Predicting the API partitioning between lipid-based drug delivery systems and water

Brinkmann, Joscha; Becker, Isabel; Kroll, Peter; Luebbert, Christian; Sadowski, Gabriele

doi:10.1016/j.ijpharm.2021.120266

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…In light of the drawbacks of the FH approach outlined above, more advanced solution theories have gained attention with respect to the modeling of ASD. The perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state (EOS) , represents one of such theories that has gained popularity in pharmaceutical applications in the past decade, including the solubility of APIs in low- M solvents, − partitioning behavior of APIs (LLE), , cocrystal screening, and phase behavior of ASD. − PC-SAFT explicitly takes into account specific interactions, including the important hydrogen bonding, and describes materials using pure-substance parameters that have a clear physical meaning. Furthermore, it approximates molecules as chains of spherical segments, which appears to be appropriately capturing the structure of (linear) polymers, in principle.…”

Section: Introductionmentioning

confidence: 99%

Can Pure Predictions of Activity Coefficients from PC-SAFT Assist Drug–Polymer Compatibility Screening?

Pavliš,

Mathers,

Fulem

et al. 2023

Mol. Pharmaceutics

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The bioavailability of poorly water-soluble active pharmaceutical ingredients (APIs) can be improved via the formulation of an amorphous solid dispersion (ASD), where the API is incorporated into a suitable polymeric carrier. Optimal carriers that exhibit good compatibility (i.e., solubility and miscibility) with given APIs are typically identified through experimental means, which are routinely labor- and cost-inefficient. Therefore, the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state, a popular thermodynamic model in pharmaceutical applications, is examined in terms of its performance regarding the computational pure prediction of API–polymer compatibility based on activity coefficients (API fusion properties were taken from experiments) without any binary interaction parameters fitted to API–polymer experimental data (that is, k ij = 0 in all cases). This kind of prediction does not need any experimental binary information and has been underreported in the literature so far, as the routine modeling strategy used in the majority of the existing PC-SAFT applications to ASDs comprised the use of nonzero k ij values. The predictive performance of PC-SAFT was systematically and thoroughly evaluated against reliable experimental data for almost 40 API–polymer combinations. We also examined the effect of different sets of PC-SAFT parameters for APIs on compatibility predictions. Quantitatively, the total average error calculated over all systems was approximately 50% in the weight fraction solubility of APIs in polymers, regardless of the specific API parametrization. The magnitude of the error for individual systems was found to vary significantly from one system to another. Interestingly, the poorest results were obtained for systems with self-associating polymers such as poly(vinyl alcohol). Such polymers can form intramolecular hydrogen bonds, which are not accounted for in the PC-SAFT variant routinely applied to ASDs (i.e., that used in this work). However, the qualitative ranking of polymers with respect to their compatibility with a given API was reasonably predicted in many cases. It was also predicted correctly that some polymers always have better compatibility with the APIs than others. Finally, possible future routes to improve the cost–performance ratio of PC-SAFT in terms of parametrization are discussed.

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Section: Introductionmentioning

confidence: 99%

Can Pure Predictions of Activity Coefficients from PC-SAFT Assist Drug–Polymer Compatibility Screening?

Pavliš,

Mathers,

Fulem

et al. 2023

Mol. Pharmaceutics

View full text Add to dashboard Cite

show abstract

“…Particularly for complex mixtures, the application of kij is often inevitable to produce reasonable results. 38 While studies on modeling/correlation of API solubility with fitted kijs are frequent (e.g., refs 29,36,[39][40][41] ), calculations without adapted kijs (termed 'pure' or 'theoretical' predictions, as they are based on pure-substance parameters) are generally underreported in the literature. Only a few publications are relevant in this regard:…”

Section: Introductionmentioning

confidence: 99%

“…While studies on modeling/correlation of API solubility with fitted k ij s are frequent (e.g., refs , , and – ), calculations without adapted k ij s (termed “pure” or “theoretical” predictions, as they are based on pure-substance parameters) are generally underreported in the literature. Only a few publications are relevant in this regard: Spyriouni et al parametrized six APIs by fitting their PC-SAFT parameters to solubility data in three different solvents and then tested their predictive power without k ij s for other API–solvent systems not included in the parametrization.…”

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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Predicting the API partitioning between lipid-based drug delivery systems and water

Cited by 2 publications

References 26 publications

Can Pure Predictions of Activity Coefficients from PC-SAFT Assist Drug–Polymer Compatibility Screening?

Can Pure Predictions of Activity Coefficients from PC-SAFT Assist Drug–Polymer Compatibility Screening?

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

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