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  3. A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

  • Sci Rep. 2022 Jan 20;12(1):1043. doi: 10.1038/s41598-022-04942-4.
Tahereh Rezaei 1,  Vesal Nazarpour 2,  Nahal Shahini 3,  Soufia Bahmani 3,  Amir Shahkar 4,  Mohammadreza Abdihaji 5,  Sina Ahmadi 6,  Farzad Tat Shahdost 7
Affiliations

Affiliations

  • 1 Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. [email protected].
  • 2 Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
  • 3 Department of Computer Engineering, Amirkabir University of Technology, Tehran, 15875-4413, Iran.
  • 4 Department of Transportation Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey.
  • 5 Department of Biology, The Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN, USA.
  • 6 Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran.
  • 7 Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Semnan, Iran.
PMID: 35058504 DOI: 10.1038/s41598-022-04942-4
Abstract

Understanding the drug solubility behavior is likely the first essential requirement for designing the supercritical technology for pharmaceutical processing. Therefore, this study utilizes different machine learning scenarios to simulate the solubility of twelve non-steroidal anti-inflammatory drugs (NSAIDs) in the supercritical carbon dioxide (SCCO2). The considered NSAIDs are Fenoprofen, Flurbiprofen, Ibuprofen, Ketoprofen, Loxoprofen, Nabumetone, Naproxen, Nimesulide, Phenylbutazone, Piroxicam, Salicylamide, and Tolmetin. Physical characteristics of the drugs (molecular weight and melting temperature), operating conditions (pressure and temperature), and solvent property (SCCO2 density) are effectively used to estimate the drug solubility. Monitoring and comparing the prediction accuracy of twelve intelligent paradigms from three categories (artificial neural networks, support vector regression, and hybrid neuro-fuzzy) approves that adaptive neuro-fuzzy inference is the best tool for the considered task. The hybrid optimization strategy adjusts the cluster radius of the subtractive clustering membership function to 0.6111. This model estimates 254 laboratory-measured solubility data with the AAPRE = 3.13%, MSE = 2.58 × 10-9, and R2 = 0.99919. The leverage technique confirms that outliers may poison less than four percent of the experimental data. In addition, the proposed hybrid paradigm is more reliable than the equations of state and available correlations in the literature. Experimental measurements, model predictions, and relevancy analyses justified that the drug solubility in SCCO2 increases by increasing temperature and pressure. The results show that Ibuprofen and Naproxen are the most soluble and insoluble drugs in SCCO2, respectively.

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