# Sustainable Copper-Catalyzed Cyanation of Aryl Iodine through a Design of Experiment Strategy

Kevin Brugemann, Moreira Pereira, Philippe Steinsoultz, Clovis Peter, Sokaina Hammoud, Martine Schmitt, Gaëlle Blond, Frédéric Bihel

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Kevin Brugemann, Moreira Pereira, Philippe Steinsoultz, Clovis Peter, Sokaina Hammoud, et al. Sustainable Copper-Catalyzed Cyanation of Aryl Iodine through a Design of Experiment Strategy. ACS Sustainable Chemistry & Engineering, 2025, 13 (30), pp. 12232-12241. 10.1021/acssuschemeng.5c04898. hal-05204136

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# Sustainable Copper-Catalyzed Cyanation of Aryl Iodine through a Design of Experiment Strategy

Kevin Brugemann,ª Thiago Moreira Pereira,ª Philippe Steinsoultz,ª Clovis Peter,ª Sokaina Hammoud,ª Martine Schmitt,ª Gaëlle Blondª and Frédéric Bihelª*

Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS, Université de Strasbourg, 67401 Illkirch, France

KEYWORDS: Aryl nitriles; Copper-catalyst; Response Surface Methodology; Cyanide Source; Green chemistry

**ABSTRACT:** Aryl nitriles are critical intermediates in organic synthesis, widely utilized in pharmaceuticals, agrochemicals, and other industrial applications. Traditional cyanation methods often rely on highly toxic cyanide sources and demanding conditions, prompting the need for greener alternatives. Here, we report a novel copper catalyst [Cu(CyDMEDA)₂Br]Br·H₂O allowing cyanation of aryl iodides using sodium nitroprusside as a non-toxic cyanide source in water, operating under mild conditions (75°C) with high efficiency. A Design of Experiment (DoE)-based optimization strategy employing Response Surface Methodology (RSM) enabled the systematic investigation of key reaction parameters, achieving yields exceeding 80% with minimal resource consumption. The robustness of the optimized protocol was validated through the synthesis of Febuxostat, an FDA-approved gout medication, in 79% yield under environmentally friendly conditions. This approach highlights a sustainable and scalable alternative for the preparation of aryl nitriles (13 examples, 29% to 81% yields), advancing the principles of green chemistry in pharmaceutical synthesis.

## 1. INTRODUCTION

Nitriles represent a crucial class of compounds in organic synthesis, with aryl nitriles being especially significant due to their prevalence as core structural motifs in a wide array of applications, including dyes, agrochemicals, pharmaceuticals, or batteries.¹⁻⁶ Additionally, the nitrile functionality serves as a highly versatile intermediate, enabling the synthesis of diverse functional groups such as amines, amidines, tetrazoles, aldehydes, amides, and other carboxylic acid derivatives.⁷⁻¹¹ Traditional methods for the synthesis of aryl nitriles, such as the Sandmeyer reaction and the Rosenmund-von Braun protocol,¹²,¹³ have been widely employed, including at preparative scales. Despite their utility, these approaches are hindered by the necessity for stoichiometric quantities of CuCN and the demanding reaction conditions. Consequently, significant efforts have been devoted to the development of alternative methodologies.¹⁴ Among these, the transition-metal-catalyzed cyanation of aryl halides and related derivatives has emerged as a particularly promising strategy, offering enhanced efficiency and broader applicability.¹⁵⁻¹⁹

Moreover, the choice of cyanide source remains a persistent challenge in cyanation chemistry, primarily due to the high toxicity of commonly used cyanides such as KCN, NaCN, TMSCN, CuCN, Zn(CN)₂, Bu₄NCN, malononitriles, DDQ, and cyanohydrins. Over time, significant efforts have been devoted to develop less toxic alternatives for cyanation reactions.

These alternatives can be broadly categorized into two main types: (i) reagents containing a tightly bound cyano group that must be released during the reaction, and (ii) systems in which

the cyanide moiety is generated *in situ* from precursors comprising carbon and nitrogen atoms in various oxidation states.²⁰ Among the first category, ferricyanides have attracted increasing attention, particularly following the seminal work of Beller et al. in 2004.²¹ Their study demonstrated the utility of potassium ferrocyanide (K₄[Fe(CN)₆]), a compound commonly used as a food additive, in palladium-catalyzed cyanation reactions. Shortly thereafter, they published a series of studies detailing the first K₄[Fe(CN)₆]-based cyanation of aryl halides (Cl, Br, I) catalyzed by copper complexes with DMEDA or N-alkylimidazoles as ligands in organic solvents such as DMAc or toluene at elevated temperatures (T > 120 °C) (Fig.1A).²²⁻²⁵ This approach has since spurred extensive research into the synthesis of aromatic nitriles.²⁰,²⁶ Additional copper-catalyzed reactions employing ferrocyanide have been reported in organic solvents at high temperatures (>140 °C), utilizing alternative ligands such as phenanthroline,²⁷ ethylenediamine (EDA),²⁸ or trans-N,N'-dimethylcyclohexane-1,2-diamine,²⁹ typically requiring high copper loadings (≥20 mol%). More recently, Nasrollahzadeh et al. reported an efficient cyanation process using carbon-supported copper nanoparticles (Cu = 0.5 mol%) in DMF at 120 °C.³⁰ To avoid the use of nitrogen-containing dipolar aprotic solvents such as DMF, DMAc, or NMP, more sustainable approaches have been explored, including ligand-free copper-catalyzed cyanation of aryl iodides in water at elevated temperatures (T > 150 °C) (Fig.1B).³¹,³² While the majority of these reactions require high thermal input (>120 °C), reports of metal-catalyzed cyanation under milder conditions (<100 °C) remain scarce.
Cu-catalyzed cyanation in organic solvents<sup>21-25, 27-30</sup>

<figure><img src="image_2.png" /></figure>

Cu-catalyzed cyanation in water.<sup>31,32</sup>

<figure><img src="image_3.png" /></figure>

Cu-catalyzed cyanation in PEG at 80°C.<sup>42</sup>

<figure><img src="image_4.png" /></figure>

This work : Cu-catalyzed cyanation in water at T°<80°C

<figure><img src="image_5.png" /><figcaption>**Figure 1.** State of the art of copper-catalyzed cyanation reaction using iron-cyanide complexes</figcaption></figure>

Only a few examples involving palladium have been described,<sup>33-41</sup> and to date, there is only one report of copper-catalyzed cyanation under such mild conditions. Specifically, Colacino et al. were the first to employ molten poly(ethylene glycol) (PEG) as a solvent for the sonochemical cyanation of aryl halides using K₄[Fe(CN)₆] in the presence of copper nanoparticles at 80 °C (Fig. 1C).<sup>42</sup> Whereas sonochemical methods are not yet widely adopted in organic laboratories, we report alternative cyanation under eco-friendly conditions, using classical thermal activation, with sodium nitroprusside as a non-toxic cyanide source, a copper complex as a non-noble metal catalyst, water as a green solvent, and an even lower reaction temperature of 75 °C (Fig. 1D). During the optimization process using the standard "One Factor At a Time" (OFAT) strategy, inconsistent results were observed, suggesting potential interactions between reagents. To address this, we employed a statistical approach known as Response Surface Methodology (RSM), based on the principles of Design of Experiments (DoE), to characterize these interactions and optimize the reaction conditions. Despite its widespread use in many scientific fields,<sup>43-46</sup> RSM remains underutilized in academic organic chemistry.<sup>47-58</sup> Nevertheless, independent recent contributions by A. Lapkin and G. S. Newman reflect a growing consensus on the future impact of DoE in chemical optimization.<sup>59,60</sup>

<table><caption>Table 1. Initial screening of ligands</caption><thead><tr><th>Ligands</th><th>Entry</th><th>Ligands</th><th>Yield (%)<sup>a,b</sup></th></tr></thead><tbody><tr><td></td><td>1</td><td>None</td><td>n.d.</td></tr><tr><td></td><td>2</td><td>1,10-Phenanthroline (L1)</td><td>n.d.</td></tr><tr><td></td><td>3</td><td>CyEDA (L2)</td><td>n.d.</td></tr><tr><td></td><td>4</td><td>CyDMEDA (L3)</td><td>50</td></tr><tr><td></td><td>5</td><td>DMEDA (L4)</td><td>n.d.</td></tr><tr><td></td><td>6</td><td>L-Proline (L5)</td><td>n.d.</td></tr><tr><td></td><td>7</td><td>Dipivaloylmethane (L6)</td><td>n.d.</td></tr></tbody></table>

<sup>a</sup> Reaction conditions: 4-iodo-anisole 1a (1 eq), Na₄Fe(CN)₆ (0.5 eq), CuBr₂·H₂O (10 mol%), Ligand (20 mol%), NaOH (2 eq); <sup>b</sup> Average yields determined by HPLC/UV using benzophenone as an external standard; <sup>c</sup> n.d.: not detected

<figure><img src="image_6.png" /><figcaption>**Figure 2.** A) Synthesis of the copper complex [Cu(L3)₂Br]Br·H₂O; Reaction conditions: CuBr₂·H₂O (1 eq), L3 (2 eq), EtOH, rt, 16h; B) Blue crystals obtained after recrystallization in THF/H₂O (95:5); C) X-ray structure of the complex [Cu(CyDMEDA)₂Br]Br·H₂O (available at the CCDC)</figcaption></figure>

In this report, we demonstrate how this methodology can facilitate the optimization process and yield deeper insights into complex reaction systems.

## 2. EXPERIMENTAL SECTION

### 2.1. Materials.
All commercial reagents were used without additional purification.

### 2.2. Catalyst preparation.
The ligand L3, (1R,2R)-N,N'-dimethyl-1,2-cyclohexanediamine (454 mg, 3.2 mmol, 2.0 eq.), was dissolved in 20 mL of absolute ethanol. Separately, CuBr₂·H₂O (356 mg, 1.6 mmol, 1.0 eq.) was dissolved in 10 mL of absolute ethanol, and the resulting solution was filtered using cotton to remove any insoluble impurities. The CuBr₂·H₂O solution was then added dropwise to the stirred ligand solution. The reaction mixture was
stirred at room temperature overnight. After completion of the reaction, the precipitate formed in the solution was collected by filtration under reduced pressure, yielding a violet powder. The solid was recrystallized from a 50 mL THF/water mixture (95:5 v/v), affording blue crystals (654 mg, 78%).

**2.3. General procedure for the cyanation reaction.** All solid reactants, including the iodide substrate (1 eq.), copper complex (7.5 mol%), and $Na_2[Fe(CN)_5NO]$ (0.35 eq.) were premixed in a ball-mill reactor for 3 minutes at 30Hz to form a homogeneous powder mixture, which was transferred in a 4 mL amber glass vial, sealed, and subjected to three cycles of vacuum/argon purging to ensure an inert environment. The reaction medium was prepared by dissolving the components in freshly degassed water and degassed NaOH solution (1.7 eq.), achieving a final concentration of 0.8 mol/L. The sealed vial was placed in a bioshaker at 1200 rpm and heated to 75°C for 4 hours. Upon reaction completion, the crude product was purified via silica gel chromatography using a pentane/diethyl ether gradient, yielding the cyanated product.

### 3. RESULTS AND DISCUSSION

Using 4-iodoanisole **1a** as the starting material, we initiated our investigation with a screening of six copper ligands under specific reaction conditions (Table 1). The conditions included sodium ferricyanide as the cyanide source, $CuBr_2$ as the metal catalyst, and NaOH as the base in water at 75°C. Among the tested ligands, no reaction was observed except in the presence of CyDMEDA (**L3**), which produced the desired product with 50% yield (Table 1, entry 4). Interestingly, closely related analogs of **L3**, such as CyEDA (**L2**), which lacks the methyl groups of CyDMEDA, and DMEDA (**L4**), showed no catalytic activity, demonstrating their complete inefficiency under the same conditions. Following the seminal work of Stephen Buchwald and colleagues,⁶¹⁻⁶⁷ CyDMEDA has been widely used as a copper ligand over the past 25 years.⁶⁸⁻⁷⁸ Copper-catalyzed transformations employing CyDMEDA are fully compatible with water as a solvent.⁷⁹,⁸⁰

Notably, MacArthur et al. reported its use in the microwave-assisted cyanation of aryl chlorides in acetonitrile at 200 °C for 1 h.²⁹ However, CyDMEDA presents significant practical challenges due to its hygroscopic nature and sensitivity to air and heat. Additionally, its commercial availability as either a solid or a liquid, introduces variability that can affect the reproducibility of catalyzed reactions. To address these limitations, we aimed to isolate the copper complex formed by the reaction of $CuBr_2 \cdot H_2O$ and **L3** in ethanol at room temperature (Fig. 2A). Remarkably, although CyDMEDA has been widely employed as a copper ligand for more than two decades, the corresponding copper complex has not, to our knowledge, been previously reported. After recrystallization, X-ray crystallography of the resulting blue crystals identified the compound as a cationic copper complex, $[Cu(CyDMEDA)_2Br]Br \cdot H_2O$ (Fig. 2B and 2C). Notably, the water molecule in the complex originates from the starting material, $CuBr_2 \cdot H_2O$. This complex demonstrated exceptional stability under ambient conditions, both in air and at room temperature, significantly simplifying its handling and storage compared to the free ligand. The copper complex $[Cu(CyDMEDA)_2Br]Br \cdot H_2O$ was subsequently employed as a catalyst for reaction optimization. The use of this isolated complex gave the same result as the complex formed *in situ*

from $CuBr_2 \cdot H_2O$ and CyDMEDA (Entry 1, Table 2 versus entry 4, Table 1). Initially, various analogs of $Na_4Fe(CN)_6$ were screened, including potassium ferrocyanide, ferricyanide, and sodium nitroprusside (Table 2, entries 2-4). The use of a potassium counterion or the presence of $Fe^{3+}$ proved detrimental to the reaction. In contrast, sodium nitroprusside (SNP) delivered the desired compound **2a** in 82% yield (Table 2, entry 4). Interestingly, after just 4 hours the same result was observed, with a significant reduction in reaction time (84%, Table 2, entry 5). Sodium nitroprusside (SNP), first identified in 1850, is a well-established FDA-approved vasodilator widely used in clinical settings to manage hypertensive crises by lowering blood pressure.⁸¹ Its therapeutic effect is primarily due to the release of nitric oxide (NO), with a minimal contribution from its cyanide content.⁸² Beyond its clinical applications, SNP has found utility in analytical chemistry, particularly for the titration of thiols, and serves as a classical reagent for nitrosation reactions in organic chemistry.⁸³⁻⁸⁶ Interestingly, despite its cyanide content, only one study by Jiang *et al.* has explored the use of SNP as a cyanide source,⁸⁷ underlining its limited application in this area. The authors demonstrated a copper-catalyzed cyanation of aryl iodides using SNP in PEG-400 at elevated temperatures (120–140°C). While potassium ferrocyanide ($K_4[Fe(CN)_6]$) is generally regarded as less toxic, SNP presents a compelling alternative as a cyanation reagent, offering distinct advantages over traditional cyanide sources in certain contexts.

<figure><img src="image_7.png" /><figcaption>**Table 2. Screening of the cyanide source and the nature of the base**</figcaption></figure>

<table><thead><tr><th>Entry</th><th>Modification<sup>a</sup></th><th>Time (h)</th><th>Yields (%)<sup>a,b</sup></th></tr></thead><tbody><tr><td>1</td><td>None</td><td>16</td><td>53</td></tr><tr><td>2</td><td>$K_4Fe(CN)_6$ instead of $Na_4Fe(CN)_6$</td><td>16</td><td>21</td></tr><tr><td>3</td><td>$K_3Fe(CN)_6$ instead of $Na_4Fe(CN)_6$</td><td>16</td><td>n.d.</td></tr><tr><td>4</td><td>$Na_2[Fe(CN)_5NO]$ instead of $Na_4Fe(CN)_6$</td><td>16</td><td>82</td></tr><tr><td>5</td><td>$Na_2[Fe(CN)_5NO]$ instead of $Na_4Fe(CN)_6$</td><td>4</td><td>84</td></tr><tr><td>6</td><td>KOH instead of NaOH</td><td>4</td><td>50</td></tr><tr><td>7</td><td>LiOH instead of NaOH</td><td>4</td><td>63</td></tr><tr><td>8</td><td>t-BuONa instead of NaOH</td><td>4</td><td>83</td></tr><tr><td>9</td><td>$K_2CO_3$ instead of NaOH</td><td>4</td><td>1</td></tr><tr><td>10</td><td>$K_3PO_4$ instead of NaOH</td><td>4</td><td>7</td></tr><tr><td>11</td><td>No base instead of NaOH</td><td>4</td><td>n.d.</td></tr></tbody></table>

Returning to the reaction optimization, a set of bases was tested as alternatives to NaOH (Table 2, entries 6-10); however, all of them resulted in lower yields, excepted for t-

<footnote><sup>a</sup> Reaction conditions: 4-iodo-anisole (1 eq), cyanide source (0.5 eq), [Cu] (10 mol%), Base (2 eq); <sup>b</sup>Average yields determined by HPLC/UV using benzophenone as an external standard.</footnote>
BuONa. However, compared to t-BuONa, NaOH is more sustainable to produce, with a lower environmental impact and toxicity, so NaOH was selected for the next optimization step. The reaction did not work without base (Table 2, entry 11). After identifying the appropriate reagents and catalyst for this cyanation in an aqueous medium, we proceeded to optimize the quantities of each reagent. Initially, we employed a classical One-Factor-At-a-Time (OFAT) strategy. However, we quickly encountered challenges due to seemingly inconsistent results. Interactions between experimental factors were suspected, highlighting a limitation of the OFAT approach, which is known to overlook such interactions.⁵⁹ To investigate the relationship between two factors, experiments must be conducted where both factors are varied simultaneously. This approach can be implemented using statistical models, commonly referred to as Design of Experiments (DoE). Response Surface Methodology (RSM) is an advanced Design of Experiments (DoE) technique designed to model and optimize responses influenced by multiple experimental variables. By employing non-linear statistical models, typically polynomial, RSM enables the exploration of complex interactions among factors and their combined effects on one or more responses. In this study, Response Surface Methodology (RSM) based on a Central Composite Design (CCD) was employed to investigate the impact of three key variables—catalyst charge, SNP and NaOH quantity—on the cyanation yield (response). The CCD framework systematically explores the factor space using three types of experimental points (Table 3): (1) *factorial points*, representing combinations of the factors at low (-1) and high (+1) levels,

<table><caption>**Table 3. Optimization through a Central Composite Design**</caption><thead><tr><th rowspan="2">Variables</th><th colspan="5">Central Composite Design (CCD)</th></tr><tr><th>-1.83</th><th>-1</th><th>0</th><th>1</th><th>1.83</th></tr></thead><tbody><tr><td>[Cu] (x mol%)</td><td>2.94</td><td>5</td><td>7.5</td><td>10</td><td>12.06</td></tr><tr><td>SNP (y eq.)</td><td>0.22</td><td>0.3</td><td>0.4</td><td>0.5</td><td>0.58</td></tr><tr><td>NaOH (z eq.)</td><td>1.15</td><td>1.4</td><td>1.7</td><td>2</td><td>2.25</td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>

forming the edges of a cube; (2) *star points*, which extend beyond the cube at extreme levels (-1.83 and +1.83), enabling the assessment of curvature in the response surface; and (3)

central points, located at the midpoint of all factors (0, 0, 0). The central points are repeated multiple times to evaluate the repeatability and ensure robust estimation of experimental variability. This design allows for a thorough examination of both linear and non-linear effects, as well as interactions between the factors, providing a comprehensive model for optimizing the cyanation yield. The experimental design for optimizing the cyanation reaction consisted of 22 runs, created using RStudio software (version 2024.09.1) with the RcmdrPlugin.DoE package (version 0.12.5). The design included 8 factorial points, 6 star points, and 8 central points. Based on the yields obtained from each experiment (Table 4), a mathematical model was deduced by applying the following second order polynomial quadratic model (Eq 1.).

$$Y = \beta_0 + \sum_{j}^{k} \beta_j \cdot X_j + \sum_{i \neq j}^{k} \beta_{ij} \cdot X_i \cdot X_j + \sum_{j}^{k} \beta_{jj} \cdot X_j^2 + \epsilon \quad \text{Equation (1)}$$

<table><caption>**Table 4. Experimental Central Composite Design**</caption><thead><tr><th>Entry</th><th>[Cu] mol%</th><th>NaOH (eq)</th><th>SNP (eq)</th><th>Yields (%)<sup>a</sup></th></tr></thead><tbody><tr><td>1</td><td>7.50</td><td>1.70</td><td>0.40</td><td>84</td></tr><tr><td>2</td><td>7.50</td><td>1.70</td><td>0.40</td><td>80</td></tr><tr><td>3</td><td>10.00</td><td>1.40</td><td>0.30</td><td>81</td></tr><tr><td>4</td><td>5.00</td><td>1.40</td><td>0.30</td><td>70</td></tr><tr><td>5</td><td>10.00</td><td>2.00</td><td>0.30</td><td>71</td></tr><tr><td>6</td><td>10.00</td><td>1.40</td><td>0.50</td><td>14</td></tr><tr><td>7</td><td>7.50</td><td>1.70</td><td>0.40</td><td>83</td></tr><tr><td>8</td><td>5.00</td><td>1.40</td><td>0.50</td><td>11</td></tr><tr><td>9</td><td>5.00</td><td>2.00</td><td>0.50</td><td>65</td></tr><tr><td>10</td><td>5.00</td><td>2.00</td><td>0.30</td><td>47</td></tr><tr><td>11</td><td>10.00</td><td>2.00</td><td>0.50</td><td>88</td></tr><tr><td>12</td><td>7.50</td><td>1.70</td><td>0.40</td><td>82</td></tr><tr><td>13</td><td>7.50</td><td>1.15</td><td>0.40</td><td>47</td></tr><tr><td>14</td><td>7.50</td><td>2.25</td><td>0.40</td><td>64</td></tr><tr><td>15</td><td>7.50</td><td>1.70</td><td>0.40</td><td>83</td></tr><tr><td>16</td><td>7.50</td><td>1.70</td><td>0.40</td><td>78</td></tr><tr><td>17</td><td>7.50</td><td>1.70</td><td>0.40</td><td>88</td></tr><tr><td>18</td><td>2.94</td><td>1.70</td><td>0.40</td><td>6</td></tr><tr><td>19</td><td>7.50</td><td>1.70</td><td>0.40</td><td>85</td></tr><tr><td>20</td><td>12.06</td><td>1.70</td><td>0.40</td><td>91</td></tr><tr><td>21</td><td>7.50</td><td>1.70</td><td>0.22</td><td>56</td></tr><tr><td>22</td><td>7.50</td><td>1.70</td><td>0.58</td><td>50</td></tr></tbody></table>

In this regression model. Y is the response. β₀ is a constant coefficient. βⱼ, βᵢⱼ and βᵢⱼ are the coefficients for the linear, quadratic and interaction effects, respectively. Xᵢ and Xⱼ are the coded levels for the independent variables. k is the number of

<footnote><sup>a</sup> Yields determined by HPLC/UV using benzophenone as an external standard.</footnote>
independent variables and ε is the random error. All the values for regression coefficient (β) are shown in Table 5. and the analysis of variance (ANOVA) in Table 6. The RSM model is statistically significant. explaining a substantial portion of the response variability (Table 6. R²=90.01%). The adjusted R² (80.93%) reflects a slight penalty for model complexity. Significant first-order. interaction. and quadratic terms validate the model's ability to capture the underlying relationships between the factors and the response. However. the significant lack of fit highlights the need for caution. as additional higher-order effects or experimental variability may influence the results. The model remains relevant for identifying trends and guiding optimization within the experimental design space. The interaction between SNP (X₁) and NaOH (X₂) was found to significantly influence the yield. as evidenced by the interaction term β₁₂ in the regression model. The coefficient for X₁: X₂ is positive (β₁₂=20.75. Table 5). indicating a synergistic effect: the simultaneous variation of SNP and NaOH enhances the response beyond the sum of their individual effects. This behavior suggests that the effect of SNP on the yield becomes more pronounced when NaOH increases. and vice versa.

<table><caption>Table 5. Values for regression coefficient for the response surface quadratic model defined by Equation 1</caption><thead><tr><th></th><th>β Value</th><th>Std. Error</th><th>t value</th><th>p-value<sup>a</sup></th></tr></thead><tbody><tr><td>Intercept</td><td>83.05702</td><td>4.59915</td><td>18.0592</td><td>***</td></tr><tr><td>X<sub>1</sub></td><td>-6.61053</td><td>2.98111</td><td>-2.2175</td><td>*</td></tr><tr><td>X<sub>2</sub></td><td>8.93438</td><td>2.98111</td><td>2.9970</td><td>*</td></tr><tr><td>X<sub>3</sub></td><td>15.08100</td><td>2.98111</td><td>5.0588</td><td>***</td></tr><tr><td>X<sub>1</sub>:X<sub>2</sub></td><td>20.75000</td><td>4.03645</td><td>5.1407</td><td>***</td></tr><tr><td>X<sub>1</sub>:X<sub>3</sub></td><td>-0.50000</td><td>4.03645</td><td>-0.1239</td><td>n.s.</td></tr><tr><td>X<sub>2</sub>:X<sub>3</sub></td><td>4.75000</td><td>4.03645</td><td>1.1768</td><td>n.s.</td></tr><tr><td>(X<sub>1</sub>)<sup>2</sup></td><td>-8.78684</td><td>2.54615</td><td>-3.4510</td><td>**</td></tr><tr><td>(X<sub>2</sub>)<sup>2</sup></td><td>-8.03684</td><td>2.54615</td><td>-3.1565</td><td>**</td></tr><tr><td>(X<sub>3</sub>)<sup>2</sup></td><td>-10.13684</td><td>2.54615</td><td>-3.9812</td><td>**</td></tr><tr><td>ε</td><td>-0.28333</td><td>4.88838</td><td>-0.0580</td><td>n.s.</td></tr></tbody></table>

The statistical significance of this interaction term (p-value = 3.2 x 10⁻⁴) confirms its relevance within the model. Based on the Response Surface Model (RSM), predictive yields under specific experimental conditions can be visualized effectively using 3D graphs (Figure 3A). However, a more standard and practical method for representing such predictive models is through contour plots, which are 2D graphical representations that display predicted yields as a function of two variables (Figure 3B–D). Contour plots provide insights into variable interactions: when there is no interaction between variables, the plots exhibit concentric circles, as seen in the combinations of [Cu] and SNP (Figure 3B) or [Cu] and NaOH (Figure 3D).

<table><caption>Table 6. Analysis of Variance (ANOVA) for the response surface quadratic model</caption><thead><tr><th>Source</th><th>Df</th><th>Sum of squares</th><th>Mean square</th><th>F-Value</th><th>p-value<sup>a</sup></th></tr></thead><tbody><tr><td>Σ<sup>k</sup><sub>j</sub> β<sub>j</sub> X<sub>j</sub></td><td>3</td><td>5147.4</td><td>1715.80</td><td>13.1637</td><td>***</td></tr><tr><td>Σ<sup>k</sup><sub>i≠j</sub> β<sub>ij</sub> X<sub>i</sub> X<sub>j</sub></td><td>3</td><td>3627.0</td><td>1209.00</td><td>9.2755</td><td>**</td></tr><tr><td>Σ<sup>k</sup><sub>j</sub> β<sub>jj</sub> X<sub>j</sub><sup>2</sup></td><td>3</td><td>4142.3</td><td>1380.78</td><td>10.5934</td><td>**</td></tr><tr><td>ε</td><td>1</td><td>0.4</td><td>0.44</td><td>0.0034</td><td>n.s.</td></tr><tr><td>Residuals</td><td>11</td><td>1433.8</td><td>130.134</td><td></td><td></td></tr><tr><td>Lack of fit</td><td>5</td><td>1372.0</td><td>274.41</td><td>26.6629</td><td>***</td></tr><tr><td>Pure error</td><td>6</td><td>61.7</td><td>10.29</td><td></td><td></td></tr><tr><td>R<sup>2</sup> = 0.9001</td><td colspan="4">Adjusted R<sup>2</sup> = 0.8093</td><td></td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>Figure 3 Predicted effects of the variables ([Cu], SNP, and NaOH on yield according the RSM model; A) 3D graph with SNP = 0.4 eq; B) Contour plot with NaOH = 1.7 eq. and SNP and [Cu] as variables; C) Contour plot with [Cu] = 7.5 mol%.. and SNP and NaOH as variables; D) Contour plot with SNP = 0.4 eq. and [Cu] and NaOH as variables.</figcaption></figure>

Conversely, the curved patterns in the contour plot of SNP and NaOH highlight non-additive responses, indicating the presence of an interaction between the two variables (Figure 3C). Consequently, the combined levels of SNP and NaOH must be carefully balanced to achieve optimal results. Such interactions are critical in understanding the system's behavior and optimizing the experimental conditions, as they demonstrate the importance of considering combined factor effects rather than treating factors independently. The predictive RSM model (Equation 1) allowed us to determine several optimal reaction conditions. The corresponding

<footnote><sup>a</sup> n.s.: non significant; * : p-value < 0.1; ** : p-value < 0.01; *** : p-value < 0.001 (full data in S.I.)</footnote>

<footnote><sup>a</sup> n.s.: non significant; * : p-value < 0.1; ** : p-value < 0.01; *** : p-value < 0.001 (full data in S.I.)</footnote>
quantities of SNP. NaOH and [Cu]. along with the predicted and
experimental yields. are summarized in Table 7 (entries 1-5).

<figure><img src="image_10.png" /><figcaption>**Table 7. Optimal conditions**</figcaption></figure>

<table><thead><tr><th>Entry</th><th>SNP (eq.)</th><th>[Cu] (mol%)</th><th>NaOH (eq.)</th><th>Pred Yield (%)<sup>a</sup></th><th>Exp. Yield (%)<sup>b,c</sup></th></tr></thead><tbody><tr><td>1</td><td>0.3</td><td>9.0</td><td>1.5</td><td>88</td><td>80</td></tr><tr><td>2</td><td>0.35</td><td>9.5</td><td>1.7</td><td>89</td><td>82</td></tr><tr><td>3</td><td>0.4</td><td>9.5</td><td>2.0</td><td>93</td><td>82</td></tr><tr><td>4</td><td>0.45</td><td>10.0</td><td>2.0</td><td>98</td><td>85</td></tr><tr><td>5</td><td>0.5</td><td>10.0</td><td>2.0</td><td>98</td><td>88</td></tr><tr><td>6</td><td>0.35</td><td>8.5</td><td>1.7</td><td>88</td><td>82</td></tr><tr><td>7</td><td>0.35</td><td>7.5</td><td>1.7</td><td>84</td><td>81</td></tr><tr><td>8</td><td>0.35</td><td>7.0</td><td>1.7</td><td>80</td><td>80</td></tr><tr><td>9</td><td>0.35</td><td>6.5</td><td>1.7</td><td>76</td><td>76</td></tr></tbody></table>

<sup>a</sup> Pred. yield : Predicted yield; <sup>b</sup> Exp. Yield : Experimental yield; <sup>c</sup> Average yields from 4 experiments determined by HPLC/UV using benzophenone as an external standard

While the experimental yields were slightly lower than the predicted values. they remained consistent. all exceeding 80%. Notably. the difference between the highest (88%) and lowest (80%) yields was relatively small. despite the highest yield requiring significantly larger amounts of reagents (SNP. NaOH) and catalyst. We identified the conditions of 0.35 equivalents of SNP. 1.7 equivalents of NaOH. and 9.5 mol% [Cu] (Table 7. entry 2) as an optimal balance between material efficiency and yield. Based on the predictive model. reducing the catalyst loading from 9.5 mol% to 7 mol%. while maintaining 0.35 eq SNP and 1.7 eq NaOH. still afforded yields exceeding 80% (Table 7. entries 6–8). Experimental validation confirmed these predictions with catalyst loadings of 8.5. 7.5. 7.0. and 6.5 mol%. achieving yields above 80% with ≥7.0 mol% of catalyst. Thus. we determined that 0.35 eq SNP. 1.7 eq NaOH. and 7.5 mol% [Cu] represent the best compromise between efficiency and material consumption. and these conditions were further evaluated for reaction scope (Figure 4). It is important to notice that the 4-methoxyiodobenzene is fully consumed. and the 12 to 20% yield loss observed can be attributed to consecutive side reactions (See SI). First. the aryl nitrile intermediate may undergo hydrolysis under the basic aqueous conditions (NaOH. 75 °C). resulting in the formation of both the corresponding primary amide (Ar–CONH₂) and carboxylic acid (Ar–COOH). This amide can then participate in a secondary Ullmann-type coupling with the aryl iodide to generate a diaryl amide byproduct (Ar–CONH–Ar). Indeed. Cu(II) in presence of CyDMEDA (L3) has been reported as a good catalytic system

for Ullmann-type N-arylation of amide in water.⁸⁰ Finally. we explored the recyclability of the catalytic system. After completion of the reaction and extraction of the cyanation product with an organic solvent. the aqueous layer was centrifuged to remove the iron-containing precipitate. The resulting blue solution was reused in the presence of aryliodide. SNP. and NaOH; however. only trace amounts of the cyanated product were detected. The addition of CyDMEDA (15 mol%) to the recycled solution yielded compound 2a. albeit in only 38% yield . and the starting material **1a** was not completely consumed.

<figure><img src="image_11.png" /></figure>

**Figure 4.** Scope and limitations. <sup>a</sup> Reaction conditions : RX **1** (0.4 mmol. 1 eq). Na₂[Fe(CN)₅NO] (0.35 eq). [Cu(CyDMEDA)₂Br]Br.H₂O (7.5 mol%). NaOH (1.7 eq). H₂O (0.5 mL); Yields correspond to isolated yields (average of two runs); <sup>b</sup> n.r. : no reaction; <sup>c</sup> scale up with 4 mmol of 4-iodoanisole ; <sup>d</sup> 2.7 eq. NaOH instead of 1.7 eq; <sup>e</sup> reaction time of 16h instead of 4h.

Under optimized conditions. no reaction was observed with 4-bromo- or 4-chloro-anisole. highlighting the limited reactivity of bromide and chloride derivatives (Figure 4). In contrast. iodoaryl substrates bearing electron-donating groups reacted efficiently. affording the corresponding cyanated products in isolated yields of 62% to 82% (**2a–f**). For compound **1e**. the presence of a phenolic moiety required the addition of an extra equivalent of NaOH to neutralize the acidity of the phenol group and maintain the reaction's basic conditions. The reaction demonstrated good tolerance for functional groups. as evidenced by the successful cyanation of the para-benzoyl-substituted substrate **1g** giving **2g** with 67% yield. In most cases. the starting material is fully consumed. and the loss in yield is primarily due to hydrolysis of the nitrile intermediate. followed by Ullmann-type N-arylation. In the presence of electron-donating groups. such as alkoxy substituents in the ortho or para positions. the nitrile functionality is less prone to
hydrolysis. In contrast. electron-withdrawing substituents increase the susceptibility of the nitrile to hydrolyze under the reaction conditions. This difference in reactivity accounts for the variation in isolated yields across different substrates. The para-acetyl-substituted substrate displayed significantly lower reactivity. yielding only trace amounts of the product **2h** after 4 hours and 29% after 16 hours. This reduced efficiency is likely due to enolization of the acetyl group under basic conditions. which may interfere with the cyanation process. The methodology also proved effective for more complex substrates. yielding 2-(cyanophenyl)acetonitrile (**2i**) and 2-naphthonitrile (**2j**) in satisfactory yields. Cyanation was similarly successful with heteroaromatic compounds. as demonstrated by the formation of cyanated quinolines at positions 7 (**2k**) and 8 (**2l**).

Finally. we applied the optimized conditions for this copper-catalyzed cyanation to synthesize Febuxostat **6** (Figure 5). an FDA-approved drug used to treat hyperuricemia in gout patients.⁸⁸ While various synthetic routes have been proposed for this drug.⁸⁹⁻⁹¹ we focused on the method patented by Chemo Iberica in 2010 (Figure 5. top panel).⁹²

<figure><img src="image_12.png" /><figcaption>Cyanation reactionª reported by Castaldi et al. - Chemo Iberica, WO2010142653A1
(2010):</figcaption></figure>

This Workᵇ:

<figure><img src="image_13.png" /><figcaption>**Figure 5** : Synthesis of Febuxostat – copper-catalyzed cyanation as an alternative to the process patented by Castaldi et al. (Chemo Iberico). a reaction conditions : **3** (4.17 mmol. 1 eq). K₄Fe(CN)₆·3H₂O (0.82 mmol. 0.2 eq). Pd(OAc)₂ (0.08 mmol. 2 mol%). Na₂CO₃ (4.17 mmol. 1 eq); b reaction conditions : **5** (0.4 mmol. 1 eq). Na₂[Fe(CN)₅NO] (0.35 eq). [Cu(CyDMEDA)₂Br]Br·H₂O (7.5 mol%). NaOH (2.7 eq). H₂O (0.5 mL). 4h. 80°C.</figcaption></figure>

This patent employed potassium ferrocyanide K₄[Fe(CN)₆] in a palladium-catalyzed cyanation reaction at 145°C over 48 hours to afford the Febuxostat ethyl ester (yield not reported). Using a similar precursor. our approach achieved Febuxostat **6** with a 79% yield in just 4 hours at 75°C in water (Figure 5. bottom panel). underscoring its potential as an efficient and environmentally friendly alternative for drug production. To assess the environmental impact and sustainability of the synthetic protocol. green chemistry metrics (See SI) were calculated for the copper-catalyzed cyanation step leading to Febuxostat **6**. The atom economy (AE) was found to be 72%. indicating efficient incorporation of reactant atoms into the desired product. The E-factor. a measure of waste generation. was calculated to be 1.52 before purification and 42.5 when including solvent and purification waste. The corresponding Process Mass Intensity (PMI) was 43.5. These values suggest a

relatively low environmental footprint at the reaction level. with most waste arising from downstream processing.

## 4. CONCLUSIONS

This study presents a significant advancement in the field of green cyanation chemistry. offering an efficient and environmentally friendly method for synthesizing aryl nitriles. By employing sodium nitroprusside as a non-toxic cyanide source and water as a green solvent. we identified and characterized a novel copper complex. [Cu(CyDMEDA)₂Br]Br·H₂O. to operate a cyanation reaction under mild conditions with good yields. The systematic optimization using Design of Experiment (DoE) and Response Surface Methodology (RSM) not only maximized reaction efficiency but also minimized resource consumption. aligning with the principles of green chemistry.

The method's applicability was demonstrated through the synthesis of Febuxostat. an FDA-approved drug. in 79% yield under significantly milder conditions compared to previously reported protocols. This highlights the potential of this approach to serve as a practical alternative for pharmaceutical and industrial applications. Furthermore. the use of sodium nitroprusside as a cyanide source broadens the scope of green cyanation strategies. offering a safer and more sustainable option compared to traditional cyanide reagents.

Future studies could expand the scope of substrates and explore the mechanistic underpinnings of this reaction to further refine its applicability and efficiency. This work underscores the importance of integrating green chemistry principles into synthetic methodologies. paving the way for more sustainable and scalable solutions in organic synthesis and drug production.

## ASSOCIATED CONTENT

### Supporting Information

The Supporting Information is available free of charge on the ACS Publications website.

Experimental procedures. detailed optimization. and ¹H.¹³C NMR spectra. X-ray structure (PDF)

### Accession codes

Deposition Number 2425496 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge via the joint Cambridge Crystallographic DataCentre (CCDC)

## AUTHOR INFORMATION

### Corresponding Author

* fbihel@unistra.fr. ORCID : 0000-0002-4122-0929

### Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Conceptualization: F.B., M.S.; Methodology: F.B., K.B.
Software: F. B., K. B.; Formal Analysis: F.B.; Investigation: K.B., T.M.P., P.S., C.P., S.H.; Writing - Original Draft: F.B., G.B., M.S., K.B.; Visualization: F.B., K.B.; Supervision: F.B., M.S., G.B.; Project Administration: F.B.; Funding Acquisition: F.B.

## Funding Sources

F.B. was funded by the CNRS, SATT Conectus, Novalix, LABEX Medalis (ANR-10-LABX-0034, Programme d'investissement d'avenir), and by the SFISTRAT'US Project (ANR-20-SFRI-0012) and IdEx Unistra (ANR-10-IDEX0002) under the framework of the French Program "Investments for the Future" given to the Strasbourg Drug Institute (IMS), as part of the Interdisciplinary Thematic Institute (ITI) 2021-2028 Program of the University of Strasbourg, CNRS, and Inserm. K.B. and F.B. were funded by ANR (21-CE07-0057). T.M.P. and M.S. were funded by CAPES-COFECUB (Campus France). F.B. and S.H. were funded by INSERM (PCSI, S22R415A). F.B. and P.S. were funded by Region Grand-Est.

## ACKNOWLEDGMENT

We thank the platform PACSI (UAR3286. Université de Strasbourg) for NMR and mass analyses and the X-ray crystallography platform (UAR2042. Université de Strasbourg) for X-ray analyses.

## ABBREVIATIONS

SNP, Sodium Nitroprusside Na2[Fe(CN)5NO]; FDA, Food & Drug Administration; RSM, Response Surface Methodology; DoE, Design of Experiment; DMEDA, N,N'-Dimethylethylenediamine; CyEDA, rac-(1R,2S)-Cyclohexane-1,2-diamine; CyDMEDA, rac-(1R,2S)-N1,N2-dimethylcyclohexane-1,2-diamine

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SYNOPSIS TOC (Word Style "SN_Synopsis_TOC").

Green copper-catalyzed cyanation of aryl iodides in water using sodium nitroprusside

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