Characterization of Dye-Loaded Poly(lactic-co-glycolic acid) Nanoparticles by Comprehensive Two-Dimensional Liquid Chromatography Combining Hydrodynamic and Reversed-Phase Liquid Chromatography.

Analytical methods for the assessment of drug-delivery systems (DDSs) are commonly suitable for characterizing individual DDS properties, but do not allow determination of several properties simultaneously. A comprehensive online two-dimensional liquid chromatography (LC × LC) system was developed that is aimed to be capable of characterizing both nanoparticle size and encapsulated cargo over the particle size distribution of a DDS by using one integrated method. Polymeric nanoparticles (NPs) with encapsulated hydrophobic dyes were used as model DDSs. Hydrodynamic chromatography (HDC) was used in the first dimension to separate the intact NPs and to determine the particle size distribution. Fractions from the first dimension were taken comprehensively and disassembled online by the addition of an organic solvent, thereby releasing the encapsulated cargo. Reversed-phase liquid chromatography (RPLC) was used as a second dimension to separate the released dyes. Conditions were optimized to ensure the complete disassembly of the NPs and the dissolution of the dyes during the solvent modulation step. Subsequently, stationary-phase-assisted modulation (SPAM) was applied for trapping and preconcentration of the analytes, thereby minimizing the risk of analyte precipitation or breakthrough. The developed HDC × RPLC method allows for the characterization of encapsulated cargo as a function of intact nanoparticle size and shows potential for the analysis of API stability.

Sample transformation in online separations: how chemical conversion advances analytical technology.

While the advent of modern analytical technology has allowed scientists to determine the complexity of mixtures, it also spurred the demand to understand these sophisticated mixtures better. Chemical transformation can be used to provide insights into properties of complex samples such as degradation pathways or molecular heterogeneity that are otherwise unaccessible. In this article, we explore how sample transformation is exploited across different application fields to empower analytical methods. Transformation mechanisms include molecular-weight reduction, controlled degradation, and derivatization. Both offline and online transformation methods have been explored. The covered studies show that sample transformation facilitates faster reactions (e.g. several hours to minutes), reduces sample complexity, unlocks new sample dimensions (e.g. functional groups), provides correlations between multiple sample dimensions, and improves detectability. The article highlights the state-of-the-art and future prospects, focusing in particular on the characterization of protein and nucleic-acid therapeutics, nanoparticles, synthetic polymers, and small molecules.

Performance evaluation of handheld Raman spectroscopy for cocaine detection in forensic case samples.

Handheld Raman spectroscopy is an emerging technique for rapid on-site detection of drugs of abuse. Most devices are developed for on-scene operation with a user interface that only shows whether cocaine has been detected. Extensive validation studies are unavailable, and so are typically the insight in raw spectral data and the identification criteria. This work evaluates the performance of a commercial handheld Raman spectrometer for cocaine detection based on (i) its performance on 0-100 wt% binary cocaine mixtures, (ii) retrospective comparison of 3,168 case samples from 2015 to 2020 analyzed by both gas chromatography-mass spectrometry (GC-MS) and Raman, (iii) assessment of spectral selectivity, and (iv) comparison of the instrument's on-screen results with combined partial least square regression (PLS-R) and discriminant analysis (PLS-DA) models. The limit of detection was dependent on sample composition and varied between 10 wt% and 40 wt% cocaine. Because the average cocaine content in street samples is well above this limit, a 97.5% true positive rate was observed in case samples. No cocaine false positives were reported, although 12.5% of the negative samples were initially reported as inconclusive by the built-in software. The spectral assessment showed high selectivity for Raman peaks at 1,712 (cocaine base) and 1,716 cm-1 (cocaine HCl). Combined PLS-R and PLS-DA models using these features confirmed and further improved instrument performance. This study scientifically assessed the performance of a commercial Raman spectrometer, providing useful insight on its applicability for both presumptive detection and legally valid evidence of cocaine presence for law enforcement.

Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms.

On-scene drug detection is an increasingly significant challenge due to the fast-changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false-positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740-1070 nm small-wavelength-range near-infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true-positive and 98% true-negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on-scene approach is that the model can almost instantly adapt to changes in the illicit-drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited-range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on-site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food-safety, and environmental domains.

Comprehensive off-line silver phase liquid chromatography × gas chromatography with flame ionization and vacuum ultraviolet detection for the detailed characterization of mineral oil aromatic hydrocarbons.

Highly purified mineral oils used for the elaboration of pharmaceutical, food and cosmetic products can contain residual mineral oil aromatic hydrocarbons (MOAH). Quantification of the MOAH level as well as detailed characterization of the aromatic species present is important for safety evaluations and for optimization of the purification process. Two comprehensive off-line silver phase liquid chromatography × gas chromatography (AgLC × GC) methods, one with flame ionization detection (FID) and another with vacuum ultraviolet detection (VUV), were developed for MOAH analysis. The methods showed a better resolution between the MOSH and MOAH groups compared to the traditional online LC-GC methods due to the different retention mechanisms employed in the two dimensions, albeit that the gain was less than seen e.g. in edible oil analysis. An important advantage of the new comprehensive AgLC × GC methods is that the use of markers to determine the MOSH/MOAH cut-point is no longer needed, because all the eluent coming from the LC separation is transferred as narrow fractions to the GC. Due to the use of silver based stationary phases in the first separation dimension, a group-type separation of the mineral oil according to the degree of aromaticity (aliphatics, mono-aromatics and poly-aromatics) was obtained. Moreover, thanks to the use of VUV detection, the new method also delivered additional structural information on the different groups of compounds present.