Determination of the solubility of anticancer drugs in supercritical carbon dioxide using empirical models and artificial neural network

Saadati Ardestani, Nedasadat; Amani, Mitra; Yeganeh Majd, Navid

Determination of the solubility of anticancer drugs in supercritical carbon dioxide using empirical models and artificial neural network

Document Type : Qualitative Research

Authors

N. Saadati Ardestani ¹

M. Amani ²

N. Yeganeh Majd ³

¹ Department of Nanotechnology and Advanced Materials, Materials and Energy Research Center, Karaj, Iran

² Department of Chemical Engineering, Islamic Azad University, Robat Karim Branch, Robat Karim, Iran.

³ b Department of Chemical Engineering, Islamic Azad University, Robat Karim Branch, Robat Karim, Iran.

Abstract

Research subject: Low solubility of pharmaceutical compounds leads to increasing the required drug dosage and their side effects as well as reducing their therapeutic efficiency. Producing pharmaceutical micro/nanoparticles with homogenous morphology and narrow size distribution is one of the confirmed approaches for their solubility enhancement. So, selection and designing an appropriate method for this purpose is one of the most important research fields of pharmaceutical industries. Over the past three decades, supercritical carbon dioxide (sc-CO₂) based methods as a clean and green technologies have been received much attention in various fields of pharmaceutical industries. However, in order to design and development of these methods for producing micro/nanoparticles, determination of the compounds solubility in sc-CO₂is essential.

Research approach: In this research, well known empirical models (Adachi and Lu, Ch and Madras, Hozahzbr et al., Bian et al., Mendez-Santiago-Teja), as well as the artificial neural network model were applied for prediction the solubility of six anticancer drugs (Aprepitant, 5-Fluorouracil, Imatinib mesylate, Capecitabine, Letrozole, Docetaxel) in sc-CO₂.

In order to evaluate the accuracy of these models, a comparison was made between the calculated solubility values and the available experimental data, based on several statistical criteria, such as the average absolute relative deviation (AARD%), adjusted correlation coefficient (R_adj) and F-value.

Main results: According to obtained results, Adachi and Lu model with AARD% value of 12.12% and R_adj value of 0.97 provided acceptable results for solubility of mentioned drugs in sc-CO₂. Also, in comparison between empirical and artificial neural network models, the latter one with AARD% value of 1.65% and R_adj value of 0.9960 was appointed as the most appropriate model for correlation of drugs solubility data.

Keywords

supercritical carbon dioxide

Solubility

Anti-cancer drug

artificial neural network

Empirical model

Subjects

Drug delivery

[1]. Amani M., Saadati Ardestani N., and Yeganeh Majd N., Utilization of supercritical CO2 gas antisolvent (GAS) for production of Capecitabine nanoparticles as anti-cancer drug: Analysis and optimization of the process conditions, Journal of CO2 Utilization, 46, 101465, 2021.
[2]. Ardestani N.S. and Amani M., Production of Anthraquinone Violet 3RN nanoparticles via the GAS process: Optimization of the process parameters using Box-Behnken design, Dyes and Pigments, 193, 109471, 2021.
[3]. Fang Z., Rapid Production of Micro- and Nano-particles Using Supercritical Water. 2010: Springer Science & Business Media
[4]. Amani M., Ardestani N.S., and Honarvar B., Experimental Optimization and Modeling of Supercritical Fluid Extraction of Oil from Pinus gerardiana, Chemical Engineering & Technology, 44(4), 578-588, 2021.
[5]. Saadati Ardestani N., Amani M., and Moharrery L., Determination of Anthraquinone Violet 3RN solubility in supercritical carbon dioxide with/without co-solvent: Experimental data and modeling (empirical and thermodynamic models), Chemical Engineering Research and Design, 159, 529-542, 2020.
[6]. Coimbra P., Duarte C.M.M., and de Sousa H.C., Cubic equation-of-state correlation of the solubility of some anti-inflammatory drugs in supercritical carbon dioxide, Fluid Phase Equilibria, 239(2), 188-199, 2006.
[7]. Huang C.-Y., Lee L.-S., and Su C.-S., Correlation of solid solubilities of pharmaceutical compounds in supercritical carbon dioxide with solution model approach, Journal of the Taiwan Institute of Chemical Engineers, 44(3), 349-358, 2013.
[8]. Peng D.-Y. and Robinson D.B., A New Two-Constant Equation of State, Industrial & Engineering Chemistry Fundamentals, 15(1), 59-64, 1976.
[9]. Soave G., Improving the treatment of heavy hydrocarbons by the SRK EOS, Fluid Phase Equilibria, 84, 339-342, 1993.
[10]. Valderrama J., eacute, and O, A Generalized Patel-Teja Equation of State for Polar and Nonpolar Fluids and Their Mixtures, Journal of Chemical Engineering of Japan, 23(1), 87-91, 1990.
[11]. Dashtizadeh A., Pazuki G.R., TaghikhaniV. and Ghotbi C., A New Two-Parameter Cubic Equation of State for Predicting Phase Behavior of Pure Compounds and Mixtures, Fluid Phase Equilibria, 242(1), 19-28, 2006.
[12]. Jouyban A., Chan H.-K., and Foster N.R., Mathematical Representation of Solute Solubility in Supercritical Carbon Dioxide Using Empirical Expressions, The Journal of Supercritical Fluids, 24(1), 19-35, 2002.
[13]. Sparks D.L., Estévez L.A., Hernandez R., Barlow K. and French T., Solubility of Nonanoic (Pelargonic) Acid in Supercritical Carbon Dioxide, Journal of Chemical & Engineering Data, 53(2), 407-410, 2008.
[14]. Su C.-S., Correlation of Solid Solubility of 1,4-bis(alkylamino)-9,10-Anthraquinones in Supercritical Carbon Dioxide Using Solution Model Approach, Fluid Phase Equilibria, 361, 266-272, 2014.
[15]. Su C-S., Correlation and Estimation of Solubilities of Fatty Acids in Supercritical Carbon Dioxide Using Solution Model Approach, The Journal of Supercritical Fluids, 81, 79-85, 2013.
[16]. Vaferi B., Karimi M., Azizi M. and Esmaeili H., Comparison Between the Artificial Neural Network, SAFT and PRSV Approach in Obtaining the Solubility of Solid Aromatic Compounds in Supercritical Carbon Dioxide, The Journal of Supercritical Fluids, 77, 44-51, 2013.
[17]. Bakhbakhi Y., Neural Network Modeling of Ternary Solubilities of 2-naphthol in Supercritical CO2: A Comparative Study, Mathematical and Computer Modelling, 55(7), 1932-1941, 2012.
[18]. Sodeifian G., Sajadian S.A., and Razmimanesh F., Solubility of an Antiarrhythmic Drug (Amiodarone Hydrochloride) in Supercritical Carbon Dioxide, Fluid Phase Equilibria, 450, 149-159, 2017.
[19]. Sodeifian Gh., Razmimanesh F., Sajadian SA. and Soltani Panah H., Solubility Measurement of an Antihistamine Drug (Loratadine) in Supercritical Carbon Dioxide: Assessment of qCPA and PCP-SAFT Equations of State, Fluid Phase Equilibria, 472, 147-159, 2018.
[20]. Ardjmand M., Mirzajanzadeh M., and Zabihi F., Measurement and Correlation of Solid Drugs Solubility in Supercritical Systems, Chinese Journal of Chemical Engineering, 22(5), 549-558, 2014.
[21]. Mehdizadeh B. and Movagharnejad K., A Comparison Between Neural Network Method and Semi Empirical Equations to Predict the Solubility of Different Compounds in Supercritical Carbon Dioxide, Fluid Phase Equilibria, 303(1), 40-44, 2011.
[22]. Gharagheizi F., Eslamimanesh A., Mohammadi AH., and Richon D., Artificial Neural Network Modeling of Solubilities of 21 Commonly Used Industrial Solid Compounds in Supercritical Carbon Dioxide, Industrial & Engineering Chemistry Research, 50(1), 221-226, 2011.
[23]. Bahmani A.R., Sabzi F., and Bahmani M., Prediction of Solubility of Sulfur Dioxide in Ionic Liquids Using Artificial Neural Network, Journal of Molecular Liquids, 211, 395-400, 2015.
[24]. Lashkarbolooki M., Vaferi B., and Rahimpour M.R., Comparison the Capability of Artificial Neural Network (ANN) and EOS for Prediction of Solid Solubilities in Supercritical Carbon Dioxide, Fluid Phase Equilibria, 308(1), 35-43, 2011.
[25]. Yang G., Li Z., Shao Q. and Feng N., Measurement and Correlation Study of Silymarin Solubility in Supercritical Carbon Dioxide With and Without a Cosolvent Using Semi-Empirical Models and Back-Propagation Artificial Neural Networks, Asian Journal of Pharmaceutical Sciences, 12(5), 456-463, 2017.
[26]. Adachi Y. and Lu B.C.Y., Supercritical Fluid Extraction With Carbon Dioxide and Ethylene, Fluid Phase Equilibria, 14, 147-156, 1983.
[27]. Ch R. and Madras G., An Association Model for the Solubilities of Pharmaceuticals in Supercritical Carbon Dioxide, Thermochimica Acta, 507-508, 99-105, 2010.
[28]. Hozhabr S.B., Mazloumi S.H., and Sargolzaei J., Correlation of Solute Solubility in Supercritical Carbon Dioxide Using a New Empirical Equation, Chemical Engineering Research and Design, 92(11), 2734-2739, 2014.
[29]. Bian X., Zhang Q., Du Z-M., Chen J. and Jaubert J-N, A Five-Parameter Empirical Model for Correlating the Solubility of Solid Compounds in Supercritical Carbon Dioxide, Fluid Phase Equilibria, 411, 74-80, 2016.
[30]. Méndez-Santiago J. and Teja A.S., The Solubility of Solids in Supercritical Fluids, Fluid Phase Equilibria, 158-160, 501-510, 1999.
[31]. Sodeifian G., Sajadian S.A., and Ardestani N.S., Determination of Solubility of Aprepitant (an antiemetic drug for chemotherapy) in Supercritical Carbon Dioxide: Empirical and Thermodynamic Models, The Journal of Supercritical Fluids, 128, 102-111, 2017.
[32]. Zhan Sh., Zhao Q., Chen Sh., Wang J., Liu Zh., and Chen Ch., Solubility and Partition Coefficients of 5-Fluorouracil in ScCO2 and ScCO2/Poly(l-lactic acid), Journal of Chemical & Engineering Data, 59(4), 1158-1164, 2014.
[33]. Sodeifian G., Razmimanesh F., and Sajadian S.A., Solubility Measurement of a Chemotherapeutic agent (Imatinib Mesylate) in Supercritical Carbon Dioxide: Assessment of New Empirical Model, The Journal of Supercritical Fluids, 146, 89-99, 2019.
[34]. Yamini Y., Hojjati M., Kalantarian P. and Vatanara A., Solubility of Capecitabine and Docetaxel in Supercritical Carbon Dioxide: Data and the Best Correlation, Thermochimica Acta, 549, 95-101, 2012.
[35]. Freidoonimehr N. and Nazari F, Parametric Analysis and Optimization of the Supercritical Ejector Refrigeration Cycle With Different Working Fluids Using Artificial Neural Network and Particle Swarm Optimization Algorithm, Journal of Modeling Engineering, 15 (50), 121-133, 2017.
[36]. Azizi S., Karimi H., and Darvishi P., Flow Pattern and Oil Holdup Prediction in Vertical Oil–Water Two–Phase Flow Using Pressure Fluctuation Signal, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 36(2), 125-141, 2017.
[37]. Ghoreishi S.M. and Heidari E., Extraction of Epigallocatechin-3-gallate From Green Tea via Supercritical Fluid Technology: Neural Network Modeling and Response Surface Optimization, The Journal of Supercritical Fluids, 74, 128-136, 2013.
[38]. Guiné R., Gonçalves Ch., Matos S., Gonçalves F., Costa D. V.T.A. and Mendes M., Modeling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 37(2), 193-212, 2018.
[39]. Zahedi Abghari S. and Imani A., Determination of Suitable Operating Conditions of Fluid Catalytic Cracking Process by Application of Artificial Neural Network and Firefly Algorithm, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 37(6), 157-168, 2018.
[40]. Garlapati C. and Madras G., New Empirical Expressions to Correlate Solubilities of Solids in Supercritical Carbon Dioxide, Thermochimica Acta, 500(1), 123-127, 2010.
[41]. Montgomery D.C., Design and Analysis of Experiments, John Wiley & Sons, 2017.
[42]. Pishnamazi M., Zabihi S., Jamshidian S., ZeinolabediniHezave H., ZeinolabediniHezave A., Shirazian S., Measuring Solubility of a Chemotherapy-Anti Cancer Drug (Busulfan) in Supercritical Carbon Dioxide, Journal of Molecular Liquids, 317, 113954, 2020.
[43]. Pishnamazi M., Zabihi S., Jamshidian S., Borousan F., ZeinolabediniHezave A., Marjani A., Shirazian S., Experimental and Thermodynamic Modeling Decitabine Anti Cancer Drug Solubility in Supercritical Carbon Dioxide, Scientific Reports, 11(1), 1075, 2021.
[44]. Zabihi S., Khoshmaram A., Phishnamazi M., Borousan F., ZeinolabediniHezave A., Marjani A., Pelalak R., AgustionoKurniawan T., Shirazian S., Thermodynamic study on solubility of brain tumor drug in supercritical solvent: Temozolomide case study, Journal of Molecular Liquids, 321, 114926, 2021.
[45]. Ongkasin K., Sauceau M., Masmoudi Y., Fages J., Badens E., Solubility of cefuroxime axetil in supercritical CO2: Measurement and modeling, The Journal of Supercritical Fluids, 152, 104498, 2019.
[46]. Ardestani N.S., Majd N.Y., and Amani M., Experimental Measurement and Thermodynamic Modeling of Capecitabine (an Anticancer Drug) Solubility in Supercritical Carbon Dioxide in a Ternary System: Effect of Different Cosolvents, Journal of Chemical & Engineering Data, 65(10), 4762-4779, 2020.
[47]. Mehdipourghazi M. and Moayyedi M., ANN and Mathematical Mass Transfer Modeling of Glycol Amin Liquid Membranes for Separation of Carbon Dioxide from the Air, Journal of Modeling in Engineering, 14(47), 51-60, 2017.
[48]. Jouyban A., Soltani S., and Asadpour Zeynali K., Solubility Prediction of Drugs in Supercritical Carbon Dioxide Using Artificial Neural Network, Iranian Journal of Pharmaceutical Research, Volume 6(Number 4), 243-250, 2010.