Communication pubs.acs.org/OPRD
Cite This: Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Photoredox Iridium−Nickel Dual-Catalyzed Decarboxylative Arylation Cross-Coupling: From Batch to Continuous Flow via SelfOptimizing Segmented Flow Reactor Hsiao-Wu Hsieh,† Connor W. Coley,‡ Lorenz M. Baumgartner,‡ Klavs F. Jensen,*,‡ and Richard I. Robinson*,† †
Global Discovery Chemistry − Chemical Technology Group, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States ‡ Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States S Supporting Information *
ABSTRACT: Photoredox decarboxylative cross-coupling via iridium−nickel dual catalysis has emerged as a valuable method for C(sp2)−C(sp3) bond formation. Herein we describe the application of a segmented flow (“microslug”) reactor equipped with a newly designed photochemistry module for material-efficient reaction screening and optimization. Through the deployment of a self-optimizing algorithm, optimal flow conditions for the model reaction were rapidly developed, simultaneously accounting for the effects of continuous variables (temperature and time) and discrete variables (base and catalyst). Temperature was found to be a critical parameter with regard to reaction rates and hence productivity in subsequent scale-up in flow. The optimized conditions identified at microscale were found to directly transfer to a Vapourtec UV-150 continuous flow photoreactor, enabling predictable scaleup operation at a scale of hundreds of milligrams per hour. This optimization approach was then expanded to other halide coupling partners that were low-yielding in batch reactions, highlighting the practical application of this optimization platform in the development of conditions for photochemical synthesis in continuous flow.
Figure 1. Proposed mechanism of Ir−Ni-catalyzed C(sp3)−C(sp2) photoredox cross-coupling.15
generally slow in a batch setting (12−72 h) and often prone to substrate-dependent anomalies. Reproducibility can also be a challenge, which can be exacerbated through inefficient light irradiation and lack of both standardized instrumentation and operating procedures.18 Moreover, the effect of temperature on photoredox reactions is seldom addressed and is often a neglected concern. Without proper temperature control, competing side reactions (e.g., dehalogenation, solvent couplings, and homo couplings) have been reported, which can lead poor reaction performance or complicated reaction mixtures.16,19−21 Converting batch reactions to flow has many advantages in terms of improving the yield and productivity, particularly in photoredox chemistry.22,23 According to the Beer−Lambert law, the light intensity (photonic flux) decreases exponentially with the depth in a given reaction medium, which suggests that the visible-light photoredox reaction might only take place in
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INTRODUCTION Ir−Ni dual-catalyzed photoredox chemistry has witnessed rapid growth in both academia and industry in the past decade, thanks primarily to newly developed photocatalysts, improved light-emitting diode (LED) technology, and deeper understanding of the single electron transfer (SET) mechanism.1−7 Compared with traditional two-electron-based cross-coupling reactions,8,9 photoredox chemistry uses relatively nonhazardous reagents and is often conducted at ambient temperature, providing a milder alternative method for forging carbon− carbon bonds. Early developments by MacMillan, Doyle, Molander, and others (Figure 1)10,11 in the field of photoredox cross-coupling applying Ir−Ni dual catalysis have demonstrated a range of substrates toward challenging C−C bond formation, including their asymmetric variants.12−14 While the methodology allows the introduction of highly desirable C(sp3)− C(sp2) and C(sp3)−C(sp3) fragments,15−17 the reactions are © XXXX American Chemical Society
Received: January 19, 2018 Published: March 26, 2018 A
DOI: 10.1021/acs.oprd.8b00018 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
Communication
Figure 2. (a) Overview of the segmented flow reactor. Adapted with permission from ref 32a. Copyright 2017 The Royal Society of Chemistry. (b) Photochemistry module to accommodate the OFR (designed by Novartis).
Figure 3. Workflow: (1) validation; (2) screening and optimization; (3) production and scale-up.
the proximal area of the vessel wall (i.e., within 2−3 mm).22b,24 Flow chemistry conducted in transparent 1/16″ to 1/8″ O.D. (1.6 mm to 3.2 mm) fluorinated ethylene propylene (FEP) tubing not only ensures exposure of the reaction mixture to sufficient photonic flux but also provides better heat and mass transfer, which can result in improved reaction yield and scaleup robustness. On its merits, this approach has been adapted in both academia and industry research laboratories in recent years.25−30 In order to bridge the reaction development gap between traditional batch and flow chemistry, Jensen and co-workers have developed a fully automated segmented flow reactor for screening and optimization (Figure 2a).31,32 Segmented flow reactions are akin to miniaturized batch reactions with flow chemistry characteristics, making the screening platform a useful tool for developing batch-to-flow processes. While very impressive high-throughput screening techniques have been introduced by the pharmaceutical industry,33 the flexibility of
adjusting both reaction time and temperature for every individual experiment remains challenging. One of the important advantages of this system, as a complementary method to the traditional plate-base screening, is that it provides the ability to explore continuous variables (e.g., temperature and residence time) and discrete variables (e.g., catalyst, base) simultaneously with high material efficiency. Recent examples highlighting the experimental efficiency of utilizing automated data feedback loops combined with algorithms for the self-adjustment of experimental conditions (self-optimization) show great potential in the field of reaction optimization and process development.34 Controlled by custom LabVIEW and MATLAB codes, the system integrates a liquid handler, syringe pumps, switching valves, and an oscillatory reactor with an integrated analytical HPLC unit. Each generated droplet “microslug” (total volume of ∼15 μL) represents a discrete reaction. Those droplets are prepared sequentially for each cycle using approximately 0.1− B
DOI: 10.1021/acs.oprd.8b00018 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
Communication
0.5 mg of material at a time. A recently developed selfoptimization algorithm was deployed on the platform to perform real-time optimization of reaction conditions with less human intervention.34a,35,36 In order to demonstrate the system’s capability to perform a photoredox cross-coupling reaction of interest, a custom photochemistry module was integrated with the previously disclosed oscillatory flow reactor (OFR) (Figure 2b). The photochemistry module is watercooled and is heated with electrical cartridge heaters, resulting in an operating temperature between 20 and 70 °C within an error of 1 °C. An additional consideration particularly relevant to photocatalyzed reactions is that the tubing dimensions (and hence the irradiating depth) deployed for the segmented flow reactor are identical to those of the Vapourtec scale-up system. In this work, building on our previous knowledge of this system, we demonstrate a successful transition from batch to flow as a part of the process development of a photoredox decarboxylative arylation in which the segmented oscillatory flow reactor was used for reaction screening and optimization. The conditions found at the 15 μL scale were transferred to a Vapourtec E-series system equipped with a UV-150 10 mL photoreactor, enabling subgram per hour production (Figure 3).
Table 1. Base Screening Using a Standard Batch Photoreactor
conjugate acid pKab
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RESULTS AND DISCUSSION In order to avoid clogging in the tubular flow reactors and develop a homogeneous reaction system, the study began with the investigation of suitable organic bases to replace carbonates (e.g., Cs2CO3) in typical photoredox decarboxylative arylation.10,28 N-Benzyloxycarbonyl-L-proline (1, Cbz-Pro-OH) and 4′-bromoacetophenone (2) were chosen to be the coupling partners, giving the corresponding C(sp2)−C(sp3)-coupled benzylic amine compound 3. Triethylamine and 2,6-lutidine were the first two bases to be tested because of the similarity of their conjugate acid pKa values with those of carbonates. Although both bases led to homogeneous reaction mixtures, no product was observed after 16 h of irradiation and vigorous stirring (Table 1, entries 2 and 3). Next, three commonly used strong organic bases were applied: 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU), 1,1,3,3-tetramethylguanidine (TMG), and 2-tert-butyl-1,1,3,3-tetramethylguanidine (tBu-TMG or Barton’s base). These bases led to yields ranging from 83% to 91% with complete consumption of starting material (Table 1, entries 4−6) . To try to accelerate the reaction further, a stronger and more rigid base, 7-methyl-1,5,7triazabicyclo[4.4.0]dec-5-ene (Me-TMD),37 was also tested. Me-TMD gave full consumption of the starting material but led to slightly lower yields, presumably because of the competing side reactions of debromination and/or aldol condensation of the ketone group (Table 1, entry 7). To avoid unwanted metal chelation, sodium tert-butoxide was also tested as a nonnitrogen-containing base, and it led to full conversion but only 81% final yield (Table 1, entry 8). It is worth noting that the tert-butoxide is highly hygroscopic. Without proper sealing of the reaction vessel, the reaction mixture tends to form gel-like precipitates, which introduces a clogging risk for flow reactors over time. The batch reactions were initially conducted as 0.05 M solutions with respect to compound 2 at the 0.125 mmol scale (∼25 mg). An initial attempt to scale-up the reaction (4-fold, 0.5 mmol, ∼100 mg) was attempted in batch. At this scale, the reactions became sluggish, requiring prolonged reaction time and leading to low yields. This was likely due to inefficient
entry
base
HPLC yield (%)a
in H2O
1 2 3 4 5 6 7 8
Cs2CO3 NEt3 2,6-lutidine DBU TMG tBu-TMG Me-TBD NaOtBu
84 0 0 83 87 91 78 81
10.3 10.7 7 13.5 13 14 13 16.5
in DMSO 9.0 4.5 12
in ACN
24 23 24 29
a
0.125 mmol scale reactions. Biphenyl (20 mol %) was added as an internal standard to determine HPLC yields. Calibration curves were established under 254 and 280 nm UV absorption. bData from refs 37 and 45.
irradiation as a result of using larger reaction vessels (Figures S9 and S10), confirming our rationale to develop a flow process. Before a larger quantity of material was committed to develop a flow procedure, the segmented flow reactor was used to screen organic bases over a range of residence time and temperature settings. The results of photoredox decarboxylative arylation using five different organic bases are shown in Figure 4. In order to gain insights into the reaction rates with different bases, the automated system was predefined with experimental conditions (run in duplicate) to investigate the key parameters of both residence time and temperature. Reaction profiles (plots of yield versus residence time) for the different bases were initially obtained at a fixed temperature of 30 °C (Figure 4a). We found that the two guanidine type bases, Barton’s base and TMG, afforded the highest reaction yield (95%) in 30 min. DBU and sodium tert-butoxide were less efficient at around 70% yield, whereas Me-TBD plateaued at around 40% as a result of significant side-product formation. Next, by elevating the temperature from 30 to 50 °C, we observed the acceleration of the reaction in both the Barton’s base and DBU cases (Figure 4b,c). Generally, all of the reactions were completed within 20 min. Compared with the initial batch study, the improvement in reaction rate (16 h to 20 min) is ideal for enabling rapid reaction condition screening and optimization. The system’s capability to change both continuous and discrete variables was demonstrated by fixing the Ni catalyst loading and varying the Ir catalyst loading (Figure 5a) and vice versa (Figure 5b) to validate the optimal combination of the dual catalyst system. In Figure 5a, the Ir catalyst was dosed from 0.5 to 2.0 mol % in the model reaction with a fixed 10 mol % loading of the Ni catalyst. The reaction did not exhibit a significant change in reaction profile, suggesting that the Ir C
DOI: 10.1021/acs.oprd.8b00018 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
Communication
Figure 4. Use of the oscillatory segmented flow reactor to obtain reaction profiles: (a) organic base screening; (b) temperature screening with tBuTMG; (c) temperature screening with DBU.
creates response surfaces for each discrete variable using a quadratic-response surface model.36,40 Next, these surfaces are iteratively refined to identify the best-performing discrete variable, which is expected to lead to the global optimum. During this refinement, the poor-performing discrete variables, accounting for model uncertainty, are removed from consideration. New experiments with the better-performing discrete variables and different continuous variable combinations are chosen using a G-optimal strategy. In this way, the model’s confidence in its predicted optimum outcome becomes higher after every experimental iteration.36 It is also worth noting that the objective function (productivity) was defined as the total yield divided by the residence time (to penalize long reaction times). The optimization was constrained to maintain at least 95% of the maximal yield. To test whether the self-optimizing algorithm agreed with our manual predefined screening results, the decarboxylative arylation model reaction was chosen for validation (Figure 6). The two continuous variables, temperature and residence time, were set between 30 and 50 °C and between 5 and 30 min, respectively. The three organic bases (Barton’s base, DBU, and Me-TBD) were set as discrete variable conditions from which the system could choose. The objective was to find the maximal yield and constrained optimum productivity conditions in the defined operating window. After a total 22 experiments and 12 h of nonstop operation, the system successfully predicted the conditions leading to the maximum of 97% yield using Barton’s base at 43 °C with a residence time of 21 min. The optimal productivity condition was found at 91% yield using the same base with slightly higher temperature of 50 °C with a residence time of just 12 min. Both conditions were validated twice in microslugs with yields matching both predicted values within 1% yield. With reduced experimental time and fewer experi-
catalyst was robust enough to generate radicals efficiently even at a relatively low loading. On the other hand, in Figure 5b, the model reaction’s Ni catalyst loading was varied from 2.5 to 20 mol % with a fixed 2 mol % loading of the Ir catalyst. The reaction rate increased significantly with increasing Ni catalyst loading, suggesting the involvement of this catalyst in the ratedetermining step. This is also consistent with literature catalyst ratios typically described in early papers.10,11 An organic dye, 1,2,3,5-tetrakis(carbazol-9-yl)-4,6-dicyanobenzene (4CzIPN), has been shown to be an effective photoredox catalyst in decarboxylative arylation.38,39 It is relatively easy and less expensive to prepare, making it a potential replacement for the expensive metal-based (Ir or Ru) catalysts. The organic dye catalyst (4CzIPN) with catalyst loadings of 1, 2, 4, and 8 mol % was applied to the model reaction, and the results are shown in Figure 5c. The conditions using the organic dye seemed to reach maximal yields faster, but the conversion flattened after 15 min, giving final yields ranging from 60 to 70%, whereas the standard 2 mol % Ir catalyst could reach 95% yield in 20 min. According to our results so far, the substitution of the Ir catalyst with 4CzIPN is feasible. Further cost-effective investigation of the reaction is ongoing within our laboratories. After this preliminary predefined screening approach, we sought to utilize a self-optimization algorithm approach to maximize the yield and productivity simultaneously. The custom MATLAB code applied was a modified algorithm evolved from previous work on solvent effects in SN2 alkylations and catalyst selection for Suzuki−Miyaura crosscouplings.34−36 By specifying ranges of the continuous variables (i.e., temperature and residence time) and different choices of the discrete variables (i.e., different base and catalyst identities), the code initiates a minimal D-optimal set of experiments and D
DOI: 10.1021/acs.oprd.8b00018 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
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Figure 5. Use of the oscillatory segmented flow reactor to obtain reaction profiles: (a) varying the Ir catalyst loading; (b) varying the Ni catalyst loading; (c) replacing the Ir catalyst with the organic dye 4CzIPN and then varying the 4CzIPN loading.
0.125 mmol scale using NaOtBu, DBU, and tBu-TMG were conducted, leading to HPLC yields of 81%, 83%, and 91%, respectively. On the basis of the knowledge from the previous optimization results, a temperature of 40 °C and a residence time of 20 min were applied to the microslug reactions using NaOtBu, DBU, and tBu-TMG, which gave yields of 70%, 75%, and 95%, respectively. Next, the same combinations of temperature and residence time were transferred to the corresponding flow rate on the Vapourtec UV-150 flow reactor, conducting 0.5 mmol scale reactions. It should be noted that the irradiation sources of the two platforms were not identical, but both offered high-intensity illumination. The continuous flow reactions gave isolated yields of 71%, 77%, and 94%, which were within error of the HPLC yields obtained in the microslug experiments. In terms of the final production rate, the Vapourtec UV-150 flow reactor could generate 345 to 435 mg of product per hour (Table 2, entries 1, 2, and 3). The predicted optimal productivity conditions for 3 (50 °C and 12 min residence time) were applied to the Vapourtec UV-150 flow reactor, which led to an isolated yield of 88% and an improved production rate of 710 mg/h (Table 2, entry 4). This reaction was also conducted at the 1.5 mmol scale using both maximal yield and optimal productivity conditions (i.e., the conditions in Table 2, entries 3 and 4) at least two times. The results led to isolated yields of 95% and 89%, respectively, under steady-state conditions, which demonstrated the transferability from microslug to continuous flow for the chemistry considered. The direct transfer based on conditions (concentrations, temperature, and residence time) works in this case
ments, the self-optimizing algorithm pleasingly reached the same ultimate conclusion as the extensive predefined screening conditions (Figure 6b), demonstrating the system’s ability to successfully identify optimal conditions. The self-optimization scope was expanded to include Ni precatalysts (Figure 7). The flexibility of choosing discrete variables suggests that the system could potentially be a platform for new catalyst development. Four Ni precatalysts commonly found in Ir−Ni dual-catalyzed reactions were chosen and prepared according to the literature procedures, using NiCl2·glyme and the corresponding bidentate ligands (A, 4,4′di-tert-butyl-2,2′-dipyridine; B; 4,4′-dimethoxy-2,2′-dipyridine; C, 2-pyridinecarboximidamide; D, 4-methoxy-2-pyridinecarboximidamide) under THF reflux conditions. 10,41−44 The precatalyst self-optimization experiment was set up using similar variables as disclosed above (see the Supporting Information): Ir[dF(CF3)ppy]2(dtbbpy)PF6 (2 mol %), Barton’s base (1.5 equiv), temperature (30−50 °C), and residence time (5−30 min) as the continuous variables and the Ni precatalyst (A, B, C, or D) as the discrete variable. After 25 experiments and a total experiment time of 18 h, the predicted maximal yield conditions were found to be 93% using NiCl2(dtbbpy) at 50 °C with a residence time of around 20 min, whereas the predicted optimal productivity conditions had 91% yield using the same precatalyst at 50 °C with a residence time of around 12 min (Figure 7). The concept of transferability between systems was demonstrated using standard 0.05 M decarboxylative arylation, as shown in Table 2. First, overnight batch reactions at the E
DOI: 10.1021/acs.oprd.8b00018 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
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Figure 6. Summary of self-optimizing base screening results: (a) visualization of yields and productivities; (b) the self-optimization result (arrows) agrees with manual predefined screening (points).
Figure 7. Summary of self-optimizing Ni precatalyst screening results.
F
DOI: 10.1021/acs.oprd.8b00018 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
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Table 2. Demonstration of Transferability: Microslug Conditions to Vapourtec UV-150 10 mL Flow Reactor
entry
base
batch HPLC yield (%)a
microslug HPLC yield (%)b
1 2 3 4
NaOtBu DBU tBu-TMG tBu-TMG
81 83 91 N/A
70 75 95 92
Vapourtec UV-150 conditionsc (flow rate) 35 35 40 50
°C, °C, °C, °C,
20 20 20 12
min min min min
(0.50 (0.50 (0.50 (0.83
mL/min) mL/min) mL/min) mL/min)
Vapourtec UV-150 isolated yield, production rate 71%, 77%, 94%, 88%,
115 124 145 142
mg/20 mg/20 mg/20 mg/20
min min min min
= = = =
345 372 435 710
mg/h mg/h mg/h mg/h
a 0.125 mmol scale, 0.05 M, rt, 16 h. b0.125 mmol scale, 0.05 M, 20 min (entries 1, 2, and 3) or 12 min (entry 4). c0.500 mmol scale, 0.05 M, 20 min (entries 1, 2, and 3) or 12 min (entry 4).
Table 3. Optimization Workflow: From Batch to Continuous Flow via Microslug
a
0.05 M, 0.125 mmol (∼25 mg) scale; HPLC yields in parentheses. b0.05 M, 0.002 mmol (∼0.5 mg) scale. Optimization ranges: 5−40 min and 30− 55 °C or 40−65 °C. Top: maximal yield conditions. Bottom: optimal productivity conditions. HPLC yields are shown in parentheses. c0.05 M, 0.500 mmol (∼100 mg) scale.
because the light activation is not the rate-determining step. Had the reaction been photon-flux-limited, it would have been necessary to include photon flux intensity differences in selecting the operating conditions for the scale-up from the microslug results. At this point, a conceptual workflow of batch-to-flow process development using the segmented flow self-optimizing reactor had been successfully established. To ensure that the protocol was not limited to the model substrate, 4′-chloroacetophenone (4), 5-acetyl-2-bromopyridine (5), 1-bromo-4-ethylbenzene (7), and 4-ethyl-1-iodobenzene (9) were selected to validate the process development via the microslug approach (Table 3). These aryl halides are less reactive than 4′-bromoacetophenone (2) in batch settings, typically exhibiting low yields (
