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This work studies the formation of deep eutectic solvents formed by one active pharmaceutical ingredient (quinine, pyrimethamine, or 2-phenylimidazopyridine) and a second component potentially acting as an excipient (betaine, choline chloride, tetramethylammonium chloride, thymol, menthol, gallic acid, vanillin, acetovanillone, 4-hydroxybenzaldehyde, syringaldehyde, propyl gallate, propylparaben, or butylated hydroxyanisole), aiming to address challenges regarding drug solubility, bioavailability, and permeability. A preliminary screening was carried out using the thermodynamic model COSMO-RS, narrowing down the search to three promising excipients (thymol, propyl gallate, and butylated hydroxyanisole). Nine solid-liquid equilibrium (SLE) phase diagrams were experimentally measured combining the three model drugs with the screened excipients, and using a combination of a visual melting method and differential scanning calorimetry. Negative deviations from thermodynamic ideality were observed in all nine systems. Furthermore, a total of four new cocrystals were found, with powder and single crystal X-ray diffraction techniques being employed to verify their unique diffraction patterns. In the thermodynamic modelling of the SLE diagrams, two COSMO-RS parametrizations (TZVP and TZVPD-FINE) were also applied, though neither consistently delivered a better description over the other., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available., Competing Interests: Competing interests statement:The authors declare no competing interest.

Lignin holds significant potential as a feedstock for generating valuable aromatic compounds, fuels, and functional materials. However, achieving this potential requires the development of effective dissolution methods. Previous works have demonstrated the remarkable capability of hydrotropes to enhance the aqueous solubility of lignin, an amphiphilic macromolecule. Notably, deep eutectic solvents (DESs) have exhibited hydrotropic behavior, significantly increasing the aqueous solubility of hydrophobic solutes, making them attractive options for lignin dissolution. This study aimed at exploring the influence of hydrogen bond acceptors (HBAs) and hydrogen bond donors (HBDs) on the performance of DESs as hydrotropes for lignin dissolution, while possible dissolution mechanisms in different water/DES compositions were discussed. The capacity of six alcohols (glycerol, ethylene glycol, 1,3-propanediol, 1,4-butanediol, 1,5-pentanediol, and 1,6-hexanediol) and cholinium chloride to enhance the solubility of Kraft lignin in aqueous media was investigated. A correlation between solubility enhancement and the alkyl chain length of the alcohol was observed. This was rationalized upon the competition between hydrotrope-hydrotrope and solute-hydrotrope aggregates with the latter being maximized for 1,4-butanediol. Interestingly, the hydrotropic effect of DESs on lignin solubility is well represented by the independent sum of the dissolving contributions from the corresponding HBAs and HBDs in the diluted region. Conversely, in the concentrated region, the solubility of lignin for a certain hydrotrope concentration was always found to be higher for the pure hydrotropes rather than their combined HBA/HBD counterparts., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Sigma profiles are quantum-chemistry-derived molecular descriptors that encode the polarity of molecules. They have shown great performance when used as a feature in machine learning applications. To accelerate the development of these models and the construction of large sigma profile databases, this work proposes a graph convolutional network (GCN) architecture to predict sigma profiles from molecule structures. To do so, the usage of molecular mechanics (force field atom types) is explored as a computationally inexpensive node-level featurization technique to encode the local and global chemical environments of atoms in molecules. The GCN models developed in this work accurately predict the sigma profiles of assorted organic and inorganic compounds. The best GCN model here reported, obtained using Merck molecular force field (MMFF) atom types, displayed training and testing set coefficients of determination of 0.98 and 0.96, respectively, which are superior to previous methodologies reported in the literature. This performance boost is shown to be due to both the usage of a convolutional architecture and node-level features based on force field atom types. Finally, to demonstrate their practical applicability, we used GCN-predicted sigma profiles as the input to machine learning models previously developed in the literature that predict boiling temperatures and aqueous solubilities. Using the predicted sigma profiles as input, these models were able to compute both physicochemical properties using significantly less computational resources and displayed only a slight decrease in performance when compared with sigma profiles obtained from quantum chemistry methods.

Are deep eutectic solvents (DESs) a promising alternative to conventional solvents? Perhaps, but their development is hindered by a plethora of misconceptions. These are carefully analyzed here, beginning with the very meaning of DESs, which has strayed far beyond its original scope of eutectic mixtures of Lewis or Brønsted acids and bases. Instead, a definition that is grounded on thermodynamic principles and distinguishes between eutectic and deep eutectic is encouraged, and the types of precursors that can be used to prepare DESs are reviewed. Landmark works surrounding the sustainability, stability, toxicity, and biodegradability of these solvents are also discussed, revealing piling evidence that numerous DESs reported thus far, particularly those that are choline based, lack sufficient sustainability-related traits to be considered green solvents. Finally, emerging DES applications are reviewed, emphasizing their most remarkable feature: the ability to liquefy a solid compound with a target property, allowing its use as a liquid solvent.

The solid-liquid phase behaviour of two tertiary alcohols, perfluoro- tert -butanol and tert -butanol, was studied here using experimental (ITC, DSC and density measurements) and theoretical (MD simulations) approaches. The phase diagram of the binary mixture reveals highly negative deviations from ideality at low concentrations, as well as the formation of co-crystals and is characterized by two eutectic points, a congruent melting point and a peritectic reaction corresponding to TBH : TBF stoichiometries of 2 : 1 and 1 : 1 respectively. Excess molar enthalpies and volumes were calculated, showing negative and positive deviations from ideality, respectively. The effect of acidity, stereochemical hindrance and phobic effects and how they affect intermolecular interactions in these binary mixtures is discussed, with the aim of designing and fine-tuning type V deep eutectic solvents. The results showed that the fluorination of tertiary alcohols can be used for the tuning of the mixing properties and solid-liquid phase diagrams.

The Abraham and NRTL-SAC semipredictive models were employed to represent the solubility of (-)-borneol, (1R)-(+)-camphor, l-(-)-menthol, and thymol in water and organic solvents, using data measured in this work and collected from the literature. A reduced set of solubility data was used to estimate the model parameters of the solutes, and global average relative deviations (ARDs) of 27% for the Abraham model and 15% for the NRTL-SAC model were obtained. The predictive capability of these models was tested by estimating the solubilities in solvents not included in the correlation step. Global ARDs of 8% (Abraham model) and 14% (NRTL-SAC model) were obtained. Finally, the predictive COSMO-RS model was used to describe the solubility data in organic solvents, with ARD of 16%. These results show the overall better performance of NRTL-SAC in a hybrid correlation/prediction approach, while COSMO-RS can produce very satisfactory predictions even in the absence of any experimental data., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

The importance of choline chloride (ChCl) is recognized due to its widespread use in the formulation of deep eutectic solvents. The controlled addition of water in deep eutectic solvents has been proposed to overcome some of the major drawbacks of these solvents, namely their high hygroscopicities and viscosities. Recently, aqueous solutions of ChCl at specific mole ratios have been presented as a novel, low viscous deep eutectic solvent. Nevertheless, these proposals are suggested without any information about the solid-liquid phase diagram of this system or the deviations from the thermodynamic ideality of its precursors. This work contributes significantly to this matter as the phase behavior of pure ChCl and (ChCl + H 2 O) binary mixtures was investigated by calorimetric and analytical techniques. The thermal behavior and stability of ChCl were studied by polarized light optical microscopy and differential scanning calorimetry, confirming the existence of a solid-solid transition at 352.2 ± 0.6 K. Additionally, heat capacity measurements of pure ChCl (covering both ChCl solid phases) and aqueous solutions of ChCl ( x ChCl < 0.4) were performed using a heat-flow differential scanning microcalorimeter or a high-precision heat capacity drop calorimeter, allowing the estimation of a heat capacity change of (ChCl) ≈ 39.3 ± 10 J K -1 mol -1 , between the hypothetical liquid and the observed crystalline phase at 298.15 K. The solid-liquid phase diagram of the ChCl + water mixture was investigated in the whole concentration range by differential scanning calorimetry and the analytical shake-flask method. The phase diagram obtained for the mixture shows an eutectic temperature of 204 K, at a mole fraction of choline chloride close to x ChCl = 0.2, and a shift of the solid-solid transition of ChCl-water mixtures of 10 K below the value observed for pure choline chloride, suggesting the appearance of a new crystalline structure of ChCl in the presence of water, as confirmed by X-ray diffraction. The liquid phase presents significant negative deviations to ideality for water while COSMO-RS predicts a near ideal behaviour for ChCl.

This work showcases the remarkable ability of sigma profiles to function as molecular descriptors in deep learning. The sigma profiles of 1432 compounds are used to train convolutional neural networks that accurately correlate and predict a wide range of physicochemical properties. The architectures developed are then exploited to include temperature as an additional feature.

The formation of deep eutectic solvents (DES) is tied to negative deviations to ideality caused by the establishment of stronger interactions in the mixture than in the pure DES precursors. This work tested thymol and menthol as hydrogen bond donors when combined with different flavonoids. Negative deviations from ideality were observed upon mixing thymol with either flavone or flavanone, two parent flavonoids that only have hydrogen bond acceptor (HBA) groups, thus forming non-ionic DES (Type V). On the other hand, the menthol systems with the same compounds generally showed positive deviations from ideality. That was also the case with the mixtures containing the more complex hydroxylated flavonoid, hesperetin, which resulted in positive deviations when mixed with either thymol or menthol. COSMO-RS successfully predicted the behavior of the solid-liquid phase diagram of the studied systems, allowing for evaluation of the impact of the different contributions to the intermolecular interactions, and proving to be a good tool for the design of DES.

Inspired by the recently proposed cooperative mechanism of hydrotropy, where water molecules mediate the aggregation of hydrotrope around the solute, this work studies the impact of apolar volume and polar group position on the performance of hydrotropes. To do so, the ability of two different families of alkanediols (1,2-alkanediols and 1, n -alkanediols) to increase the aqueous solubility of syringic acid is initially investigated. Interestingly, it is observed that in the dilute region (low hydrotrope concentration), the relative position of the hydroxyl groups of the alkanediols does not impact their performance. Instead, their ability to increase the solubility of syringic acid correlates remarkably well with the size of their alkyl chains. However, this is not the case for larger hydrotrope concentrations, where 1,2-alkanediols are found to perform, in general, better than 1, n -alkanediols. These seemingly contradictory findings are reconciled using theoretical and experimental techniques, namely the cooperative model of hydrotropy and chemical environment probes (Kamlet-Taft and pyrene polarity scales). It is found that the number of hydrotropes aggregated around a solute molecule does not increase linearly with the apolar volume of the former, reaching a maximum instead. This maximum is discussed in terms of competing solute-hydrotrope and hydrotrope-hydrotrope interactions. The results suggest that hydrotrope self-aggregation is more prevalent in 1, n -alkanediols, which negatively impacts their performance as hydrotropes. The results reported in this work support the cooperative model of hydrotropy and, from an application perspective, show that hydrotropes should be designed taking into consideration not only their apolar volume but also their ability to stabilize their self-aggregation in water, which negatively impacts their performance as solubility enhancers.

The differences on the impact of water on the intermolecular interactions present in the deep eutectic solvents betaine/urea and choline/urea are investigated in this work by measuring the solid-liquid phase diagrams of these mixtures with different amounts of added water. These data are analyzed in terms of ternary systems, rather than the usual pseudo-binary approach, and are used to calculate activity coefficients, which provide precious insight into how water affects the interactions of these systems. It is found that the addition of water greatly enhances the intermolecular interactions of betaine/urea near its eutectic composition, hinting at the formation of a 1:1:1 betaine/urea/water aggregate. On the other hand and contrary to what is commonly believed, water has an asymmetric impact on the interactions present in the choline/urea system. The addition of water to choline-rich mixtures leads to weaker interactions, whereas its addition to urea-rich mixtures leads to stronger interactions. This shows that the decrease in the melting temperature of choline/urea mixtures due to the presence of water does not necessarily mean that intermolecular interactions are strengthened. Finally, a minimum in the activity coefficient of urea in the choline/urea system with 2 wt. % of water was found, which coincides with several anomalies in the properties of this system previously reported in the literature.

Hydrotropy is a well-established strategy to enhance the aqueous solubility of hydrophobic drugs, facilitating their formulation for oral and dermal delivery. However, most hydrotropes studied so far possess toxicity issues and are inefficient, with large amounts being needed to achieve significant solubility increases. Inspired by recent developments in the understanding of the mechanism of hydrotropy that reveal ionic liquids as powerful hydrotropes, in the present work the use of cholinium vanillate, cholinium gallate, and cholinium salicylate to enhance the aqueous solubility of two model drugs, ibuprofen and naproxen, is investigated. It is shown that cholinium vanillate and cholinium gallate are able to increase the solubility of ibuprofen up to 500-fold, while all three ionic liquids revealed solubility enhancements up to 600-fold in the case of naproxen. Remarkably, cholinium salicylate increases the solubility of ibuprofen up to 6000-fold. The results obtained reveal the exceptional hydrotropic ability of cholinium-based ionic liquids to increase the solubility of hydrophobic drugs, even at diluted concentrations (below 1 mol·kg -1 ), when compared with conventional hydrotropes. These results are especially relevant in the field of drug formulation due to the bio-based nature of these ionic liquids and their low toxicity profiles. Finally, the solubility mechanism in these novel hydrotropes is shown to depend on synergism between both amphiphilic ions., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The efficiency of an ionic hydrotrope is shown to increase with the hydrophobicity of its counterion, challenging the common view that ionic hydrotropes should possess a small, densely charged counterion such as sodium or chloride.

Aiming to contribute to drug pre-formulation, new eutectic mixtures were developed. Thymol, coumarin, or quaternary ammonium chlorides as excipients, were combined with the active pharmaceutical ingredients (APIs) acetylsalicylic acid, acetaminophen, ibuprofen, ketoprofen, or lidocaine. Their solid-liquid equilibrium (SLE) binary phase diagrams were measured to study eventual phase separation between the compounds, preventing manufacturing problems, and to study the molecular interactions between the APIs and ionic or non-ionic excipients. The Conductor-like Screening Model for Real Solvents (COSMO-RS) capability to predict the SLE of mixtures containing non-ionic excipients was further evaluated. COSMO-RS gives a good quantitative description of the experimental SLE being a tool with great potential in the screening of eutectic systems containing APIs and non-ionic excipients. While thymol presents strong interactions with the APIs, and consequently negative deviations to thermodynamic ideality, systems containing coumarin follow a quasi-ideal behavior. Regarding the ionic excipients, both choline chloride and the tetraalkylammonium chlorides are unable to establish relevant interactions with the APIs, and no significant negative deviations to ideality are observed. The liquefaction of the APIs here studied is favored by using non-ionic excipients, such as thymol, due to the strong interactions it can establish with the APIs., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The mechanism of formation of betaine-based deep eutectic solvents (DES) is presented for the first time. Due to its polarity unbalance, it was found that betaine displays strong negative deviations from ideality when mixed with a variety of different organic substances. These results pave the way for a comprehensive design of novel deep eutectic solvents. A connection to biologically relevant systems was made using betaine (osmolyte) and urea (protein denaturant), showing that these two compounds formed a DES, the molecular interactions of which were greatly enhanced in the presence of water., (© 2020 Wiley-VCH GmbH.)

A recent proposal attributes the origin of hydrotropy to the water-mediated aggregation of hydrotrope molecules around the solute. Experimental evidence for this phenomenon is reported for the first time in this work, using 1 H-NMR. A new computational technique to quantify apolarity is introduced and is used to show that apolarity of both solute and hydrotrope is the driving force of hydrotropy.

The criterion to distinguish a simple eutectic mixture from a deep eutectic solvent (DES) lies in the deviations to thermodynamic ideality presented by the components in the system. In this work, the current knowledge of the molecular interactions in types III and V DES is explored to liquefy a set of three fatty acids and three fatty alcohols, here used as model compounds for carboxyl and hydroxyl containing solid compounds. This work shows that thymol, a stronger than usual hydrogen bond donor, is able to form deep eutectic solvents of type V with the fatty alcohols studied. This is particularly interesting, since these DES formed are hydrophobic. Regarding type III DES, the results suggest that the prototypical DES hydrogen bond acceptor, cholinium chloride, is unable to induce negative deviations to ideality in the model molecules studied. By substituting choline with tetramethylammonium chloride, it is shown that the choline hydroxyl group is responsible for the difficulty in forming choline-based deep eutectic solvents and that its absence induces strong negative deviations to ideality in the alkylammonium side. Finally, it is demonstrated that tetrabutylammonium chloride acts as a chloride donning agent, causing significant negative deviations to ideality in both fatty acids and alcohols and leading to the formation of deep eutectic solvents of type III.

It was recently shown that tetramethylammonium chloride presented negative deviations to ideality when mixed with tetraethylammonium chloride or tetrapropylammonium chloride, leading to a strong decrease of the melting points of these salt mixtures, in a behavior akin to that observed in the formation of deep eutectic solvents. To better rationalize this unexpected melting point depression between two structurally similar compounds devoid of dominant hydrogen bonding capability, new solid-liquid equilibria data for tetramethylammonium-based systems were measured and analyzed in this work. Molecular dynamics was used to show that the strong negative deviations from ideality presented by these systems arise from a synergetic share of the chloride ions. A transfer of chloride ions seems to occur from the bigger cation in the mixture (which possesses a more disperse charge) to the smaller cation (tetramethylammonium), resembling the formation of metal-chloride complexes in type I deep eutectic solvents. This rearrangement of the charged species leads to an energetic stabilization of both components in the mixture, inducing the negative deviations to the ideality observed. The conclusions presented herein emphasize the often-neglected contribution of charge delocalization in deep eutectic solvents formation and its applicability toward the design of new ionic liquid mixtures.

Mixtures of non-ionic compounds have been reported as DES but most are just ideal mixtures. In the thymol-menthol system, an abnormal strong interaction was identified stemming from the acidity difference of the phenolic and aliphatic hydroxyl groups. This type of interaction is found to be the key to prepare non-ionic DES, that may be classified as type V.

There is a lack of fundamental knowledge on deep eutectic solvents, even for the most extensively studied mixtures, such as the mixture of cholinium chloride and urea, which prevents a judicious choice of components to prepare new solvents. The objective of this work is to study and understand the fundamental interactions between cholinium chloride and urea that lead to the experimentally observed melting temperature depression. To do so, the structure of urea was strategically and progressively modified, in order to block certain interaction centres, and the solid-liquid equilibrium data of each new binary system was experimentally measured. Using this approach, it was concluded that the most important interaction between cholinium chloride and urea occurs through hydrogen bonding between the chloride anion and the amine groups. Any blockage of these groups severely hampers the melting point depression effect. Raman spectroscopy and DFT calculations were utilized to study in more detail this hydrogen bonding and its nuances.