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.

Comparison of several strategies for the deployment of a multivariate regression model on several handheld NIR instruments. Application to the quality control of medicines.

Chemometrics applied to spectroscopic measurements such as near-infrared are gaining more and more importance for quality control of pharmaceutical products. Handheld near-infrared devices show great promise as a medicines quality screening technique for post-marketing surveillance. These devices are able to detect substandard and falsified medicines in pharmaceutical supply chains and enable rapid action before these medicines reach patients. The instrumental and environmental changes, expected or not, can adversely affect the analytical performances of prediction models developed for routine applications. Based on a previous study, PLS prediction models were developed and validated on three similar handheld NIR transmission spectrophotometers of the same model and from same company. These models have shown to be effective in analyzing metformin tablet samples, but significant spectral differences between handheld systems complicated their deployment for routine analysis. In this study, different strategies have been applied and compared to correct the instrumental variations, including global modelling (GM) and calibration transfer methods (Direct Standardization, DS; Spectral Space Transformation, SST and Slope/Bias correction, SBC), considering the RMSEP and the accuracy profile as assessment criteria. The transfer methods showed good capabilities to maintain the predictive performances comparable to that of the global modelling approach, except for a remaining slight bias. This approach is interesting since very few standardization samples are required to develop an adequate transfer model. GM, SST and SBC were able to correct/handle drifts in the spectral responses of different handheld instruments and thus may help to avoid the need for a long, laborious, and costly full recalibration process due to inter-instrument variations.

Composition analysis of falsified chloroquine phosphate samples seized during the COVID-19 pandemic.

The proliferation of falsified medicines can cause serious public health issues, particularly in the context of a global pandemic such as the actual COVID-19 pandemic. Our study involved eight chloroquine phosphate medicines seized in Cameroon, Democratic Republic of Congo and Niger during March and May 2020. These suspect samples were first analyzed in a screening phase using field tools such as handheld Raman spectroscopy (TruScan) and then in a confirmation phase using laboratory tools such as hyperspectral Raman imaging and High Performance Liquid Chromatography (HPLC). The results confirmed the falsified nature of the samples, highlighting the presence of metronidazole at low dose in four samples (16.6, 15.2, 15.2 and 14.5 mg/tab), too low levels of chloroquine in two samples (2.4 and 20.2 mg/tab), and substitution of chloroquine phosphate by paracetamol in one sample (255.7 mg/tab). The results also confirmed that four samples had been adulterated with paracetamol in trace amounts and two of them presented traces of chloramphenicol.

Raman imaging as a new analytical tool for the quality control of the monitoring of osteogenic differentiation in forming 3D bone tissue.

In this study, adipose-derived stem cells (ASCs) are used to produce 3D bone grafts. The safety and the feasibility of using these bone grafts have been already showed and quality controls are already implemented. However, a cheaper, fast and non-destructive technique is required to monitor the osteogenic differentiation process. Here, the use of Raman imaging to monitor the synthesis of the extracellular matrix and its progressive mineralization occurring during the osteogenic differentiation process is investigated for the first time on a 3D in forming bone tissue. The attention was focused on Raman bands related to this matrix belonging to phosphate, phenylalanine and hydroxyproline, which are very distinctive and intense. The kinetic of the osteogenic differentiation process was first compared between a 2D and a 3D forming bone tissue. It was observed that the kinetics of the osteogenic differentiation process is slower in 3D in forming bone tissue. In a second step, an evaluation of the reliability of the Raman imaging method was performed including a study of the influence of the harvest biopsies position on the forming 3D bone tissue. The repeatability and the specificity of this method were also demonstrated. In a last step, several batches of ASCs were cultured and analyzed in 3D at different time points using Raman imaging. From the mean Raman spectra, mineral to matrix ratios (MTMR) were determined and used to evaluate the formation of mineral deposits accompanying the extracellular matrix synthesis which is indicative of an ongoing osteogenic differentiation process. These ratios peaked between the day 35 and 49. This observation was very interesting since it corresponds to the time at which the 3D bone grafts are used for the patient surgery. To conclude, Raman imaging allowed fast acquisition and time-resolved monitoring in vitro of the mineralization of extracellular matrix during osteogenic differentiation.

Quantitation of active pharmaceutical ingredient through the packaging using Raman handheld spectrophotometers: A comparison study.

Handheld Raman spectroscopy is actually booming. Recent devices improvements aim at addressing the usual Raman spectroscopy issues: fluorescence with shifted-excitation Raman difference spectroscopy (SERDS), poor sensitivity with surface enhanced Raman scattering (SERS) and information only about the sample surface with spatially offset Raman spectroscopy (SORS). While qualitative performances of handheld devices are generally well established, the quantitative analysis of pharmaceutical samples remains challenging. The aim of this study was to compare the quantitative performances of three commercially available handheld Raman spectroscopy devices. Two of them (TruScan and IDRaman mini) are equipped with a 785 nm laser wavelength and operate in a conventional backscattering mode. The IDRaman has the Orbital Raster Scanning (ORS) option to increase the analyzed surface. The third device (Resolve) operates with an 830 nm laser wavelength both in backscattering and in SORS modes. The comparative study was carried out on ibuprofen-mannitol-microcrystalline cellulose ternary mixtures. The concentration of ibuprofen ranged from 24 to 52% (w/w) while the proportions of the two excipients were varied to avoid cross-correlation as much as possible. Analyses were performed either directly through a glass vial or with the glass vial in an opaque polypropylene flask, using a validated FT-NIR spectroscopy method as a reference method. Chemometric analyses were carried out with the Partial Least Squares Regression (PLS-R) algorithm. The quantitative models were validated using the total error approach and the ICH Q2 (R1) guidelines with ±â€¯15% as acceptance limits.

Comparing the qualitative performances of handheld NIR and Raman spectrophotometers for the detection of falsified pharmaceutical products.

Over the last decade, the growth of the global pharmaceutical market has led to an overall increase of substandard and falsified drugs especially on the African market (or emerging countries). Recently, several methods using handheld/portable vibrational spectroscopy have been developed for rapid and on-field drug analysis. The objective of this work was to evaluate the performances of various NIR and Raman handheld spectrophotometers in specific brand identification of medicines through their primary packaging. Three groups of drug samples (artemether-lumefantrine, paracetamol and ibuprofen) were used in tablet or capsule forms. In order to perform a critical comparison, the analytical performances of the two analytical systems were compared statistically using three methods: hierarchical clustering algorithm (HCA), data-driven soft independent modelling of class analogy (DD-SIMCA) and hit quality index (HQI). The overall results show good detection abilities for NIR systems compared to Raman systems based on Matthews's correlation coefficients, generally close to one. Raman systems are less sensitive to the physical state of the samples than the NIR systems, it also suffers of the auto-fluorescence phenomenon and the signal of highly dosed active pharmaceutical ingredient (e.g. paracetamol or lumefantrine) may mask the signal of low-dosed and weaker Raman active compounds (e.g. artemether). Hence, Raman systems are less effective for specific product identification purposes but are interesting in the context of falsification because they allow a visual interpretation of the spectral signature (presence or absence of API).

Critical review of surface-enhanced Raman spectroscopy applications in the pharmaceutical field.

Surface-enhanced Raman spectroscopy (SERS) is a sensitive analytical tool used in the pharmaceutical field in recent years. SERS keeps all the advantages of classical Raman spectroscopy while being is more sensitive allowing its use for the detection and the quantification of low-dose substances contained in pharmaceutical samples. However, the analytical performance of SERS is limited due to the difficulty to implement a quantitative methodology correctly validated. Nevertheless, some studies reported the development of SERS quantitative methods especially in pharmaceutical approaches. In this context, this review presents the main concepts of the SERS technique. The different steps that need to be applied to develop a SERS quantitative method are also deeply described. The last part of the present manuscript gives a critical overview of the different SERS pharmaceutical applications that were developed for a non-exhaustive list of pharmaceutical compounds with the aim to highlights the validation criteria for each application.

Development of a SERS strategy to overcome the nanoparticle stabilisation effect in serum-containing samples: Application to the quantification of dopamine in the culture medium of PC-12 cells.

The analysis of serum samples by surface-enhanced Raman spectroscopy (SERS) has gained ground over the last few years. However, the stabilisation of colloids by the proteins contained in these samples has restricted their use in common practice, unless antibodies or aptamers are used. Therefore, this work was dedicated to the development of a SERS methodology allowing the analysis of serum samples in a simple and easy-to-implement way. This approach was based on the pre-aggregation of the colloid with a salt solution. Gold nanoparticles (AuNPs) were used as the SERS substrate and, owing to its physiopathological importance, dopamine was chosen as a model to implement the SERS approach. The presence of this neurotransmitter could be determined in the concentration range 0.5-50 ppm (2.64-264 µM) in the culture medium of PC-12 cells, with a R2 of 0.9874, and at even lower concentrations (0.25 ppm, 1.32 µM) in another matrix containing fewer proteins. Moreover, the effect of calcium and potassium on the dopamine exocytosis from PC-12 cells was studied. Calcium was shown to have a predominant and dose-dependant effect. Finally, PC-12 cells were exposed to dexamethasone in order to increase their biosynthesis and release of dopamine. This increase was monitored with the developed SERS approach.

Development of an analytical method for crystalline content determination in amorphous solid dispersions produced by hot-melt extrusion using transmission Raman spectroscopy: A feasibility study.

The development of a quantitative method determining the crystalline percentage in an amorphous solid dispersion is of great interest in the pharmaceutical field. Indeed, the crystalline Active Pharmaceutical Ingredient transformation into its amorphous state is increasingly used as it enhances the solubility and bioavailability of Biopharmaceutical Classification System class II drugs. One way to produce amorphous solid dispersions is the Hot-Melt Extrusion (HME) process. This study reported the development and the comparison of the analytical performances of two techniques, based on backscattering and transmission Raman spectroscopy, determining the crystalline remaining content in amorphous solid dispersions produced by HME. Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were performed on preprocessed data and tended towards the same conclusions: for the backscattering Raman results, the use of the DuoScan™ mode improved the PCA and PLS results, due to a larger analyzed sampling volume. For the transmission Raman results, the determination of low crystalline percentages was possible and the best regression model was obtained using this technique. Indeed, the latter acquired spectra through the whole sample volume, in contrast with the previous surface analyses performed using the backscattering mode. This study consequently highlighted the importance of the analyzed sampling volume.

Global approach for the validation of an in-line Raman spectroscopic method to determine the API content in real-time during a hot-melt extrusion process.

Since the Food and Drug Administration (FDA) published a guidance based on the Process Analytical Technology (PAT) approach, real-time analyses during manufacturing processes are in real expansion. In this study, in-line Raman spectroscopic analyses were performed during a Hot-Melt Extrusion (HME) process to determine the Active Pharmaceutical Ingredient (API) content in real-time. The method was validated based on a univariate and a multivariate approach and the analytical performances of the obtained models were compared. Moreover, on one hand, in-line data were correlated with the real API concentration present in the sample quantified by a previously validated off-line confocal Raman microspectroscopic method. On the other hand, in-line data were also treated in function of the concentration based on the weighing of the components in the prepared mixture. The importance of developing quantitative methods based on the use of a reference method was thus highlighted. The method was validated according to the total error approach fixing the acceptance limits at ±15% and the α risk at ±5%. This method reaches the requirements of the European Pharmacopeia norms for the uniformity of content of single-dose preparations. The validation proves that future results will be in the acceptance limits with a previously defined probability. Finally, the in-line validated method was compared with the off-line one to demonstrate its ability to be used in routine analyses.

A simple calibration approach based on film-casting for confocal Raman microscopy to support the development of a hot-melt extrusion process.

When developing a new formulation, the development, calibration and validation steps of analytical methods based on vibrational spectroscopy are time-consuming. For each new formulation, real samples must be produced and a "reference method" must be used in order to determine the Active Pharmaceutical Ingredient (API) content of each sample. To circumvent this issue, the paper presents a simple approach based on the film-casting technique used as a calibration tool in the framework of hot-melt extrusion process. Confocal Raman microscopic method was successfully validated for the determination of itraconazole content in film-casting samples. Then, hot-melt extrusion was carried out to produce real samples in order to confront the results obtained with confocal Raman microscopy and Ultra High Performance Liquid Chromatography (UHPLC). The agreement between both methods was demonstrated using a comparison study based on the Bland and Altman's plot.

Monitoring of anatabine release by methyl jasmonate elicited BY-2 cells using surface-enhanced Raman scattering.

A new application of surface-enhanced Raman scattering (SERS) in the field of plant material analysis is proposed in this study. The aim was to monitor the release of anatabine by methyl jasmonate (MeJa) elicited Bright Yellow-2 (BY-2) cells. Gold nanoparticles (AuNps) were used as SERS substrate. The first step was to study the SERS activity of anatabine in a complex matrix comprising the culture medium and BY-2 cells. The second step was the calibration. This one was successfully performed directly in the culture medium in order to take into account the matrix effect, by spiking the medium with different concentrations of anatabine, leading to solutions ranging from 250 to 5000µgL(-1). A univariate analysis was performed, the intensity of a band situated at 1028cm(-1), related to anatabine, was plotted against the anatabine concentration. A linear relationship was observed with a R(2) of 0.9951. During the monitoring study, after the MeJa elicitation, samples were collected from the culture medium containing BY-2 cells at 0, 24h, 48h, 72h and 96h and were analysed using SERS. Finally, the amount of anatabine released in the culture medium was determined using the response function, reaching a plateau after 72h of 82µg of anatabine released/g of fresh weight (FW) MeJa elicited BY-2 cells.

A simple approach for ultrasensitive detection of bisphenols by multiplexed surface-enhanced Raman scattering.

Bisphenol A (BPA) is well known for its use in plastic manufacture and thermal paper production despite its risk of health toxicity as an endocrine disruptor in humans. Since the publication of new legislation regarding the use of BPA, manufacturers have begun to replace BPA with other phenolic molecules such as bisphenol F (BPF) and bisphenol B (BPB), but there are no guarantees regarding the health safety of these compounds at this time. In this context, a very simple, cheap and fast surface-enhanced Raman scattering (SERS) method was developed for the sensitive detection of these molecules in spiked tap water solutions. Silver nanoparticles were used as SERS substrates. An original strategy was employed to circumvent the issue of the affinity of bisphenols for metallic surfaces and the silver nanoparticles surface was functionalized using pyridine in order to improve again the sensitivity of the detection. Semi-quantitative detections were performed in tap water solutions at a concentrations range from 0.25 to 20 µg L(-1) for BPA and BPB and from 5 to 100 µg L(-1) for BPF. Moreover, a feasibility study for performing a multiplex-SERS detection of these molecules was also performed before successfully implementing the developed SERS method on real samples.

Vibrational spectroscopy and microspectroscopy analyzing qualitatively and quantitatively pharmaceutical hot melt extrudates.

Since the last decade, more and more Active Pharmaceutical Ingredient (API) candidates have poor water solubility inducing low bioavailability. These molecules belong to the Biopharmaceutical Classification System (BCS) classes II and IV. Thanks to Hot-Melt Extrusion (HME), it is possible to incorporate these candidates in pharmaceutical solid forms. Indeed, HME increases the solubility and the bioavailability of these drugs by encompassing them in a polymeric carrier and by forming solid dispersions. Moreover, in 2004, the FDA's guidance initiative promoted the usefulness of Process Analytical Technology (PAT) tools when developing a manufacturing process. Indeed, the main objective when developing a new pharmaceutical process is the product quality throughout the production chain. The trend is to follow this parameter in real-time in order to react immediately when there is a bias. Vibrational spectroscopic techniques, NIR and Raman, are useful to analyze processes in-line. Moreover, off-line Raman microspectroscopy is more and more used when developing new pharmaceutical processes or when analyzing optimized ones by combining the advantages of Raman spectroscopy and imaging. It is an interesting tool for homogeneity and spatial distribution studies. This review treats about spectroscopic techniques analyzing a HME process, as well off-line as in-line, presenting their advantages and their complementarities.

Development, validation and comparison of NIR and Raman methods for the identification and assay of poor-quality oral quinine drops.

Poor quality antimalarial drugs are one of the public's major health problems in Africa. The depth of this problem may be explained in part by the lack of effective enforcement and the lack of efficient local drug analysis laboratories. To tackle part of this issue, two spectroscopic methods with the ability to detect and to quantify quinine dihydrochloride in children's oral drops formulations were developed and validated. Raman and near infrared (NIR) spectroscopy were selected for the drug analysis due to their low cost, non-destructive and rapid characteristics. Both of the methods developed were successfully validated using the total error approach in the range of 50-150% of the target concentration (20%W/V) within the 10% acceptance limits. Samples collected on the Congolese pharmaceutical market were analyzed by both techniques to detect potentially substandard drugs. After a comparison of the analytical performance of both methods, it has been decided to implement the method based on NIR spectroscopy to perform the routine analysis of quinine oral drop samples in the Quality Control Laboratory of Drugs at the University of Kinshasa (DRC).

Data processing of vibrational chemical imaging for pharmaceutical applications.

Vibrational spectroscopy (MIR, NIR and Raman) based hyperspectral imaging is one of the most powerful tools to analyze pharmaceutical preparation. Indeed, it combines the advantages of vibrational spectroscopy to imaging techniques and allows therefore the visualization of distribution of compounds or crystallization processes. However, these techniques provide a huge amount of data that must be processed to extract the relevant information. This review presents fundamental concepts of hyperspectral imaging, the basic theory of the most used chemometric tools used to pre-process, process and post-process the generated data. The last part of the present paper focuses on pharmaceutical applications of hyperspectral imaging and highlights the data processing approaches to enable the reader making the best choice among the different tools available.

Development of a quantitative approach using surface-enhanced Raman chemical imaging: first step for the determination of an impurity in a pharmaceutical model.

This publication reports, for the first time, the development of a quantitative approach using surface-enhanced Raman chemical imaging (SER-CI). A pharmaceutical model presented as tablets based on paracetamol, which is the most sold drug around the world, was used to develop this approach. 4-Aminophenol is the main impurity of paracetamol and is actively researched in pharmaceutical formulations because of its toxicity. As its concentration is generally very low (<0.1%, w/w), conventional Raman chemical imaging cannot be used. In this context, a SER-CI method was developed to quantify 4-aminophenol assessing a limit of quantification below its limit of specification of 1000 ppm. Citrate-reduced silver nanoparticles were used as SERS substrate and these nanoparticles were functionalized using 1-butanethiol. Different ways to cover the tablets surface by butanethiol-functionalized silver nanoparticles were tested and a homogeneity study of the silver nanoparticles covering was realized. This homogeneity study was performed in order to choose the best way to cover the surface of tablets by silver colloid. Afterwards, the optimization of the SER-CI approach was necessary and different spectral intensity normalizations were tested. Finally, a quantitative approach using SER-CI was developed enabling to quantify 4-aminophenol from 0.025% to 0.2% in paracetamol tablets. This quantitative approach was tested on two different series of tablets using different batches of silver nanoparticles.

Evaluation of the residual solvent content of counterfeit tablets and capsules.

A group of counterfeit samples of Viagra and Cialis were screened for their residual solvent content and compared to the content of the genuine products. It was observed that all counterfeit samples had higher residual solvent contents compared to the genuine products. A more diverse range of residual solvents was found as well as higher concentrations. In general these concentrations did not exceed the international imposed maximum limits. Only in a few samples the limits were exceeded. A Projection Pursuit analysis revealed clusters of samples with similar residual solvent content, possibly enabling some future perspectives in forensic research.

Determination of 4-aminophenol in a pharmaceutical formulation using surface enhanced Raman scattering: from development to method validation.

A surface enhanced Raman scattering (SERS) method able to quantify 4-aminophenol in a pharmaceutical formulation based on acetaminophen, also called paracetamol, was developed and, for the first time, successfully validated. In this context, silver nanoparticles were synthesized according to the method described by Lee-Meisel and used as SERS substrate. The repeatability of the silver colloid synthesis was tested using different methods to characterize the size and the zeta potential of silver nanoparticles freshly synthesized. To optimize the SERS samples preparation, a design of experiments implicating concentrations of citrate-reduced silver nanoparticles and aggregating agent was performed in order to maximize the Raman signal enhancement. Finally, an approach based on tolerance intervals and accuracy profiles was applied in order to thoroughly validate the method in a range of concentrations comprised from 3 to 15 µg mL(-1) using normalized band intensities. The standard addition method was selected as method calibration. Therefore, measurements were carried out on 4-aminophenol spiked solutions of the pharmaceutical formulation. Despite the well-known stability and reproducibility problems of SERS, the validation was performed using two operators and five batches of nanoparticles, one for each validation day.

Classification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines.

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourier Transformed Infrared spectra, the near infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra(®) and Cialis(®). Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra(®) and the Cialis(®) data set respectively.