A new criterion to assess distributional homogeneity in hyperspectral images of solid pharmaceutical dosage forms

Development of a Versatile Lipid Core for Nanostructured Lipid Carriers (NLCs) Using Design of Experiments (DoE) and Raman Mapping

The objective of this study was to develop a versatile lipid core for the 'brick-dust type of drugs' (poorly water-soluble and poorly lipid-soluble drugs). In the first step, excipients of different polarities were classified according to their behavior in aqueous solutions. Subsequently, binary mixtures were prepared with cetyl palmitate (Crodamol™ CP pharma, Campinas, São Paulo, Brazil) as the solid lipid, and its miscibility with other excipients was evaluated using Raman mapping and classical least squares (CLS). Based on the results, the excipients Crodamol™ CP pharma (hydrophobic), Super Refined™ DMI (dimethyl isosorbide; hydrophilic, Mill Hall, PA, USA), and Super Refined™ Lauryl Lactate (lauryl lactate, medium polarity, Mill Hall, PA, USA) were chosen to compose the lipid core. The ideal proportion of these excipients was determined using a mixture design and the standard deviation (STD) of image histograms as the response variables. After statistical evaluation of the DoE results, the final composition was determined, and drugs with different logP (0 to 10) and physicochemical characteristics were evaluated in the optimized mixture. The drugs butamben (Sigma-Aldrich Co., Spruce Street, St. Louis, MO, USA), tacrolimus (NutriFarm, São Paulo, Brazil), atorvastatin calcium, and resveratrol (Botica da Terra, Campinas, Brazil) presented a homogeneous distribution in the optimized lipid core, indicating that this is a promising system to be used in nanostructured lipid carrier (NLC) formulations of such types of drugs.

When it is too much: Identifying butamben excess on the surface of pharmaceutical preformulation samples by Raman mapping

Butamben is a topical local anesthetic which formulation in lipid-based drug delivery systems (DDS) is challenging due to its affinity for hydrophilic excipients. This means that a medium polarity excipient is preferred for the development of a stable nanostructured lipid carrier (NLC) formulation. In turn, in NLC, the type and number of excipients will determine the active pharmaceutical ingredient (API) solubility and the maximum drug upload. To solve this dilemma and get the best formulation, a throughout screening study to evaluate API solubilization in different excipients was carried out. Subsequently, excipients with different solubilization capacities were selected for microscopic evaluation by Raman mapping, and in turn analysis of the distributional homogeneity index (DHI) and standard deviation of the histograms allowed solving the posed question. Design of experiments (DoE) was employed to understand better the interactions between the excipients; linear and higher-order models were obtained with R2 above of 0.8824. Even though DHI is a good parameter to be used as response, an API concentration higher than 30% (w/w) provided a homogeneous surface in case of good miscibility and, in this case, this parameter needs to be employed with an inspection and/or evaluation of other parameters. A curve of concentration vs. mean scores of images proved to be an alternative to identify the saturation/limit of linear range.

Impact of Raman mapping area and intra-tablet homogeneity on the accuracy of sustained-release tablet dissolution prediction

This exploratory study investigated the minimum required Raman mapping area for predicting sustained-release tablet dissolution profiles based on intra-tablet homogeneity. The aim was to minimize scanning time while achieving reliable dissolution profile predictions. To construct the sample set, we controlled the blending time to introduce variability in the homogeneity of the tablets. The dissolution prediction models were established using the partial least squares regression under different Raman mapping area. The accuracies of the prediction results were evaluated according to the difference factor f1 and Intersection-Union two one-sided t-tests (IU TOST) methods, and the implications conveyed by the results were discussed. The results showed that the homogeneity of sustained-release tablet affects the minimum required mapping area, and the tablets with higher homogeneity show higher prediction accuracy when using the same mapping area to model the dissolution profiles of tablets.

Characterization of Pharmaceutical Tablets by X-ray Tomography

Solid dosage forms such as tablets are extensively used in drug administration for their simplicity and large-scale manufacturing capabilities. High-resolution X-ray tomography is one of the most valuable non-destructive techniques to investigate the internal structure of the tablets for drug product development as well as for a cost effective production process. In this work, we review the recent developments in high-resolution X-ray microtomography and its application towards different tablet characterizations. The increased availability of powerful laboratory instrumentation, as well as the advent of high brilliance and coherent 3rd generation synchrotron light sources, combined with advanced data processing techniques, are driving the application of X-ray microtomography forward as an indispensable tool in the pharmaceutical industry.

The Application of Hyperspectral Imaging to the Measurement of Pressure Injury Area

Wound size measurement is an important indicator of wound healing. Nurses measure wound size in terms of length × width in wound healing assessment, but it is easy to overestimate the extent of the wound due to irregularities around it. Using hyperspectral imaging (HIS) to measure the area of a pressure injury could provide more accurate data than manual measurement, ensure that the same tool is used for standardized assessment of wounds, and reduce the measurement time. This study was a pilot cross-sectional study, and a total of 30 patients with coccyx sacral pressure injuries were recruited to the rehabilitation ward after approval by the human subjects research committee. We used hyperspectral images to collect pressure injury images and machine learning (k-means) to automatically classify wound areas in combination with the length × width rule (LW rule) and image morphology algorithm for wound judgment and area calculation. The results calculated from the data were compared with the calculations made by the nursing staff using the length × width rule. The use of hyperspectral images, machine learning, the length × width rule (LW rule), and an image morphology algorithm to calculate the wound area yielded more accurate measurements than did nurses, effectively reduced the chance of human error, reduced the measurement time, and produced real-time data. HIS can be used by nursing staff to assess wounds with a standardized approach so as to ensure that proper wound care can be provided.

Neither too little nor too much: Finding the ideal proportion of excipients using confocal Raman and chemometrics

The applications of Raman imaging in pharmaceutical field are ever-increasing due its ability to obtain spatial and spectral information simultaneously, once it allows determine the chemical distribution of compounds. In this sense, it is used to study homogeneity, of paramount importance during the development of pharmaceutical formulations due to its relation to stability, safety and efficacy. Commonly, just surface is analyzed, but confocal Raman spectroscopy can also characterize the inner part of samples, allowing to determine phase separation in the early stages. In this sense, confocal 3D Raman microscopy was crucial to obtain the optimal proportion of Apifil®, Capryol® 90 and Transcutol® to promote controlled release of the local anesthetic butamben (BTB). 3D chemical maps were obtained by classical least squares (CLS) using pure compound spectra as S matrix, showing that chemical distribution throughout the material was different. Knowing that the composition of samples affects the homogeneity parameter, standard deviation and distributional homogeneity index (DHI) were used in mixture experimental design (DoE). From this analysis, it was revealed that a correct amount of Capryol® 90 enhances both miscibility and solubility. Furthermore, suitable miscibility was observed in two ratio proportions of excipients with a desirability of 0.783 and 0.742. These results unequivocally demonstrated that confocal Raman microscopy combined to DoE can bring pharmaceutical development to a higher level.

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.

A Review of Pharmaceutical Robot based on Hyperspectral Technology

The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

Evaluation of distributional homogeneity of pharmaceutical formulation using laser direct infrared imaging

The distributional homogeneity of chemicals is a key parameter of solid pharmaceutical formulations. Indeed, it may affect the efficacy of the drug and consequently its safety. Chemical imaging offers a unique insight enabling the visualisation of the different constituents of a pharmaceutical tablet. It allows identifying ingredients poorly distributed offering the possibility to optimize the process parameters or to adapt characteristics of incoming raw materials to increase the final product quality. Among the available chemical imaging tools, Raman imaging is one of the most widely used since it offers a high spatial resolution with well-resolved peaks resulting in a high spectral specificity. However, Raman imaging suffers from sample autofluorescence and long acquisition times. Recently commercialised, laser direct infrared reflectance imaging (LDIR) is a quantum cascade laser (QCL) based imaging technique that offers the opportunity to rapidly analyse samples. In this study, a typical pharmaceutical formulation blend composed of two active pharmaceutical ingredients and three excipients was aliquoted at different mixing timepoints. The collected aliquots were tableted and analysed using both Raman and LDIR imaging. The distributional homogeneity indexes of one active ingredient image were then computed and compared. The results show that both techniques achieved similar conclusions. However, the analysis times were drastically different. While Raman imaging required a total analysis time of 4 h per tablet to obtain the distribution map of acetylsalicylic acid with a step size of 100 µm, it only took 7.5 min to achieve the same result with LDIR. The results obtained in the present study show that LDIR is a promising technique for the analysis of pharmaceutical formulations and that it could be a valuable tool when developing new pharmaceutical formulations.

Application of reverse engineering in the field of pharmaceutical tablets using Raman mapping and chemometrics

Raman micro-spectroscopy technique offers a combination of relatively high spatial resolution with identification of components or mixtures of components in different sample areas, e.g. on the surface or the cross-section of a sample. This study is focused on the analysis of the tablets from pharmaceutical development with different technological parameters: (1) the manufacturing technology, (2) the particle size of the input API (active pharmaceutical ingredient) and (3) the quantitative composition of the individual excipients. These three mentioned parameters represent the most frequently solved problems in the field of reverse engineering in pharmacy. The investigation aims to distinguish tablets with the above-described technological parameters with limited subjective steps by Raman microscopy. Furthermore, non-subjective methods of Raman data analysis using advanced statistical analysis have been proposed, namely Principal Component Analysis, Soft Independent Modelling of Class Analogy and Linear Discriminant Analysis. The methods successfully distinguished and identified even very small differences in the analysed tablets within our study and provided objective statistic evaluation of Raman maps. The information on component and particle size distribution including their small differences, which is the critical parameter in the development of the original and generic products, was obtained due to combination of these methods. Even though each of these chemometric methods evaluates the data set from a different perspective, their mutual application on the problem of Raman maps evaluation confirmed and specified results on level that would be unattainable with the use of only one them.

SWiVIA - Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging

Controlling blending processes of solid material using advanced real-time sensing technologies tools is crucial to guarantee the quality attributes of manufactured products from diverse industries. The use of process analytical technology (PAT) tools based on chemical imaging systems are useful to assess heterogeneity information during mixing processes. Recently, a powerful procedure for heterogeneity assessment based on the combination of off-line acquired chemical images and variographic analysis has been proposed to provide specific heterogeneity indices related to global and distributional heterogeneity. This work proposes a novel PAT tool combining in situ chemical imaging and variogram-derived quantitative heterogeneity indices for the real-time monitoring of blending processes. The proposed method, so called sliding window variographic image analysis (SWiVIA), derives heterogeneity indices in real-time associated with a sliding image window that moves continuously until the full blending time interval is covered. The SWiVIA method is thoroughly assessed paying attention at the effect of relevant factors for continuous blending monitoring and heterogeneity description, such as the scale of scrutiny needed for heterogeneity definition or the blending period defined to set the sliding image window. SWiVIA is tested on blending runs of pharmaceutical and food products monitored with an in situ near-infrared chemical imaging system. The results obtained help to detect abnormal mixing phenomena and can be the basis to establish blending process control indicators in the future. SWiVIA is adapted to study blending behaviors of the bulk product or compound-specific blending evolutions.

Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations

Hyperspectral imaging has been widely used for different kinds of applications and many chemometric tools have been developed to help identifying chemical compounds. However, most of those tools rely on factorial decomposition techniques that can be challenging for large data sets and/or in the presence of minor compounds. The present study proposes a pixel-based identification (PBI) approach that allows readily identifying spectral signatures in Raman hyperspectral imaging data. This strategy is based on the identification of essential spectral pixels (ESP), which can be found by convex hull calculation. As the corresponding set of spectra is largely reduced and encompasses the purest spectral signatures, direct database matching and identification can be reliably and rapidly performed. The efficiency of PBI was evaluated on both known and unknown samples, considering genuine and falsified pharmaceutical tablets. We showed that it is possible to analyze a wide variety of pharmaceutical formulations of increasing complexity (from 5 to 0.1% (w/w) of polymorphic impurity detection) for medium (150 x 150 pixels) and big (1000 x 1000 pixels) map sizes in less than 2 min. Moreover, in the case of falsified medicines, it is demonstrated that the proposed approach allows the identification of all compounds, found in very different proportions and, sometimes, in trace amounts. Furthermore, the relevant spectral signatures for which no match is found in the reference database can be identified at a later stage and the nature of the corresponding compounds further investigated. Overall, the provided results show that Raman hyperspectral imaging combined with PBI enables rapid and reliable spectral identification of complex pharmaceutical formulations.

Design of Heterogeneity Indices for Blending Quality Assessment Based on Hyperspectral Images and Variographic Analysis

Heterogeneity characterization is crucial to define the quality of end products and to describe the evolution of processes that involve blending of compounds. The heterogeneity concept describes both the diversity of physicochemical characteristics of sample fragments (constitutional heterogeneity) and the diversity of spatial distribution of the materials/compounds in the sample (distributional heterogeneity, DH). Hyperspectral images (HSIs) are unique analytical measurements that provide physicochemical and spatial information on samples and, hence, are ideal to perform heterogeneity studies. This work proposes a new methodology combining HSI and variographic analysis to obtain a good qualitative and quantitative description of global heterogeneity (GH) and DH for samples and blending processes. An initial step of image unmixing provides a set of pure distribution maps of the blending constituents as a function of time that allows a qualitative visualization of the heterogeneity variation along the blending process. These maps are used as seeding information for a subsequent variographic analysis that furnishes the newly designed quantitative global heterogeneity index (GHI) and distributional uniformity index (DUI), related to GH and DH indices, respectively. GHI and DUI indices can be described at a sample level and per component within the sample. GHI and DUI curves of blending processes are easily interpretable and adaptable for blending monitoring and control and provide invaluable information to understand the sources of the abnormal blending behavior.

Hyperspectral imaging for skin assessment in systemic sclerosis: a pilot study

OBJECTIVE

Hyperspectral imaging (HSI) is a novel technology for obtaining quantitative measurements from transcutaneous spatial and spectral information. In patients with SSc, the severity of skin tightness is associated with internal organ involvement. However, clinical assessment using the modified Rodnan skin score is highly variable and there are currently no universal standardized protocols. This study aimed to compare the ability to differentiate between SSc patients and healthy controls using skin scores, ultrasound and HSI.

METHODS

Short-wave infrared light was utilized to detect the spectral angle mapper (SAM) of HSI. In addition, skin severity was evaluated by skin scores, ultrasound to detect dermal thickness and strain elastography. Spearman's correlation was used for assessing skin scores, strain ratio, thickness and SAM. Comparisons of various assessment tools were performed by receiver operating characteristic curves.

RESULTS

In total, 31 SSc patients were enrolled. SAM was positively correlated with skin scores and dermal thickness. In SSc patients with normal skin scores, SAM values were still significantly higher than in healthy controls. SAM exhibited the highest area under the curve (AUC: 0.812, P < 0.001) in detecting SSc compared with skin scores (AUC: 0.712, P < 0.001), thickness (AUC: 0.585, P = 0.009) and strain ratio by elastography (AUC: 0.522, P = 0.510). Moreover, the severity of skin tightness was reflected by the incremental changes of waveforms in the spectral diagrams.

CONCLUSION

SAM was correlated with skin scores and sufficiently sensitive to detect subclinical disease. HSI can be used as a novel, non-invasive method for assessing skin changes in SSc.

Determination and Quantification of the Distribution of CN-NL Nanoparticles Encapsulating Glycyrrhetic Acid on Novel Textile Surfaces with Hyperspectral Imaging

Chitin Lignin nanoparticles (CN-NL), standalone and encapsulating glycyrrhetic acid (GA), were applied on novel substrates for textiles to obtain antibacterial, antioxidant properties. Their homogeneous application is an important parameter that can strongly influence the final performance of the investigated textiles for its cosmetic and medical use. In this paper, hyperspectral imaging techniques combined with chemometric tools were investigated to study the distribution and quantification of CN-NL/GA on chitosan and CN-NL on pullulan substrates. To do so, samples of chitosan and pullulan impregnated with CN-NL/GA and CN-NL were analysed through Short Wave Infrared (SWIR) and Visible-Near Infrared (VisNIR) hyperspectral cameras. Two different chemometric tools for qualitative and quantitative analysis have been applied, principal component analysis (PCA) and partial least square regression (PLSR) models. Promising results were obtained in the VisNIR range, which made it possible for us to visualize the CN-NL/GA compound on chitosan and CN-NL on pullulan substrates. Additionally, the PLSR model results had determination coefficient ( R C 2 ) for calibration and cross-validation ( R C V 2 )   values of 0.983 and 0.857, respectively. Minimum values of root-mean-square error for calibration (RMSEC) and cross-validation (RMSECV) of CN-NL/GA were 0.333 and 0.993 g, respectively. The results demonstrate that hyperspectral imaging combined with chemometrics offers a powerful tool for studying the distribution on chitosan and pullulan substrates and to quantify the content of CN-NL/GA compounds on chitosan substrates.

Local anomaly detection and quantitative analysis of contaminants in soybean meal using near infrared imaging: The example of non-protein nitrogen

The melamine scandal indicates that traditional targeted detection methods only detect the specifically listed forms of contamination, which leads to the failure to identify new adulterants in time. In order to deal with continually changing forms of adulterations in food and feed and make up for the inadequacy of targeted detection methods, an untargeted detection method based on local anomaly detection (LAD) using near infrared (NIR) imaging was examined in this study. In the LAD method, with a particular size of window filter and at a 99% level of confidence, a specific value of Global H (GH, modified Mahalanobis distance) can be used as a threshold for anomalous spectra detection and quantitative analysis. The results showed an acceptable performance for the detection of contaminations with the advantage of no need of building a 'clean' library. And, a high coefficient of determination (R2LAD = 0.9984 and R2PLS-DA = 0.9978) for the quantitative analysis of melamine with a limit of detection lower than 0.01% was obtained. This indicates that the new strategy of untargeted detection has the potential to move from passive to active for food and feed safety control.

Convolutional neural network for hyperspectral data analysis and effective wavelengths selection

Fusion of spectral and spatial information has been proved to be an effective approach to improve model performance in near-infrared hyperspectral data analysis. Regardless, most of the existing spectral-spatial classification methods require fairly complex pipelines and exact selection of parameters, which mainly depend on the investigator's experience and the object under test. Convolutional neural network (CNN) is a powerful tool for representing complicated data and usually works with few "hand-engineering", making it an appropriate candidate for developing a general and automatic approach. In this paper, a two-branch convolutional neural network (2B-CNN) was developed for spectral-spatial classification and effective wavelengths (EWs) selection. The proposed network was evaluated by three classification data sets, including herbal medicine, coffee bean and strawberry. The results showed that the 2B-CNN obtained the best classification accuracies (96.72% in average) when compared with support vector machine (92.60% in average), one dimensional CNN (92.58% in average), and grey level co-occurrence matrix based support vector machine (93.83% in average). Furthermore, the learned weights of the two-dimensional branch in 2B-CNN were adopted as the indicator of EWs and compared with the successive projections algorithm. The 2B-CNN models built with wavelengths selected by the weight indicator achieved the best accuracies (96.02% in average) among all the examined EWs models. Different from the conventional EWs selection method, the proposed algorithm works without any additional retraining and has the ability to comprehensively consider the discriminative power in spectral domain and spatial domain.

Evaluation of miscibility and polymorphism of synthetic and natural lipids for nanostructured lipid carrier (NLC) formulations by Raman mapping and multivariate curve resolution (MCR)

Nanostructured lipid carriers (NLC) belong to youngest lipid-based nanocarrier class and they have gained increasing attention over the last ten years. NLCs are composed of a mixture of solid and liquid lipids, which solubilizes the active pharmaceutical ingredient, stabilized by a surfactant. The miscibility of the lipid excipients and structural changes (polymorphism) play an important role in the stability of the formulation and are not easily predicted in the early pharmaceutical development. Even when the excipients are macroscopically miscible, microscopic heterogeneities can result in phase separation during storage, which is only detected after several months of stability studies. In this sense, this work aimed to evaluate the miscibility and the presence of polymorphism in lipid mixtures containing synthetic (cetyl palmitate, Capryol 90®, Dhaykol 6040 LW®, Precirol ATO5® and myristyl myristate) and natural (beeswax, cocoa and shea butters, copaiba, sweet almond, sesame and coconut oils) excipients using Raman mapping and multivariate curve resolution - alternating least squares (MCR-ALS) method. The results were correlated to the macroscopic stability of the formulations. Chemical maps constructed for each excipient allowed the direct comparison among formulations, using standard deviation of the histograms and the Distributional Homogeneity Index (DHI). Lipid mixtures of cetyl palmitate/Capryol®; cetyl palmitate/Dhaykol®; myristyl myristate/Dhaykol® and myristyl myristate/coconut oil presented a single histogram distribution and were stable. The sample with Precirol®/Capryol® was not stable, although the histogram distribution was narrower than the samples with cetyl palmitate, indicating that miscibility was not the factor responsible for the instability. Structural changes before and after melting were identified for cocoa butter and shea butter, but not in the beeswax. Beeswax + copaiba oil sample was very homogenous, without polymorphism and stable over 6 months. Shea butter was also homogeneous and, in spite of the polymorphism, was stable. Formulations with cocoa butter presented a wider histogram distribution and were unstable. This paper showed that, besides the miscibility evaluation, Raman imaging could also identify the polymorphism of the lipids, two major issues in lipid-based formulation development that could help guide the developer understand the stability of the NLC formulations.

Label-Free Raman Spectroscopy Reveals Signatures of Radiation Resistance in the Tumor Microenvironment

Delay in the assessment of tumor response to radiotherapy continues to pose a major challenge to quality of life for patients with nonresponsive tumors. Here, we exploited label-free Raman spectroscopic mapping to elucidate radiation-induced biomolecular changes in tumors and uncovered latent microenvironmental differences between treatment-resistant and -sensitive tumors. We used isogenic radiation-resistant and -sensitive A549 human lung cancer cells and human head and neck squamous cell carcinoma (HNSCC) cell lines (UM-SCC-47 and UM-SCC-22B, respectively) to grow tumor xenografts in athymic nude mice and demonstrated the molecular specificity and quantitative nature of Raman spectroscopic tissue assessments. Raman spectra obtained from untreated and treated tumors were subjected to chemometric analysis using multivariate curve resolution-alternating least squares (MCR-ALS) and support vector machine (SVM) to quantify biomolecular differences in the tumor microenvironment. The Raman measurements revealed significant and reliable differences in lipid and collagen content postradiation in the tumor microenvironment, with consistently greater changes observed in the radiation-sensitive tumors. In addition to accurately evaluating tumor response to therapy, the combination of Raman spectral markers potentially offers a route to predicting response in untreated tumors prior to commencing treatment. Combined with its noninvasive nature, our findings provide a rationale for in vivo studies using Raman spectroscopy, with the ultimate goal of clinical translation for patient stratification and guiding adaptation of radiotherapy during the course of treatment. SIGNIFICANCE: These findings highlight the sensitivity of label-free Raman spectroscopy to changes induced by radiotherapy and indicate the potential to predict radiation resistance prior to commencing therapy.

[Applications of Raman Spectroscopy on Quality Control of Hospital Formulations]

Hospital formulation has several advantages, including the flexibility of customization as per the disease state or the patients' precise requirements. However, compared with commercial formulations, hospital formulations are usually not under the same level of quality check. In the present study, we tested mixed powder formulations prepared in a hospital pharmacy using Raman spectroscopy to investigate the feasibility of applying Raman spectroscopy as a quality-control tool of hospital formulations. For this purpose, we first established a numerical evaluation method to determine the uniformity of a powder mixture using Raman chemical imaging data with atropine sulfate/lactose mixture samples and revealed that the mixing uniformity correlated to the experience level of the pharmacist. Next, we developed a content quantification method in a one-dose packaged powder formulation by measuring the Raman spectra from the outside of the package. Because this method allows for quantification of the components inside the package in a non-destructive and non-contact manner, it can be applied for content confirmation after one-dose packaging. Using this method, the content uniformity of the mixed powder formulation in the one-dose package was compared between the formulations prepared by the pharmacists and those prepared by a pharmacy robot. Our study indicates the possibility of applying Raman spectroscopy as a quality-control tool of hospital formulations. Studies on further applications of Raman spectroscopy in the field of clinical pharmacology are expected.

Investigation of the distributional homogeneity on chlorpheniramine maleate tablets using NIR-CI

Homogeneity is the basic element of pharmaceutical analysis. Distributional Homogeneity Index (DHI) was proposed to assess the distributional homogeneity of commercial chlorpheniramine maleate (CPM) tablets. Furthermore, the divergence value of DHI value from expectation DHI (value = 1) was calculated to obtain the CPM distributional homogeneity. The distribution of commercial CPM tablets from six brands was successfully visualized using near infrared chemical imaging (NIR-CI) coupled with characteristic wavenumber method and binary image. Besides, content homogeneity of CPM was obtained through calculating the proportion of white region in the binary image. The result demonstrated that the distributional homogeneity of brand 4 was to be the best among all the brands, following by brand 2, brand 3, brand 5, brand 6 and brand 1. Furthermore, the sequence of the content uniformity was different from the distributional homogeneity, which demonstrated that content uniformity could not represent the distributional homogeneity. This work was a significant method guideline to assess the distributional homogeneity in pharmaceutical field.

Electron microscopy/energy dispersive X-ray spectroscopy of drug distribution in solid dispersions and interpretation by multifractal geometry

Much contemporary research of poorly water-soluble drugs focuses on amorphous solid dispersions (SDs) for oral drug delivery. Recently, a multifractal formalism has been introduced to describe the distribution of an inorganic carrier in SDs. The present work attempts to directly image model drugs by means of scanning electron microscopy and energy dispersive X-ray spectroscopy. The compounds amlodipine, felodipine, glyburide, and indomethacine, which include halogens to enable sufficient scattering in energy dispersive X-ray spectroscopy, were employed to prepare SDs with hydroxypropyl methylcellulose acetate succinate (HPMCAS) by using a microwave method. Following chemical imaging, it was demonstrated that drug distribution was best described by multifractals, which was clearly superior to a monofractal assumption. The obtained fractal dimensions were influenced by drug loading and it was possible to detect microstructural changes upon addition of the plasticizer urea. Accordingly, the multifractal approach bears much potential to better explore the analytical results of chemical formulation imaging. Insights can be gained from the microstructural organization of SDs, which is interesting to further study formulation and process factors as well as physical stability.

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.

Raman spectroscopy in pharmaceutical product design

Almost 100 years after the discovery of the Raman scattering phenomenon, related analytical techniques have emerged as important tools in biomedical sciences. Raman spectroscopy and microscopy are frontier, non-invasive analytical techniques amenable for diverse biomedical areas, ranging from molecular-based drug discovery, design of innovative drug delivery systems and quality control of finished products. This review presents concise accounts of various conventional and emerging Raman instrumentations including associated hyphenated tools of pharmaceutical interest. Moreover, relevant application cases of Raman spectroscopy in early and late phase pharmaceutical development, process analysis and micro-structural analysis of drug delivery systems are introduced. Finally, potential areas of future advancement and application of Raman spectroscopic techniques are discussed.

Raman imaging of drug delivery systems

This review article includes an introduction to the principals of Raman spectroscopy, an outline of the experimental systems used for Raman imaging and the associated important considerations and limitations of this method. Common spectral analysis methods are briefly described and examples of interesting published studies which utilised Raman imaging of pharmaceutical and biomedical devices are discussed, along with summary tables of the literature at this point in time.