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Prediction of solubility of some statin drugs in supercritical carbon dioxide using classification and regression tree analysis and adaptive neuro-fuzzy inference systems

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Abstract

A quantitative structure-solubility relationship was developed to predict the solubility of some statin drugs in supercritical carbon dioxide (SC-CO2). The solubility of lovastatin, simvastatin, atorvastatin, rosuvastatin, and flovastatin in SC-CO2 at 225 different states of temperature and pressure were predicted. Classification and regression tree (CART) was successfully used as a descriptor selection method. Three descriptors (pressure, temperature, and molecular weight) were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). The root mean square errors for the calibration, prediction, and validation sets were 0.09, 0.14, and 0.11, respectively. In comparison with other methods, CART-ANFIS is a powerful model for prediction of solubilities of these statins in SC-CO2.

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Authors and Affiliations

  1. School of Chemistry, Damghan University, Damghan, Iran

    K. Zarei & F. Taheri

Authors
  1. K. Zarei
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  2. F. Taheri
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Correspondence to K. Zarei.

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Published in Russian in Izvestiya Akademii Nauk. Seriya Khimicheskaya, No. 4, pp. 1131—1138, April, 2016.

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Zarei, K., Taheri, F. Prediction of solubility of some statin drugs in supercritical carbon dioxide using classification and regression tree analysis and adaptive neuro-fuzzy inference systems. Russ Chem Bull 65, 1131–1138 (2016). https://doi.org/10.1007/s11172-016-1424-x

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  • DOI: https://doi.org/10.1007/s11172-016-1424-x

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