Predicción de la solubilidad de fármacos
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Date
2024
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Publisher
Universidad de Concepción
Abstract
Se evaluó la solubilidad de cuatro fármacos de uso común seleccionados arbitrariamente: paracetamol, ibuprofeno, aspirina y naproxeno, en tres solventes: etanol, agua y 1-octanol, que simulan las condiciones del cuerpo humano. Para ello, en primer lugar se evaluaron los sistemas binarios compuestos de fármaco y solvente, y luego los sistemas ternarios compuestos por el fármaco, agua y 1-octanol. Esta mezcla de solventes se estudió para distintas relaciones de masa 1-octanol/agua simulando las condiciones dentro del cuerpo humano: medio acuoso (agua) y membrana (1-octanol).
Para la ejecución del estudio se desarrolló un código en Python que permitió calcular la solubilidad en función de la temperatura para la solubilidad de cada fármaco, en el caso ideal y en el no ideal según los modelos NRTL y UNIFAC. Los datos necesarios para el desarrollo de este estudio fueron la entalpía y temperatura de fusión de los fármacos, los parámetros de interacción para el modelo NRTL y la descomposición de los compuestos según los grupos funcionales que los conforman junto con sus parámetros de interacción según el modelo predictivo UNIFAC. Además, para corroborar la precisión de los modelos y comprobar el ajuste de los modelos, se obtuvieron datos experimentales para los sistemas estudiados desde la literatura. Para cuantificar esto, se calculó el error de cada uno de los modelos.
En conclusión, se comprueba que el modelo predictivo UNIFAC satisface la necesidad de un modelo universal para determinar la solubilidad. Teniendo como principal ventaja el hecho de que no es necesario depender de datos experimentales para generar los parámetros necesarios, a diferencia del modelo NRTL.
The solubility of four arbitrarily selected commonly used drugs: paracetamol, ibuprofen, aspirin and naproxen, was evaluated in three solvents: ethanol, water and 1-octanol, which simulate the conditions of the human body. For this purpose, binary systems composed of drug and solvent were evaluated first, followed by ternary systems composed of drug, water and 1-octanol. This mixture of solvents was studied for different 1-octanol/water mass ratios simulating the conditions inside the human body: aqueous medium (water) and membrane (1-octanol). For the execution of the study, a Python code was developed to calculate the solubility as a function of temperature for the solubility of each drug, in the ideal and non-ideal case according to the NRTL and UNIFAC models. The data required for the development of this study were the enthalpy and melting temperature of the drugs, the interaction parameters for the NRTL model and the decomposition of the compounds according to their constituent functional groups together with their interaction parameters according to the predictive UNIFAC model. In addition, to corroborate the accuracy of the models and to check the fit of the models, experimental data for the systems studied were obtained from the literature. To quantify this, the error of each of the models was calculated. In conclusion, it is found that the UNIFAC predictive model satisfies the need for a universal model to determine solubility. Its main advantage is that it is not necessary to rely on experimental data to generate the necessary parameters, unlike the NRTL model.
The solubility of four arbitrarily selected commonly used drugs: paracetamol, ibuprofen, aspirin and naproxen, was evaluated in three solvents: ethanol, water and 1-octanol, which simulate the conditions of the human body. For this purpose, binary systems composed of drug and solvent were evaluated first, followed by ternary systems composed of drug, water and 1-octanol. This mixture of solvents was studied for different 1-octanol/water mass ratios simulating the conditions inside the human body: aqueous medium (water) and membrane (1-octanol). For the execution of the study, a Python code was developed to calculate the solubility as a function of temperature for the solubility of each drug, in the ideal and non-ideal case according to the NRTL and UNIFAC models. The data required for the development of this study were the enthalpy and melting temperature of the drugs, the interaction parameters for the NRTL model and the decomposition of the compounds according to their constituent functional groups together with their interaction parameters according to the predictive UNIFAC model. In addition, to corroborate the accuracy of the models and to check the fit of the models, experimental data for the systems studied were obtained from the literature. To quantify this, the error of each of the models was calculated. In conclusion, it is found that the UNIFAC predictive model satisfies the need for a universal model to determine solubility. Its main advantage is that it is not necessary to rely on experimental data to generate the necessary parameters, unlike the NRTL model.
Description
Tesis presentada para optar al título profesional de Ingeniera Civil Química
Keywords
Solubilidad, Medicamentos, Soluciones (Química)