Diffuse reflectance near infrared-chemometric methods development and validation of amoxicillin capsule formulations

Abstract

Objective:The aim of present study was to establish near infrared-chemometric methods that could be effectively used for quality profiling through identification and quantification of amoxicillin (AMOX) in formulated capsule which were similar to commercial products. In order to evaluate a large number of market products easily and quickly, these methods were modeled.Materials and Methods:Thermo Scientific Antaris II near infrared analyzer with TQ Analyst Chemometric Software were used for the development and validation of the identification and quantification models. Several AMOX formulations were composed with four excipients microcrystalline cellulose, magnesium stearate, croscarmellose sodium and colloidal silicon dioxide. Development includes quadratic mixture formulation design, near infrared spectrum acquisition, spectral pretreatment and outlier detection. According to prescribed guidelines by International Conference on Harmonization (ICH) and European Medicine Agency (EMA) developed methods were validated in terms of specificity, accuracy, precision, linearity, and robustness.Results:On diffuse reflectance mode, an identification model based on discriminant analysis was successfully processed with 76 formulations; and same samples were also used for quantitative analysis using partial least square algorithm with four latent variables and 0.9937 correlation of coefficient followed by 2.17% root mean square error of calibration (RMSEC), 2.38% root mean square error of prediction (RMSEP), 2.43% root mean square error of cross-validation (RMSECV).Conclusion:Proposed model established a good relationship between the spectral information and AMOX identity as well as content. Resulted values show the performance of the proposed models which offers alternate choice for AMOX capsule evaluation, relative to that of well-established high-performance liquid chromatography method. Ultimately three commercial products were successfully evaluated using developed methods.

Device-Independent Discrimination of Falsified Amoxicillin Capsules Using Heterogeneous Near-Infrared Spectroscopic Devices for Training and Testing of a Support Vector Machine

“…As reported by other researchers, the standard medical products of amoxicillin can be discriminated from the lowquality products using a bench-top spectrometer. 28,29,[31][32][33] The homogeneous device discrimination using FT-NIR also gave fine discrimination results even for the substandard products. We considered the causes that give low discrimination performance for the substandard products by the heterogeneous device models.…”

Quality of Amoxicillin Trihydrate/Potassium Clavulanate Tablets Commercially Available in Libyan Pharmaceutical Market

“…There are many and various analytical methods previously reported for assay of co-clavimox tablets which includes thin layer chromatography (TLC) [19][20][21][22], ultraviolet (UV) spectrophotometry [23,24], titration [25], high-performance liquid chromatography (HPLC) [26][27][28][29], capillary electrophoresis (CE) [30], and infrared spectroscopy (IR) [31], in both British Pharmacopoeia (B. P) and United States Pharmacopoeia (U. S. P). Among then, HPLC is recommended for the analysis of co-clavimoxin pure forms and pharmaceutical preparations.…”

Following drug degradation and consequent taste deterioration of an oral reconstituted paediatric suspension during dosing interval via electronic tongue

“…Ttraditional assessment of the taste of a pharmaceutical product is usually conducted on human volunteers, which is costly, time-consuming and sometimes harmful( Committee for Medicinal Products for Human Use (CHMP) 2013 ). Accordingly, reliable and predictive in vitro methods have been developed to assess the taste of pharmaceutical products ( Hilton and Thomas, 2003 , Khan et al, 2016 , Mohamed-Ahmed et al, 2016 , Zontov et al, 2016 ). One of the newly used methods is the electronic tongue (ET), which is a multi-sensor system composed of a number of low-selective and cross-sensitive sensors, and uses advanced mathematical algorithms for signal processing based on multivariate analysis, e.g., pattern recognition (PARC) and artificial neural networks (ANNs) ( Vlasov et al, 2005 , Gebicki, 2016 , Mendez and Preedy, 2016 ).…”