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Using the Artificial Neural Networks to Predict the Solubility Effects of Theophylline Drug in Hydrotropic Solutions
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| Author:
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CHINNAKANNU JAYAKUMAR, REDDY PRASAD DONIPATHI MOGILI, VEERAMANI MANSA DEVI, GANESAN SURENDRAN
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| Abstract:
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Theophylline is used to treat respiratory problems like COPD and asthma (bronchitis, emphysema). To
prevent wheezing and shortness of breath, it has to be used daily. This study is to measure the solubility of the
Theophylline drug among the chemical substances Using the ANN model. The experimental datasets are
trained together with a determination of the hydrotropes and analyzed physicochemical effects are now used
in-silico to set up an ANN system to engage Theophylline tranquilize solubilization. In the presence of
hydrotropes, the trained ANN system predicted exactly good estimations of Theophylline drug solubility. It
was verified for that purpose to provide a valuable capacity by which hydrotrope sensitivity could be
computationally screened in the same way. An ANN system was developed using MATLAB 2019 version to
predict the solubility properties of the hydrotropic-ester. From the observation, the Theophylline is more
soluble in sodium salicylate hydrotrope than other three hydrotropes. Since it is a water soluble molecular
structure that is more fitting in the system. The Theophylline affinity of the hydrophobic cavity in ionized form
and hence, greater hydrophilic form, should explain this effect. It is concluded that the use of artificial neural
networks through in-silico screening of drug/hydrotrope structures is explicitly possible to minimize the need
for large-scale laboratory testing of these systems in terms of decreased costs and time to upgrade the framework estimate the solubility of Theophylline.
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| Keyword:
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Theophylline, Solubility, Hydrotropes, Mathematical Model, Artificial Neural Networks
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2021.13.02.344
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| Download:
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