Abstract
To determine the solubility of the drug compounds in supercritical fluid carbon dioxide, several models could be developed to avoid the time-consuming computations with poor results. Theoretical methods are applied to limit the expensive experimental apparatus and strenuous manual labor. In this study, a radial basis function neural network (RBFNN) was used to predict the solubility of some 1,4-dihydropyridine derivative drugs in supercritical fluid carbon dioxide. The solubility of drugs was predicted based on the pressure, temperature, molecular weight, melting point, density, carbon number, and hydrogen number. The predicted solubility obtained by RBFNN was compared to experimental data. The root mean square error (RMSE), determination coefficient (R2), mean bias error, mean absolute error, modified agreement index (md), and modified Nash and Sutcliffe efficiency were determined. The square regression coefficient was obtained between 0.981 and 0.99 for each and overall compound. According to the results, this model can be reliably used to investigate the solubility of drugs by known physical properties. Finally, the sensitivity analyses considered the effects of inputs using a nonlinear relation. The results showed that pressure and density indicated the highest effective input variables while temperature was the lowest sensitive variable.
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This work was supported by the University of Zabol (Grant No. IR-UOZ-GR-8175).
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Byabani-Givo, A., Khajeh, M., Bohlooli, M. et al. Prediction of solubility of some dihydropyridine derivative drugs in supercritical fluid carbon dioxide by RBFNN. Netw Model Anal Health Inform Bioinforma 11, 37 (2022). https://doi.org/10.1007/s13721-022-00380-4
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DOI: https://doi.org/10.1007/s13721-022-00380-4