Predicting the Solvation of Organic Compounds in Aqueous Environments: From Alkanes and Alcohols to Pharmaceuticals

Prediction of water anomalous properties by introducing the two-state theory in SAFT

Water is one of the most abundant substances on earth, but it is still not entirely understood. It shows unusual behavior, and its properties present characteristic extrema unlike any other fluid. This unusual behavior has been linked to the two-state theory of water, which proposes that water forms different clusters, one with a high density and one with a low density, which may even form two distinct phases at low temperatures. Models incorporating the two-state theory manage to capture the unusual extrema of water, unlike traditional equations of state, which fail. In this work, we have derived the framework to incorporate the two-state theory of water into the Statistical-Associating-Fluid-Theory (SAFT). More specifically, we have assumed that water is an ideal solution of high density water molecules and low density water molecules that are in chemical equilibrium. Using this assumption, we have generalized the association term SAFT to allow for the simultaneous existence of the two water types, which have the same physical parameters but different association properties. We have incorporated the newly derived association term in the context of the Perturbed Chain-SAFT (PC-SAFT). The new model is referred to as PC-SAFT-Two-State (PC-SAFT-TS). Using PC-SAFT-TS, we have succeeded in predicting the characteristic extrema of water, such as its density and speed of sound maximum, etc., without loss of accuracy compared to the original PC-SAFT. This new framework is readily extended to mixtures, and PC-SAFT-TS manages to capture the solubility minimum of hydrocarbons in water in a straightforward manner.

Modeling the Thermodynamic Properties of Saturated Lactones in Nonideal Mixtures with the SAFT-γ Mie Approach

The prediction of the thermodynamic properties of lactones is an important challenge in the flavor, fragrance, and pharmaceutical industries. Here, we develop a predictive model of the phase behavior of binary mixtures of lactones with hydrocarbons, alcohols, ketones, esters, aromatic compounds, water, and carbon dioxide. We extend the group-parameter matrix of the statistical associating fluid theory SAFT-γ Mie group-contribution method by defining a new cyclic ester group, denoted cCOO. The group is composed of two spherical Mie segments and two association electron-donating sites of type e1 that can interact with association electron-accepting sites of type H in other molecules. The model parameters of the new cCOO group interactions (1 like interaction and 17 unlike interactions) are characterized to represent target experimental data of physical properties of pure fluids (vapor pressure, single-phase density, and vaporization enthalpy) and mixtures (vapor-liquid equilibria, liquid-liquid equilibria, solid-liquid equilibria, density, and excess enthalpy). The robustness of the model is assessed by comparing theoretical predictions with experimental data, mainly for oxolan-2-one, 5-methyloxolan-2-one, and oxepan-2-one (also referred to as γ-butyrolactone, γ-valerolactone, and ε-caprolactone, respectively). The calculations are found to be in very good quantitative agreement with experiments. The proposed model allows for accurate predictions of the thermodynamic properties and highly nonideal phase behavior of the systems of interest, such as azeotrope compositions. It can be used to support the development of novel molecules and manufacturing processes.

Factors Influencing the Bioavailability of Organic Molecules to Bacterial Cells-A Mini-Review

The bioavailability of organic compounds to bacterial cells is crucial for their vital activities. This includes both compounds that are desirable to the cells (e.g., sources of energy, carbon, nitrogen, and other nutrients) and undesirable compounds that are toxic to the cells. For this reason, bioavailability is an issue of great importance in many areas of human activity that are related to bacteria, e.g., biotechnological production, bioremediation of organic pollutants, and the use of antibiotics. This article proposes a classification of factors determining bioavailability, dividing them into factors at the physicochemical level (i.e., those related to the solubility of a chemical compound and its transport in aqueous solution) and factors at the microbiological level (i.e., those related to adsorption on the cell surface and those related to transport into the cell). Awareness of the importance of and the mechanisms governing each of the factors described allows their use to change bioavailability in the desired direction.

Improving the SAFT-γ Mie equation of state to account for functional group interactions in a structural (s-SAFT-γ Mie) framework: Linear and branched alkanes

The group contribution SAFT-γ Mie EoS is based on the statistical associating fluid theory for fused heteronuclear molecules. While the chain term of the model has been modified to account for the new functional group-specific parameters, it does not impose a bonding order to these functional groups, only considering intergroup interactions in the monomer reference fluid. This leaves the model unable to account for the different physical properties of structural isomers and implicitly introducing modeling bias to species where the molecular structure mimics those used in the parameter regression. In this work, a simple but physically meaningful modification to the chain term in SAFT-γ Mie is proposed that accounts for the number of intergroup bonds, thereby encoding structural information in the model, without introducing an additional regressed parameter. The resulting structural SAFT-γ Mie (s-SAFT-γ Mie) requires reparameterization of the group parameters, which we present for linear and branched alkanes (CH₃, CH₂, CH, and C groups) here. Following an identical parameterization procedure to the original model, validation showed that the modification actually improves prediction accuracy for linear alkanes while addressing the original inability of the framework to distinguish between structural isomers. The good predictive performance seen in this work, for both pure component and mixture properties, lays a good foundation for expansion to other functional groups in future work.

Representation of Multiphase OH Oxidation of Amorphous Organic Aerosol for Tropospheric Conditions

Organic aerosol (OA) is ubiquitous in the atmosphere and, during transport, can experience chemical transformation with consequences for air quality and climate. Prediction of the chemical evolution of OA depends on its reactivity with atmospheric oxidants such as the OH radical. OA particles undergo amorphous phase transitions from liquid to solid (glassy) states in response to temperature changes, which, in turn, will impact its reactivity toward OH oxidation. To improve the predictability of OA reactivity toward OH oxidation, the reactive uptake coefficients (γ) of OH radicals reacting with triacontane and squalane serving as amorphous OA surrogates were measured at temperatures from 213-293 K. γ increases strongest with temperature when the organic species is in the liquid phase, compared to when being in the semisolid or solid phase. The resistor model is applied, accounting for the amorphous phase state changes using the organic species' glass transition temperature and fragility, to evaluate the physicochemical parameters of the temperature dependent OH uptake process. This allows for the derivation of a semiempirical formula, applicable to models, to predict the degree of oxidation and chemical lifetime of the condensed-phase organic species for typical tropospheric temperature and humidity when OA particle viscosity is known.

Computer Aided Design of Solvent Blends for Hybrid Cooling and Antisolvent Crystallization of Active Pharmaceutical Ingredients

Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAMbD) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing.

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R2) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol-1 for the training set. The best PLS model includes six latent variables, and has an R2 value of 0.91 and a RMSE of 0.52 kcal mol-1. The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.

Modelling and prediction of the thermophysical properties of aqueous mixtures of choline geranate and geranic acid (CAGE) using SAFT-γ Mie

Deep-eutectic solvents and room temperature ionic liquids are increasingly recognised as appropriate materials for use as active pharmaceutical ingredients and formulation additives. Aqueous mixtures of choline and geranate (CAGE), in particular, have been shown to offer promising biomedical properties but understanding the thermophysical behaviour of these mixtures remains limited. Here, we develop interaction potentials for use in the SAFT-γ Mie group-contribution approach, to study the thermodynamic properties and phase behaviour of aqueous mixtures of choline geranate and geranic acid. The determination of the interaction parameters between chemical functional groups is carried out in a sequential fashion, characterising each group based on those previously developed. The parameters of the groups relevant to geranic acid are estimated using experimental fluid phase-equilibrium data such as vapour pressure and saturated-liquid density of simple pure components (n-alkenes, branched alkenes and carboxylic acids) and the phase equilibrium data of mixtures (aqueous solutions of branched alkenes and of carboxylic acids). Geranate is represented by further incorporating the anionic carboxylate group, COO⁻, which is characterised using aqueous solution data of sodium carboxylate salts, assuming full dissociation of the salt in water. Choline is described by incorporating the cationic quaternary ammonium group, N⁺, using data for choline chloride solutions. The osmotic pressure of aqueous mixtures of CAGE at several concentrations is predicted and compared to experimental data obtained as part of our work to assess the accuracy of the modelling platform. The SAFT-γ Mie approach is shown to be predictive, providing a good description of the measured data for a wide range of mixtures and properties. Furthermore, the new group-interaction parameters needed to represent CAGE extend the set of functional groups of the group-contribution approach, and can be used in a transferable way to predict the properties of systems beyond those studied in the current work.

The properties of aqueous solutions of the CAGE deep eutectic solvent are predicted with the SAFT-γ Mie approach.