Métodos multivariantes aplicados a la efectividad de los disolventes en la industria farmacéutica
Date
2018-08-01
2018-08-01
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Citar con el siguiente link: http://hdl.handle.net/11323/2435
https://revistascientificas.cuc.edu.co/ingecuc/article/view/1743
Citar con el siguiente link: http://hdl.handle.net/11323/2435
https://revistascientificas.cuc.edu.co/ingecuc/article/view/1743
Abstract
Introducción: Los disolventes son agentes químicos usados en la industria farmacéutica y su importancia radica en que su presencia puede acelerar o retardar la reacción de un fármaco hasta un millón de veces. Objetivo: La presente investigación analiza los diferentes tipos de disolventes para evaluar la existencia de grupos en los cuales se puedan identificar patrones relacionados con la efectividad de esos disolventes en la producción de fármacos. Metodología: El estudio consta de 4 fases: 1) análisis de componentes principales, 2) análisis de clúster, 3) análisis discriminante, y 4) interpretación de resultados y conclusiones. Resultados: Se identificaron tres conglomerados categorizados como supercríticos, microesféricos y biodegradables. La prueba de T de Hotelling arroja un p-valor de 0, poniendo en evidencia la diferencia entre grupos. El discriminante cuadrático arroja una precisión del 96% para la clasificación de disolventes. Conclusiones: El análisis multivariante permite modelar la efectividad de los disolventes en la industria farmacéutica, generando una metodología objetiva de decisión para la clasificación de disolventes según un enfoque de efectividad.
Referencias:
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Garrard, «A comprehensive classification of solvent systems used for natural product purifications in countercurrent and centrifugal partition chromatography,» Natural Product Reports, vol. 32, no. 11, pp. 1556-1561, 2015. https://doi.org/10.1039/C5NP00061K [12] K. Dunn, « Solvents-OpenMV.net Datasets,» 14 Diciembre 2017. [En línea]. Available: www.http://openmv.net/info/solvents. [Último acceso: 14 Diciembre 2017]. [13] M. Aulton, La ciencia del diseño de las formas farmacéuticas, Leicester: Elsevier, 2004. [14] S. F. Sneddon, D. J. Tobias y C. L. Brooks III, «Thermodynamics of amide hydrogen bond formation in polar and apolar solvents,» Journal of molecular biology, vol. 209, no. 4, pp. 817-820, 1989. https://doi.org/10.1016/0022-2836(89)90609-8 [15] C. Cuadras, Nuevos métodos de análisis multivariante, Barcelona: CMC Editions, 2014. [16] H. Breu, J. GIL, D. Kirlpatrick y M. Werman, «Linear Time Euclidean distance transform algorithms,» IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 5, pp. 529-533, 1995. https://doi.org/10.1109/34.391389 [17] S. Le, J. Josse y F. Husson, «FactoMiner: A package for Multivariate Analysis,» Journal of Statistical Software, vol. 25, no. 1, pp. 1-18, 2008. https://doi.org/10.18637/jss.v025.i01 [18] P. Trodler, R. D. Schmid y J. Pleiss, «Modeling of solventdependent conformational transitions in Burkholderia cepacia lipase,» BMC Structural Biology, vol. 9, no. 1, pp. 1-13, 2009. https://doi.org/10.1186/1472-6807-9-38. [19] F. Kerton y R. MarriotT, Alternative Solvents for green chemistry, vol. 77, London: RSC Publishing, 2014. https://doi.org/10.1186/1472-6807-9-38. [20] J. H. Ward Jr, «Hierarchical grouping to optimize an objective function,» Journal of the American Statistical Association, vol. 58, no. 301, pp. 236-244, 1963. https://doi.org/10.1080/01621459.1963.10500845 [21] U. Maulik y S. Bandyopadhyay, «Performance evaluation of some clustering algorithms and validity indices,» IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1650-1654, 2002. https://doi.org/10.1109/TPAMI.2002.1114856 [22] F. Tang, T. Hess, J. Valacich y J. Sweeney, «The effects of visualization and interactivity on calibration in financial decision-making,» Behavioral Research in Accounting, vol. 26, no. 1, pp. 25-28, 2013. https://doi.org/10.2308/bria-50589 [23] R. Swedberg, «Can You Visualize Theory? On the Use of Visual Thinking in Theory Pictures, Theorizing Diagrams, and Visual Sketches,» Sociological Theory, vol. 34, no. 3, pp. 250-275, 2016. https://doi.org/10.1177/0735275116664380 [24] B. P. Whim y P. G. Johnson, Directory of solvents, London: Springer Science & Business Media, 2012. [25] B. Trèmillon, «Quemistry in non-aqueous solvents,» Reidel, p. 289, 1971.
[1] V. Sáenz, E. Hernáez, L. Sanz y I. Katime, «Liberación controlada de fármacos. Microparticulas,» Revista Iberoamericana de Polímeros, vol. 5, no. 2, pp. 87-101, 2004. [2] C. Capello, U. Fisher y K. Hungerbühler, «What is a green solvent? A comprehensive framework for the environmental assessment of solvents,» Green Chemistry, vol. 9, no. 9, pp. 927-934, 2007. http://dx.doi.org/10.1039/b617536h [3] S. C. Shen, W. K. Ng, J. Hu, K. Letchmanan, J. Ng y R. B. H. Tan, «Solvent-free direct formulation of poorly-soluble drugs to amorphous solid dispersion via melt-absortion,» Advanced Powder Technology, vol. 28, no. 5, pp. 1316-1324, 2017. https://doi.org/10.1016/j.apt.2017.02.020 [4] O. S. Hammond, D. Bowron, A. J. Jackson, T. Arnold, A. Sanchez-Fernandez, N. Tsapatsaris y K. J. Edler, «Resilience of Malic Acid Natural Deep Eutetic Solvent Nanostructure to solidification and Hydration,» The Journal of Physical Chemistry B, vol. 121, no. 31, pp. 7473-7483, 2017. https://doi.org/10.1021/acs.jpcb.7b05454 [5] R. Minphimai, S. Piriyaprasarth y P. Sriamornsak, «Application of Artificial Neural Networks for Prediction of Ibuprofen Yield from Ultrasound-Assisted Anti-Solvent Crystallization,» Advanced Materials Research, vol. 1060, pp. 145-148, 2015. https://doi.org/10.4028/www.scientific.net/AMR.1060.145 [6] A. Parker, «Protic-dipolar aprotic solvent effects on rates of bimolecular reactions,» Chemical Reviews, vol. 69, no. 1, pp. 1-32, 1969. https://doi.org/10.1021/cr60257a001 [7] A. de Juan, G. Fonrodona y E. Casassas, «Solvent classification based on solvatochromic parameters: a comparison with the Snyder approach,» TraC trends in Analytical Chemistry, vol. 16, no. 1, pp. 52-62, 1997. https://doi.org/10.1016/S0165-9936(96)00079-9 [8] A. Katritzky, D. Fara , M. Kuanar, E. Hur y M. Karelson, «The classification of solvents by combining classical QSPR methodology with principal component analysis,» The Journal of Physical Chemistry A, vol. 109, no. 45, pp. 10323 - 10341, 2005. https://doi.org/10.1021/jp050395e [9] L. Snyder, «Classification of the solvent properties of common liquids,» Journal of Chromatography A, vol. 92, no. 2, pp. 223-230, 1974. https://doi.org/10.1016/S0021-9673(00)85732-5 [10] N. Bondarev, «Exploratory analysis in thermodynamics of equilibria. Classification and prediction of benzoic acid strength in aqueous organic solvents,» Russian Journal of General Chemistry, vol. 86, no. 6, pp. 1221-1228, 2016. https://doi.org/10.1134/S1070363216060025 [11] K. Skalicka-Wozniack y I. Garrard, «A comprehensive classification of solvent systems used for natural product purifications in countercurrent and centrifugal partition chromatography,» Natural Product Reports, vol. 32, no. 11, pp. 1556-1561, 2015. https://doi.org/10.1039/C5NP00061K [12] K. Dunn, « Solvents-OpenMV.net Datasets,» 14 Diciembre 2017. [En línea]. Available: www.http://openmv.net/info/solvents. [Último acceso: 14 Diciembre 2017]. [13] M. Aulton, La ciencia del diseño de las formas farmacéuticas, Leicester: Elsevier, 2004. [14] S. F. Sneddon, D. J. Tobias y C. L. Brooks III, «Thermodynamics of amide hydrogen bond formation in polar and apolar solvents,» Journal of molecular biology, vol. 209, no. 4, pp. 817-820, 1989. https://doi.org/10.1016/0022-2836(89)90609-8 [15] C. Cuadras, Nuevos métodos de análisis multivariante, Barcelona: CMC Editions, 2014. [16] H. Breu, J. GIL, D. Kirlpatrick y M. Werman, «Linear Time Euclidean distance transform algorithms,» IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 5, pp. 529-533, 1995. https://doi.org/10.1109/34.391389 [17] S. Le, J. Josse y F. Husson, «FactoMiner: A package for Multivariate Analysis,» Journal of Statistical Software, vol. 25, no. 1, pp. 1-18, 2008. https://doi.org/10.18637/jss.v025.i01 [18] P. Trodler, R. D. Schmid y J. Pleiss, «Modeling of solventdependent conformational transitions in Burkholderia cepacia lipase,» BMC Structural Biology, vol. 9, no. 1, pp. 1-13, 2009. https://doi.org/10.1186/1472-6807-9-38. [19] F. Kerton y R. MarriotT, Alternative Solvents for green chemistry, vol. 77, London: RSC Publishing, 2014. https://doi.org/10.1186/1472-6807-9-38. [20] J. H. Ward Jr, «Hierarchical grouping to optimize an objective function,» Journal of the American Statistical Association, vol. 58, no. 301, pp. 236-244, 1963. https://doi.org/10.1080/01621459.1963.10500845 [21] U. Maulik y S. Bandyopadhyay, «Performance evaluation of some clustering algorithms and validity indices,» IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1650-1654, 2002. https://doi.org/10.1109/TPAMI.2002.1114856 [22] F. Tang, T. Hess, J. Valacich y J. Sweeney, «The effects of visualization and interactivity on calibration in financial decision-making,» Behavioral Research in Accounting, vol. 26, no. 1, pp. 25-28, 2013. https://doi.org/10.2308/bria-50589 [23] R. Swedberg, «Can You Visualize Theory? On the Use of Visual Thinking in Theory Pictures, Theorizing Diagrams, and Visual Sketches,» Sociological Theory, vol. 34, no. 3, pp. 250-275, 2016. https://doi.org/10.1177/0735275116664380 [24] B. P. Whim y P. G. Johnson, Directory of solvents, London: Springer Science & Business Media, 2012. [25] B. Trèmillon, «Quemistry in non-aqueous solvents,» Reidel, p. 289, 1971.
URI: http://hdl.handle.net/11323/2435
https://revistascientificas.cuc.edu.co/ingecuc/article/view/1743
https://revistascientificas.cuc.edu.co/ingecuc/article/view/1743
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