Utilizing Design of Experiments
(DoE) for Efficiently “Optimizing”
Impurity Profile of the API
What is DoE ?
The statistical analysis
of a randomized series
of planned experiments
in which multiple
p variables are varied by
yp
pre-determined
amounts within a single experiment
to determine the effect of all possible combinations
of variables tested on the result.
Types of DoE
•
Full Factorial : all experiments performed based on the formula:
number of levels to the power of the number of variables.
e.g. for four factors at two levels each : 24 = 16 experiments.
•
Fractional Factorial :
a reduced number of experiments based on statistically removing
experiments that provide information on higher order interactions.
Used primarily to screen for main effects, often followed by another DoE to refine the data set.
e.g. to gather information on seven factors at two levels each : 27-3 = 16 experiments.
•
Response
p
Surface Methods (RSM)
(
) : Used to identifyy true maxima rather than local maxima.
•
Central Composite Design (CCD) : Used to increase knowledge of design space from
cubic to spherical.
Uses of DoE
•
Range Finding :
– Identify major factors that affect an outcome.
•
Process Optimization :
– Optimize the conditions to obtain the desired outcome.
– Target reduction of specific process impurities.
•
Process Validation :
– Establish all combinations that will provide an acceptable result,
or conversely, an unacceptable result.
– Determine acceptable design space.
Benefits of DoE –
Doing More With Less
•
Save time and material
– by performing fewer experiments to reach the same outcome as a One Factor
At a Time approach (OFAT)
– 6 factors at 2 levels results in 26 experiments (64) with the traditional method, but
using fractional factorial DoE of level IV resolution results in just 26-2 experiments (16).
– OFAT approach has problems when multiple responses relate differently to the factors
factors.
•
Significant increase in design space knowledge
– If you study 3 factors at 2 levels using the traditional method you only gain knowledge
regarding the vertices of a cube, while by using DoE you gain knowledge of every
point within the cube.
The DoE Trap
•
•
•
Generating too much data with little benefit if improperly designed.
Using DoE to solve every problem
problem.
DoE if not properly applied can actually slow down early development
by consuming valuable resources & precious time.
CML’s philosophy on DoE is to utilize this tool where
maximum benefit can be realized to solve development
challenges in a RAPID timeframe.
How is CML Using DoE?
CML uses Design-Expert from Stat-Ease to:
•
•
•
Establish critical parameters and Proven Acceptable Ranges
(PAR) for process validation.
Quickly determine the Edge of Failure (EoF) for process steps.
E t bli h operating
Establish
ti parameters
t
ffor:
• Optimization of yield, cycle time, and throughput.
• Reduction/elimination of specific process impurities.
The common thread: Maximizing the results
with the Minimum number of experiments.
Case Study –
Triflate Formation
R2
R1
O
Tf2O
R2
R1
OTf
B
Base
• Goals :
R3
R3
– Support validation protocol by establishing proven
acceptable
t bl ranges and
dd
determine
t
i which
hi h combinations
bi ti
off
factors would result in failed batches.
– D
Determine
i conditions
di i
that
h will
ill minimize
i i i specific
ifi process
impurities without negatively impacting product yield.
Variables
Tf2O charge
Base charge
R
Reaction
ti T
Temperature
t
Tf2O addition time
Base addition time
Levels
1.05, 1.10, 1.15 eq.
1.00, 1.05, 1.10 eq.
-10,
10 -2.5,
2 5 5 °C
0.33, 0.67, 1 h
1, 2, 3 h
Advantages of DoE
Case Study : Triflate reaction
The “Traditional”
Approach (OFAT*)
CML DoE Approach
Variables Studied
5
5
# of experiments
32 (25)
16 + control(s)
Timeline – Experimental
2+ weeks
1 week
Timeline – Data analysis
1 week
4 hours
Prediction of optimal
conditions
No
Yes
Analysis of variable
interactions
No
Yes
* One Factor at a Time (OFAT)
DoE Uncovers
Hidden Relationships
Relationship between temperature
and base-addition time is NOT intuitive
Interaction
Design-Expert® Software
C: Reaction Temp
Impurity X
3.90
Impact of temperature / base
addition rate on formation of
impurity X.
C- -10.000
C+ 5.000
2.98
Actual Factors
A: Tf2O addn rate = 0
0.67
67
D: Tf2O = 1.10
E: DIPEA = 1.05
As base addition time
p yX
increases,, impurity
decreases @ +5°C;
but @ -10°C ……
the opposite occurs !
Impurity X
X1 = B: Base Addition Time
X2 = C: Reaction Temp
2 05
2.05
1.13
0.20
1.00
1.50
2.00
Imp rit X actually
Impurity
act all increases –
B: Base addition time
A Two Factor Interaction (2FI)
2.50
3.00
DoE Shows Us
Two Factor Interactions
• DoE uncovers information across the entire design space.
• This results in knowledge that would not usually be gained by a more
traditional “One Factor At a Time” (OFAT) approach.
• Had a traditional OFAT study of base addition time been carried out at
a temperature
t
t
off +5°C,
+5°C th
the conclusion
l i would’ve
ld’ b
been:
“To reduce impurity X, increase the base addition time as
much as possible.”
• This conclusion, while correct at +5°C, does not hold at -10°C.
• DoE enables discovery of these Two Factor Interactions (2FI).
3D Plot of Temperature
vs. Base Addition Rate
• DoE offers a significant increase in
design space knowledge.
• The
Th predictive
di ti power off D
DoE
E can b
be
shown as a simple 3D contour plot.
Design-Expert® Software
Impurity X
3.86
0.28
3.90
X1 = B: Base Addition Time
X2 = C: Reaction Temp
Actual Factors
A: Tf2O addn rate = 0
0.67
67
D: Tf2O = 1.10
E: DIPEA = 1.05
Impurity X
2.98
2.05
1.13
0.20
Levels of Impurity X
can be reduced by
modification of
reaction conditions
along the 3D surface.
5
1.00
1.25
1.50
-2.5
2.00
-6.25
2.50
B: Base Addition Time
3 00
3.00
-10
10
C: Reaction Temp
Comparing
Experimental Results
•
•
Original Reaction
Conditions
DoE Predicted
Conditions
Base Addition Time
3h
1h
Temperature
0 °C
-5 °C
Triflate yield
69 1%
69.1%
68 5%
68.5%
Impurity X
3.1%
1.0%
DoE highlighted reaction conditions that reduced impurity
X without significantly affecting triflate yield.
“Designer impurity profiles” can be created using DoE
prediction
di i tools.
l
Case Study - Conclusions
•
The use of DoE provided a refined set of conditions to yield optimal results
2-3 times faster than a “One Factor At a Time” (OFAT) approach.
•
DoE afforded material with lower impurity X content while maintaining overall
product yield
•
DoE identified the critical parameters for process validation
validation.
•
DoE provided confidence of success over the entire design space rather than
onlyy at the isolated points that a OFAT approach would have provided.
•
DoE provided data to predict the effect of changing multiple variables at
once, and uncovered “Hidden Relationships” between variables.