Development of a Near Infrared Spectroscopy method for the in-line quantitative bilastine drug determination during pharmaceutical powders blending

A highly sensitive spectrofluorimetric method for the determination of bilastine in human plasma: Application of content uniformity testing

Bilastine, a new second generation antihistaminic drug, has been widely used for relieving symptoms of allergic rhinitis and urticaria without a sedative effect. A simple, cost-effective, and highly sensitive fluorimetric method was developed for the estimation of bilastine in human plasma, in addition to its pure state and tablets. The suggested method depended on binary complex formation of eosin with bilastine in a buffered medium at pH 4.2. The formed complex resulted in quantitative quenching of eosin emission at 538 nm after excitation at 335 nm. This method demonstrates a broad range of linearity, spanning from 200 to 1000 ng/mL, and exhibits exceptional sensitivity, with a limit of detection and quantitation of 30.85 and 93.48 ng/mL, respectively. In addition, this spectrofluorimetric method may be employed to determine the amount of bilastine in human plasma and tablets with satisfactory accuracy and excellent precision. Furthermore, the content uniformity of bilastine in commercially available tablets was successfully tested by this approach. Compared with the reference method, there were no significant variations in terms of precision or accuracy. In conclusion, the proposed protocol is highly recommended to quantitatively estimate bilastine in different quality control settings.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Assessing Polymorphic Purity of Rifampicin in Double and Triple-Drug Fixed-Dose Combination Products

First-line tuberculostatic agents, Rifampicin (RIF), Isoniazid (ISH), Ethambutol (ETB), and Pyrazinamide (PZA) are generally administered as a fixed-dose combination (FDC) for improving patient adherence. The major quality challenge of these FDC products is their variable bioavailability, where RIF and its solid state are key factors. In this work, the analysis of the impact of the polymorphism in the performance of RIF in RIF-ISH and PZA-RIF-ISH combined products was carried out by an overall approach that included the development and validation of two methodologies combining near-infrared (NIR) spectroscopy and partial least squares (PLS) to the further evaluation of commercial products. For NIR-PLS methods, training and validation sets were prepared with mixtures of Form I/Form II of RIF, and the appropriate amount of ISH (for double associations) or ISH-PZA (for triple associations). The corresponding matrix of the excipients was added to the mixture of APIs to simulate the environment of each FDC product. Four PLS factors, reduced spectral range, and the combination of standard normal variate and Savitzky-Golay 1st derivative (SNV-D') were selected as optimum data pre-treatment for both methods, yielding satisfactory recoveries during the analysis of validation sets (98.5±2.0%, and 98.7±1.8% for double- and triple-FDC products, respectively). The NIR-PLS model for RIF-ISH successfully estimated the polymorphic purity of Form II in double-FDC capsules (1.02 ± 0.02w/w). On the other hand, the NIR-PLS model for RIF-ISH-PZA detected a low purity of Form II in triple FDC tablets (0.800 ± 0.021w/w), these results were confirmed by X-ray powder diffraction. Nevertheless, the triple-FDC tablets showed good performance in the dissolution test (Q=99-102%), implying a Form II purity about of 80% is not low enough to affect the safety and efficacy of the product.

Development of a green synchronous spectrofluorimetric technique for simultaneous determination of Montelukast sodium and Bilastine in pharmaceutical formulations

For the treatment of rhinitis and asthma, a combination of Montelukast sodium and Bilastine has just been approved. Based on the first derivative of synchronous fluorescence, the current work developed a green, highly accurate, sensitive, and selective spectroscopic approach for estimating Montelukast sodium and Bilastine in pharmaceutical dosage form without previous separation. The selected technique focuses on measuring the synchronized fluorescence of the studied medications at a fixed wavelength range (Δλ) = 110 nm, and using the amplitude of the first derivative's peak at 381 and 324 nm, for quantitative estimation of Montelukast sodium and Bilastine, respectively. The impacts of different factors on the referred drugs' synchronized fluorescence intensity were investigated and adjusted. The calibration plots for were found to be linear over concentration ranges of 50-2000 ng mL-1 for Montelukast sodium and 50-1000 ng mL-1 for Bilastine. Montelukast sodium and Bilastine have LODs of 16.5 and 10.9 ng mL-1, respectively. In addition, LOQs were: 49.9 and 33.0 ng mL-1, for both drugs, respectively. The developed method was successfully employed to quantify the two drugs in synthetic tablets mixture and in laboratory prepared mixtures containing varied Montelukast and Bilastine ratios. To compare the results with the published analytical approach, a variance ratio F-test and a student t-test were used, which revealed no significant differences.

Development of a CH2-dependent analytical method using near-infrared spectroscopy via the integration of two algorithms: non-dominated sorting genetic-II and competitive adaptive reweighted sampling (NSGAII-CARS)

In most of the near-infrared studies, near-infrared spectra (NIRS) were often mathematically treated. However, these algorithms selected a large number of variables and latent variables, and they caused the over-fitting phenomenon, which became very common. The large number of variables made it impossible to extract the "chemical information" directly from the NIRS. To build robust and interpretable mathematical models, the non-dominated sorting genetic-II-competitive adaptive reweighted sampling (NSGAII-CARS) algorithm was proposed to determine influential functional groups for quantitative analysis. In this research, data on a primary mixture of two amino acids (AAs), namely NH₂(CH₂)₃COOH and HOOC(NH₂)CH(CH₂)₂COOH, was used to illustrate the algorithm. The principle of the algorithm was first to find out the different characteristic spectral regions of two amino acids by extreme points according to Non-dominated Sorting Genetic-II (NSGAII). Second, based on the absolute value of the regression coefficient, we found out [ν(CH₂) + 2δ(CH₂)] and [2ν(CH₂)], where the wavenumber ranged from 6165 to 5683 cm^-1, were the influential functional groups for quantitative analysis. Finally, the CARS (competitive adaptive reweighted sampling) algorithm was combined with NSGAII to find the specific fingerprint points for the determination of two AAs. Compared with the previous results, the NSGAII-CARS algorithm not only pointed out the influential quantitative functional groups but also used only 6 points for HOOC(NH₂)CH(CH₂)₂COOH and 18 points for NH₂(CH₂)₃COOH to achieve the full-spectrum quantitative effect. The results proposed a general algorithm for the quantitative analysis of NIRS obtained in the binary or ternary mixed systems. The MATLAB codes of the NSGAII-CARS algorithm are available on the website: https://github.com/Mark1988NK/NSGAII-CARS-Algorithm.git.

Improved Principal Component Analysis (IPCA): A Novel Method for Quantitative Calibration Transfer between Different Near-Infrared Spectrometers

Given the labor-consuming nature of model establishment, model transfer has become a considerable topic in the study of near-infrared (NIR) spectroscopy. Recently, many new algorithms have been proposed for the model transfer of spectra collected by the same types of instruments under different situations. However, in a practical scenario, we need to deal with model transfer between different types of instruments. To expand model applicability, we must develop a method that could transfer spectra acquired from different types of NIR spectrometers with different wavenumbers or absorbance. Therefore, in our study, we propose a new methodology based on improved principal component analysis (IPCA) for calibration transfer between different types of spectrometers. We adopted three datasets for method evaluation, including public pharmaceutical tablets (dataset 1), corn data (dataset 2), and the spectra of eight batches of samples acquired from the plasma ethanol precipitation process collected by FT-NIR and MicroNIR spectrometers (dataset 3). In the calibration transfer for public datasets, IPCA displayed comparable results with the classical calibration transfer method using piecewise direct standardization (PDS), indicating its obvious ability to transfer spectra collected from the same types of instruments. However, in the calibration transfer for dataset 3, our proposed IPCA method achieved a successful bi-transfer between the spectra acquired from the benchtop and micro-instruments with/without wavelength region selection. Furthermore, our proposed method enabled improvements in prediction ability rather than the degradation of the models built with original micro spectra. Therefore, our proposed method has no limitations on the spectrum for model transfer between different types of NIR instruments, thus allowing a wide application range, which could provide a supporting technology for the practical application of NIR spectroscopy.

In-Line Vis-NIR Spectral Analysis for the Column Chromatographic Processes of the Ginkgo biloba L. Leaves. Part II: Batch-to-Batch Consistency Evaluation of the Elution Process

An in-line monitoring method for the elution process of Ginkgo biloba L. leaves using visible and near-infrared spectroscopy in conjunction with multivariate statistical process control (MSPC) was established. Experiments, including normal operating batches and abnormal ones, were designed and carried out. The MSPC model for the elution process was developed and validated. The abnormalities were detected successfully by the control charts of principal component scores, Hotelling T2, or DModX (distance to the model). The results suggested that the established method can be used for the in-line monitoring and batch-to-batch consistency evaluation of the elution process.

NIR Spectroscopy as an Online PAT Tool for a Narrow Therapeutic Index Drug: Toward a Platform Approach Across Lab and Pilot Scales for Development of a Powder Blending Monitoring Method and Endpoint Determination

An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.

Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer

For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.