A simple HPLC method for the determination of halcinonide in lipid nanoparticles: development, validation, encapsulation efficiency, and  in vitro drug permeation

Lopes, Clarissa Elize; Langoski, Gisele; Klein, Traudi; Ferrari, Priscileila Colerato; Farago, Paulo Vítor

doi:10.1590/s2175-97902017000215250

Cited by 20 publications

(19 citation statements)

References 23 publications

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“…The precision data were expressed as RSD obtained for repeatability and intermediate precision are described in Table 3. All the RSD values were below 5%, which confirm an appropriate precision for the evaluated method [35].…”

Section: Validation Methodssupporting

confidence: 61%

“…The robustness was based on the RSD values obtained by changing analytical parameters, such as the flow rate, the mobile phase composition, and the column temperature [26]. An analytical method is classified as robust when it remains unchanged at small variations that eventually occur during the analysis process [26,35]. The RSD results depicted in Table 5 show that there was no significant difference in the peak area and the UA retention time after the proposed changes.…”

Section: Validation Methodsmentioning

confidence: 99%

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Development and Validation of a Fast and Sensitive Uhplc-Pda Method for the Quantification of Ursolic Acid in Poly(l-Lactic Acid) Nanocapsules

Lyra

Koga

Novatski

et al. 2020

Asian J Pharm Clin Res

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Objective: The aim of the present study is to develop and validation of a ultra-high performance liquid chromatography (UHPLC) method to determine the ursolic acid content and its encapsulation efficiency (EE) in lipid-core nanocapsules prepared from poly (L-lactic acid). Methods: A simple UHPLC-PDA method was developed and validated for the quantitative determination of ursolic acid in poly(L-lactic acid) nanocapsules. The chromatographic conditions used were: RP-C18 column, isocratic mobile phase containing acetonitrile:water (92:8, v/v), flow rate of 0.8 ml/min, column temperature of 50°C, and detection at 203 nm. The following parameters were evaluated: Specificity, linearity, limits of detection and quantification, precision, accuracy, and robustness. Results: The method was specific to the ursolic acid and linear (r=0.9998) in the range of 10–100 μg/ml. The limits of detection and quantification were 1.35 and 4.10 μg/ml, respectively. The precision was demonstrated by a relative standard deviation less than 2%. Adequate accuracy (98.35%±0.82) was obtained. Changes in flow rate, mobile phase, and column temperature did not significantly alter the peak area and the retention time of the ursolic acid. The mean EE was 99.89%. Conclusion: The method proved to be fast, sensitive, and simple for quantifying ursolic acid in nanocapsules and was successfully used for determining the EE.

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Section: Validation Methodssupporting

confidence: 61%

Section: Validation Methodsmentioning

confidence: 99%

Development and Validation of a Fast and Sensitive Uhplc-Pda Method for the Quantification of Ursolic Acid in Poly(l-Lactic Acid) Nanocapsules

Lyra

Koga

Novatski

et al. 2020

Asian J Pharm Clin Res

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“…Halcinonide is a corticosteroid that is used to treat various skin conditions. It is a glucocorticoid and [18] lists hypertension as an adverse effect for glucocorticoids. Note that none of the above findings are reported in SIDER.…”

Section: Discussionmentioning

confidence: 99%

DeepSide: A Deep Learning Framework for Drug Side Effect Prediction

Uner

Cinbiş

Taştan

et al. 2019

Preprint

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Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features. The state-of-the-art approach that aims at using context specific information relies on only the highquality experiments in LINCS L1000 and discards a large portion of the experiments. In this study, our goal is to boost the prediction performance by utilizing this data to its full extent. We experiment with 5 deep learning architectures. We find that a multi-modal architecture produces the best predictive performance among multi-layer perceptron-based architectures when drug chemical structure (CS), and the full set of drug perturbed gene expression profiles (GEX) are used as modalities. Overall, we observe that the CS is more informative than the GEX. A convolutional neural network-based model that uses only SMILES string representation of the drugs achieves the best results and provides 13.0% macro-AUC and 3.1% micro-AUC improvements over the state-of-the-art. We also show that the model is able to predict side effect-drug pairs that are reported in the literature but was missing in the ground truth side effect dataset. DeepSide is available at

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“…The robustness of an analytical method indicates the reliability of the method against small variations of the analytical parameters [25]. The RSD values obtained after these changes did not exceed 5%, which represented that the proposed HPLC method is robust [26].…”

Section: Hplc Methods Validationmentioning

confidence: 99%

PEG-PCL Nanocapsules Containing Curcumin: Validation of HPLC Method for Analyzing Drug-loading Efficiency, Stability Testing and Cytotoxicity on NIH-3T3 Cell Line

Rudnik

Lyra

Barboza

et al. 2020

Braz. arch. biol. technol.

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Curcumin (CUR) shows potential use for treating cancer. However, CUR has low solubility and reduced bioavailability, which limit its clinical effect. Therefore, the development of nanocarriers can overcome these problems and can ensure the desired pharmacological effect. In addition, it is mandatory to prove the quality, the efficacy, and the safety for a novel nanomedicine to be approved. In that sense, this paper aimed (a) to prepare CUR-loaded polyethylene glycol-poly(ε-caprolactone) nanocapsules; (b) to validate an analytical method by high performance liquid chromatography (HPLC) for quantifying CUR in these nanoformulations; (c) to evaluate the physicochemical stability of these formulations; and to investigate their cytotoxicity on NIH-3T3 mouse fibroblast cells. The HPLC method was specific to CUR in the loaded nanocapsules, linear (r = 0.9994) in a range of 10.0 to 90.0 µg.mL-1 with limits of detection and quantification of 0.160 and 0.480 µg.mL-1 , respectively. Precision was demonstrated by a relative standard deviation lower than 5%. Suitable accuracy (102.37 ± 0.92%) was obtained. Values of pH, particle size, polydispersity index, and zeta potential presented no statistical difference (p > 0.05) for CUR-loaded nanoparticles. No cytotoxicity HIGHLIGHTS  A simple and low-cost HPLC method for determining curcumin in nanocapsules was validated.  PEG-PCL nanocapsules containing curcumin showed suitable stability over 60 days.  No cytotoxicity was observed against NIH-3T3 mouse embryo fibroblast cell line.  PEG-PCL nanocapsules containing curcumin can be further investigated for cancer treatment. 2 Rudnik, L.A.C.; et al.

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A simple HPLC method for the determination of halcinonide in lipid nanoparticles: development, validation, encapsulation efficiency, and in vitro drug permeation

Cited by 20 publications

References 23 publications

Development and Validation of a Fast and Sensitive Uhplc-Pda Method for the Quantification of Ursolic Acid in Poly(l-Lactic Acid) Nanocapsules

Development and Validation of a Fast and Sensitive Uhplc-Pda Method for the Quantification of Ursolic Acid in Poly(l-Lactic Acid) Nanocapsules

DeepSide: A Deep Learning Framework for Drug Side Effect Prediction

PEG-PCL Nanocapsules Containing Curcumin: Validation of HPLC Method for Analyzing Drug-loading Efficiency, Stability Testing and Cytotoxicity on NIH-3T3 Cell Line

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