Understanding the effect of pH on the solubility of Gamavuton-0 in the aqueous solution: Experimental and COSMO-RS modelling

Kurnia, Kiki A.; Harimurti, Sabtanti; Yung, Hue Keat; Baraheng, Alfanee; Alimin, Mohammad Azlie Sham; Dagang, Nurul Safiah Mat; Fadhilah, Alfan; Rosyadi, Rivki; Yahya, Wan Zaireen Nisa; Bustam, Mohamad Azmi

doi:10.1016/j.molliq.2019.111845

Cited by 10 publications

(2 citation statements)

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“…COSMO-RS is considered a useful tool for qualitatively predicting API solubility and other phase equilibria and is reported to be an established solvent screening tool in the pharmaceutical industry. 48,57,58 Further information and examples of the performance of COSMO-RS for API solubility can be found in refs 21,47,48,52,57,[59][60][61][62] . Other studies that demonstrate the usefulness of COSMO-based approaches in the pharmaceutical field (also beyond the solubility problem), can be found, e.g., in refs [63][64][65][66][67][68][69] .…”

Section: Introductionmentioning

confidence: 99%

“…However, we would like to emphasize that this approach is not followed in this study as it would contradict our ultimate goal to show and assess only pure predictions. COSMO-RS is considered a useful tool for qualitatively predicting API solubility and other phase equilibria and is reported to be an established solvent screening tool in the pharmaceutical industry. ,, Further information and examples of the performance of COSMO-RS for API solubility can be found in refs , , , , and , − . Other studies that demonstrate the usefulness of COSMO-based approaches in the pharmaceutical field (also beyond the solubility problem) can be found, for example, in refs − .…”

Section: Introductionmentioning

confidence: 99%

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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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