What is it about?
Solubility data of solid drugs in supercritical carbon dioxide is of vital importance for the design and optimization of relevant processes in the pharmaceutical industry. Obtaining such data from QSAR/QSPR perspectives is worth investigating. For the first time, a quantitative structure-property relationship (QSPR) modeling was investigated to predict drug and drug-like compounds solubility in supercritical carbon dioxide. A dataset of 148 drug\drug-like compounds, accounting for 3971 experimental data points (EDPs), was collected and used for modelling the relationship between selected molecular descriptors and solubility fraction data achieved by a nonlinear approach (Artificial neural network, ANN) based on molecular descriptors.
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Why is it important?
A reasonably good solubility measurement for a single solute in a single supercritical fluid requires at least 15 and possibly up to 40 experimental conditions (exp. 5–10 pressures at 3 or 4 different temperatures). However, for low solubilities these measurements are not easy because they are time-consuming and expensive. This background explained why data on drugs solubility in SC-CO2 is still not available for most pharmaceutical compounds. Despite this, a large amount of experimental data on the solubility of solids (including solid drugs) in SC-CO2 is now available, which allowed further attempts to develop models analogously to the measurement of the solubility. The objective behind the development of these models is to minimize the use of experiments, to offer the possibility of interpolating between the points measured experimentally, and to provide a tool to support process design and analysis.
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This page is a summary of: QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide, Molecular Informatics, April 2022, Wiley, DOI: 10.1002/minf.202200026.
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