PREDICTION OF THE DEGREE OF DANGER/RISK OF RESIDUAL SOLVENTS IN MEDICINAL PRODUCTS USING CHEMOMETRIC METHODS
DOI:
https://doi.org/10.11603/2312-0967.2023.3.13985Keywords:
residual solvent, classification, descriptor, artificial neural networkAbstract
The aim of the work. To investigate the applicability of chemometric methods for prediction of risk assessment for residual solvents in pharmaceuticals based on a set of molecular descriptors.
Materials and Methods. The object of the study is the classification of residual solvents in drug substances, excipients, and in drug products according to the degree of risk/danger to human health. Research methods ‒ Kruskal-Wallis test; probabilistic neural network. Software ‒ software packages MATLAB R2022b and ChemOffice 2020.
Results and Discussion. It was established that the classification of solvents according to their risk to human health is most influenced by the following molecular descriptors (their values change significantly depending on the class of solvents): number of HBond acceptors, logarithm of the solubility of a substance, polar surface area, shape coefficient, sum of valence degrees, total valence connectivity. The correct training of probabilistic neural network was archived in the case of using above six molecular descriptors at different values of spread parameter of the Gaussian activation function (there are no classification errors for testing and validation subsets).
Conclusions. Probabilistic neural network is the effective tool for risk assessment of residual solvents.
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