PC-SAFT Modeling of Phase Equilibria Relevant for Lipid-Based Drug Delivery Systems

Brinkmann, Joscha; Exner, Lara; Verevkin, Sergey P.; Luebbert, Christian; Sadowski, Gabriele

doi:10.1021/acs.jced.0c00912

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…The solid–liquid equilibrium phase diagrams are shown in Figure . A previous publication reported that the solubility data of felodipine in ethanol at 298.15 K in mass fraction was 0.16351 . In this work, the mass fraction solubility data of felodipine in ethanol at 298.15 K is 0.10113.…”

Section: Resultsmentioning

confidence: 52%

“…A previous publication reported that the solubility data of felodipine in ethanol at 298.15 K in mass fraction was 0.16351. 23 In this work, the mass fraction solubility data of felodipine in ethanol at 298.15 K is 0.10113. This error is acceptable, which may be owing to different experimental methods, temperature control accuracy, and test conditions.…”

Section: Resultsmentioning

confidence: 93%

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Solid–Liquid Phase Equilibrium and the Thermodynamic Correlation of Felodipine Form I in Pure and Mixed Solvents

Nie

Liu

et al. 2022

J. Chem. Eng. Data

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Because felodipine form I is used in marketed products, a co-solvent (ethyl acetate) is applied to enhance the solubility of poorly soluble form I in the present work. The solid−liquid equilibrium phase diagrams in pure and mixed solvents were established, and the effects of the solvent properties and intermolecular interaction on the solubility of felodipine were also studied. In pure solvent, the largest and smallest solubility data were obtained in ethyl acetate and toluene, respectively. The solubility increased to the maximum value at w = 0.8 with a mass fraction of ethyl acetate in (isopropanol + ethyl acetate) mixtures and then decreased with the increasing composition of the co-solvent. The modified Apelblat equation and the Jouyban−Acree model were applied to evaluate the solubility profile. All of the values of RAD were no more than 5%. More importantly, preferential solvation parameters (δ x1,3 ) of felodipine in (isopropanol + ethyl acetate) mixtures were calculated using the inverse Kirkwood−Buff integrals method. The results indicated that the values of δx 1,3 were all negative in ethyl acetate-rich and isopropanol-rich regions but positive in the composition of 0.45 < x 1 < 0.80. Felodipine is preferentially solvated by isopropanol in ethyl acetate-rich and isopropanol-rich regions. In the composition of 0.45 < x 1 < 0.80, felodipine is preferentially solvated by ethyl acetate.

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Section: Resultsmentioning

confidence: 52%

Section: Resultsmentioning

confidence: 93%

Solid–Liquid Phase Equilibrium and the Thermodynamic Correlation of Felodipine Form I in Pure and Mixed Solvents

Nie

Liu

et al. 2022

J. Chem. Eng. Data

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“…The solubility of the drug in polymers PEG 6000 or PVP K30 (the mass ratios of the drug in the polymers are 40, 60, and 80%) was determined by DSC. , The DSC was calibrated in weight (standard calibration weight), temperature and enthalpy of fusion (zinc), and heat capacity (sapphire). The drug and polymer physical mixtures were mixed by a mortar and pestle for 30 min.…”

Section: Methodsmentioning

confidence: 99%

“…The drug is usually considered a solute and the polymer is considered a solvent. The melting temperature of the drug is usually considered as the dissolving temperature of the drug in the polymer. , Thus, the solubility of the drug in the polymer (the heating rate is 0 K/min) was obtained by extrapolation from experiment data.…”

Section: Methodsmentioning

confidence: 99%

Measurement and Thermodynamic Modeling of Oxaprozin Solubility in Polymers and Mixed Solutions

Wu,

Jiang,

Shen

et al. 2024

J. Chem. Eng. Data

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Measuring and modeling the solubility of drugs in different solvent systems is helpful to guide the selection of appropriate solvents at various stages of drug formulation development. In this work, the gravimetrical method was used to determine the solubility data of oxaprozin (OXA) in different compositions of water/organic solvents (methanol and ethanol) binary mixtures within the range of 293.15 to 333.15 K. Subsequently, the differential scanning calorimetry method was used to measure the solubility of the drug in polymers (Polyethylene Glycol 6000 and Polyvinylpyrrolidone K30) at above 400 K. Then, the solubility of OXA in ultrapure water and polymer aqueous solution was acquired by UV spectrophotometry or HPLC within a temperature range of 303.15 to 323.15 K. Finally, the experimental values were compared with the calculated values from the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) to investigate the prediction accuracy of this model in different complex mixed solvent systems. The average relative deviations (ARD) were used to evaluate the model performance of PC-SAFT. Furthermore, PC-SAFT combined with solid−liquid equilibrium theory not only modeled the phase behavior between pure or mixed solvents and drugs independent of the molecular weight of the solvent but also did not require any experimental data or model parameters from the ternary system to predict the phase behavior of OXA in binary solvents. The results of this work illustrate that PC-SAFT is a beneficial model in drug development.

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“…Over the last decade, interest regarding the use of ML algorithms across diverse disciplines in pharmaceutical design and development has grown [ 11 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. While ML models have been produced to optimise lipid-based formulation (LBF) development [ 3 , 22 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], the application of more novel ML approaches for bio-enabling formulations currently focuses on solid dispersions (SDs) [ 21 , 34 , 35 ]. However, their application to LBFs, particularly supersaturated LBFs (sLBFs), remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

et al. 2021

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In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.

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PC-SAFT Modeling of Phase Equilibria Relevant for Lipid-Based Drug Delivery Systems

Cited by 7 publications

References 36 publications

Solid–Liquid Phase Equilibrium and the Thermodynamic Correlation of Felodipine Form I in Pure and Mixed Solvents

Solid–Liquid Phase Equilibrium and the Thermodynamic Correlation of Felodipine Form I in Pure and Mixed Solvents

Measurement and Thermodynamic Modeling of Oxaprozin Solubility in Polymers and Mixed Solutions

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

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