Article Cite This: Org. Process Res. Dev. 2019, 23, 1343−1351
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Synergistic Solvation Effects: Enhanced Compound Solubility Using Binary Solvent Mixtures Jun Qiu,* Jacob Albrecht,* and Jacob Janey
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Product Development, Bristol-Myers Squibb Company, One Squibb Drive, New Brunswick, New Jersey 08903, United States ABSTRACT: We explain the concept of solubility synergistic solvation (also known as parabolic solubility), wherein a binary solvent mixture exhibits a higher solubility than either of the component solvents alone. In cases where a compound has poor solubility in pure solvents, a binary solvent mixture may be used to enhance its solubility; however, the obvious challenge is how to identify such solvent pairs efficiently and economically as the number of all possible binary solvent combinations is prohibitively large. After analyzing 33 879 pieces of compound solubility data that were collected from solvent mixtures, we conclude that synergistic solvation may be more common than generally appreciated, the magnitude of which oftentimes is significant enough to make the use of binary mixtures more advantageous than pure solvents. For charged solutes, we confirmed that adding a small amount of water to organic solvents would often improve their solubility substantially. As for noncharged solutes, we identified four “privileged solvent pairs” that are strongly synergistic. In this report, four case studies are discussed which illustrate how synergistic effects have been used to support chemical process development in different ways. KEYWORDS: binary solvent mixtures, synergistic solvation, synergistic effect, enhanced solubility, privileged solvent pairs
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INTRODUCTION In organic chemical process development, knowledge concerning all relevant compounds’ thermodynamic solubility is critical in enabling efficient reaction, workup, and isolation decisions.1,2 When comparing different solvent options, besides chemical compatibility, sustainability, and cost considerations, solvents are often evaluated by whether they can provide high solubility of certain process components, such as starting materials, reagents, catalysts, products, byproducts, and impurities. Through the course of process development, we are oftentimes faced with solubility challenges of key materials in pure solvents and can be limited in terms of solvent choices due to the nature of the chemical transformation or constraints imposed in the subsequent workup and isolation. Hence, the question arises whether solvent mixtures can be employed to improve overall solubility attributes. As background, the solubility profile of a solute in binary mixtures can be theoretically categorized as either normal or synergistic (Figure 1). For a given compound, the solvent with the higher solubility is generally referred to as Solvent 1 or solvent, and the solvent with the lower solubility as Solvent 2 or antisolvent. A normal solubility profile of binary solvent mixtures is defined as such: when the fraction of antisolvent in the mixture increases, solubility of the compound decreases, either linearly or nonlinearly, and across the whole range, the solubility from any mixture is always between the compound’s solubility in pure Solvent 1 and pure Solvent 2. In our automated workflows, we have never observed a compound’s solubility from any binary solvent mixture to be lower than its lowest solubility in either pure Solvent 1 or pure Solvent 2, except when form changes have occurred. We have identified a single literature reference where negative synergistic effects were reported.3 In this report, the magnitude of the negative synergistic effects reported was within 5% of the © 2019 American Chemical Society
Figure 1. Solubility profile of binary solvent mixtures for a solute.
lowest pure solvent solubility and may have been influenced by experimental error, as the data was collected by a dynamic (synthetic) method, less accurate than the shake flask method. Given the limited amount of reliable data on negative synergistic effects, we do not explore the effect in this work. On the other hand, positive synergistic solvation can be a genuine, rather frequent, event. For certain compounds in specific binary solvent mixtures, as the fraction of antisolvent increases, solubility of the compound may increase, at least across some of the solvent ratio ranges. What is more, sometimes the compound’s solubility in binary solvent mixtures can rise so much that it distinctly exceeds the compound’s solubility in pure Solvent 1 (i.e., the better solvent). We define a synergistic effect as precisely where the Received: February 15, 2019 Published: June 21, 2019 1343
DOI: 10.1021/acs.oprd.9b00077 Org. Process Res. Dev. 2019, 23, 1343−1351
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requested help in finding a suitable solvent for the preparation of API A’s GC sample preparation. Several genotoxic impurities (GTIs) could be present in the isolated API, and thus a GC method was being developed to monitor the levels of these GTIs. As detection limits of the GTIs must be set at extremely low levels, the GC samples had to be injected at 50 mg/mL A or higher to achieve an acceptable signal-to-noise ratio. Although API A’s solubility in NMP or DMSO was sufficient, other method constraints excluded the use of these high boiling solvents. In addition, API A was poorly soluble in all pure volatile solvents (Table 1), and thus the analytical development effort encountered a significant challenge.
compound’s solubility in binary solvent mixtures has exceeded its solubility in both pure Solvent 1 and pure Solvent 2. Looser definitions of synergistic effects have been reported by others, such as when a compound’s solubility rises where it is expected to fall (i.e., it curves up but does not surpass the solubility in either pure solvent).4 In this paper, however, we would use our aforementioned, strict definition of synergistic effect without exception. In addition, it should be clear that any confirmed solid form change in relevant solvents during the solubility measurement would disqualify this mixture from being counted as exhibiting a synergistic effect. For pharmaceutical compounds in water and organic solvents, there are many approaches that have been explored to model and predict mixture solubility, including synergistic behavior. Semiempirical activity coefficient approaches such as regressing UNIFAC group contributions 5 as well as COSMO6,7 based models are becoming mainstream for pharmaceutically relevant molecules. Recently, machine learning approaches8 have also been applied to develop quantitative structure−property relationship models to predict solubility in solvent mixtures. Often, published models for solubility in organic solvent mixtures are developed using relatively small sets of data (fewer than 50 solutes) collected for model compounds in selected solvent pairs. Our aim here is to provide additional guidance to researchers developing solubility models by reporting the solubility phenomena observed across hundreds of solutes. Overall, little has been published in the literature on synergistic solvation effects. Knowledge of its prevalence or underlying physical principles is very limited. Wypych et al. discussed synergistic effect on polymeric solutes,9 which do not much resemble pharmaceutical molecules and intermediates. Domanska et al. speculated on the cause of this observed synergistic effect,10 albeit with limited data or experimental evidence. In our view, two issues are evident in this prior work: (1) the reported experimental methods did not produce accurate, consistent, and large data sets compared to current standards, and (2) experimental error was not considered in drawing these conclusions. In contrast, this study attempts to use statistical analysis on our large collection of highly accurate, diverse data to identify solvent pairs that are most likely to afford a synergistic effect such that future empirical data collection and model improvement efforts can be focused on these fertile areas of the relevant solvent space. Hypothesizing as to the fundamental physical causes of this synergistic solvation phenomena is left for future study and discussions as more data become available. On the application side, a synergistic solvation effect can often be very useful. While a normal solubility profile is generally expected by default, synergistic effects had been empirically observed from our comprehensive, automated solubility screens rather routinely. In more than a few instances, we have successfully exploited this effect to improve the chemical processes in various ways. Straightforward applications include taking advantage of the enhanced solubility from solvent pairs to dissolve poorly soluble substrates or reagents, and thus carry out homogeneous chemical reactions rather than challenging heterogeneous mixtures, or to simply dissolve compounds to prepare analytical samples, such as in Case Study 1. Case Study 1. Preparation of GC Samples for a Poorly Soluble API (A). Some years ago, our analytical colleagues
Table 1. Solubility of API A in Pure Solvents at RT solvent
solubility (mg/mL)b
note
NMP DMSO DMF DMPUa DMAca MeOH formamide MeCN acetone THF EtOAc EtOH water 1,2-DCEa chloroform
66.8 46.8 41.9 30.4 27.2 7.5 6.2 5.1 1.9 1.2 1.1 0.8 0.1 50 1.5 1.2 1.9 7.4 3.6 2.0 4.4 37.8 gel 2.8 8.0 gel gel 47.5 gel
4.6 9.0 5.7 18.9 >50 >50 1.2 1.1 2.5 3.9 5.8 1.7 6.1 37.1 21.1 2.3 gel gel 24.1 46.9 gel
5.0 7.7 5.7 14.3 46.9 gel 0.8 0.9 1.9 2.1 2.6 2.3 6.1 22.0 7.4 2.3 3.0 8.1 17.9 31.0 gel
4.3 5.3 4.6 15.0 23.3 17.8 0.7 0.7 1.1 1.5 1.4 2.1 4.3 14.8 gel 1.9 2.1 4.2 11.5 14.0 gel
Figure 2. Solubility profile of Compound B.
Table 4. Solubility of Compound B in Binary Solvent Mixtures at Different Temperatures solvent MeCN 90% MeCN/10% toluene 80% MeCN/20% toluene 70% MeCN/30% toluene 60% MeCN/40% toluene 50% MeCN/50% toluene 40% MeCN/60% toluene 30% MeCN/70% toluene 20% MeCN/80% toluene 10% MeCN/90% toluene toluene
a
20% S1−S2:20% Solvent 1/80% Solvent 2.
effects is to be able to anticipate and prevent unexpected crystallization issues, such as what occurred in the following case study. Case Study 2. Undesired Dissolution of a Crystallization Seedbed. MeCN/toluene was chosen as the solvent/antisolvent system for the crystallization and isolation of compound B, based upon the known solubility data in pure solvents (Table 3). The crystallization process was planned to Table 3. Solubility of Compound B in Pure Solvents at Different Temperatures solvent
50 °C (mg/mL)
25 °C (mg/mL)
MeCN toluene
238.3 9.5
68.7 3.4
50 °C (mg/mL)
40 °C (mg/mL)
25 °C (mg/mL)
10 °C (mg/mL)
238.3 291.2
135.4 191.4
68.7 90.6
37.9 52.8
311.2
220.1
112.4
63.3
319.6
221.8
124.8
73.6
307.7
221.6
127.0
70.7
293.0
207.0
123.7
66.8
247.2
180.0
108.8
67.1
189.2
132.2
80.5
49.0
89.9
77.2
48.4
27.9
24.3
26.3
18.3
11.6
9.5
3.4
avoids solvent ratios that exhibit the highest solubility and risk seedbed formation and subsequent dissolution. Not only did this change prevent the seedbeds from dissolving, but it also significantly improved the process’s Vmax/ Vmin ratio. The Vmax/Vmin ratio is defined as the ratio of maximum reactor volume used to minimum reactor volume used among all unit operations in a given batch process. A smaller ratio indicates a more volume efficient process, as a larger amount of product may be processed in reactors of the same size. As Compound B’s solubility in 50% MeCN/toluene was higher than in pure MeCN, and because at the new seed point, over half of the toluene antisolvent that was required to reach the isolation point had already been added, the Vmax/Vmin ratio was lowered substantially. This unexpected synergistic effect improved Vmax/Vmin by 50%, and turned from a risk to a benefit. As illustrated by the aforementioned case studies, there were different needs for a better understanding of solubility synergistic effects in general, especially regarding their frequency and magnitude, from common solvent pairs. Hence, after our previous report on the solubility correlations
proceed as follows: a saturated solution of Compound B in MeCN was seeded to form a seedbed at high temperature, followed by an addition of toluene as the antisolvent with cooling at the end in order to increase the overall yield. However, the seedbed was observed to dissolve during the course of the toluene addition. This puzzling observation was explained after comprehensive solubility studies were carried out on MeCN/toluene mixtures at different temperatures and solvent ratios. The MeCN/toluene solvent pair was found to exhibit synergistic effects with Compound B (Figure 2 and Table 4).Once this complete solubility profile was acquired with our automated solubility workflow, this undesired seedbed dissolution during the toluene “anti-solvent” addition was thus explainable based on this synergistic effect. Hence, the crystallization protocol was modified to the following: a saturated solution of Compound B in 50% MeCN/toluene is seeded to form a seedbed at high temperature, followed by a toluene antisolvent addition and subsequent cooling as before. This therefore 1345
DOI: 10.1021/acs.oprd.9b00077 Org. Process Res. Dev. 2019, 23, 1343−1351
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̈ the analysis results, we believe it is necessary to use only naive data sets. ̈ era of data collection, our standard 96-well During this naive plate screen typically included 25−40 common, pure solvents and 48 binary mixtures. Among these mixtures, the majority used heptane or water as the antisolvent, while a smaller number used MTBE or toluene as the antisolvent. Other binary mixtures were rationalized as rarely used in process development and so were not screened regularly. Currently, however, we typically collect solubility data from pure solvents in the first screen, and depending upon the compound’s overall solubility trends, adapt and customize the follow-up screens using a wider range of binary mixtures. For example, if a compound is considered to have high, broad solubility overall, solvent/antisolvent pairs with heptane/water/MTBE/toluene as antisolvent will be screened next. On the other hand, if the compound has overall poor solubility and there is a desire to enhance its solubility with binary mixtures, the follow-up screen will instead focus on solvent pairs that are predicted to have high synergistic potential.
for charged and noncharged (i.e., neutral) molecules in pure solvents using the subset of pure solvent data15 (Table 5), we turned our attention to the analysis of aggregated solubility data from binary solvent mixtures (Table 6). Table 5. Number of Solubility Data by Solute and Temperature in Pure Solvents solvent
solutea (# of solutes)
pure
noncharged (692) charged (202)
temperature RT (20−25 °C) other temperatures RT (20−25 °C) other temperatures
total
number of measurements 19951 2895 5775 740 29361
a
Primarily organic compounds with a typical MW range between 100 and 1000.
Table 6. Number of Solubility Data by Solute and Temperature in Mixed Solvents solvent
solutea (# of solutes)
mixed
noncharged (623) charged (180)
temperature RT (20−25 °C) other temperatures RT (20−25 °C) other temperatures
total
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number of measurements
DATA ANALYSIS METHODOLOGY Prior to any analysis, non-numeric solubility data from the aggregate data set were preprocessed by applying the following rules, in the same way and for the same reasons as previously reported:12 • All fully dissolved data points (i.e., reported as “>X mg/ mL”) were excluded. • All numerical solubility data less than 0.1 mg/mL and data reported as “below detection limit” were converted to 0.1 mg/mL. To quantitate how often synergistic events occurred, a custom Python program was written to analyze each data point, using the strictest rules of positive identification. A solubility data point was counted as synergistic only when it met all the following conditions: • The solubility value is higher than the values from both pure solvents • All three values were measured at the same temperature • All three measurements used the same lot of solute • All three measurements were part of same parallel experiment
22925 4030 6420 504 33879
a
Primarily organic compounds with a typical MW range between 100 and 1000.
Note that the vast majority of solvent mixtures in Table 6 are binary systems. A negligible fraction of these solvent mixtures consisted of three or more solvents, and are excluded from further analysis. It is also important to note that an additional 10 000+ pieces of solubility data have been collected since we started this endeavor from an initial data set that contained 63 240 pieces of solubility data.15 We decided to freeze the data source for subsequent analysis, as we had already applied a number of key discoveries from the initial analysis to improve the efficiency of subsequent solubility screens. The later screens were thus considered influenced by our retrospective analysis, and not as ̈ as the earlier screens. In order to avoid any distortion in naive Table 7. Statistics of Synergistic Events with Charged Solutes solvent A
solvent B
pure solvent correlation R2
# of data points
% with synergistic event
median synergy power
max synergy power
acetone EtOAc IPA i-PrOAc MEK THF acetone DMF EtOH IPA MeCN MeOH NMP THF
heptane heptane heptane heptane heptane heptane water water water water water water water water
0.03 0.08 0.10 0.11 0.05 0.04 0.00 0.03 0.06 0.03 0.00 0.15 0.01 0.04
268 309 302 279 242 279 373 291 93 523 143 382 289 328
10 7 8 9 9 7 27 1 16 30 20 9 4 28
1.53 1.64 1.42 1.44 1.40 1.42 2.63 1.26 1.47 2.48 7.38 1.53 2.54 2.62
4 3 4 2 4 3 59 1 4 33 79 5 3 55
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Table 8 presents the full statistics on noncharged solutes. Several solvent pairs, for example, MeOH/toluene and MeOH/MTBE, produced synergistic events frequently. Note that the % with synergistic event value represented the lower bound; in reality, synergistic events may be more frequent than what is reported herein. When developing crystallization processes with these pairs as the solvent/antisolvent system, it would be especially advisible to examine whether synergistic effects pose any risk of crystal seedbed dissolution. We then plotted the median synergy power versus the pure solvent correlation value R2 in Figure 4. With the exception of MeOH/toluene and IPA/toluene, there appeared to be a moderate inverse relationship between median synergy power and R.2 It is interesting to note that alcohol/toluene mixtures also exhibited significant synergistic effects as reported in their use for coal extraction.16 At this point, we identified a few binary mixtures as being most likely to improve the solubility of poorly soluble, noncharged solutes. With further ideation and experimentation, we were able to confirm four “privileged solvent pairs”: MeOH/DCM, MeOH/THF, MeOH/toluene, and MeOH/ anisole. MeOH/DCM is well-known for its synergistic effect on solubility.17−19 On the other hand, MeOH/toluene is suspected to exhibit a synergistic effect based on our observations, and indisputably confirmed by our data mining exercise. Although IPA/toluene has a higher synergy power than MeOH/toluene, we should note that when these binary mixtures are used to enhance solubility, the absolute solubility value is most important, not the synergy power per se. As we have previously reported,12 MeOH’s dissolving power is on average 1.9 times as high as IPA’s, so MeOH/toluene is almost always a stronger solvent pair than IPA/toluene. MeOH/THF and MeOH/anisole were also unknown previously and proposed through a process of rational design. Our data mining found MeOH/MTBE, as we knew THF and MTBE had high correlations,12 with THF being a much better solvent, and we hypothesized that MeOH/THF would exhibit even stronger synergistic effect than MeOH/MTBE. Similarly, we hypothesized that MeOH/anisole could exhibit a stronger synergistic effect than MeOH/toluene. These solvent mixtures were thusly included in later solubility screens when we desired enhanced solubility. Both of these new pairs have performed well in subsequent empirical studies on a number of challenging solutes, such as API C and API D as described in Case study 3 and Case study 4, respectively. In our experience, all four “privileged solvent pairs” exhibited synergistic effects well over 50% of the time in later non̈ and rationally designed screens, making them very naive reliable choices for improving the solubility of poorly soluble, noncharged compounds. Case Study 3. Applying Synergistic Solvent Pairs To Provide Crystallization Optionality. API C was a noncharged compound. On the basis of its solubility properties (Table 9), a crystallization protocol was being developed in DCM/antisolvent systems; however, all isolated solids from such systems were contaminated with residual DCM that could not be removed by drying. Therefore, there was a desire to replace DCM as a solvent, but other strong solvents, such as NMP and DMSO, presented operational issues that precluded their use in the crystallization process development. With these limited solvent options at our disposal, follow-up solubility studies explored binary mixtures with enhanced
Thus, this analysis methodology was very conservative: a positively identified synergistic event was unambiguously measured, while some synergistic events could be miscategorized or ignored due to various reasons such as a screen conducted across multiple experiments or solute lots. In essence, it established the lower bound of how frequent synergistic events were observed from common binary solvent mixtures. For each positively identified synergistic event, its synergy power was calculated as follows: Synergy power = solubility in solvent mixture/max (solubility in pure Solvent 1, solubility in pure Solvent 2) A filter was set to eliminate rare binary mixtures: if the total number of appearance for a binary mixture (all different Solvent 1−Solvent 2 ratios combined) in the whole data set did not exceed 50, the solvent pair would be excluded from further analysis in order to ensure statistical robustness.
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RESULTS AND DISCUSSION Among the 300−780 distinct binary mixtures that were combinatorically possible from the 25−40 most common solvents, the vast majority appeared fewer than 50 times in the aggregate data set. This was not surprising as even with high throughput automated workflows; typically, no more than 48 binary mixtures were accommodated in the screening panel, so only the binary mixtures that were perceived to be the most useful would be routinely included in empirical measurements. Because of the smaller number of charged solutes and fewer related data points in the data set, only the following water and heptane mixtures met the 50 times requirement. Pure solvent correlation R2 values have been defined and reported previously,15 which were the R2 values generated from linear regressions of solubility (log scale) in solvent pairs at RT. Table 7 presents the full statistics on charged solutes, and the median synergy power vs the pure solvent correlation value R2 for charged solutes were plotted in Figure 3. It was long observed that adding water to an organic solvent often improved the solubility of organic salts significantly, which was confirmed in our analysis (Table 7). The best results came from adding a small amount of water (10−20 vol %) most of the time.
Figure 3. Synergy power of binary mixture vs pure solvent correlation R2 with charged solutes. 1347
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Table 8. Statistics of Synergistic Events with Noncharged Solutes solvent A
solvent B
pure solvent correlation R2
# of data points
% with synergistic event
median synergy power
max synergy power
acetone DCM EtOAc IPA i-PrOAc MEK n-BuOAc n-BuOH n-PrOAc THF acetone DCM EtOAc IPA MeOH THF acetone DCM EtOAc IPA MeOH THF acetone DMAc DMF DMSO EtOH IPA MeCN MeOH NMP THF
heptane heptane heptane heptane heptane heptane heptane heptane heptane heptane MTBE MTBE MTBE MTBE MTBE MTBE toluene toluene toluene toluene toluene toluene water water water water water water water water water water
0.17 0.17 0.26 0.21 0.30 0.18 0.30 0.21 0.28 0.11 0.61 0.41 0.80 0.56 0.34 0.48 0.50 0.64 0.70 0.35 0.19 0.41 0.00 0.00 0.00 0.02 0.09 0.08 0.01 0.14 0.00 0.03
1008 105 1236 1152 1065 880 169 99 171 1042 104 103 105 105 99 107 97 92 93 96 92 101 1295 173 1164 165 393 1942 207 1318 1134 1113
3 2 1 12 1 1 1 21 1 1 12 12 13 42 34 3 14 5 11 45 33 3 13 1 1 1 8 21 19 4 1 15
1.25 2.82 1.36 1.45 1.24 1.36 1.08 1.61 2.56 1.30 1.28 1.35 1.31 1.41 1.58 1.02 1.44 1.54 1.16 2.61 2.59 1.10 1.96 1.02 1.12 1.08 1.58 2.15 1.76 1.28 1.24 2.23
4 4 3 7 5 3 1 10 3 3 2 2 8 3 2 1 3 2 2 21 15 1 23 1 2 1 14 23 17 4 6 20
Table 9. Solubility of API C in Pure Solvents at RT
Figure 4. Synergy power of binary mixture vs pure solvent correlation R2 with noncharged solutes.
compound solubility. Two solvent systems were observed to exhibit significant synergistic effects (Figure 5 and Table 10). From these data sets, two possible crystallization pathways for each solvent pair seemed viable. Using MeOH/anisole for example, an API C solution in 30% MeOH/70% anisole could be seeded at high temperature, and then either MeOH or anisole could be added as the antisolvent, followed by cooling 1348
solvent
solubility (mg/mL)
DCM NMP DMSO THF MEK anisole acetone MeCN MeOH 2-MeTHF EtOAc n-BuOAc n-BuOH EtOH toluene CPME MIBK i-PrOAc t-amyl alcohol heptane IPA water MTBE
>100 63.5 34.6 9.1 4.7 2.8 2.1 1.9 1.2 0.99 0.81 0.62 0.45 0.30 0.22 0.20 0.16 0.14 0.07 0.01 0.01 0.01 0.01
DOI: 10.1021/acs.oprd.9b00077 Org. Process Res. Dev. 2019, 23, 1343−1351
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Table 11. Solubility of API D in Pure Solvents at RT
Figure 5. Solubility profile of API C.
Table 10. Solubility of API C in Binary Mixtures at RT solvent MeOH 90% MeOH/10% toluene 80% MeOH/20% toluene 70% MeOH/30% toluene 60% MeOH/40% toluene 50% MeOH/50% toluene 40% MeOH/60% toluene 30% MeOH/70% toluene 20% MeOH/80% toluene 10% MeOH/90% toluene toluene
solubility (mg/mL) 1.2 2.1 3.9 6.5 9.8 10.3 12.3 15.5 13.6 7.5 0.2
solvent MeOH 90% MeOH/10% anisole 80% MeOH/20% anisole 70% MeOH/30% anisole 60% MeOH/40% anisole 50% MeOH/50% anisole 40% MeOH/60% anisole 30% MeOH/70% anisole 20% MeOH/80% anisole 10% MeOH/90% anisole anisole
solubility (mg/mL) 1.2 2.7 5.4
solvent
solubility (mg/mL)
NMP DMSO DMF DMAc acetic acid MeOH EtOH n-BuOH IPA water acetone MEK MIBK MeCN EtOAc anisole THF 2-MeTHF MTBE DCM toluene heptane
>86.6 >80.6 78.6 73.7 67.7 5.4 1.2 0.88 0.53 0.05
