Modeling and Prediction of Solid Solubility by GE Models

Cunico, Larissa P.; Tula, Anjan K.; Ceriani, Roberta; Gani, Rafiqul

doi:10.1002/9781118700686.ch10

Cited by 3 publications

(4 citation statements)

References 50 publications

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“…To validate this procedure, 10 examples of literature on natural products extracted from various sources were used, whose basic information is summarized in Table 2, while Table 3 summarizes the experimental fusion enthalpy values and those estimated by Joback and Reid, used in the study, as well as the absolute error of the estimation respect to the experimental value. [24] 26.72 9.96 Vanillic acid 484.90 32.80 [32] 27.24 16.96 Rosmarinic acid 444.52 51.27 [26] 56. 45 10.11 β-carotene 456.00 56.00 [31] 49.80 11.07 Betulinic acid 588.45 42.23 [36] 43.26 2.44 Coumarin 344.…”

Section: Results Methodology Validationmentioning

confidence: 99%

“…Another study developed a prediction method for solids solubility's using excess Gibbs energy models (GE models) together with analysis of data, model parameter estimation, and calculations of solids solubilitys [16]. In this work a computer-aided model-based framework for solids solubility's calculations and for solvent selection and design, called SolventPro was developed.…”

Section: Introductionmentioning

confidence: 99%

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Use of Group Contribution Methods, Hansen’s Theory of Solubility and Microsoft Excel in the Selection of Solvents for the Extraction of Natural Products

Ríos,

Pérez

2023

Preprint

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Usually, Hansen's theory and the COSMO continuous solvation models have been used for the selection of solvents related to a solute, using the HSPiP and COSMO-RS computational tools, respectively. Those tools are not always available to many researchers; for this reason, Microsoft Excel has been used for the estimation of good solvents in the extraction of natural products of high value and interesting for the food, pharmaceutical and cosmetic industries. This method is applicable to cases where there is little, or practically no information about the compound of interest, for which functional group contribution methods are used, among them, the Joback method for the estimation of properties necessary for the determination of solubility models, and Van Krevelen method for the initial estimation of the Hansen solubility parameters. For solubility estimates, the Flory-Hugging model is used, from which and its classification, according to the North American Pharmacopeia, the good and bad solvents are defined in the selected database for the final estimation of Hansen's parameters and the solubility region. The methodology was validated with ten study cases reported in the literature.

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Section: Results Methodology Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Group Contribution Methods, Hansen’s Theory of Solubility and Microsoft Excel in the Selection of Solvents for the Extraction of Natural Products

Ríos,

Pérez

2023

Preprint

View full text Add to dashboard Cite

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“…Various solubility prediction models can be employed for the selection of optimal solvent systems for crystallization design before going into the laboratory. − Commonly used organic solvents have been grouped by toxicity under the ICH guidance. Class 3 (with low toxic potential) and class 2 (to be limited) solvents are listed in Table S3.…”

Section: Landscape Of Computational Support For Drug Developmentmentioning

confidence: 99%

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Abramov

Sun²,

Zeng³

2022

J. Chem. Inf. Model.

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Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

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“…The unavailability of experimental data to train the model parameters may also be a limiting factor in the case of conventional semi-empirical excess Gibbs energy ( G E ) models such as NRTL-SAC and UNIFAC . The latter generally show a limited capability to predict the API solubility, which is mainly attributed to the failure of the group additivity assumption and the parameter unavailability in the case of complex multifunctional molecules such as APIs. , Empirical approaches that utilize various forms of the solubility parameter ,− have proven in the past to be relatively useful for rapid and qualitative solvent screening in cases where accurate solubility estimates and their temperature dependence are not demanded. ,, Reviews and more information on the API solubility estimations using various computational techniques can be found, for example, in refs , , and , − .…”

Section: Introductionmentioning

confidence: 99%

Purely Predicting the Pharmaceutical Solubility: What to Expect from PC-SAFT and COSMO-RS?

Klajmon

2022

Mol. Pharmaceutics

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A pair of popular thermodynamic models for pharmaceutical applications, namely, the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state and the conductor-like screening model for real solvents (COSMO-RS), are thoroughly benchmarked for their performance in predicting the solubility of active pharmaceutical ingredients (APIs) in pure solvents. The ultimate goal is to provide an illustration of what to expect from these progressive frameworks when applied to the thermodynamic solubility of APIs based on activity coefficients in a purely predictive regime without specific experimental solubility data (the fusion properties of pure APIs were taken from experiments). While this kind of prediction represents the typical modus operandi of the first-principles-aided COSMO-RS, PC-SAFT is a relatively highly parametrized model that relies on experimental data, against which its pure-substance and binary interaction parameters (kij) are fitted. Therefore, to make this benchmark as fair as possible, we omitted any binary parameters of PC-SAFT (that is, kij = 0 in all cases) and preferred pure-substance parameter sets for APIs not trained to experimental solubility data. This computational approach, together with a detailed assessment of the obtained solubility predictions against a large experimental data set, revealed that COSMO-RS convincingly outperformed PC-SAFT both qualitatively (that is, COSMO-RS was better in solvent ranking) and quantitatively, even though the former is independent of both substance-and mixture-specific experimental data. Regarding quantitative comparison, COSMO-RS outperformed PC-SAFT for nine of the ten APIs and for 63 % of the API-solvent systems, with root-mean-square deviations of the predicted data from the entire experimental data set being 0.82 and 1.44 log units, respectively.The results were further analyzed to expand the picture of the performance of both models with respect to the individual APIs and solvents. Interestingly, in many cases, both models were found to qualitatively incorrectly predict the direction of deviations from ideality. Furthermore, we examined how the solubility predictions from both models are sensitive to different API parametrizations.

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Modeling and Prediction of Solid Solubility by GE Models

Cited by 3 publications

References 50 publications

Use of Group Contribution Methods, Hansen’s Theory of Solubility and Microsoft Excel in the Selection of Solvents for the Extraction of Natural Products

Use of Group Contribution Methods, Hansen’s Theory of Solubility and Microsoft Excel in the Selection of Solvents for the Extraction of Natural Products

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Purely Predicting the Pharmaceutical Solubility: What to Expect from PC-SAFT and COSMO-RS?

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