RESUMEN
Ambient ionization (AI) is a rapidly growing field in mass spectrometry (MS). It allows for the direct analysis of samples without any sample preparation, making it a promising technique for the detection of explosives. Previous studies have shown that AI can be used to detect a variety of explosives, but the exact gas-phase reactions that occur during ionization are not fully understood. This is further complicated by differences in mass spectrometers and individual experimental set ups between researchers. This study investigated the gas-phase ion reactions of five different explosives using a variety of AI techniques coupled to a Waters QDa mass spectrometer to identify selective ions for explosive detection and identification based on the applied ambient ionization technique. The results showed that the choice of the ion source can have a significant impact on the number of ions observed. This can affect the sensitivity and selectivity of the data produced. The findings of this study provide new insights into the gas-phase ion reactions of explosives and could lead to the development of more sensitive and selective AI-based methods for their detection.
RESUMEN
The utilization of ambient ionization (AI) techniques for mass spectrometry (MS) has significantly grown due to their ability to facilitate rapid and direct sample analysis with minimal sample preparation. This study investigates the performance of various AI techniques, including atmospheric solids analysis probe (ASAP), thermal desorption corona discharge (TDCD), direct analysis in real time (DART), and paper spray coupled to a Waters QDa mass spectrometer. The focus is on evaluating the linearity, repeatability, and limit of detection (LOD) of these techniques across a range of analytes, including amino acids, drugs, and explosives. The results show that each AI technique exhibits distinct advantages and limitations. ASAP and DART cover high concentration ranges, which may make them suitable for semiquantitative analysis. TDCD demonstrates exceptional linearity and repeatability for most analytes, while paper spray offers surprising LODs despite its complex setup (between 80 and 400 pg for most analytes). The comparison with electrospray ionization (ESI) as a standard method shows that ambient ionization techniques can achieve competitive LODs for various compounds such as PETN (80 pg ESI vs 100 pg ASAP), TNT (9 pg ESI vs 4 pg ASAP), and RDX (4 pg ESI vs 10 pg ASAP). This study underscores the importance of selecting the appropriate ambient ionization technique based on the specific analytical requirements. This comprehensive evaluation contributes valuable insights into the selection and optimization of AI techniques for diverse analytical applications.
RESUMEN
Nanomedicines have emerged as a promising approach for targeted therapeutic delivery and specifically as a beneficial alternative to conventional cancer therapies as they can deliver higher concentrations of chemotherapeutic agents at the tumour site compared to healthy tissue, thus providing improved drug efficacy and lower systemic toxicity. Long acting injectables are increasingly becoming the focus of pharmaceutical research, as they can reduce dosing frequency and improve the life quality of patients. Development of an in vitro release (IVR) method for modified release nanomedicines is challenging because of the uniqueness and range of different formulation design approaches, as well as the complex nature of drug release mechanisms which may result in inherent variability. Regulatory guidance on the development of dissolution or release methods for parenteral products is limited relative to oral products. This article details the extensive in vitro release method development work conducted on a polymeric nanoparticle to develop the release media composition and selection of suitable apparatus and sampling technique to separate the released drug from the formulation. The aim was to develop a suitably robust analytical method that generated clinically relevant in vitro release data.