Comparison of different chemometric methods to extract chemical and physical information from Raman images of homogeneous and heterogeneous semi-solid pharmaceutical formulations

Fresh Carrier for an Old Topical Local Anesthetic: Benzocaine in Nanostructured Lipid Carriers

Nanostructured lipid carriers (NLC) have emerged as innovative drug delivery systems, offering distinct advantages over other lipid-based carriers, such as liposomes and solid lipid nanoparticles. Benzocaine (BZC), the oldest topical local anesthetic in use, undergoes metabolism by pseudocholinesterase, leading to the formation of p-aminobenzoic acid, a causative agent for allergic reactions associated with prolonged BZC usage. In order to mitigate adverse effects and enhance bioavailability, BZC was encapsulated within NLC. Utilizing a 23 factorial design, formulations comprising cetyl palmitate (solid lipid), propylene glycol monocaprylate (liquid lipid), and Pluronic F68 as surfactants were systematically prepared, with variations in the solid/liquid lipid mass ratios (60:40-80:20%), total lipid contents (15-25%), and BZC concentrations (1-3%). The optimized formulation underwent characterization by dynamic light scattering, differential scanning calorimetry, Raman imaging, X-ray diffraction, small-angle neutron scattering, nanotracking analysis, and transmission electron microscopy (TEM)/cryo-TEM, providing insights into the nanoparticle structure and the incorporation of BZC into its lipid matrix. NLCBZC exhibited a noteworthy encapsulation efficiency (%EE = 96%) and a 1 year stability when stored at 25 °C. In vitro kinetic studies and in vivo antinociceptive tests conducted in mice revealed that NLCBZC effectively sustained drug release for over 20 h and prolonged the anesthetic effect of BZC for up to 18 h. We therefore propose the use of NLCBZC to diminish the effective anesthetic concentration of benzocaine (from 20 to 3% or less), thus minimizing allergic reactions that follow the topical administration of this anesthetic and, potentially, paving the way for new routes of BZC administration in pain management.

New insights into the surface Enhanced Raman Scattering (SERS) response of adenine using chemometrics

The SERS response of adenine is one of the most studied, due to its outstanding exaltation. However, the spectra obtained strongly differ according to the experimental conditions and still remain not well understood. To be able to search for the presence of this molecule in complex environments, it is essential to better understand the SERS response of adenine alone. After a brief presentation of the literature on the subject, we present results suggesting that the experimental spectra would result from the overlap of different spectroscopic signatures, that may probably be due to different non-covalent interactions or different electromagnetic fields experienced by adenine molecules. An independent component analysis is reported. Our results underline the difficulty to precisely analyze the experimental data, the need to continue this research and to constitute data banks that would allow comparing the spectra obtained in different laboratories according to the experimental conditions.

Programmable hyperspectral coherent anti-Stokes Raman scattering microscopy

Hyperspectral coherent Raman scattering microscopy provides a significant improvement in acquisition time compared to spontaneous Raman scattering yet still suffers from the time required to sweep through individual wavenumbers. To address this, we present the use of a pulse shaper with a 2D spatial light modulator for phase- and amplitude-based shaping of the Stokes beam to create programmable spectrally tailored excitation envelopes. This enables collection of useful spectral information in a more rapid and efficient manner.

Exploring the immunomodulatory properties of glucan particles in human primary cells

The immunomodulatory properties of β-glucans have sparked interest among various medical fields. As vaccine adjuvants, glucan particles offer additional advantages as antigen delivery systems. This study reported the immunomodulatory properties of glucan particles with different size and chemical composition. The effect of glucan microparticles (GPs) and glucan nanoparticles (Glu 130 and 355 NPs) was evaluated on human immune cells. While GPs and Glu 355 NPs demonstrated substantial interaction with Dectin-1 receptor on monocytes, Glu 130 NPs exhibited reduced activation of this receptor. This observation was substantiated by blocking Dectin-1, resulting in inhibition of reactive oxygen species production induced by GPs and Glu 355 NPs. Notably, monocyte-derived dendritic cells (moDCs) stimulated by Glu 355 NPs exhibited phenotypic and functional maturation, essential for antigen cross-presentation. The immunomodulatory efficacy was investigated using an autologous mixed lymphocyte reaction (AMLR), resulting in considerable rates of lymphocyte proliferation and an intriguing profile of cytokine and chemokine release. Our findings highlight the importance of meticulously characterizing the size and chemical composition of β-glucan particles to draw accurate conclusions regarding their immunomodulatory activity. This in vitro model mimics the human cellular immune response, and the results obtained endorse the use of β-glucan-based delivery systems as future vaccine adjuvants.

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.

Evaluation of the Miscibility of Novel Cocoa Butter Equivalents by Raman Mapping and Multivariate Curve Resolution-Alternating Least Squares

Cocoa butter (CB) is an ingredient traditionally used in the manufacturing of chocolates, but its availability is decreasing due to its scarcity and high cost. For this reason, other vegetable oils, known as cocoa butter equivalents (CBE), are used to replace CB partially or wholly. In the present work, two Peruvian vegetable oils, coconut oil (CNO) and sacha inchi oil (SIO), are proposed as novel CBEs. Confocal Raman microscopy (CRM) was used for the chemical differentiation and polymorphism of these oils with CB based on their Raman spectra. To analyze their miscibility, two types of blends were prepared: CB with CNO, and CB with SIO. Both were prepared at 5 different concentrations (5%, 15%, 25%, 35%, and 45%). Raman mapping was used to obtain the chemical maps of the blends and analyze their miscibility through distribution maps, histograms and relative standard deviation (RSD). These values were obtained with multivariate curve resolution-alternating least squares. The results show that both vegetable oils are miscible with CB at high concentrations: 45% for CNO and 35% for SIO. At low concentrations, their miscibility decreases. This shows that it is possible to consider these vegetable oils as novel CBEs in the manufacturing of chocolates.

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

Current pharmaceutical research directions tend to follow a systematic approach in the field of applied research and development. The concept of quality-by-design (QbD) has been the focus of the current progress of pharmaceutical sciences. It is based on, but not limited, to risk assessment, design of experiments and other computational methods and process analytical technology. These tools offer a well-organized methodology, both to identify and analyse the hazards that should be handled as critical, and are therefore applicable in the control strategy. Once implemented, the QbD approach will augment the comprehension of experts concerning the developed analytical technique or manufacturing process. The main activities are oriented towards the identification of the quality target product profiles, along with the critical quality attributes, the risk management of these and their analysis through in silico aided methods. This review aims to offer an overview of the current standpoints and general applications of QbD methods in pharmaceutical development.

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.

A pre-formulation study of tetracaine loaded in optimized nanostructured lipid carriers

Tetracaine (TTC) is a local anesthetic broadly used for topical and spinal blockade, despite its systemic toxicity. Encapsulation in nanostructured lipid carriers (NLC) may prolong TTC delivery at the site of injection, reducing such toxicity. This work reports the development of NLC loading 4% TTC. Structural properties and encapsulation efficiency (%EE > 63%) guided the selection of three pre-formulations of different lipid composition, through a 2³ factorial design of experiments (DOE). DLS and TEM analyses revealed average sizes (193-220 nm), polydispersity (< 0.2), zeta potential |- 21.8 to - 30.1 mV| and spherical shape of the nanoparticles, while FTIR-ATR, NTA, DSC, XRD and SANS provided details on their structure and physicochemical stability over time. Interestingly, one optimized pre-formulation (CP-TRANS/TTC) showed phase-separation after 4 months, as predicted by Raman imaging that detected lack of miscibility between its solid (cetyl palmitate) and liquid (Transcutol) lipids. SANS analyses identified lamellar arrangements inside such nanoparticles, the thickness of the lamellae been decreased by TTC. As a result of this combined approach (DOE and biophysical techniques) two optimized pre-formulations were rationally selected, both with great potential as drug delivery systems, extending the release of the anesthetic (> 48 h) and reducing TTC cytotoxicity against Balb/c 3T3 cells.

Multi-window sparse spectral sampling stimulated Raman scattering microscopy

Stimulated Raman scattering (SRS) is a nondestructive and rapid technique for imaging of biological and clinical specimens with label-free chemical specificity. SRS spectral imaging is typically carried out either via broadband methods, or by tuning narrowband ultrafast light sources over narrow spectral ranges thus specifically targeting vibrational frequencies. We demonstrate a multi-window sparse spectral sampling SRS (S4RS) approach where a rapidly-tunable dual-output all-fiber optical parametric oscillator is tuned into specific vibrational modes across more than 1400 cm-1 during imaging. This approach is capable of collecting SRS hyperspectral images either by scanning a full spectrum or by rapidly tuning into select target frequencies, hands-free and automatically, across the fingerprint, silent, and high wavenumber windows of the Raman spectrum. We further apply computational techniques for spectral decomposition and feature selection to identify a sparse subset of Raman frequencies capable of sample discrimination. Here we have applied this novel method to monitor spatiotemporal dynamic changes of active pharmaceutical ingredients in skin, which has particular relevance to topical drug product delivery.

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.

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.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

Raman mapping coupled to self-modelling MCR-ALS analysis to estimate active cosmetic ingredient penetration profile in skin

Confocal Raman mapping (CRM) is a powerful, label free, non-destructive tool, enabling molecular characterization of human skin with applications in the dermo-cosmetic field. Coupling CRM to multivariate analysis can be used to monitor the penetration and permeation of active cosmetic ingredients (ACI) after topical application. It is presently illustrated how multivariate curve resolution alternating least squares (MCR-ALS) can be applied to detect and semi-quantitatively describe the diffusion profile of Delipidol, a commercially available slimming ACI, from Raman spectral maps. Although the analysis outcome can be critically dependent on the a priori selection of the number of regression components, it is demonstrated that profiling of the kinetics of diffusion into the skin can be established with or without additionnal spectral equality constraints in the multivariate analysis, with similar results. Ultimately, MCR-ALS, applied without spectral equality contraints, specifically identifies the ACI as one of main spectral components enabling to investigate its distribution and penetration into the stratum corneum and underlying epidermis layers.

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.

Raman mapping of fentanyl transdermal delivery systems with off-label modifications

Raman mapping is a powerful and emerging tool in characterization of pharmaceuticals and provides non-destructive chemical and structural identification with minimal sample preparation. One pharmaceutical form that is suitable but has not been studied in-depth with Raman mapping is transdermal delivery systems (TDS). TDS are dosage forms designed to deliver a therapeutically effective amount of active pharmaceutical ingredient (API) across a patient's skin. To enhance drug delivery through the skin, the API in the formulation is often close to a saturated or supersaturated state. Thus, improper use or off-label modifications can lead to occurrence of unwanted API changes, specifically, crystallization over time. Here, off-label modifications were mimicked on a set of fentanyl drug-in-adhesive TDS sold on the U.S. market by four different manufacturers via die cutting, and then the die cut TDS were investigated through confocal Raman mapping for structural and chemical changes. Using Multivariate Curve Resolution (MCR), not only was morphological and chemical characterization of transdermal systems provided, but also fentanyl crystals in certain products due to off-label modifications were identified. The chemometric model used in analysis of Raman maps allowed precise identification of fentanyl as the crystalline material as confirmed by the hit-quality-index correlation of component spectra from the chemometric model with library spectra of a fentanyl reference standard. The results show that confocal Raman mapping with MCR can be utilized in assessing pharmaceutical quality of TDS. This method has the potential to be widely used in characterization of such systems as an alternative to existing techniques.

Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry

Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

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.