Raman Chemical Mapping of Low-Content Active Pharmaceutical Ingredient Formulations. III. Statistically Optimized Sampling and Detection of Polymorphic Forms in Tablets on Stability

Review of the Application of Raman Spectroscopy in Qualitative and Quantitative Analysis of Drug Polymorphism

Drug polymorphism is an important factor affecting the drugs quality and clinical efficacy. Therefore, great attention should be paid to the crystal analysis of drugs with their researching and evaluating part. With the booming development of Raman spectroscopy in recent years, more and more crystal analysis investigations were based on vibrational spectroscopy. This review mainly discussed the qualitative and quantitative analysis of active pharmaceutical ingredients (API) and pharmaceutical preparation with Raman spectroscopy. On basis of the determination of the vibration mode of drug molecules and the analysis of their chemical structure, this method had the advantages of universal, non-destructive, fast determination, low samples and cost, etc. This review provides theoretical and technical support for crystal structure, which are worth popularizing. It is expected that it will be helpful to relevant government management institutions, pharmaceutical scientific research institutions and pharmaceutical manufacturers.

A User-Friendly Platform for Single-Cell Raman Spectroscopy Analysis

The optimization of Raman instruments greatly expands our understanding of single-cell Raman spectroscopy. The improvement in the speed and sensitivity of the instrument and the implementation of advanced data mining methods help to reveal the complex chemical and biological information within the Raman spectral data. Here we introduce a new Matlab Graphical User-Friendly Interface (GUI), named "CELL IMAGE" for the analysis of cellular Raman spectroscopy data. The three main steps of data analysis embedded in the GUI include spectral processing, pattern recognition and model validation. Various well-known methods are available to the user of the GUI at each step of the analysis. Herein, a new subsampling optimization method is integrated into the GUI to estimate the minimum number of spectral collection points. The introduction of the signal-to-noise ratio (SNR) of the analyte in the binomial statistical model means the new subsampling model is more sophisticated and suitable for complicated Raman cell data. These embedded methods allow "CELL IMAGE" to transform spectral information into biological information, including single-cell visualization, cell classification and biomolecular/ drug quantification.

Research progress on the application of spectral imaging technology in pharmaceutical tablet analysis

Tablet as a traditional dosage form in pharmacy has the advantages of accurate dosage, ideal dissolution and bioavailability, convenient to carry and transport. The most concerned tablet quality attributes include active pharmaceutical ingredient (API) contents and polymorphic forms, components distribution, hardness, density, coating state, dissolution behavior, etc., which greatly affect the bioavailability and consistency of tablet final products. In the pharmaceutical industry, there are usually industry standard methods to analyze the tablet quality attributes. However, these methods are generally time-consuming and laborious, and lack a comprehensive understanding of the properties of tablets, such as spatial information. In recent years, spectral imaging technology makes up for the shortcomings of traditional tablet analysis methods because it provides non-contact and rich information in time and space. As a promising technology to replace the traditional tablet analysis methods, it has attracted more and more attention. The present paper briefly describes a series of spectral imaging techniques and their applications in tablet analysis. Finally, the possible application prospect of this technology and the deficiencies that need to be improved were also prospected.

Raman Mapping-Based Reverse Engineering Facilitates Development of Sustained-Release Nifedipine Tablet

The development of generic preparations that are bioequivalent to a reference listed drug (RLD) is faced with challenges because some critical attributes of RLDs are commonly unknown to developers. In order to determine these attributes, Raman mapping-based reverse engineering in this study to analyze a model sustained-release tablet of nifedipine. The Raman mapping results indicate that the size and size distribution of nifedipine are critical to its release pattern and bioavailability. The tablets with a particle size of nifedipine comparable to that of a commercial product, Adalat^®-L, showed similar in vitro release profiles to the RLD. Moreover, a pharmacokinetic study in human volunteers proved the bioequivalence of the two preparations. In conclusion, Raman mapping-based reverse engineering has the potential to facilitate the development of generic preparations.

Variety classification of coated maize seeds based on Raman hyperspectral imaging

As an essential factor in quality assessment of maize seeds, variety purity profoundly impacts final yield and farmers' economic benefits. In this study, a novel method based on Raman hyperspectral imaging system was applied to achieve variety classification of coated maize seeds. A total of 760 maize seeds including 4 different varieties were evaluated. Raman spectral data of 400-1800 cm^-1 were extracted and preprocessed. Variable selection methods involved were modified competitive adaptive reweighted sampling (MCARS), successive projections algorithm (SPA), and their combination. In addition, MCARS was proposed for the first time in this paper as a stable search technology. The performance of support vector machine (SVM) models optimized by genetic algorithm (GA) was analyzed and compared with models based on random forest (RF) and back-propagation neural network (BPNN). Same models based on Vis-NIR spectral data were also established for comparison. Results showed that the MCARS-GA-SVM model based on Raman spectral data obtained the best performance with calibration accuracy of 99.29% and prediction accuracy of 100%, which were stable and easily replicated. In addition, the accuracy on the independent validation set was 96.88%, which proved that the model can be applied in practice. A more simplified MCARS-SPA-GA-SVM model, which contained only 3 variables, had more than 95% accuracy on each data set. This procedure can help to develop a real-time detection system to classify coated seed varieties with high accuracy, which is of great significance for assessing variety purity and increasing crop yield.

Application of UV dissolution imaging to pharmaceutical systems

UV-vis spectrometry is widely used in the pharmaceutical sciences for compound quantification, alone or in conjunction with separation techniques, due to most drug entities possessing a chromophore absorbing light in the range 190-800 nm. UV dissolution imaging, the scope of this review, generates spatially and temporally resolved absorbance maps by exploiting the UV absorbance of the analyte. This review aims to give an introduction to UV dissolution imaging and its use in the determination of intrinsic dissolution rates and drug release from whole dosage forms. Applications of UV imaging to non-oral formulations have started to emerge and are reviewed together with the possibility of utilizing UV imaging for physical chemical characterisation of drug substances. The benefits of imaging drug diffusion and transport processes are also discussed.

Fiber-Array-Based Raman Hyperspectral Imaging for Simultaneous, Chemically-Selective Monitoring of Particle Size and Shape of Active Ingredients in Analgesic Tablets

The particle shape, size and distribution of active pharmaceutical ingredients (API) are relevant quality indicators of pharmaceutical tablets due to their high impact on the manufacturing process. Furthermore, the bioavailability of the APIs from the dosage form depends largely on these characteristics. Routinely, particle size and shape are only analyzed in the powder form, without regard to the effect of the formulation procedure on the particle characteristics. The monitoring of these parameters improves the understanding of the process; therefore, higher quality and better control over the biopharmaceutical profile can be ensured. A new fiber-array-based Raman hyperspectral imaging technique is presented for direct simultaneous in-situ monitoring of three different active pharmaceutical ingredients- acetylsalicylic acid, acetaminophen and caffeine- in analgesic tablets. This novel method enables a chemically selective, noninvasive assessment of the distribution of the active ingredients down to 1 µm spatial resolution. The occurrence of spherical and needle-like particles, as well as agglomerations and the respective particle size ranges, were rapidly determined for two commercially available analgesic tablet types. Subtle differences were observed in comparison between these two tablets. Higher amounts of acetaminophen were visible, more needle-shaped and bigger acetylsalicylic acid particles, and a higher incidence of bigger agglomerations were found in one of the analgesic tablets.

Comparison of the crystal structures and physicochemical properties of novel resveratrol cocrystals

Resveratrol (RSV) is one of the most extensively investigated natural polyphenol with potential cardioprotective effects and various biological activities. However, the polymorphism and solvates of RSV cocrystals have not been studied comprehensively. In addition, the relationship between the crystal packing modes and their physicochemical properties of RSV cocrystals remains poorly understood. In this paper, seven novel RSV cocrystals were prepared and characterized by powder X-ray diffraction, single-crystal X-ray diffraction, thermogravimetric analysis, differential scanning calorimetry, dynamic vapor sorption, Raman and Fourier transform infrared spectroscopy. Five RSV–4,4′-vinylenedipyridine (DPE) cocrystals were synthesized with polymorphs and solvates, such as RSV–DPE (1:2) in form (I) [RSV–2DPE form (I)], RSV–DPE (1:2) in form (II) [RSV–2DPE form (II)], RSV–DPE (1:1) (RSV–DPE), RSV–DPE (2:3)·acetone (RSV–1.5DPE·0.5ACE), RSV–DPE (1:1.5)·MeOH (RSV–1.5DPE·MeOH). However, RSV–4,4′-ethylenedipyridine (BPE) and RSV–4,4′-azobispyridine (AZPY) cocrystals were prepared as their single crystal forms, that is, RSV–BPE (1:1.5) (RSV–1.5BPE) and RSV–AZPY (1:2) (RSV–2AZPY). RSV–2DPE form (II) can be transformed from RSV–2DPE form (I) during the heating process from single crystal to single crystal. The physicochemical properties of RSV cocrystals are closely related to their crystal packing modes. Also, the conformation and molecular packing of RSV among different cocrystals is flexible. The solubility of RSV–1.5BPE and RSV–2DPE form (II) exhibit higher than RSV in the buffer solution of pH 4.6 and 2.0, respectively. This study may provide a valuable insight into the crystal packing modes of cocrystals which may affect their physicochemical properties.

Quantification and spatial distribution of salicylic acid in film tablets using FT-Raman mapping with multivariate curve resolution

In this study, we proposed a rapid and sensitive method for quantification and spatial distribution of salicylic acid in film tablets using FT-Raman spectroscopy with multivariate curve resolution (MCR). For this purpose, the constituents of film tablets were identified by using FT-Raman spectroscopy, and then eight different concentrations of salicylic acid tablets were visualized by Raman mapping. MCR was applied to mapping data to expose the active pharmaceutical ingredients in the presence of other excipients by monitoring distribution maps and combination of FT-Raman mapping with MCR enabled the determination of lower salicylic acid concentrations. In addition, the distribution of major excipient, lactose, was examined in the tablet form. A calibration curve was obtained by plotting the intensity of the Raman signal at 1635 cm⁻¹ versus the concentration of salicylic acid and the correlation was found to be linear within the range of 0.5%–3.9% with a correlation coefficient of 0.99. The limit of detection for the technique was determined 0.35%. The ability of the technique to quantify salicylic acid in tablet test samples was also investigated.

Kernel principal component analysis residual diagnosis (KPCARD): An automated method for cosmic ray artifact removal in Raman spectra

A new, fully automated, rapid method, referred to as kernel principal component analysis residual diagnosis (KPCARD), is proposed for removing cosmic ray artifacts (CRAs) in Raman spectra, and in particular for large Raman imaging datasets. KPCARD identifies CRAs via a statistical analysis of the residuals obtained at each wavenumber in the spectra. The method utilizes the stochastic nature of CRAs; therefore, the most significant components in principal component analysis (PCA) of large numbers of Raman spectra should not contain any CRAs. The process worked by first implementing kernel PCA (kPCA) on all the Raman mapping data and second accurately estimating the inter- and intra-spectrum noise to generate two threshold values. CRA identification was then achieved by using the threshold values to evaluate the residuals for each spectrum and assess if a CRA was present. CRA correction was achieved by spectral replacement where, the nearest neighbor (NN) spectrum, most spectroscopically similar to the CRA contaminated spectrum and principal components (PCs) obtained by kPCA were both used to generate a robust, best curve fit to the CRA contaminated spectrum. This best fit spectrum then replaced the CRA contaminated spectrum in the dataset. KPCARD efficacy was demonstrated by using simulated data and real Raman spectra collected from solid-state materials. The results showed that KPCARD was fast (<1 min per 8400 spectra), accurate, precise, and suitable for the automated correction of very large (>1 million) Raman datasets.

An iterative approach for compound detection in an unknown pharmaceutical drug product: Application on Raman microscopy

Raman chemical imaging provides both spectral and spatial information on a pharmaceutical drug product. Even if the main objective of chemical imaging is to obtain distribution maps of each formulation compound, identification of pure signals in a mixture dataset remains of huge interest. In this work, an iterative approach is proposed to identify the compounds in a pharmaceutical drug product, assuming that the chemical composition of the product is not known by the analyst and that a low dose compound can be present in the studied medicine. The proposed approach uses a spectral library, spectral distances and orthogonal projections to iteratively detect pure compounds of a tablet. Since the proposed method is not based on variance decomposition, it should be well adapted for a drug product which contains a low dose product, interpreted as a compound located in few pixels and with low spectral contributions. The method is tested on a tablet specifically manufactured for this study with one active pharmaceutical ingredient and five excipients. A spectral library, constituted of 24 pure pharmaceutical compounds, is used as a reference spectral database. Pure spectra of active and excipients, including a modification of the crystalline form and a low dose compound, are iteratively detected. Once the pure spectra are identified, multivariate curve resolution-alternating least squares process is performed on the data to provide distribution maps of each compound in the studied sample. Distributions of the two crystalline forms of active and the five excipients were in accordance with the theoretical formulation.

High-Throughput Raman Spectroscopy Screening of Excipients for the Stabilization of Amorphous Drugs

Low aqueous solubility of active pharmaceutical ingredients (APIs) is an enduring problem in pharmaceutical development, and it is becoming increasingly prevalent among new drug candidates. It is estimated that about 40% of drugs in the development pipeline and approximately 60% of the drugs coming directly from discovery suffer from poor aqueous solubility and slow dissolution, thereby reducing their bioavailability and efficacy and thus preventing their commercialization. It is well known that utilizing the amorphous form of a drug can be a useful approach to improve the dissolution rate and solubility of poorly water-soluble APIs. Amorphous compounds are thermodynamically unstable, but they can be stabilized by combining them with a carrier polymer (excipient) to form a solid dispersion. High-throughput Raman spectroscopy was used in this study to identify excipients that promote formation and stabilization of the amorphous drug form in solid dispersions. Four model APIs were used as poorly soluble drug candidates: ketoprofen, danazol, griseofulvin, and probucol. The Raman signals of excipients were generally negligible, and therefore Raman bands from the drugs were used with minimal spectral pre-processing. By comparing Raman spectra collected from the APIs in the crystalline and molten state, appropriate spectral features and regions were identified for the development of semi-quantitative methods to determine the amorphous content for each API. It is demonstrated that methods based on peak intensity ratio, peak width, peak distance, and classical least squares can all be effective methods for the screening of excipients. Interesting excipient-dependent phase transformation behavior was also observed for probucol.

In situ Raman mapping for identifying transient solid forms

In situ Raman surface mapping on a trihydrate sample provides evidence for the existence of a transient phase X on the route to its stable anhydrous polymorph I. The phase transformation events are observed in the order trihydrate → transient phase → form I.