Development and optimization of a one step process for the production and sterilization of liposomes using supercritical CO2

Liposomes are very interesting drug delivery systems for pharmaceutical and therapeutic purposes. However, liposome sterilization as well as their industrial manufacturing remain challenging. Supercritical carbon dioxide is an innovative technology that can potentially overcome these limitations. The aim of this study was to optimize a one-step process for producing and sterilizing liposomes using supercritical CO2. For this purpose, a design of experiment was conducted. The analysis of the experimental design showed that the temperature is the most influential parameter to achieve the sterility assurance level (SAL) required for liposomes (≤10-6). Optimal conditions (80 °C, 240 bar, 30 min) were identified to obtain the fixed critical quality attributes of liposomes. The conditions for preparing and sterilizing empty liposomes of various compositions, as well as liposomes containing the poorly water-soluble drug budesonide, were validated. The results indicate that the liposomes have appropriate physicochemical characteristics for drug delivery, with a size of 200 nm or less and a PdI of 0.35 or less. Additionally, all liposome formulations demonstrated the required SAL and sterility at concentrations of 5 and 45 mM, with high encapsulation efficiency.

A multi-target QSRR approach to model retention times of small molecules in RPLC

Quantitative structure-retention relationship models (QSRR) have been utilized as an alternative to costly and time-consuming separation analyses and associated experiments for predicting retention time. However, achieving 100 % accuracy in retention prediction is unrealistic despite the existence of various tools and approaches. The limitations of vast data availability and time complexity hinder the use of most algorithms for retention prediction. Therefore, in this study, we examined and compared two approaches for modelling retention time using a dataset of small molecules with retention times obtained at multiple conditions, referred to as multi-targets (five pH levels: 2.7, 3.5, 5, 6.5, and 8 at gradient times of 20 min of mobile phase). The first approach involved developing separate models for predicting retention time at each condition (single-target approach), while the second approach aimed to learn a single model for predicting retention across all conditions simultaneously (multi-target approach). Our findings highlight the advantages of the multi-target approach over the single-target modelling approach. The multi-target models are more efficient in terms of size and learning speed compared to the single-target models. These retention prediction models offer two-fold benefits. Firstly, they enhance knowledge and understanding of retention times, identifying molecular descriptors that contribute to changes in retention behaviour under different pH conditions. Secondly, these approaches can be extended to address other multi-target property prediction problems, such as multi-quantitative structure Property(X) relationship studies (mt-QS(X)R).

In-Field Implementation of Near-Infrared Quantitative Methods for Analysis of Medicines in Tropical Environments

Near-infrared (NIR) spectroscopy is actually a well-established technique that demonstrates its performance in the frame of detection of poor-quality medicines. The use of low-cost handheld NIR spectrophotometers in low-resource contexts can allow an inexpensive and more rapid detection compared to laboratory methods. Considering these points, it was decided to develop, validate, and transfer methods for the quantification of ciprofloxacin and metronidazole tablet samples using a NIR handheld spectrophotometer in transmission mode (NIR-M-T1) coupled to chemometrics such as partial least squares regression (PLSR) algorithm. All of the models were validated with the total error approach using an accuracy profile as a decision tool, with ±10% specifications and a risk α set at 5%. Quantitative PLSR models were first validated in Belgium, which is a temperate oceanic climate zone. Second, they were transferred to Cameroon, a tropical climate zone, where issues regarding the prediction of new validation series with the initial models were highlighted. Two augmentation strategies were then envisaged to make the predictive models robust to environmental conditions, incorporating the potential variability linked to environmental effects in the initial calibration sets. The resulting models were then used for in-field analysis of ciprofloxacin and metronidazole tablet samples collected in three cities in Cameroon. The contents results obtained for each sample with the two strategies were close and not statistically different. Nevertheless, the first one is easier to implement and the second is the best regarding model diagnostic measures and accuracy profiles. Two samples were found to be noncompliant in terms of content, and these results were confirmed using high-performance liquid chromatography taken as the reference method.

Usefulness of medicine screening tools in the frame of pharmaceutical post-marketing surveillance

The negative consequences of Substandard and falsified (SF) medicines are widely documented nowadays and there is still an urgent need to find them in more efficient ways. Several screening tools have been developed for this purpose recently. In this study, three screening tools were used on 292 samples of ciprofloxacin and metronidazole collected in Cameroon. Each sample was then analyzed by HPLC and disintegration tests. Seven additional samples from the nitro-imidazole (secnidazole, ornidazole, tinidazole) and the fluoroquinolone (levofloxacin, ofloxacin, norfloxacin, moxifloxacin) families were analyzed to mimic falsified medicines. Placebo samples that contained only inert excipients were also tested to mimic falsified samples without active pharmaceutical ingredient (API). The three screening tools implemented were: a simplified visual inspection checklist, a low-cost handheld near infrared (NIR) spectrophotometer and paper analytical devices (PADs). Overall, 61.1% of the samples that failed disintegration and assay tests also failed the visual inspection checklist test. For the handheld NIR, one-class classifier models were built to detect the presence of ciprofloxacin and metronidazole, respectively. The APIs were correctly identified in all the samples with sensitivities and specificities of 100%. However, the importance of a representative and up-to-date spectral database was underlined by comparing models built with different calibration set spanning different variability spaces. The PADs were used only on ciprofloxacin samples and detected the API in all samples in which the presence of ciprofloxacin was confirmed by HPLC. However, these PADs were not specific to ciprofloxacin since they reacted like ciprofloxacin to other fluoroquinolone compounds. The advantages and drawbacks of each screening tool were highlighted. They are promising means in the frame of early detection of SF medicines and they can increase the speed of decision about SF medicines in the context of pharmaceutical post-marketing surveillance.

Optimization of silver nanoparticles synthesis by chemical reduction to enhance SERS quantitative performances: Early characterization using the quality by design approach

Surface-enhanced Raman scattering (SERS) is a vibrational widely used technique thanks to its multiple advantages such as its high specificity and sensitivity. The Raman signal exaltation comes from the use of metallic nanoparticles (Nps) acting as antennas by amplifying the Raman scattering. Controlling the Nps synthesis is a major point for the implementation of SERS in routine analysis and especially in quantitative applications. Effectively, nature, size and shape of these Nps considerably influence the SERS response intensity and repeatability. The Lee-Meisel protocol is the most common synthesis route used by the SERS community due to the low cost, rapidity and ease of manufacturing. However, this process leads to a significant heterogeneity in terms of particle size and shape. In this context, this study aimed to synthesize repeatable and homogeneous silver nanoparticles (AgNps) by chemical reduction. The Quality by Design strategy from quality target product profile to early characterization design was considered to optimize this reaction. The first step of this strategy aimed to highlight critical parameters by the means of an early characterization design. Based on an Ishikawa diagram, five process parameters were studied: the reaction volume as categorical variable and the temperature, the time of reaction, the trisodium citrate concentration and pH as continuous variables. A D-Optimal design of 35 conditions was performed. Three critical quality attributes were selected to maximize the SERS intensity, minimize the variation coefficient on SERS intensities and the polydispersity index of the AgNps. Considering these factors, it appeared that concentration, pH and time of reaction were identified as having a critical impact on the Nps formation and can then be considered for the further optimization step.

Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy

Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.

Prevalence of Poor Quality Ciprofloxacin and Metronidazole Tablets in Three Cities in Cameroon

Quality is one of the essential components of medicines and needs to be ensured to preserve the population's health. This can be achieved through post-marketing quality control of medicines and is one of the most important duties of national regulatory authorities. In collaboration with the Cameroonian National Drug Quality Control and Valuation Laboratory, the decision was made to initiate a prevalence study to assess the quality of antiinfective medicines in Cameroon. A total of 150 samples of ciprofloxacin tablets and 142 samples of metronidazole tablets were collected from 76 licensed pharmacies and 75 informal vendors in three cities in Cameroon using a random strategy wherever possible and a mystery shopper approach. Three tests were carried out on each of the samples. Visual inspection allowed to find two falsified samples (0.7%) due to lack of information about the manufacturing company, and five more samples (1.7%) were deemed to be substandard due to flaws in the product. An additional 13 samples (4.5%) failed disintegration testing, and six (2.1%) others failed high-performance liquid chromatography assay testing due to insufficient active pharmaceutical ingredient (API) content. All samples were found to contain some API. A prevalence of 7.9% substandard or falsified (SF) medicines was found. Moreover, the prevalence of outlets selling SF medicines was greater in the informal sector (26.7%) than in the formal sector (2.6%). Although the prevalence of SF medicines found was low, efforts need to be made by national regulatory authorities to monitor the pharmaceutical market more closely.

User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules

In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure-retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user's decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps.

Optimizing the soft independent modeling of class analogy (SIMCA) using statistical prediction regions

The ultimate goal of a one-class classifier like the "rigorous" soft independent modeling of class analogy (SIMCA) is to predict with a certain confidence probability, the conformity of future objects with a given reference class. However, the SIMCA model, as currently implemented often suffers from an undercoverage problem, meaning that its observed sensitivity often falls far below the desired theoretical confidence probability, hence undermining its intended use as a predictive tool. To overcome the issue, the most reported strategy in the literature, involves incrementing the nominal confidence probability until the desired sensitivity is obtained in cross-validation. This article proposes a statistical prediction interval-based strategy as an alternative strategy to properly overcome this undercoverage issue. The strategy uses the concept of predictive distributions sensu stricto to construct statistical prediction regions for the metrics. Firstly, a procedure based on goodness-of-fit criteria is used to select the best-fitting family of probability models for each metric or its monotonic transformation, among several plausible candidate families of right-skewed probability distributions for positive random variables, including the gamma and the lognormal families. Secondly, assuming the best-fitting distribution, a generalized linear model is fitted to each metric data using the Bayesian method. This method enables to conveniently estimate uncertainties about the parameters of the selected distribution. Propagating these uncertainties to the best-fitting probability model of the metric enables to derive its so-called posterior predictive distribution, which is then used to set its critical limit. Overall, the evaluation of the proposed approach on a diversity of real datasets shows that it yields unbiased and more accurate sensitivities than existing methods which are not based on predictive densities. It can even yield better specificities than the strategy that attempts to improve sensitivities of existing methods by "optimizing" the type 1 error, especially in low sample sizes' contexts.

Emerging analytical techniques for pharmaceutical quality control: Where are we in 2022?

Quality control is a fundamental and critical activity in the pharmaceutical industry that guarantees the quality of medicines. QC analyses are currently performed using several well-known techniques, mainly liquid and gas chromatography. However, current trends are focused on the development of new techniques to reduce analysis time and cost, to improve the performances and decrease ecological footprint. In this context, analytical scientists developed and studied emerging technologies based on spectroscopy and chromatography. The present review aims to give an overview of the recent development of vibrational spectroscopy, supercritical fluid chromatography and multi-dimensional chromatography. Selected emerging techniques are discussed using SWOT analysis and published pharmaceutical QC applications are discussed.

A pharmaceutical-related molecules dataset for reversed-phase chromatography retention time prediction built on combining pH and gradient time conditions

There is a rising interest in the modeling and predicting of chromatographic retention. The progress towards more complex and comprehensive models emphasized the need for broad reliable datasets. The present dataset comprises small pharmaceutical compounds selected to cover a wide range in terms of physicochemical properties that are known to impact the retention in reversed-phase liquid chromatography. Moreover, this dataset was analyzed at five pH with two gradient slopes. It provides a reliable dataset with a diversity of conditions and compounds to support the building of new models. To enhance the robustness of the dataset, the compounds were injected individually, and each sequence of injections included a quality control sample. This unambiguous detection of each compound as well as a systematic analysis of a quality control sample ensured the quality of the reported retention times. Moreover, three different liquid chromatographic systems were used to increase the robustness of the dataset.

A new alternative tool to analyse glycosylation in pharmaceutical proteins based on infrared spectroscopy combined with nonlinear support vector regression

Almost 60% of commercialized pharmaceutical proteins are glycosylated. Glycosylation is considered a critical quality attribute, as it affects the stability, bioactivity and safety of proteins. Hence, the development of analytical methods to characterise the composition and structure of glycoproteins is crucial. Currently, existing methods are time-consuming, expensive, and require significant sample preparation steps, which can alter the robustness of the analyses. In this work, we suggest the use of a fast, direct, and simple Fourier transform infrared spectroscopy (FT-IR) combined with a chemometric strategy to address this challenge. In this context, a database of FT-IR spectra of glycoproteins was built, and the glycoproteins were characterised by reference methods (MALDI-TOF, LC-ESI-QTOF and LC-FLR-MS) to estimate the mass ratio between carbohydrates and proteins and determine the composition in monosaccharides. The FT-IR spectra were processed first by Partial Least Squares Regression (PLSR), one of the most used regression algorithms in spectroscopy and secondly by Support Vector Regression (SVR). SVR has emerged in recent years and is now considered a powerful alternative to PLSR, thanks to its ability to flexibly model nonlinear relationships. The results provide clear evidence of the efficiency of the combination of FT-IR spectroscopy, and SVR modelling to characterise glycosylation in therapeutic proteins. The SVR models showed better predictive performances than the PLSR models in terms of RMSECV, RMSEP, R2CV, R2Pred and RPD. This tool offers several potential applications, such as comparing the glycosylation of a biosimilar and the original molecule, monitoring batch-to-batch homogeneity, and in-process control.

Interpretable One-Class Classification of Raman Spectra Using Prediction Bands Estimated by Wavelet Regression

Previously, we introduced a novel one-class classification (OCC) concept for spectra. It uses as acceptance space for genuine spectra of the target chemical, a prediction band in the wavelengths' space. As a decision rule, test spectra falling substantially outside this band are rejected as noncomplying with the target, and their deviations are documented in the wavelengths' space. This band-based OCC concept was applied to smooth signals like near-infrared (NIR) spectra. A regression model based on a smoothed principal component (PC) representation of the training spectra was used to predict unseen trajectories of future spectra. The boundaries of the most central predicted trajectories were chosen as critical trajectories. We now propose a methodology to construct a similar band-based one-class classifier for Raman spectra, which are sharper and noisier than NIR spectra. The spectra are transformed by a composition of wavelet and principal component (wPC) expansions instead of just a PC expansion in the previous methodology for NIR spectra. Wavelets can capture sharp features of Raman signals and provide a framework to efficiently denoise them. A multinormal prediction model is then used to derive predictions of future wPC scores of unseen spectra. These predicted wPC scores are then backtransformed to obtain predictions of future trajectories of unseen spectra in the wavelengths' space, whose most central region defines the acceptance band or space. This band-based one-class classifier successfully classified the first derivatives of real pharmaceutical Raman spectra, while enjoying the advantage of documenting deviations from the critical trajectories in the wavelengths' space and hence is more interpretable.

Poor-Quality Medicines in Cameroon: A Critical Review

Poor-quality medicines are the cause of many public health and socioeconomic problems. We conducted a review to acquire an overview of the situation concerning such medicines in Cameroon. Different searches were performed on databases from several websites of the WHO, the Ministry of Public Health of Cameroon, the Anti-Counterfeit Medicine Research Institute, the Global Pharma Health Fund, and the Infectious Disease Data Observatory. We identified 92 publications comprised of 19 peer-reviewed studies and 73 alerts. Based on studies completed, 1,664 samples were analyzed, and the prevalence of substandard and falsified (SF) medicines could be estimated for 1,440 samples. A total of 67.5% of these samples were collected from the informal sector, 20.9% from the formal sector, and 11.6% from both sectors. We found a prevalence of SF medicines across the peer-reviewed studies of 26.9%, whereas most of the SF medicines belonged to the anti-infective class. The problem of SF medicines is not studied sufficiently in Cameroon; therefore, efforts should be made to conduct adequate studies in terms of representativity and methodology.

New perspective for the in-field analysis of cannabis samples using handheld near-infrared spectroscopy: A case study focusing on the determination of Δ9-tetrahydrocannabinol

The aim of the present study was to explore the feasibility of applying near-infrared (NIR) spectroscopy for the quantitative analysis of Δ⁹-tetrahydrocannabinol (THC) in cannabis products using handheld devices. A preliminary study was conducted on different physical forms (entire, ground and sieved) of cannabis inflorescences in order to evaluate the impact of sample homogeneity on THC content predictions. Since entire cannabis inflorescences represent the most common types of samples found in both the pharmaceutical and illicit markets, they have been considered priority analytical targets. Two handheld NIR spectrophotometers (a low-cost device and a mid-cost device) were used to perform the analyses and their predictive performance was compared. Six partial least square (PLS) models based on reference data obtained by UHPLC-UV were built. The importance of the technical features of the spectrophotometer for quantitative applications was highlighted. The mid-cost system outperformed the low-cost system in terms of predictive performance, especially when analyzing entire cannabis inflorescences. In contrast, for the more homogeneous forms, the results were comparable. The mid-cost system was selected as the best-suited spectrophotometer for this application. The number of cannabis inflorescence samples was augmented with new real samples, and a chemometric model based on machine learning ensemble algorithms was developed to predict the concentration of THC in those samples. Good predictive performance was obtained with a root mean squared error of prediction of 1.75 % (w/w). The Bland-Altman method was then used to compare the NIR predictions to the quantitative results obtained by UHPLC-UV and to evaluate the degree of accordance between the two analytical techniques. Each result fell within the established limits of agreement, demonstrating the feasibility of this chemometric model for analytical purposes. Finally, resin samples were investigated by both NIR devices. Two PLS models were built by using a sample set of 45 samples. When the analytical performances were compared, the mid-cost spectrophotometer significantly outperformed the low-cost device for prediction accuracy and reproducibility.

Development of a prototype device for near real-time surface-enhanced Raman scattering monitoring of biological samples

With the fast growth of bioanalytical surface-enhanced Raman scattering (SERS), analytical methods have had to adapt to the complex nature of biological samples. In particular, interfering species and protein adsorption onto the SERS substrates have been addressed by sample preparation steps, such as precipitation or extraction, and by smart SERS substrate functionalisation. These additional handling steps however result in irreversible sample alteration, which in turn prevents sample monitoring over time. A new methodology, that enables near real-time, non-invasive and non-destructive SERS monitoring of biological samples, is therefore proposed. It combines solid SERS substrates, benefitting from liquid immersion resistance for extended periods of time, with an original protein filtering device and an on-field detection by means of a handheld Raman analyser. The protein removal device aims at avoiding protein surface fouling on the SERS substrate. It consists of an ultracentrifugation membrane fixed under a cell culture insert for multi-well plates. The inside of the insert is dedicated to containing biological samples. The solid SERS substrate and a simple medium, without any protein, are placed under the insert. By carefully selecting the membrane molecular weight cutoff, selective diffusion of small analytes through the device could be achieved whereas larger proteins were retained inside the insert. Non-invasive SERS spectral acquisition was then carried out through the bottom of the multi-well plate. The diffusion of a SERS probe, 2-mercaptopyridine, and of a neurotransmitter having a less intense SERS signal, serotonin, were first successfully monitored with the device. Then, the latter was applied to distinguish between subclones of cancerous cells through differences in metabolite production. This promising methodology showed a high level of versatility, together with the capability to reduce cellular stress and contamination hazards.

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.

Effect of the functionalisation agent on the surface-enhanced Raman scattering (SERS) spectrum: Case study of pyridine derivatives

Nowadays, the use of functionalised surface-enhanced Raman scattering (SERS) substrates has become common. These surface modifying agents notably act as Raman reporters, as sensors of biological processes (pH, redox probes) or to increase the sensitivity and/or the specificity of SERS detections. However, the effects of the functionalisation agents are deeply examined in very few studies, even though they can affect the aggregation behaviour of the SERS substrate. Moreover, depending on their concentration and on the pH, their spectral signature can be modified and they can even degrade if stored inappropriately. In this context, this paper aims at emphasising the importance of the different aspects previously listed in the selection of a functionalisation agent. Pyridine derivatives were picked out to highlight these parameters, as some of these compounds are commonly used to be grafted onto SERS substrates. Two widespread syntheses of nanoparticles were selected as SERS substrates: citrate-reduced gold and silver nanoparticles. The surface of the nanoparticles was functionalised with several pyridine derivatives at different concentrations and in several solvents. It was observed that the molecules under study had a concentration-dependent effect on nanoparticle aggregation. A stability study was furthermore conducted in order to determine the best preservation conditions of the grafting solutions. In conclusion, this paper shines a light on the relevance of the investigation of the too-often neglected behaviour of the surface modifying agents. Before their application in SERS analyses, parameters such as the label concentration should therefore be included in an experimental design to optimise the sample preparation.

Evaluation of the analytical performances of two Raman handheld spectrophotometers for pharmaceutical solid dosage form quantitation

This paper addresses the issue of pharmaceutical solid dosage form quantitation using handheld Raman spectrophotometers. The two spectrophotometers used are designed with different technologies: one allows getting a more representative sampling with the Orbital Raster Scanning technology and the other one allows setting acquisition parameters. The goal was to evaluate which technology could provide the best analytical results. Several parameters were optimized to get the lowest prediction error in the end. The main objective of this study was to evaluate if this kind of instrument would be able to identify substandard medicines. For that purpose, two case-study were explored. At first, a full ICH Q2 (R1) compliant validation was performed for moderate Raman scatterer active pharmaceutical ingredient (API) in a specific formulation. It was successfully validated in the ±15% relative total error acceptance limits, with a RMSEP of 0.85% (w/w). Subsequently, it was interesting to evaluate the influence of excipients when the API is a high Raman scatterer. For that purpose, a multi-formulation model was developed and successfully validated with a RMSEP of 2.98% (w/w) in the best case. These two studies showed that thanks to the optimization of acquisition parameters, Raman handheld spectrophotometers methods were validated for two different case-study and could be applied to identify substandard medicines.

Raman chemical imaging, a new tool in kidney stone structure analysis: Case-study and comparison to Fourier Transform Infrared spectroscopy

BACKGROUND AND OBJECTIVES

The kidney stone's structure might provide clinical information in addition to the stone composition. The Raman chemical imaging is a technology used for the production of two-dimension maps of the constituents' distribution in samples. We aimed at determining the use of Raman chemical imaging in urinary stone analysis.

MATERIAL AND METHODS

Fourteen calculi were analyzed by Raman chemical imaging using a confocal Raman microspectrophotometer. They were selected according to their heterogeneous composition and morphology. Raman chemical imaging was performed on the whole section of stones. Once acquired, the data were baseline corrected and analyzed by MCR-ALS. Results were then compared to the spectra obtained by Fourier Transform Infrared spectroscopy.

RESULTS

Raman chemical imaging succeeded in identifying almost all the chemical components of each sample, including monohydrate and dihydrate calcium oxalate, anhydrous and dihydrate uric acid, apatite, struvite, brushite, and rare chemicals like whitlockite, ammonium urate and drugs. However, proteins couldn't be detected because of the huge autofluorescence background and the small concentration of these poor Raman scatterers. Carbapatite and calcium oxalate were correctly detected even when they represented less than 5 percent of the whole stones. Moreover, Raman chemical imaging provided the distribution of components within the stones: nuclei were accurately identified, as well as thin layers of other components. Conversion of dihydrate to monohydrate calcium oxalate was correctly observed in the centre of one sample. The calcium oxalate monohydrate had different Raman spectra according to its localization.

CONCLUSION

Raman chemical imaging showed a good accuracy in comparison with infrared spectroscopy in identifying components of kidney stones. This analysis was also useful in determining the organization of components within stones, which help locating constituents in low quantity, such as nuclei. However, this analysis is time-consuming, making it more suitable for research studies rather than routine analysis.

Towards a spray-coating method for the detection of low-dose compounds in pharmaceutical tablets using surface-enhanced Raman chemical imaging (SER-CI)

Surface-enhanced Raman chemical imaging (SER-CI) is a highly sensitive analytical tool recently used in the pharmaceutical field owing to the possibility to obtain high sensitivity along with spatial information. However, the covering method of the pharmaceutical samples such as tablets with metallic nanoparticles is a major issue for SER-CI analyses due to the difficulty to obtain a homogeneous covering of tablet surface with the SERS substrates. In this context, a spray-coating method was proposed in order to fully exploit the potential of SER-CI. A homemade apparatus has been developed from an electrospray ionization (ESI) probe in order to cover the pharmaceutical tablets with the colloidal suspension in a homogeneous way. The silver substrate was pulled through the airbrush by a syringe pump which was then nebulized into small droplets due to the contact of the solution with the gas flow turbulence. A robust optimization of the process was carried out by adjusting experimental parameters such as the liquid flow rate and the spraying time. Besides, the performances of this spraying technique were compared with two others covering methods found in the literature which are drop casting and absorption coating. A homogeneity study, conducted by SER-CI and matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) applied to the different covering techniques was performed. The influence of the metallic nanoparticles deposit on soluble compounds was also investigated in order to highlight the advantages of using this new spray coating approach.

Moisture content determination in an antibody-drug conjugate freeze-dried medicine by near-infrared spectroscopy: A case study for release testing

The use of Near-infrared spectroscopy (NIRS) as a fast and non-destructive technique was employed for moisture content (MC) determination in Antibody-drug conjugates (ADCs) in replacement to Karl Fischer (KF) method. The lab analysis of ADCs, high potent medicines, should be performed in conditions ensuring the operator's safety and using secured analytical tools like NIRS. A NIRS method was first developed and validated in compliance with current guidelines. The novelty of this work first lies in the large number of samples prepared for a wide moisture calibration range of 0.51%-4.01%. Then, the classical Partial Least Square (PLS) regression was used as chemometric tool for the computation of the model. Excellent predictive calibration results were shown. A coefficient of correlation (r) value of 0.99 was obtained. An intercept value of 0.02 and a slope of 0.99 were observed, while the root mean square error of calibration (RMSEC) and the root mean square error of prediction (RMSEP) were respectively 0.10% and 0.12%. In addition, instrumentation, model performances and robustness of the method were evaluated, demonstrating the validation results. Calibration transfer issue and impact of the number of samples were also evaluated. Consequently, a validation strategy was introduced as a basis for submission to the health authorities' for release and stability activities in a cGMP environment in replacement of the KF method.

Poplar-Root Knot Nematode Interaction: A Model for Perennial Woody Species

Plant root-knot nematode (RKN) interaction studies are performed on several host plant models. Though RKN interact with trees, no perennial woody model has been explored so far. Here, we show that poplar (Populus tremula × P. alba) grown in vitro is susceptible to Meloidogyne incognita, allowing this nematode to penetrate, to induce feeding sites, and to successfully complete its life cycle. Quantitative reverse transcription-polymerase chain reaction analysis was performed to study changes in poplar gene expression in galls compared with noninfected roots. Three genes (expansin A, histone 3.1, and asparagine synthase), selected as gall development marker genes, followed, during poplar-nematode interaction, a similar expression pattern to what was described for other plant hosts. Downregulation of four genes implicated in the monolignol biosynthesis pathway was evidenced in galls, suggesting a shift in the phenolic profile within galls developed on poplar roots. Raman microspectroscopy demonstrated that cell walls of giant cells were not lignified but mainly composed of pectin and cellulose. The data presented here suggest that RKN exercise conserved strategies to reproduce and to invade perennial plant species and that poplar is a suitable model host to study specific traits of tree-nematode interactions.

Active content determination of pharmaceutical tablets using near infrared spectroscopy as Process Analytical Technology tool

The aim of this study was to develop Near infrared (NIR) methods to determine the active content of non-coated pharmaceutical tablets manufactured from a proportional tablet formulation. These NIR methods intend to be used for the monitoring of the active content of tablets during the tableting process. Firstly, methods were developed in transmission and reflection modes to quantify the API content of the lowest dosage strength. Secondly, these methods were fully validated for a concentration range of 70-130% of the target active content using the accuracy profile approach based on β-expectation tolerance intervals. The model using the transmission mode showed a better ability to predict the right active content compared to the reflection one. However, the ability of the reflection mode to quantify the API content in the highest dosage strength was assessed. Furthermore, the NIR method based on the transmission mode was successfully used to monitor at-line the tablet active content during the tableting process, providing better insight of the API content during the process. This improvement of control of the product quality provided by this PAT method is thoroughly compliant with the Quality by Design (QbD) concept. Finally, the transfer of the transmission model from the off-line to an on-line spectrometer was efficiently investigated.