A modification of the extended Hildebrand approach to predict the solubility of structurally related drugs in solvent mixtures

Comparative assessment of physics-based in silico methods to calculate relative solubilities

Relative solubilities, i.e. whether a given molecule is more soluble in one solvent compared to others, is a critical parameter for pharmaceutical and agricultural formulation development and chemical synthesis, material science, and environmental chemistry. In silico predictions of this crucial variable can help reducing experiments, waste of solvents and synthesis optimization. In this study, we evaluate the performance of different physics-based methods for predicting relative solubilities. Our assessment involves quantum mechanics-based COSMO-RS and molecular dynamics-based free energy methods using OPLS4, the open-source OpenFF Sage, and GAFF force fields, spanning over 200 solvent-solute combinations. Our investigation highlights the important role of compound multimerization, an effect which must be accounted for to obtain accurate relative solubility predictions. The performance landscape of these methods is varied, with significant differences in precision depending on both the method used and the solute considered, thereby offering an improved understanding of the predictive power of physics-based methods in chemical research.

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute-solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute-solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ ([Formula: see text]) and mixing the artificially liquid solute into the solvent ([Formula: see text]). In this approach [Formula: see text] is predicted using machine learning models, and the [Formula: see text] is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

Drug-plasticizer interactions causing solid state transitions of rifaximin

Solid phase interactions are often the reason for incompatibilities in solid dosage forms. A special situation occurs, if the incompatible compounds are able to migrate within the solid matrix. This study describes for the first time the migration of a plasticizer from the coating into the core and its interaction with the active ingredient located there. This behavior was observed in rifaximin gastro-resistant granules and resulted in the formation of solvates with altered dissolution behavior. For a detailed study, rifaximin was incubated with five plasticizers of different solubility and miscibility as well as different molecular geometry (linear vs branched), (dibutyl sebacate, tributyl citrate, triacetin, polyethylene glycol 400, and propylene glycol). The resulting solid states were analyzed by means of PXRD, supported by thermogravimetric analysis, infrared spectroscopy, and quantitative H NMR. No direct correlation could be demonstrated between the resulting type of solvate/hydrate and the affinity of rifaximin with the respective plasticizers. Interestingly all plasticizers that are able to form type I solvates/hydrates have linear structures. This common feature, which distinguishes them from the more bulky TAC and TBC, seems to be a key characteristic. Rifaximin-PG-solvate formation was not only detected after direct incubation trials, but also observed in enteric coated granules.

Extended Hildebrand Solubility Approach: Prediction and Correlation of the Solubility of Itraconazole in Triacetin: Water Mixtures at 298.15°K

Objectives

The aim of the study is to explore the suitability of an empirical approach for the extended Hildebrand solubility approach (EHSA) to predict and correlate the solubility of the crystalline drug itraconazole (ITRA) in triacetin: water mixtures.

Materials and Methods

The physicochemical properties of ITRA like fusion enthalpy, solubility parameter, and ideal mole fraction solubility were estimated. The solubilities of ITRA in mixed solvent blends comprising triacetin: water were determined at 298.15°K. Theoretical solubilities were back calculated using a polynomial regression equation of the interaction energy parameter W as a function of the solubility parameter (δ₁) of the solvent mixture. Similarly, the solubilities were predicted by direct method based on the use of logarithmic experimental solubilities (logX₂ ) against the solubility parameter (δ₁) of the solvent mixture. The predictive capabilities of both EHSA and the direct method were compared using mean percent deviations.

Results

The solubility of ITRA was increased in all the triacetin: water blends and was highest in the blend in which the solubility parameter of ITRA equaled that of the solvent mixture. The prediction capacities of the direct method (mean % deviation was -1.89%) were better than those of EHSA (mean % deviation was 9.76%) in the fifth order polynomial.

Conclusion

The results indicated that the solubility of any crystalline solute can be adequately predicted and correlated with the mere knowledge of physicochemical properties and EHSA. The information could be of help in process and formulation development.

Application of the solubility parameter concept to assist with oral delivery of poorly water-soluble drugs – a PEARRL review

Objectives

Solubility parameters have been used for decades in various scientific fields including pharmaceutics. It is, however, still a field of active research both on a conceptual and experimental level. This work addresses the need to review solubility parameter applications in pharmaceutics of poorly water-soluble drugs.

Key findings

An overview of the different experimental and calculation methods to determine solubility parameters is provided, which covers from classical to modern approaches. In the pharmaceutical field, solubility parameters are primarily used to guide organic solvent selection, cocrystals and salt screening, lipid-based delivery, solid dispersions and nano- or microparticulate drug delivery systems. Solubility parameters have been applied for a quantitative assessment of mixtures, or they are simply used to rank excipients for a given drug.

Summary

In particular, partial solubility parameters hold great promise for aiding the development of poorly soluble drug delivery systems. This is particularly true in early-stage development, where compound availability and resources are limited. The experimental determination of solubility parameters has its merits despite being rather labour-intensive because further data can be used to continuously improve in silico predictions. Such improvements will ensure that solubility parameters will also in future guide scientists in finding suitable drug formulations.

The Application of Modeling and Prediction to the Formation and Stability of Amorphous Solid Dispersions

Amorphous solid dispersion (ASD) formulation development is frequently difficult owing to the inherent physical instability of the amorphous form, and limited understanding of the physical and chemical interactions that translate to initial dispersion formation and long-term physical stability. Formulation development for ASDs has been historically accomplished through trial and error or experience with extant systems; however, rational selection of appropriate excipients is preferred to reduce time to market and decrease costs associated with development. Current efforts to develop thermodynamic and computational models attempt to rationally direct formulation and show promise. This review compiles and evaluates important methods used to predict ASD formation and physical stability. Recent literature in which these methods are applied is also reviewed, and limitations of each method are discussed.

Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering

OBJECTIVES

The fact that novel drug candidates are becoming increasingly insoluble is a major problem of current drug development. Computational tools may address this issue by screening for suitable solvents or by identifying potential novel cocrystal formers that increase bioavailability. In contrast to other more specialized methods, the fluid phase thermodynamics approach COSMO-RS (conductor-like screening model for real solvents) allows for a comprehensive treatment of drug solubility, solvate and cocrystal formation and many other thermodynamics properties in liquids. This article gives an overview of recent COSMO-RS developments that are of interest for drug development and contains several new application examples for solubility prediction and solvate/cocrystal screening.

METHODS

For all property predictions COSMO-RS has been used. The basic concept of COSMO-RS consists of using the screening charge density as computed from first principles calculations in combination with fast statistical thermodynamics to compute the chemical potential of a compound in solution.

KEY FINDING

The fast and accurate assessment of drug solubility and the identification of suitable solvents, solvate or cocrystal formers is nowadays possible and may be used to complement modern drug development. Efficiency is increased by avoiding costly quantum-chemical computations using a database of previously computed molecular fragments.

SUMMARY

COSMO-RS theory can be applied to a range of physico-chemical properties, which are of interest in rational crystal engineering. Most notably, in combination with experimental reference data, accurate quantitative solubility predictions in any solvent or solvent mixture are possible. Additionally, COSMO-RS can be extended to the prediction of cocrystal formation, which results in considerable predictive accuracy concerning coformer screening. In a recent variant costly quantum chemical calculations are avoided resulting in a significant speed-up and ease-of-use.

Solubility prediction of drugs in mixed solvents using partial solubility parameters

Solubility of drugs in binary and ternary solvent mixtures composed of water and pharmaceutical cosolvents at different temperatures were predicted using the Jouyban-Acree model and a combination of partial solubility parameters as interaction descriptors in the solution. The generally trained version of the model produced the overall mean percentage deviation values for the back-calculated solubility of drugs in binary solvents of 34.3% and the predicted solubilities in ternary solvent mixtures of 38.0%. In addition, the applicability of the trained model for predicting the solvent composition providing the maximum solubility of a drug was investigated. The results of collected solubility data of drugs in various mixed solvents and the newly measured solubility data of five drugs in ethanol + propylene glycol + water mixtures at 25°C showed that the model provided acceptable predictions and could be used in the pharmaceutical industry.

Solubility behavior and prediction for antihelmintics at several temperatures in aqueous and nonaqueous mixtures

A model based on solubility parameters is proposed to predict the solubility curves of antihelmintic drugs at several temperatures, including aqueous and non-aqueous mixtures. The solubility of the drugs was measured in ethanol-water and ethanol-ethyl acetate mixtures at 15-35 degrees C (mebendazole) and at 25 degrees C (thiabendazole and metronidazole). The solid phases were analyzed by differential scanning calorimerty. The polymorphic form A of mebendazole was also characterized from infrared spectroscopy. Markedly different solubility profile shapes were obtained against the solubility parameter of the mixtures: two symmetrical peaks (metronidazole), two maxima of different height (mebendazole) and a single peak (thiabendazole). The solubility parameter of the drugs was related to the co-solvent action of both mixtures and to the solubility peaks. The single equation proposed was able to predict solubility profiles of different shape, including both mixtures and all temperatures, providing reasonable physical meaning for the regression coefficients. The model was successfully tested for its predictive capability using a limited number of experimental data. More than 100 solubilities were predicted at several temperatures using 20 data point for each drug.

Thermodynamics of cosolvent action: phenacetin, salicylic acid and probenecid

The solubility of phenacetin, salicylic acid, and probenecid in ethanol-water and ethanol-ethyl acetate mixtures at several temperatures (15-40 degrees C) was measured. The solubility profiles are related to medium polarity changes. The apparent thermodynamic magnitudes and enthalpy-entropy relationships are related to the cosolvent action. Salicylic acid and probenecid show a single peak against the solubility parameter delta(1) of both solvent mixtures, at 40% (delta(1) = 21.70 MPa(1/2)) and 30% (delta(1) = 20.91 MPa(1/2)) ethanol in ethyl acetate, respectively. Phenacetin displays two peaks at 60% ethanol in ethyl acetate (23.30 MPa(1/2)) and 90% ethanol in water (delta(1) = 28.64 MPa(1/2)). The apparent enthalpies of solution display a maximum at 30% (phenacetin and salicylic acid) and 40% (probenecid) ethanol in water, respectively. Two different mechanisms, entropy at low ethanol ratios, and enthalpy at high ethanol ratios control the solubility enhancement in the aqueous mixture. In the nonaqueous mixture (ethanol-ethyl acetate) enthalpy is the driving force throughout the whole solvent composition for salicylic acid and phenacetin. For probenecid, the dominant mechanism shifts from entropy to enthalpy as the ethanol in ethyl acetate concentration increases. The enthalpy-entropy compensation plots corroborate the different mechanisms involved in the solubility enhancement by cosolvents.

Computational models to predict aqueous drug solubility, permeability and intestinal absorption

In the last decade, poor intestinal absorption of candidate drugs intended for oral administration has been identified as a major bottleneck in drug development. Poor intestinal absorption can often be related to poor aqueous solubility and/or poor permeability across the intestinal wall. Other factors, such as poor stability and the metabolism of the compounds, can also decrease the amount of compound absorbed. In an effort to design compounds with enhanced absorption profile, theoretical predictions of solubility and permeability, among other factors, have gained increased interest, and a large number of papers have been published. In this review, the databases and techniques used for the development of in silico absorption models will be discussed. The focus is on aqueous drug solubility, which has become a major problem in drug development.

Solubility parameter of drugs for predicting the solubility profile type within a wide polarity range in solvent mixtures

The solubility enhancement produced by two binary mixtures with a common cosolvent (ethanol-water and ethyl acetate-ethanol) was studied against the solubility parameter of the mixtures (delta1) to characterize different types of solubility profiles. Benzocaine, salicylic acid and acetanilide show a single peak in the least polar mixture (ethanol-ethyl acetate) at delta1=22.59, 21.70 and 20.91 MPa1/2, respectively. Phenacetin displays two solubility maxima, at delta1=25.71 (ethanol-water) and at delta1=23.30 (ethyl acetate-ethanol). Acetanilide shows an inflexion point in ethanol-water instead of a peak, and the sign of the slope does not vary when changing the cosolvent. The solubility profiles were compared to those obtained in dioxane-water, having a solubility parameter range similar to that covered with the common cosolvent system. All the drugs reach a maximum at about 90% dioxane (delta1=23 MPa1/2). A modification of the extended Hildebrand method is applicable for curves with a single maximum whereas a model including the Hildebrand solubility parameter delta1 and the acidic partial solubility parameter delta1a is required to calculate more complex solubility profiles (with inflexion point or two maxima). A single equation was able to fit the solubility curves of all drugs in the common cosolvent system. The polarity of the drug is related to the shape of the solubility profile against the solubility parameter delta1 of the solvent mixtures. The drugs with solubility parameters below 24 MPa1/2 display a single peak in ethanol-ethyl acetate. The drugs with delta2 values above 25 MPa1/2 show two maxima, one in each solvent mixture (ethanol-water and ethanol-ethyl acetate). The position of the maximum in ethanol-ethyl acetate shifts to larger polarity values (higher delta1 values) as the solubility parameter of the drug delta2 increases.

Relationship between swelling of hydroxypropylmethylcellulose and the Hansen and Karger partial solubility parameters

A model that relates the equilibrium swelling of hydroxypropylmethylcellulose to the partial solubility parameters of both the polymer and the solvents is proposed to interpret and correlate the experimental data. The non-specific interactions are expressed as the dispersion delta(d) and polar delta(p) solubility parameters of Hansen, or as a combination of both. Hydrogen bonding is represented by the acidic delta(a) and the basic delta(b) Karger solubility parameters. The results are compared with models including the same parameters for non-specific interactions (delta(d) and delta(p)) and the Hansen hydrogen bonding parameter delta(h). Equilibrium swelling of this hydrophilic polymer that is widely used in drug formulation is measured in pure solvents covering a wide polarity range. In a qualitative way, swelling increases in solvents with higher Hildebrand solubility parameters and stronger hydrogen bonding capability, and it decreases in non-polar solvents. Single polarity indexes, such as the Hildebrand solubility parameter or the partition coefficient (PC), do not fit well the overall experimental data. The best correlations were obtained with the proposed model, providing at the same time an interpretation consistent with the physical meaning of the terms included in the equation. Swelling increases as the non-specific interactions of the polymer and the solvents become alike, and as the Lewis acid-base interactions of the polymer (1) and the solvent (2) represented by the products delta(1a)delta(2b) and delta(1b)delta(2a) become greater. Conversely, hydrogen bonding self association of the solvents (the product delta(1a)delta(1b)) lowers swelling. The results show that the Karger hydrogen bonding parameters provide a better approach than the Hansen hydrogen bonding parameter to correlate the swelling behavior of a hydrophilic polymer.

A unified cosolvency model for calculating solute solubility in mixed solvents

Organic solvents are amongst the most powerful solubilization agents for a large number of water-insoluble drugs. A number of equations has been reported for mathematical representation of solute solubility in mixed solvents. The question is then posed--is there a mathematical difference between these models? To address this point, it has been demonstrated that all cosolvency models could be made equivalent by using algebraic manipulations. In order to familiarize the readers with the available cosolvency models, they are briefly reviewed. The models can be divided into two mathematical categories, i.e. linear and non-linear models. The linear models include: the log-linear, extended Hildebrand solubility approach, excess free energy equations, combined nearly ideal binary solvent/Redlich-Kister equation and Margule equations which can be converted to a general single model which expresses the logarithm of mole fraction solubility of a solute as a power series of volume fraction of the cosolvent. The non-linear models include the mixture response surface methods, two step solvation model and modified Wilson model which can be converted to a non-linear general form. Also, it has been shown that both the general single model and a non-linear general model are mathematically identical. To show the applicability of the models on real experimental data, 35 data sets have been collected from the literature. Both linear and nonlinear models produced comparable accuracies when an equal number of constant terms was employed in numerical analyses.

Prediction of Solubility of Drugs by Conductor-Like Screening Model for Real Solvents

The solubility of drugs in solvents is fundamentally important for drug development and manufacturing. As the experimental measurements of the solubility are extremely laborious tasks, reliable prediction methods are highly required. We have employed the conductor-like screening model for real solvents (COSMO-RS) in predicting the solubility of drugs and drug-like compounds in various solvent systems. We also evaluated the salt effect on the solubility of caffeine using this method. The present results demonstrated that COSMO-RS has reasonably reproduced the experimental data and have proved that this method is generally available in predicting the solubility of drugs.

Influence of solvent composition on the solid phase at equilibrium with saturated solutions of quinolones in different solvent mixtures

The dissolution profiles and solubilities of three quinolonic drugs (oxolinic, pipemidic, and nalidixic acids) in different solvent mixtures were studied. The behavior of the solid phase, during solubility experiments was in-depth investigated with the aim of detecting possible crystalline modifications, such as polymorphic transitions or solvate formations, that might modify drug stability and/or solubility properties. In order to test the influence of both the nature and polarity of the co-solvents, aqueous and non-aqueous binary mixtures have been prepared by using Lewis base (dioxane and ethyl acetate) and amphiprotic co-solvents (ethanol and water). Differential scanning calorimetry (DSC), hot stage microscopy, IR spectroscopy and X-ray powder diffraction were used in combination with solubility and dissolution studies to characterize and investigate the solid state properties of the original powders and the corresponding ones at equilibrium with the different pure solvents and solvent mixtures examined. The solid phases of nalidixic and oxolinic acids did not show any change after equilibration with the various pure solvents or binary solvent mixtures, regardless the chemical nature of the examined solvents. On the contrary, in the case of pipemidic acid, the different analytical techniques used to characterize the drug solid state enabled identification of a solvated form at equilibrium with pure dioxane and a trihydrated form in aqueous mixtures of water with both ethanol (amphiprotic) or dioxane (Lewis base) in a concentration range from 10 to 100% water.

Modeling drug solubility in water-cosolvent mixtures using an artificial neural network

Application of the artificial neural network (ANN) to calculate the solubility of drugs in water-cosolvent mixtures was shown using 35 experimental data sets. The networks employed were feedforward backpropagation errors with one hidden layer. The topology of neural network was optimized and the optimum topology achieved was a 6-5-1 architecture. All data points in each set were used to train the ANN and the solubilities were back-calculated employing the trained networks. The differences between calculated solubilities and experimental values was used as an accuracy criterion and defined as mean percentage deviation (MPD). The overall MPD (OMPD) and its S.D. obtained for 35 data sets was 0.90 +/- 0.65%. To assess the prediction capability of the method, five data points in each set were used as training set and the solubility at other solvent compositions were predicted using trained ANNs whereby the OMPD (+/-S.D.) for this analysis was 9.04 +/- 3.84%. All 496 data points from 35 data sets were used to train a general ANN model, then the solubilities were back-calculated using the trained network and MPD (+/-S.D.) was 24.76 +/- 14.76%. To test the prediction capability of the general ANN model, all data points with odd set numbers from 35 data sets were employed to train the ANN model, the solubility for the even data set numbers were predicted and the OMPD (+/-S.D.) was 55.97 +/- 57.88%. To provide a general ANN model for a given cosolvent, the experimental data points from each binary solvent were used to train ANN and back-calculated solubilities were used to calculate MPD values. The OMPD (+/-S.D.) for five cosolvent systems studied was 2.02 +/- 1.05%. A similar numerical analysis was used to calculate the solubility of structurally related drugs in a given binary solvent and the OMPD (+/-S.D.) was 4.70 +/- 2.02%. ANN model also trained using solubility data from a given drug in different cosolvent mixtures and the OMPD (+/-S.D.) obtained was 3.36 +/- 1.66%. The results for different numerical analyses using ANN were compared with those obtained from the most accurate multiple linear regression model, namely the combined nearly ideal binary solvent/Redlich-Kister equation, and the ANN model showed excellent superiority to the regression model.

Determination of partial solubility parameters of five benzodiazepines in individual solvents

Three and four component partial solubility parameters for diazepam, lorazepam, oxazepam, prazepam and temazepam were determined using the extended and expanded Hansen regression models. A comparison was made also with solubility parameters calculated by the group contribution method proposed by Van Krevelen. Although a limited number of solvents was used, the results from the present study indicate that the partial solubility parameters obtained from the experimental regression models clearly reflect the structural differences in these five structurally related molecules. High R(2)-values were observed in the regression models (0.932 < or =R(2)< or =0.984), except for lorazepam (0.606 < or =R(2)< or =0.825). This was attributed to difficulties in obtaining reliable values of the temperature and heat of fusion due to thermal decomposition of this compound. Introduction of the Flory-Huggins size correction parameter did not improve the R(2)- and F-values in any of the regression models used.

Lipophilicity determination of psoralens used in therapy through solubility and partitioning: comparison of theoretical and experimental approaches

The aim of this study was to determine and to compare experimental and theoretical solubilities (S) as well as partition coefficients (PC) in an octanol/water system of psoralen (P), 8-methoxypsoralen (8-MOP), 5-methoxypsoralen (5-MOP) and 4,5',8-trimethylpsoralen (TMP). For each psoralen, experimental results were performed in triplicate with a spectrofluorimetric technique. The measurements were achieved 10 times for each solution. The obtained order of the solubilities in pure octanol was 5-MOP approximately TMP > P > 8-MOP, while in water-saturated octanol it was expressed as follows: TMP approximately 5-MOP > P > 8-MOP. However, the following order was found for hydrophobicity: TMP > 5-MOP > 8-MOP > P. The solubility ratios (SR) in pure octanol and water were assessed (mean +/- SD): 3.13 +/- 0.01 (P), 2.60 +/- 0.01 (8-MOP), 3.75 +/- 0.01 (5-MOP), and 5.11 +/- 0.01 (TMP). In saturated phases, they were 3.27 +/- 0.01, 2.63 +/- 0.01, 3.85 +/- 0.01, and 5.32 +/- 0.01, respectively. The PCs were determined with low concentrations according to the Dearden and Bresnen32 method and they were 1.67 +/- 0.01, 1.93 +/- 0.01, 2.00 +/- 0.01, and 3.14 +/- 0.01, respectively. Solubility parameters (delta), in Hildebrand unit (H) or in (cal/cm3)1/2, were evaluated. They confirmed the polarity of psoralens, previously expressed through the PC, although the positional isomers (5-MOP and 8-MOP) revealed no difference. Hildebrand's approach to the solubility of regular solutions and Yalkowsky's concept of the solubility of nonelectrolytes and weak electrolytes in an octanol/water system permitted a comparison of the theoretical and experimental results. The perspective of this work is to use the physicochemical properties of the psoralens in practice for insuring convenient experimental assays and the prediction, in vitro, of the percutaneous absorption of these compounds.

Predicting the solubility of drugs in solvent mixtures: multiple solubility maxima and the chameleonic effect

An approach to reproduce the solubility profile of a drug in several solvent mixtures showing two solubility maxima is proposed in this work. The solubility of sulphamethoxypyridazine was determined at 25 degrees C in several mixtures of varying polarity (hexane:ethyl acetate, ethyl acetate:ethanol and ethanol:water). Sulphamethoxypyridazine was chosen as a model drug because of its proton-donor and proton-acceptor properties. A plot of the mole fraction of the drug vs the solubility parameter of the solvent mixtures shows two solubility peaks. The two peaks found for sulphamethoxypyridazine demonstrate the chameleonic effect as described by Hoy and suggest that the solute-solvent interaction does not vary uniformly from one mixture to another. The different behaviour of the drug in mixtures of two proton-donor and proton-acceptor solvents (alcohol and water), and in mixtures of one proton acceptor (ethyl acetate) and one proton donor-proton acceptor (ethanol) is rationalized in terms of differences in the proton donor-acceptor ability of the solvent mixtures. An approach based on the acidic and basic partial solubility parameters together with the Hildebrand solubility parameter of the solvent mixtures is developed to reproduce the experimental results quantitatively. The equation predicts the two solubility maxima as found experimentally, and the calculated values closely correspond to the experimental values through the range composition of the solvent mixtures. These results show that the chameleonic effect can be described in a quantitative way in terms of Lewis acid-base interactions; this approach can assist the product formulator to choose the proper solvent mixture for a new drug.