Combined use of Design of Experiment (DoE) and Process
Automation for the Efficient Optimization of New Synthetic
Transformations Universita dellInsubria-Dipartimento di Chimica Via
Valleggio n o 11 22100 Como (Italy) www.uninsubria.it R&D
Chemistry Research Centre Via Lorenzini n o 8 20139 Milano (Italy)
www.boehringer-ingelheim.it Literature meeting May 2 nd 2005
Federica Stazi Ph.D Thesis Slide 2 Reasons for DoE at the Chemistry
Research Centre Boehringer Ingelheim Pharma KG CRC Milan, Italy
Boehringer Ingelheim Pharma KG Biberach, Germany CRC Drug
Development Drug Discovery Intermediates Building blocks Test
compounds Pre-dev. Candidates Metabolites Process impurities D D
iversity O O riented S S ynthesis T T arget O O riented S S
ynthesis Slide 3 Target Oriented Synthesis (TOS) and DoE DoE-driven
search for optimal conditions Target Slide 4 Diversity-Oriented
Synthesis (DOS) and DoE Same starting material and rxn conditions
different RX Same RX and rxn conditions different starting material
DoE-driven search for optimal conditions Slide 5 The DoE Concept:
Basic Principles Inputs x1x1 x2x2 xpxp Outputs System z1z1 z2z2
zqzq controllable factors uncontrollable factors y (starting
materials) (products) Slide 6 OFAT (One Factor at A Time) Approach
OFAT results in a set of experiment in which only one factors is
varied S M P AB C incomplete picture of the overall process factor
interactions are not revealed number of experiments not fixed not
possible to perform experiments in parallel - Slide 7 DoE (Design
of Experiment) Approach S M P AB C DOE results in a set of
pre-planned experiments in which factors are varied at the same
time factor interactions are revealed precise estimation of factors
effect 2-level Factorial Design 12 34 5 6 87 experimental matrix
mathematical model of the chemical process based on statistical
analysis possibility to perform experiment in parallel Slide 8 Doe
Simplified: Practical Tools for Effective Experimentation Mark J.
Anderson, Patrick J. Whitcomb Productivity Press, 2000 Design and
Optimization in Organic Synthesis R. Carlson Elsevier Science, 1997
Design and Analysis of Experiments, 5th Edition D.C. Montgomery
Wiley, 2000 + Chemical Journals Statistical Background and DoE
Tools Slide 9 Statistical Background and DOE Tools: Examples S.V.
Ley et al. Organic Process Research Development, 2002, 6, 823 R: Et
4 F Res IV, 8 exps + 2 centres A.A. (equiv) PS-DIEA (equiv) Rnx
time (hours) Conc.(volumes) Pre DoE:40% Post DoE: 91% 5 different R
groups Yields: 81-96% S.V. Ley et al. Synlett, 2000, 11, 1603 5 F
ResIV, 16 exps + 4 centres PS-DCC (equiv) Conc. (volumes) Rnx time
(hours) Solvent T1 Solvent T2 Post DoE: 97% 8 different R groups 10
different R groups 80 cpds. Hit rate 95% 4 F ResIV, 8 exps + 1
centre PS-DCC (equiv) Conc. (volumes) Amine (equiv) Slide 10
Advantage Series 2050 (Argonaut) SK233 React Array Workstation
(Anachem) Carousel (Radley) ? ? ? ? Statistical background and DoE
Tools Design Expert 6.0.4 by Stat-Ease MODDE 7.0.0 by Umetrics
Slide 11 React. rack Reagent Solvent racks UV/Vis Detector PC
Reaction Control HPLC control Needle Syringes HPLC Statistical
Background and DOE Tools Slide 12 The Sequential Workflow of DoE 2.
Planning the experiment: State experimental objectives Choice of
factors, levels and response variable choice of experimental design
3. Performing the experiment 4. Data analysis and modeling 5.
Interpretation and confirmations 6. Reiteration 1. Synthetic
Problem ? Slide 13 1. cytochrome P450 2. UDPG transferases Putting
the Theory into Practice Step 1. Defining the Synthetic Problem: a
Problematic Glucuronidation Slide 14 Putting the Theory into
Practice Step 1. Defining the Synthetic Problem: O-Glucuronidation
Background UDPG transferases For a review, see: Stachulski, A. V.;
Jenkins, N. J. Nat. Prod. Rep. 1998, 173. Slide 15 Putting the
Theory into Practice Step 1. Defining the Synthetic Problem:
O-Glucuronidation Background For a review, see: Stachulski, A. V.;
Jenkins, N. J. Nat. Prod. Rep. 1998, 173. Slide 16 Putting the
Theory into Practice Step 1. Defining the Synthetic Problem: A New
Strategy Modified Koenigs-Knorr cond.: 25% yield (Ag 2 O, mol
sieves, 18 h CH 3 CN + TMEDA 10 eq, R=Piv) Typical Koenigs-Knorr
cond.: 3% yield (Ag 2 O, mol sieves, 18 h CH 3 CN, R=Ac or R=Piv)
Slide 17 Step 2. Planning the Experiment Find the best starting
point: small-scale parallel reagent screening (10 mg scale). Amine
vs. Ag pKa : 11.0 9.110.310.4 9.2 HMTTA works best. The silver
source does not significantly influence yields. influence of amine
complexing ability a amine basicity silver source Step 2. Planning
the Experiment 7 factors to be investigated in a screening
factorial design A complete investigation of 7 factors over 2
levels requires: 2 7 = 128 exps 128 parameters are estimable: 1
constant term, 7 linear terms, 21 2-FI, 35 3-FI, 64 4/7-FI FI
relative importance: 2-FI > 3-FI >> 4/7-FI Slide 21 Step
2. Planning the Experiment: Full vs. Fractional Factorial Designs n
o of factors n o of experiments 2 3 45 6 78 9 4 8 16 32 64 128 256
Fractional Factorials exploit the redundancy of Full Factorials to
reduce the n o of exps 7 factors can also be studied in only a
fraction of the original full factorial design. Full Fractional
Slide 22 Step 2. Planning the Experiment: Final Output of
Pre-Experimental Plan 7 factors to be investigated in a 2 7-4
Resolution III design: 8 exps + 3 center points (50mg scale)
Experimental matrix: center points for curvature detection for
calculation of pure error Slide 23 Step 3. Performing the
Experiment Use randomization to reduce the influence of nuisance
factors If possible, operate in parallel since we rely on a
previous experimental plan Monitor and record values of
uncontrolled factors Perform a scoping study: check -- - vs. +++
and reproducibility. Slide 24 Step 4. Data Analysis and Modeling:
ANOVA Testing (Analysis of Variance) of changing variable Ag 2 CO 3
Br-sugar HMTTA Slide 25 Step 5. Interpretation and Confirmation
After stepwise modifying the insignificant terms we obtain the
definitive linear model y = + * A+ * C - *D + * E + Is this linear
model adequately modeling the response? Slide 26 Step 6.
Reiteration: Altering Factors Ranges The contour plot directs us
outside the investigated region modify factors ranges to explore a
better experimental region Slide 27 Different options when the
linear model is not adequate. Many are extensions of the 2-level
factorial design 2-level FD CCDCCF3-level FD Factor levels 533
Number of Experiments 14+3 27+3 Geometries of the Explored Space
sphericalcubic Characteristics: Box-Behnken 3 12+3 spherical
Response Surface Modelling (RSM): an Overview Slide 28 Optimizing
Glucuronidation Yield using CCD: Performing the Experiment
factorial axial center 20 exps on (100mg scale) Slide 29 Optimizing
Glucuronidation Yield using CCD: Data Analysis and Model Building
Definitive coded model yield = 76.91 - 9.58 A + 0.70 B + 2.57 C-
0.75 A 2 + 1.44 A B + 2.51 A 3 Maximum Slide 30 Optimizing
Glucuronidation Yield using CCD: Empirical Model Interrogation
Program optimization tools indicate the best conditions found and
the confidence intervals FactorNameLevel Low Level High Level
AHMTTA0.700.22.5 BAg 2 CO 3 3.763.35.5 CBr-sugar2.422.02.5
PredictionSE Mean95% CI low95% CI high P yield86.51.3483.7189.33
Qty phenol in situ yield isolated yield 1 gr 86.0 80.6 1 gr 87.2
81.0 3.5 gr 85.7 80.0 Model validation Slide 31 Optimized
conditions: Ag 2 CO 3 3.76 eq Br-sugar 2.4 eq HMTTA 0.7 eq 1h CH 3
CN in situ yield 86.0% isolated yield 80.5% Reagents Screening 10
exp DoE Factorial Screening 11 exp DoE CCD Optimization 20 exp
Initial conditions: Ag 2 O 2.7 eq Br-sugar 1 eq mol sieves 18 h CH
3 CN isolated yield 3% Optimizing Glucuronidation Yield Using CCD:
Conclusion Slide 32 Ag + Ag 2 O >> Ag 2 CO 3 Ag 2 CO 3 no
Br-Sugar Ag + dissolution / activation Ag + competitive
complexation Mechanistic Modelling: the Manifold Actions of HMTTA
Ag+ active ! Slide 33 Mechanistic Modelling: the Manifold Actions
of HMTTA Positive effects of HMTTA : competitive ligand for SM
complexation activator of Ag + Max competitive binding to Ag + Max
Ag + activation SM Complexation > Ag + activation F.Stazi, G.
Palmisano, M. Turconi, S. Clini, and M. Santagostino, J. Org. Chem,
2004, 69, 1097-1103. The postulated irreversible binding of
starting material (SM) to Ag + ions is really operative. The
presence of the tetramine additive (HMTTA) influences the
complexation equilibria. The relationship between complexation of
SM and concentration of HMTTA is non-linear. Excess favours the
formation of unwanted side product Base (pKa=9.23, 8.47, 5.36,
1.68) on the Br-sugar (-HBr) Negative effect of HMTTA : Consistent
depletion of Br- sugar Slide 34 Scope and Limitation of the
Methodology % isolated yield : optimized conditions% isolated
yield: classical Koenigs-Knorr conditions + HMTTA 0.2-0.7 eq 71%80%
88%54% 86% 80% 30%74%85% 79% 0% 20% 3% 65% 0% 15%mix 0% Slide 35
Other Applications Pd-Catalysed Cyanation of aryl bromide at room
temperature F.Stazi, G.Palmisano, M.Turconi, M.Santagostino
Tetrahedron Letters, 46 (2005) 1815-1818. Regioseletive Alkylation
of 3,4-dihydroxybenzaldehyde Unpublished Results Slide 36 Summary
and Conclusions A mathematical regression model is generated. This
model is empirical and valid only within the studied factor range.
A better understanding and control of the process are gained by
interacting with the model. Use of non-statistical knowledge of the
problem for choosing factors and their levels, interpreting the
results... Using statistics is no substitute for thinking about the
problem. Design and analysis of Experiments D.C. Montgomery DOE
results in a set of experiments in which factors are varied at the
same time in an organized and systematic approach Slide 37
Suggestion If you find DoE applied to boring chemistry problem ..
Using DoE to Spend Less Time in The Traffic Screening Ingredients
(for Homemade Bread) Most Efficiently with Two- Level Design of
Experiment Applied DoE to Microwave Popcorn and more and more. By
Mark J. Anderson, consultant, Stat-Ease, Inc., Minneapolis, MN
Slide 38 Acknowledgment Prof. Giovanni Palmisano Universita
dellInsubria-Dipartimento di Chimica Dr. Marco Santagostino
Boehringer-Ingelheim R&D Chemistry Research Centre