automating chemical synthesis through flow chemistry
TRANSCRIPT
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Automating Chemical Synthesis Through Flow ChemistryNeal SachSenior Principal ScientistOncology Medicinal Chemistry10777 Science Centre DriveLa Jolla, CA 92130, [email protected]
1st RSC/SCI Symposium on Continuous Processing and Flow Chemistry GSK Stevenage, UKThursday 4th November 2010
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Contents
Conjure Flow Reactor
Applications and Examples of Conjure Flow Reactor to Discovery and Development
Towards Automated Synthesis
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Flow - Key Applications to Drug Discovery
Substrates not accessible using traditional chemical
synthesis techniques
Stab
ility
Reactivity
Medicinal - Expanding Chemical Space Forbidden chemistries Library synthesis Extending temperature ranges (vs. microwave)
Process - Enabling and Economics Scaling Forbidden chemistries (diazotation etc.) Scale-up of Discovery microwave chemistry Minimised scale-up considerations
Early implementation of flow chemistry can significantly ease the transitions from Discovery to Process
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Flow Chemistry Limitations
Emerging Technology (at laboratory scale) Heterogeneous ReactionsMaterial RequirementsTime Requirements
2004 Collaboration started with Wyeth and Accendo
nano micro meso kilo lab pilot plant manufacturing
Analytical Scale=0.5ml/min (HPLC like flow rates)
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Feed (working solvent)Fluid Prep
interface
Reactortemperature
Discrete segments inserted and maintained in main flow
mix
DiscoveryLibrary synthesis (vary monomers)
ProcessOptimization (vary conditions)
Note mixing is uniquely independent of flow rate
Draw from up to 4 of 36 source vials
Analytics and sample collection
Schematic
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Pass? Fail?
One-size-fits-all initial conditions
HPLC/MSProduct Y/N?Yield & Purity
Greater percentage of successful library elements (discovery). Optimized reactions (process)
L H H
DOEcalculates best conditions
L
Library elements (Discovery)Reaction Conditions (Process)
m
n
Vary prescribed reaction parameters around initial conditions. Automatically done based on Simplex and DOE statistical optimization tools.
(flowing segments)
Collect via Fraction Collector (Discovery)Prep via repetitive segments (Process)
Adding Artificial Intelligence
l
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Fluid prep module
Reactor module
HPLC stack
Feed module
Conjure Flow Path
Fractioncollector
ELSD
MS
Output flow from Reactor
Sample Dilutor
Sample Dilutor Detail
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Total Segment Size 150-800uL. Typically 300uL, 5mg Substrate
Multiple Segments Active Within Reactor (up to 4)
No contamination between reaction segments, key.
Conjure Square-Wave Reaction Segments
50uL He or
FL
300uL ReactionMixture
50uLHe or
FLCarrier Solvent Carrier Solvent
min15 20 25 30 35
mAU
0
200
400
600
800
1000
1200
1400
DAD1 E, Sig=310,2 Ref=off (N70807A\6X6X3TEMPACC 2007-08-07 17-49-10\001-0101.D)
16.43
7
25.6
49
34.49
2
No Dead Reckoning
Peaks On ScaleBubble or
UV Detection
1 2 3 4
Each peak is a 5mg reactionExact Stoichiometry (fluid prep)Exact Reaction Time (flow rate)Exact Temperature (within 0.1C)
Heart cut is sampled and directed for LC/MS Analysis (ultra-fast gradients <2min run-times). Enables real-time results between segments.
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Dilutionsyringe2500 L
Samplesyringe25 L
Solvent
Waste
Mixer tube
Primary flow from reactor
Collection or waste
HPLC flow
HPLC column
Sample loop
Injection loop
Sample Dilutor Module
20 L
5 L
Sampling “Heart Cuts” from the Flowing Segments
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Mixing with Diluent
Dilutionsyringe2500 L
Samplesyringe25 L
Solvent
Waste
Mixer tube
Primary flow from reactor
Collection or waste
HPLC flow
HPLC column
Sample loop
Injection loop
Sampling “Heart Cuts” from the Flowing Segments
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Injecting onto HPLC
Primary flow
Primary flow
Dilutionsyringe2500 L
Samplesyringe25 L
Solvent
HPLC flow
HPL°C flowWaste
Mixer tube
Sample loop
Injection loop
Sampling “Heart Cuts” from the Flowing Segments
Return
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Demonstration SNAr Library Preparation
6 Amines x 6 Aryls = 36 Library 0.5M Solutions 100uL Each Total Segment Volume = 300uL 3 Temperatures 150°C, 200°C, 250°C 10 Minute Reaction Time NMP Carrier Solvent 108 Experiments = 15 hours Run On-line HPLC/UV/MS/ELSD Analysis
X Cl
R1R4R2
R3 X N
R1R2
R3
R4
X1
X2
X1 NHX2
NMP
X=C-NO2 or N+ HCl
Hamper, Bruce C.; Tesfu, Eden. Direct uncatalyzed amination of 2-chloropyridine using a flow reactor. Synlett (2007), (14), 2257-2261
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150-250°C in-situ Yield by 254nm HPLC/MS Analysis
150°C - 42% 200°C - 61% 250°C - 22%
Overall Library Success Rate : 88%
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Demonstration Singleton DOE Optimisation for Process
3 Level 2 Factor Design Temp – 100°C, 150°C, 200 °C Equivalents – 1eq. 2eq. 3eq.
11 Reactions 13-20mg each 2 Centre Points
Total = 184mg Total Time = 116 minutes
N
ClCl
Cl
NH
N
O O
N
N
O O
N
ClCl
+ HCl
NMP
min0 20 40 60 80 100
mAU
0
500
1000
1500
2000
2500
DAD1 A, Sig=254,2 Ref=off (N70809B\DOEACC 2007-08-09 18-43-11\001-0101.D)
1-Vial 8
1-Vial 9
1-Vial 1
0
1-Vial 1
1
1-Vial 1
2
1-Vial 1
3
1-Vial 1
4
1-Vial 1
5
1-Vial 1
6
1-Vial 1
7
1-Vial 1
8
11.55
3
17.98
5
24.25
5
48.76
9
55.19
2
61.59
5 67.84
2
74.17
1
98.14
4
104.7
61
112.0
50
100°C 150°C 200°C
50°C5min
50°C5min
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Demonstration Singleton DOE Optimisation for Process
Yield
Investigation: 3,4,5-Trichloropyridine Optimisation (MLR)Contour Plot
66.6320063.25220051.18120035.07215035.41215040.73315035.38215025.2911506.4231005.3721003.411100YieldEquivalentsTemp
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Obs
erve
d
Predicted
Yield with Experiment Number labels
N=11 R2=0.996 R2 Adj.=0.993DF=5 Q2=0.961 RSD=1.9017
1
2
3
4
5
6
7
8
9
1011
y=1*x+7.531e-006R2=0.9963
Investigation: 3,4,5-Trichloropyridine Optimisation (MLR)
MODDE 7 - 8/14/2007 10:25:40 AM
Predicted
Obs
erve
d
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Knife edge optimal conditions 275°C, 2000psi, 15 minutes >50% product
Conclusions Key template accessed IP free chemical space Substrate of interest to
multiple TA’s Flow processes are scalable
Flow De-Carboxylation
Issues Extreme temperatures Extreme pressures Not possible in batch
NN
OH
OH
O
NN
OH
IPA
-CO2
p
21.9291
30.1321
38.3351
38.3351
46.5382
46.5382
54.7412
54.7412
4Sharp Decreasein Yield
Sharp Decreasein Yield
Flow Experimental Accendo DOE optimization
completed in 1hr CCC Design – 15 experiments
300°C200°C Temp5 min
15 minTime
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Triazoles Metabolically stable pieces Significantly lower logD of a potential drug
molecule (improved ADME properties) 1,4-substituted-1,2,3-triazoles can be
easily prepared in excellent yield using Click Chemistry
R2 N3
R3
R1
N
NN
R2
R1
R3CuSO4(0.05eq.)NaAscorbate (0.1eq.)tBuOH / H2O
Triazoles and Click Chemistry
Click Chemistry Advantages Extremely fast, clean reactions Installing acetylenes in drug
molecules is convergent with existing and common halide intermediates
Functional group tolerance enables diverse pieces to be stitched together
Disadvantages Preparing low MW organic azides
is extremely dangerous in batch
Kolb, Hartmuth C.; Finn, M. G.; Sharpless, K. Barry. Click chemistry: diverse chemical function from a few good reactions. Angewandte Chemie, International Edition (2001), 40(11), 2004-2021
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Azide Formation Can we prepare low MW azides in-situ
from halides and NaN3 in flow ?
The click reaction is known to work with extremely low concentrations of Cu
Triazole Click Chemistry Conditions
DMF / Water required for NaN3solubility and solubility of triazole products
2hr Accendo DOE OptimizationNaXR2 X
R3
R1 N
NN
R2
R1
R3
X=Cl, Br or I
+
NaN3 (1eq.)
5min175°C>75% y
NaXOHBr
N
NN
OH
+
0.25M NaN3
(1eq.)DMF / H2O 10:1150oC / 5min
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Scope of in situ Click Chemistry
ICl
BrOH
NH
OCl
N Cl
Cl
N
NO
O
O
O
O
Br
Alkyl Halides
Terminal Acetylenes
0.25M NaN3 (1eq.)DMF / H2O 10:1150°C / 5min
FlowCu Reactor
Bogdan, Andrew R.; Sach, Neal W. The use of copper flow reactor technology for the continuous synthesis of 1,4-disubstituted 1,2,3-triazoles. Advanced Synthesis & Catalysis (2009), 351(6), 849-854
N
NNN
NCl
12, 46 %
N
NNN NH
O
11, 88 %
N
NNN
O
10, 72 %
N
NNN OH
9, 60 %
N
NNN
8, 40 %
N
NNN
7, 70 %
NNN
1, 72 %
NNN
2, 28 %
NNN OH
3, 72 %
NNN
O
4, 43%
NNN NH
O
5, 61%
NNN
NCl
6, 50%
NNN
13, 30 %
NNN OH
14, 76 %
NNN
O
15, 39 %
NNN NH
O
16, 63 %
NNN
NCl17, 60 %
O
O NNN
NCl
23, 77 %
O
O NNN NH
O
22, 63 %
O
O NNN
O
21, 26 %
O
O NNN OH
20, 92 %
O
O NNN
19, 72 %
O
O NNN
18, 51 %
O
ON
NN
24, 50 %
O
ON
NN OH
25, 32 %
O
ON
NNO
26, 21%
O
ON
NN NHO
27, 47 %
N
NNN
NCl
30, 50 %
N
NNN NH
O
29, 65 %
N
NNN
O
28, 58 %
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Butyne Sonogashira
Issues Butyne b.p. 8°C Pressurized system Dangerous in batch Selectivity challanges
N
N
I
Br
N
NBrPd(P(Ph)3)4
DIPEAEtOH/Dioxane
"Cu"
+ Cu Reactor 125C, 4 min.
Hastalloy Reactor 125C, 4 min.
Conclusions Scalable process (24g/day) Cu reactor removes
requirements for Cu additive 1.2g delivered to project team
SM
SM
Product
Product
Flow Experimental Accendo optimization
completed in 2hrs Demonstrates 125°C as
optimal for conversion/time
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Production Mode
Reproduce automatically optimized chemistry Scale out not up, as a function of time
10 min 500mg 1hour 3g 1day 72g 1week 0.5kg
4 0 6 0 8 0 1 0 0
m A U
0
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
D A D 1 E , S ig = 3 1 0 ,2 R e f= o f f ( A : \F L O W _ C H . . .O W _ C H E M I S T R Y _ 2 4 6 C L _ D O E \R E P E A T S A C C 2 0 0 7 -0 8 - 1 0 1 5 -1 9 -4 2 \0 0 1 -0 1 0 1 .D )
27.98
6
33.33
6
38.6
98
44.0
75
49.4
12
54.7
88
60.14
5
65.5
66
70.88
2
76.26
3
81.59
8
86.9
78
92.3
41 97.7
45
103.1
51
108.5
35
113.8
82
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DOE Software
The Next Step – Automated DOE Optimization
InstrumentControl
Design Wizard
ReagentsDatabase
HPLC/MSAnalysis eLNB
Data Collation
and Interpretation
Closing this loop enables automated
reaction optimization
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The Next Step – Automated DOE Optimization
Automated Optimization Set initial DOE space Set theoretical parameter space Set objective (LC/MS)
Enable Automated Simplex Optimization Iteration 1 Iteration 2 Iteration 3
Objective Met System automatically scales
chemistry using optimized conditions
L H
L H
L H
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Conclusions
Demonstrated Preparation of discreet square wave reaction segments Library optimization and enumeration (Discovery) Singleton optimization (Process) Singleton scale-out (Discovery and Process) Comment - We don’t do flow, for flows sake
Next steps Continue to identify forgotten chemistries (long forbidden) Close DOE loop for automated optimization Multiple step synthesis
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Acknowledgements
Technology Joel Hawkins Jan Hughes (Accendo) Terry Long (Accendo) Kristin Price Larry Truesdale
Chemistry Andrew Bogdan (Cornell) Valery Folkin (Scripps) Jason Hein (Scripps) Peter Huang Kevin Bunker Paul Richardson