole
• Analyse response against both mixture and process factors simultaneously
• Uses D-optimal design
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Benefits of mixture design
• A key deliverable is amount of active in a fixed weight/volume
• Mixture design allows everything to be varied while fixing the final weight/volume
• This would be very hard to achieve with all other design types as factors are completely separate from each other and therefore dose weight/volume would vary considerably• All at low would give very low weight/volume and vice
versa
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Mixture vs Factorial designs
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Case Study: blending parameters
• Quality Risk Assessment (QRA) on a tableting process shows Active Pharmaceutical Ingredient (API) particle size, moisture control, blending and lubrication steps have the potential to affect the assay and content uniformity critical quality attributes (CQAs)
• A study of the parameters likely to affect blending was conducted to develop a design space
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DoE for blending: factors & ranges
• Factors investigated• Blender type
• Rotation speed
• Blending time
• API particle size
• Purpose: to assure that the blend is uniform• Analysed by NIR, target uniformity of <0.01
• Perform DoE to develop the design space
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DoE: Select and perform design
Exp no Runblending
timerpm
blender
type
particle
sizeuniformity
1 2 2 10 v type 5 0.015
2 7 16 10 v type 40 0.005
3 10 2 30 v type 40 0.005
4 5 16 30 v type 5 0.004
5 6 2 10 drum 40 0.015
6 1 16 10 drum 5 0.004
7 8 2 30 drum 5 0.005
8 11 16 30 drum 40 0.004
9 3 9 20 v type 20 0.0045
10 12 9 20 drum 20 0.005
11 9 9 20 v type 20 0.0055
12 4 9 20 drum 20 0.0051
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Overview plot
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Interaction, Ble*rpm
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Contour plot
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Optimum conditions: 30 rpm, 11 min blending time for
uniformity of 0.0025
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• Particle size and blender type are insignificant
• Model explains 99% and predicts 98% of the data• Squared term required, additional experiments
recommended to define squared term
• Uniformity of <0.01 required• levels as low as 0.0025 are predicted to be possible
• If you wanted to achieve 0.0025 uniformity, another experiment can be carried out to confirm the conditions
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DoE Summary
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DoE Summary
• Model identifies the important factors
• Model identifies setting for important factors
• Model requires quadratic and interaction terms
• DoE does this efficiently (12 experiments, additional experiments required to validate model)
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Summary
• DoE is a powerful tool
• You need to avoid the pitfalls• Incorrect factor selection
• Investigation of appropriate ranges
• Inappropriate or inaccurate responses
• Validate the model by carrying out the prediction
• A good DoE will give you much more information for a fraction of the work
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Design of Experiments for Formulators
• New two day training course
• December 4th and 5th 2018
• East Midlands UK
• Early Bird £995 plus VAT before 1st October
• £1149 plus VAT after
• More details and registration• https://iformulate.biz/design-of-experiments-for-
formulators/
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Thanks for listening
• Any questions • Paul Murray
• +44 7833 384027
• www.catalysisconsulting.co.uk
• David Calvert• +44 7860 519582
• www.iformulate.biz
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