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Abolghasem Jouyban - One of the best experts on this subject based on the ideXlab platform.
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Solubility Prediction of Solutes in Non-Aqueous Binary Solvent Mixtures
Journal of the Brazilian Chemical Society, 2020Co-Authors: Abolghasem Jouyban, Maryam Khoubnasabjafari, Shahla Soltanpour, Elnaz Tamizi, Somaieh Soltani, William E. AcreeAbstract:Foi investigada a possibilidade de substituir os parâmetros de Abraham calculados teoricamente pelos parâmetros experimentais, na previsao da solubilidade de solutos nao-aquoso em misturas de solventes binarios, utilizando-se o modelo de Jouyban-Acree. As solubilidades de 90 conjuntos de dados, coletados a partir da literatura, foram preditas utilizando-se estes parâmetros, os coeficientes de solventes e tambem as solubilidades de sistemas mono-solventes. A precisao das solubilidades previstas foi avaliada calculando-se a media percentual do desvio (MPD) e tambem dos desvios percentuais (IPDs) individuais. O MPD global para a analise utilizando os parâmetros de Abraham, experimentais e teoricos, foram os mesmos e
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Solubility Prediction of drugs in water-cosolvent mixtures using Abraham solvation parameters.
Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, 2020Co-Authors: Abolghasem Jouyban, Hakkim Chan, Somaieh Soltani, Soltanpour Sh, William E. AcreeAbstract:PURPOSE. To provide predictive cosolvency models, the Abraham solvation parameters of solutes and the solvent coefficients were combined with the Jouyban-Acree and the log-linear models. These models require two and one Solubility data points to predict the Solubility of drugs in water-cosolvent mixtures. Ab initio Prediction methods also were employed and the results were discussed. METHOD. The Jouyban- Acree model constants were correlated with variables derived from the Abraham solvation parameters of solutes and the solvent coefficients to present quantitative structure property relationship (QSPR) models. The calculated model constants using the QSPR models were used to predict the Solubility in water-cosolvent mixtures. The mean percentage deviation (MPD), average absolute error (AAE) and root mean square error (RMSE) criteria were calculated to show the accuracy of the Predictions. RESULTS. The overall MPD (±SD) of the proposed method employing Solubility data in mono-solvents, i.e. two data points for each set, was 18.5±12.0 which indicates an acceptable Prediction error from the practical point of view. The best cosolvency model employing aqueous Solubility data was produced overall MPD of 75.2±72.6. The overall MPD of the proposed ab initio method was 74.9±19.3%. The models produced the same accuracy pattern considering MPD, AAE and RMSE criteria. CONCLUSION. The proposed model employing two Solubility data points for each set produced acceptable Prediction error (≈18 %) and could be recommended for practical applications in pharmaceutical industry. MPD, AAE and RMSE criteria produced similar results considering various models. However, MPD criterion was preferred since its numerical values could be compared with experimental relative standard deviations for repeated experiments.
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Computational tools for Solubility Prediction of celecoxib in the binary solvent systems
Journal of Molecular Liquids, 2020Co-Authors: Elaheh Rahimpour, William E. Acree, Esmail Mohammadian, Abolghasem JouybanAbstract:Abstract Some linear and non-linear predictive cosolvency models in combination with Abraham and Hansen parameters are proposed for Prediction of celecoxib (CXB) Solubility in various cosolvency systems. To train the cosolvency models, collected Solubility data for CXB in 11 cosolvency systems are correlated with the proposed models and their accuracy is evaluated by calculating the overall mean relative deviation (MRD%) for back-calculated and cross-validated data. These trained models present the essential information in different stages of drug discovery, development and industrial procedures and permit the scientist for the choice of the best solvent mixture for CXB.
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A new computational method for drug Solubility Prediction in methanol + water mixtures
Journal of Molecular Liquids, 2019Co-Authors: Sina Dadmand, William E. Acree, Farzin Kamari, Abolghasem JouybanAbstract:Abstract General Solubility Prediction models were developed using both classic least square and a novel method, in order to predict the Solubility of the solutes in methanol + water binary solvent system. The novel approach to the regression analysis was investigated using an error minimization method. This aim was achieved by using a user-defined loss function regression, instead of the classic least square regression approach. To examine the results of the novel methodology, previous Solubility data of 41 solutes were used for comparison. Both least square and novel methods were applied to the Jouyban-Acree model, Jouyban-Acree model in combination with Abraham parameters, and the modified Wilson model. The generally trained versions of the mentioned models produced more accurate Predictions using the novel method than the least square method that has been confirmed by t -test analyses. The Jouyban-Acree model was the most accurate model among other generally trained models. Finally, the results were validated using a cross-validation analysis which produced the acceptable Prediction accuracy of 24.6% mean percentage deviation (MPD) for the new methodology against 32.1% of the least square method. Also a new arithmetically transformed version of aforementioned models was introduced in this study to make the calculations easier to execute.
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A global version of modified Wilson model for Solubility Prediction of drugs in methanol + water mixtures
Journal of Molecular Liquids, 2018Co-Authors: Mohammad Barzegar-jalali, William E. Acree, Elaheh Rahimpour, Abolghasem JouybanAbstract:Abstract The trained version of modified Wilson model is proposed employing Solubility data of 41 drugs and/or drug-like compounds in methanol + water mixtures at various temperatures. For covering the physicochemical properties of the solute to provide a QSPR model, the Abraham solvation parameters of solutes are combined with the modified Wilson model. The mean relative deviations for the correlated data are 32.5% and 27.1%, respectively for the modified Wilson model and its combined version with Abraham solvation parameters. Furthermore, an attempt is also made to correlate the Solubility data with the modified Wilson model combined with van't Hoff equation and the Abraham general solvation parameters to provide a full predictive model for drug Solubility Prediction in methanol + water mixtures at various temperatures.
Daan Frenkel - One of the best experts on this subject based on the ideXlab platform.
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Computational methodology for Solubility Prediction: Application to sparingly soluble organic/inorganic materials.
Journal of Chemical Physics, 2018Co-Authors: Lunna Li, Tim S Totton, Daan FrenkelAbstract:The Solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling Solubility Predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal Solubility Predictions.The Solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling Solubility Predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal Solubility Predictions.
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computational methodology for Solubility Prediction application to sparingly soluble organic inorganic materials
Journal of Chemical Physics, 2018Co-Authors: Lunna Li, Tim S Totton, Daan FrenkelAbstract:The Solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling Solubility Predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal Solubility Predictions.The Solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling Solubility Predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal Solubility Predictions.
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computational methodology for Solubility Prediction application to the sparingly soluble solutes
Journal of Chemical Physics, 2017Co-Authors: Lunna Li, Tim S Totton, Daan FrenkelAbstract:The authors would like to acknowledge the funding and technical support including BP’s High Performance Computing facility, from BP through the BP International Centre for Advanced Materials (BP-ICAM) which made this research possible. All the simulations in the work were hence conducted using the HPC resources from BP and computer resources at the Department of Chemistry, Cambridge.
William E. Acree - One of the best experts on this subject based on the ideXlab platform.
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Solubility Prediction of drugs in water-cosolvent mixtures using Abraham solvation parameters.
Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, 2020Co-Authors: Abolghasem Jouyban, Hakkim Chan, Somaieh Soltani, Soltanpour Sh, William E. AcreeAbstract:PURPOSE. To provide predictive cosolvency models, the Abraham solvation parameters of solutes and the solvent coefficients were combined with the Jouyban-Acree and the log-linear models. These models require two and one Solubility data points to predict the Solubility of drugs in water-cosolvent mixtures. Ab initio Prediction methods also were employed and the results were discussed. METHOD. The Jouyban- Acree model constants were correlated with variables derived from the Abraham solvation parameters of solutes and the solvent coefficients to present quantitative structure property relationship (QSPR) models. The calculated model constants using the QSPR models were used to predict the Solubility in water-cosolvent mixtures. The mean percentage deviation (MPD), average absolute error (AAE) and root mean square error (RMSE) criteria were calculated to show the accuracy of the Predictions. RESULTS. The overall MPD (±SD) of the proposed method employing Solubility data in mono-solvents, i.e. two data points for each set, was 18.5±12.0 which indicates an acceptable Prediction error from the practical point of view. The best cosolvency model employing aqueous Solubility data was produced overall MPD of 75.2±72.6. The overall MPD of the proposed ab initio method was 74.9±19.3%. The models produced the same accuracy pattern considering MPD, AAE and RMSE criteria. CONCLUSION. The proposed model employing two Solubility data points for each set produced acceptable Prediction error (≈18 %) and could be recommended for practical applications in pharmaceutical industry. MPD, AAE and RMSE criteria produced similar results considering various models. However, MPD criterion was preferred since its numerical values could be compared with experimental relative standard deviations for repeated experiments.
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Solubility Prediction of Solutes in Non-Aqueous Binary Solvent Mixtures
Journal of the Brazilian Chemical Society, 2020Co-Authors: Abolghasem Jouyban, Maryam Khoubnasabjafari, Shahla Soltanpour, Elnaz Tamizi, Somaieh Soltani, William E. AcreeAbstract:Foi investigada a possibilidade de substituir os parâmetros de Abraham calculados teoricamente pelos parâmetros experimentais, na previsao da solubilidade de solutos nao-aquoso em misturas de solventes binarios, utilizando-se o modelo de Jouyban-Acree. As solubilidades de 90 conjuntos de dados, coletados a partir da literatura, foram preditas utilizando-se estes parâmetros, os coeficientes de solventes e tambem as solubilidades de sistemas mono-solventes. A precisao das solubilidades previstas foi avaliada calculando-se a media percentual do desvio (MPD) e tambem dos desvios percentuais (IPDs) individuais. O MPD global para a analise utilizando os parâmetros de Abraham, experimentais e teoricos, foram os mesmos e
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Computational tools for Solubility Prediction of celecoxib in the binary solvent systems
Journal of Molecular Liquids, 2020Co-Authors: Elaheh Rahimpour, William E. Acree, Esmail Mohammadian, Abolghasem JouybanAbstract:Abstract Some linear and non-linear predictive cosolvency models in combination with Abraham and Hansen parameters are proposed for Prediction of celecoxib (CXB) Solubility in various cosolvency systems. To train the cosolvency models, collected Solubility data for CXB in 11 cosolvency systems are correlated with the proposed models and their accuracy is evaluated by calculating the overall mean relative deviation (MRD%) for back-calculated and cross-validated data. These trained models present the essential information in different stages of drug discovery, development and industrial procedures and permit the scientist for the choice of the best solvent mixture for CXB.
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A new computational method for drug Solubility Prediction in methanol + water mixtures
Journal of Molecular Liquids, 2019Co-Authors: Sina Dadmand, William E. Acree, Farzin Kamari, Abolghasem JouybanAbstract:Abstract General Solubility Prediction models were developed using both classic least square and a novel method, in order to predict the Solubility of the solutes in methanol + water binary solvent system. The novel approach to the regression analysis was investigated using an error minimization method. This aim was achieved by using a user-defined loss function regression, instead of the classic least square regression approach. To examine the results of the novel methodology, previous Solubility data of 41 solutes were used for comparison. Both least square and novel methods were applied to the Jouyban-Acree model, Jouyban-Acree model in combination with Abraham parameters, and the modified Wilson model. The generally trained versions of the mentioned models produced more accurate Predictions using the novel method than the least square method that has been confirmed by t -test analyses. The Jouyban-Acree model was the most accurate model among other generally trained models. Finally, the results were validated using a cross-validation analysis which produced the acceptable Prediction accuracy of 24.6% mean percentage deviation (MPD) for the new methodology against 32.1% of the least square method. Also a new arithmetically transformed version of aforementioned models was introduced in this study to make the calculations easier to execute.
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A global version of modified Wilson model for Solubility Prediction of drugs in methanol + water mixtures
Journal of Molecular Liquids, 2018Co-Authors: Mohammad Barzegar-jalali, William E. Acree, Elaheh Rahimpour, Abolghasem JouybanAbstract:Abstract The trained version of modified Wilson model is proposed employing Solubility data of 41 drugs and/or drug-like compounds in methanol + water mixtures at various temperatures. For covering the physicochemical properties of the solute to provide a QSPR model, the Abraham solvation parameters of solutes are combined with the modified Wilson model. The mean relative deviations for the correlated data are 32.5% and 27.1%, respectively for the modified Wilson model and its combined version with Abraham solvation parameters. Furthermore, an attempt is also made to correlate the Solubility data with the modified Wilson model combined with van't Hoff equation and the Abraham general solvation parameters to provide a full predictive model for drug Solubility Prediction in methanol + water mixtures at various temperatures.
Faiyaz Shakeel - One of the best experts on this subject based on the ideXlab platform.
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Solution thermodynamics and Solubility Prediction of glibenclamide in Transcutol + water co-solvent mixtures at 298.15–333.15 K
Archives of Pharmacal Research, 2014Co-Authors: Gamal A. Shazly, Faiyaz ShakeelAbstract:Solution thermodynamics and Solubility of glibenclamide (GBN) in binary co-solvent mixtures of Transcutol + water at temperature range of 298.15–333.15 K were investigated in present study. The modified Apelblat model was used to predict the Solubility of GBN in co-solvent mixtures at various temperatures. The highest and lowest Solubility of GBN were observed in pure Transcutol and pure water, respectively. Moreover, all co-solvent mixtures had highest Solubility at 333.15 K. The experimental Solubility data of GBN was correlated well with the modified Apelblat model at each temperature studied with relative absolute deviation in the range of 0.008–5.903 %. The correlation coefficients in co-solvent mixtures were observed in the range of 0.995–0.999 which indicated good fitting of experimental data with calculated one. The enthalpies and entropies for GBN dissolution were observed in the range of 2.012–38.215 kJ mol^−1 and 6.748–114.709 J mol^−1 K^−1, respectively indicating its dissolution is endothermic and an entropy-driven process. These results indicated that Transcutol can be used as a co-solvent in preformulation studies and formulation development of GBN.
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Solubility Prediction of indomethacin in peg 400 water mixtures at various temperatures
Journal of Molecular Liquids, 2013Co-Authors: Faiyaz Shakeel, Fars K Alanazi, Ibrahim A AlsarraAbstract:Abstract The objective of this study was to determine the equilibrium saturated Solubility as well as mole fraction Solubility of indomethacin in distilled water, polyethylene glycol 400 (PEG 400) and PEG 400 + water mixtures at the temperature range of 293.15 to 318.15 K. The equilibrium solubilities of indomethacin were determined by shake flask method and resulting data were analyzed by regression analysis. The experimental data were well correlated with the modified Apelblat model at various temperatures studied. The solubilities of indomethacin were found to be increased exponentially with increase in temperature in mono-solvents as well as in PEG 400 + water mixtures. The equilibrium saturated Solubility as well as mole fraction Solubility of indomethacin was found to be significantly higher in pure PEG 400 than distilled water and PEG 400 + water mixtures. The equilibrium and mole fraction Solubility of indomethacin in pure PEG 400 at the temperature of 298.15 K were found to be 0.0831 g/g and 0.093, respectively. The relative absolute deviation (AD) between experimental and theoretical mole fraction Solubility was found to be less than 1% in mono-solvents as compared to PEG 400 + water mixtures. Solubility data of present study indicate that PEG 400 could be successfully applied as a cosolvent in preformulation studies and formulation development of indomethacin as an alternate cosolvent of ethanol and propylene glycol.
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Solubility Prediction of indomethacin in PEG 400 + water mixtures at various temperatures
Journal of Molecular Liquids, 2013Co-Authors: Faiyaz Shakeel, Fars K Alanazi, Ibrahim A AlsarraAbstract:Abstract The objective of this study was to determine the equilibrium saturated Solubility as well as mole fraction Solubility of indomethacin in distilled water, polyethylene glycol 400 (PEG 400) and PEG 400 + water mixtures at the temperature range of 293.15 to 318.15 K. The equilibrium solubilities of indomethacin were determined by shake flask method and resulting data were analyzed by regression analysis. The experimental data were well correlated with the modified Apelblat model at various temperatures studied. The solubilities of indomethacin were found to be increased exponentially with increase in temperature in mono-solvents as well as in PEG 400 + water mixtures. The equilibrium saturated Solubility as well as mole fraction Solubility of indomethacin was found to be significantly higher in pure PEG 400 than distilled water and PEG 400 + water mixtures. The equilibrium and mole fraction Solubility of indomethacin in pure PEG 400 at the temperature of 298.15 K were found to be 0.0831 g/g and 0.093, respectively. The relative absolute deviation (AD) between experimental and theoretical mole fraction Solubility was found to be less than 1% in mono-solvents as compared to PEG 400 + water mixtures. Solubility data of present study indicate that PEG 400 could be successfully applied as a cosolvent in preformulation studies and formulation development of indomethacin as an alternate cosolvent of ethanol and propylene glycol.
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Thermodynamics-based mathematical model for Solubility Prediction of glibenclamide in ethanol–water mixtures
Pharmaceutical Development and Technology, 2013Co-Authors: Faiyaz Shakeel, Fars K Alanazi, Ibrahim A AlsarraAbstract:AbstractTemperature-dependent Solubility data of glibenclamide (GBN) in various ethanol–water mixtures is not reported in literature so far. Therefore, the aim of this study was to determine the mole fraction Solubility of GBN in various ethanol–water mixtures at the temperature range of 293.15 to 318.15 K. The Solubility of GBN was determined by reported shake flask method and the experimental data was fitted in thermodynamics-based modified Apelblat model. The Solubility of GBN was found to be increased with increase in temperature and mass fraction of ethanol in ethanol–water mixtures. The experimental data of GBN was well correlated with the modified Apelblat model at each temperature range with correlation coefficient of 0.9940–1.0000. The relative absolute deviation (AD) was found to be less than 0.1% except in pure ethanol and water. The positive values of enthalpies and entropies for GBN dissolution indicated that its dissolution is endothermic and an entropy-driven process.
Lunna Li - One of the best experts on this subject based on the ideXlab platform.
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Computational methodology for Solubility Prediction: Application to sparingly soluble organic/inorganic materials.
Journal of Chemical Physics, 2018Co-Authors: Lunna Li, Tim S Totton, Daan FrenkelAbstract:The Solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling Solubility Predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal Solubility Predictions.The Solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling Solubility Predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal Solubility Predictions.
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computational methodology for Solubility Prediction application to sparingly soluble organic inorganic materials
Journal of Chemical Physics, 2018Co-Authors: Lunna Li, Tim S Totton, Daan FrenkelAbstract:The Solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling Solubility Predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal Solubility Predictions.The Solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling Solubility Predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal Solubility Predictions.
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computational methodology for Solubility Prediction application to the sparingly soluble solutes
Journal of Chemical Physics, 2017Co-Authors: Lunna Li, Tim S Totton, Daan FrenkelAbstract:The authors would like to acknowledge the funding and technical support including BP’s High Performance Computing facility, from BP through the BP International Centre for Advanced Materials (BP-ICAM) which made this research possible. All the simulations in the work were hence conducted using the HPC resources from BP and computer resources at the Department of Chemistry, Cambridge.
