Faculty of Pharmacy

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Author: search.filters.author.Atanasova, Ana

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    Transformation model to green HPLC methods for lipophilic acidic compounds based on isoeluotropic series of elution solvents
    (Elsevier BV, 2025-06)
    Trifunovska, Bojana Vulovska
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    Atanasova, Ana
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    Antovska, Packa
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    Lazova, Jelena
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    FT-NIR models for predicting film quality parameters in titanium dioxide-free tablet coatings
    (Elsevier BV, 2025-02-01)
    Gorachinov, Filip
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    Koviloska, Monika
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    Tnokovska, Katerina
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    Atanasova, Ana
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    Antovska, Packa
    This study leverages Fourier Transform Near-Infrared (FT-NIR) spectroscopy to monitor the coating process of pharmaceutical tablets using PVA-based TiO2-free films, with talc and iron oxides as opacifiers. By employing a combination of multivariate analytical techniques, the correlation between film coating progression and film thickness was evaluated. Assessment of coating thickness for different coating levels was performed by optical microscopy. Additionally, using colorimetric analysis by scanner method, the color progression for different coating levels was evaluated and expressed as the a* value from CIELAB color space. The coordinate value a* showed predictable changes with the progression of the coating process and film thickness values, indicating its utility as a robust reference method for quality control and process optimization. The predictive capability of the OPLS models, validated against measured film thickness and the a* value, demonstrated low prediction errors and confirmed the models' effectiveness in distinguishing coating levels and accurately predicting film coating progression. The OPLS model used knowledge-based peaks of interest, which were further confirmed by loading and coefficient plots. The study demonstrated that film thickness, as a destructive, and a* value from CIELAB color space, as a non-destructive reference method for coating progression could be used during a controlled pharmaceutical coating process for product quality assessment and pharmaceutical process endpoint determination.
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    Optimization of self-emulsifying drug delivery system of cefuroxime axetil
    (Macedonian Pharmaceutical Association, 2021)
    Trajanovska, Eleonora
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    Simonoska Crcarevska, Maja
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    Jovanovikj, Frosina
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    Atanasova, Ana
    <jats:p>Abstract Overcoming solubility problems is the greatest challenge during formulation of poorly soluble active pharmaceutical ingredients (API’s) into oral solid dosage forms. Different formulation approaches were used to surpass this problem and enhance their solubility in the gastrointestinal (GI) fluids, in order to achieve a faster dissolution and better absorption, which will directly influence their therapeutic effect. In this paper, an evaluation of the potential of a self-emulsifying drug delivery system (SEDDS) to improve the solubility of the active ingredient cefuroxime axetil (CA) was done. Screening of the solubility of the API in different excipients was done, and Tween 80, PEG 400, and Olive oil as a surfactant, co-solvent, and oil, respectively, were chosen as the most convenient system constituents. An optimal self-emulsification and solubilization ability of this system was assessed using mixture experimental design statistical tools based on the response surface methodology (RSM). The prepared CA-SEDDS were evaluated for droplet size (d10, d50, d90 in µm), droplet size distribution (Span factor), and absorbance. As a complementary approach, for better representation of the non-linear relationship between the formulation compositions and the observed dispersion characteristics an artificial neural network (ANN) was used. Optimal formulation that consists of 10% (w/w) Tween 80 as surfactant, 80% (w/w) PEG 400 as co-solvent and 10% (w/w) Olive oil, was obtained. Both, mixture experimental design and ANN were combined for a comprehensive evaluation of CA-SEDDS and the obtained results suggested that formulation of SEDDS is a useful approach for improving the solubility of the CA. Keywords: self-emulsifying drug delivery systems (SEDDS), cefuroxime axetil, design of experiment, artificial neural network (ANN)</jats:p>