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Researchers have developed an eco-friendly method using chemometric techniques and artificial neural networks for simultaneous determination of aspirin, clopidogrel, and either atorvastatin or rosuvastatin in their fixed-dose combination (FDC) formulations using ultraviolet (UV) spectrophotometry.
Eco-friendly approaches in spectroscopic analysis have been an ongoing area of focus for researchers. This focus has bled into drug analysis as well. A recent study that was done at Cairo University in Egypt examined the amount of aspirin, clopidogrel, and either rosuvastatin or atorvastatin in fixed-dose combination (FDC) formulas while using a new, eco-friendly method to do so (1). Their research was published in the Journal of Chemometrics (1).
When FDCs formulas are analyzed, it is common that each component in a FDC required a different model to determine their concentration. The issue with this archaic strategy was that it was timewasting and often complex (1). The researchers sought to overcome these issues by using UV spectrophotometry coupled with chemometric techniques, integrating artificial neural networks (ANNs) during the process as well (1).
Three chemometric techniques were deployed in this study: principal component regression (PCR); partial least squares (PLS); and classical least squares (CLS) (1). These techniques were coupled with the radial basis function–ANN (RBF-ANN) (1). Two tertiary mixtures were laboratory-prepared, each containing aspirin and clopidogrel; however, one of the mixtures also contained atorvastatin, whereas the other mixture contained rosuvastatin (1).
The RBF-ANN was integral to this study because its functions helped calculate the distance between the input data and the neuron’s center, converting the input into an activation value (1). The hidden layer’s neurons then converted this value into a weighted output, which was processed in the output layer (1). The researchers used RBF-ANN in this study because it is an application designed to resolve complex problems, and it has a proven record of being able to simultaneously determine the concentrations of drugs in FDC formulations (1).
The researchers also needed to create an absorbance data matrix in this study. To accomplish this task, they took zero-order spectra measurements in the range of 250–280 nm with intervals of 0.2 nm (1). Then, the researchers used the concentration and absorbance data matrices to predict the unknown concentrations by using regression or calibration (1). The RBF-ANN had an input layer that was comprised of 151 neurons, with two hidden layers and three output neurons, which was key in determining the abovementioned drugs in their formulations (1).
The researchers assessed the green profile of the developed methods and compared them with previously reported spectrophotometric techniques. By introducing this novel approach, the researchers have successfully addressed the challenges associated with the determination of multiple components in fixed-dose combinations, while also considering environmental impact (1).
This research opens up new possibilities for the pharmaceutical industry, providing a more efficient and sustainable solution for the simultaneous determination of multiple active ingredients in fixed-dose combinations (1). The findings of this study have the potential to significantly impact the development and quality control of pharmaceutical formulations, ultimately benefiting patients worldwide (1). This study helps lead the way for further exploration of eco-friendly methods that can aid in pharmaceutical analysis. The applicability of the approach the researchers used will be further tested, with the hope that it can lead the way for improved therapeutic outcomes (1). The result will be that new advancements in this field can lead to and ensure the safety and efficacy of multi-component medications.
(1) Al-Sawy, N. S.; El-Kady, E. F.; Mostafa, E. A.A novel eco-friendly methods for simultaneous determination of aspirin, clopidogrel, and atorvastatin or rosuvastatin in their fixed-dose combination using chemometric techniques and artificial neural networks. J. Chemom. 2023, ASAP. DOI: 10.1002/cem.3474