Solubility of pharmaceutical ingredients in triglycerides

Brinkmann, Joscha; Huxoll, Fabian; Luebbert, Christian; Sadowski, Gabriele

doi:10.1016/j.ejpb.2019.10.012

Cited by 16 publications

(39 citation statements)

References 27 publications

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“…Thus, a PC-SAFT approach has been applied to study drug solubility in triglycerides. 97,98 Entire phase diagrams were modeled with a special emphasis on fatty acid chain length and saturation effects on drug solubility. Another study used COSMO-RS theory to predict solubility in glycerides.…”

Section: Lipids As Complex Solvent Systemsmentioning

confidence: 99%

Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions

Kuentz

Bergström

2021

Journal of Pharmaceutical Sciences

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Several approaches to predict and model drug solubility have been used in the drug discovery and development processes during the last decades. Each of these approaches have their own benefits and place, and are typically used as standalone approaches rather than in concert. The synergistic effects of these are often overlooked, partly due to the need of computational experts to perform the modeling and simulations as well as analyzing the data obtained. Here we provide our views on how these different approaches can be used to retrieve more information on drug solubility, ranging from multivariate data analysis over thermodynamic cycle modeling to molecular dynamics simulations. We are discussing aqueous solubility as well as solubility in more complex mixed solvents and media with colloidal structures present. We conclude that the field of computational pharmaceutics is in its early days but with a bright future ahead. However, education of computational formulators with broad knowledge of modeling and simulation approaches is imperative if computational pharmaceutics is to reach its full potential.

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Section: Lipids As Complex Solvent Systemsmentioning

confidence: 99%

Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions

Kuentz

Bergström

2021

Journal of Pharmaceutical Sciences

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“…The solubility enhancement achieved is proposed to be predicted by an interfacial Gibbs energy thermodynamic model [15]. In a dissolution reaction, the molar Gibbs energy will be described via Equation (25):…”

Section: Gibbs Energy Enhancement and Solubility Model Implementationmentioning

confidence: 99%

Integrating Elastic Tensor and PC-SAFT Modeling with Systems-Based Pharma 4.0 Simulation, to Predict Process Operations and Product Specifications of Ternary Nanocrystalline Suspensions

Davidopoulou

Kachrimanis

2021

Pharmaceutics

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Comminution of BCS II APIs below the 1 μm threshold followed by solidification of the obtained nanosuspensions improves their dissolution properties. The breakage process reveals new crystal faces, thus creating altered crystal habits of improved wettability, facilitated by the adsorption of stabilizing polymers. However, process-induced transformations remain unpredictable, mirroring the current limitations of our atomistic level of understanding. Moreover, conventional equations of estimating dissolution, such as Noyes–Whitney and Nernst–Brunner, are not suitable to quantify the solubility enhancement due to the nanoparticle formation; hence, neither the complex stabilizer contribution nor the adsorption influence on the interfacial tension occurring between the water and APIs is accounted for. For such ternary mixtures, no numeric method exists to correlate the mechanical properties with the interfacial energy, capable of informing the key process parameters and the thermodynamic stability assessment of nanosuspensions. In this work, an elastic tensor analysis was performed to quantify the API stability during process implementation. Moreover, a novel thermodynamic model, described by the stabilizer-coated nanoparticle Gibbs energy anisotropic minimization, was structured to predict the material’s system solubility quantified by the application of PC-SAFT modeling. Comprehensively merging elastic tensor and PC-SAFT analysis into the systems-based Pharma 4.0 algorithm provided a validated, multi-level, built-in method capable of predicting the critical material quality attributes and corresponding key process parameters.

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“…The Perturbed-Chain Statistical Associating Flluid Theory (PC-SAFT) has proven to be a powerful tool for the prediction of phase equilibria, as demonstrated among others for n -alkanes and active pharmaceutical ingredients . In addition, PC-SAFT has also been successfully used to model solid–liquid equilibria of triglyceride mixtures, which also have very low vapor pressures that are difficult to measure . However, due to the lack of thermodynamic pure-component data, so far no PC-SAFT parameters are available for biogenic amines.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic Properties of Biogenic Amines and Their Solutions

et al. 2021

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Vapor pressures of the biologically and industrially relevant amines 2-phenylethan-1-amine, 2-amino-1-phenylethanol, α-(methylaminomethyl)benzyl alcohol, 1-phenylmethanamine, and N,N-diethylundecan-1-amine were measured via the transpiration method. Pure-component parameters for the thermodynamic model PC-SAFT were fitted to these vapor pressures and to liquid densities. The pure-component parameters were validated with measured liquid densities of binary mixtures dimethylsulfoxid + 4-(2-aminoethyl)phenol, dimethylsulfoxid + 2-amino-1-phenylethanol, dimethylsulfoxid + α-(methylaminomethyl)benzyl alcohol, and dimethylsulfoxid + 1-phenylmethanamine at 0.102 MPa and temperatures from 298.15 to 343.15 K at different amine mass fractions. Solid−liquid equilibria at 0.1 MPa were measured in binary mixtures of α-(methylaminomethyl)benzyl alcohol + water and 4-(2-aminoethyl)phenol + water at 298.15 and 308.15 K. Finally, the presence of liquid−liquid phase separation for these systems was qualitatively predicted using PC-SAFT based on the solid−liquid equilibria only and validated for the system α-(methylaminomethyl)benzyl alcohol + water by experiments at 293.15 and 323 K at 0.1 MPa.

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Solubility of pharmaceutical ingredients in triglycerides

Cited by 16 publications

References 27 publications

Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions

Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions

Integrating Elastic Tensor and PC-SAFT Modeling with Systems-Based Pharma 4.0 Simulation, to Predict Process Operations and Product Specifications of Ternary Nanocrystalline Suspensions

Thermodynamic Properties of Biogenic Amines and Their Solutions

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