Direct analysis in real time mass spectrometry, a process analytical technology tool for real-time process monitoring in botanical drug manufacturing

Micro simulated moving bed chromatography-mass spectrometry as a continuous on-line process analytical tool

Continuous manufacturing is becoming increasingly important in the (bio-)pharmaceutical industry, as more product can be produced in less time and at lower costs. In this context, there is a need for powerful continuous analytical tools. Many established off-line analytical methods, such as mass spectrometry (MS), are hardly considered for process analytical technology (PAT) applications in biopharmaceutical processes, as they are limited to at-line analysis due to the required sample preparation and the associated complexity, although they would provide a suitable technique for the assessment of a wide range of quality attributes. In this study, we investigated the applicability of a recently developed micro simulated moving bed chromatography system (µSMB) for continuous on-line sample preparation for MS. As a test case, we demonstrate the continuous on-line MS measurement of a protein solution (myoglobin) containing Tris buffer, which interferes with ESI-MS measurements, by continuously exchanging this buffer with a volatile ammonium acetate buffer suitable for MS measurements. The integration of the µSMB significantly increases MS sensitivity by removing over 98% of the buffer substances. Thus, this study demonstrates the feasibility of on-line µSMB-MS, providing a versatile PAT tool by combining the detection power of MS for various product attributes with all the advantages of continuous on-line analytics.

An online, non-destructive method for simultaneously detecting chemical, biological, and physical properties of herbal injections using hyperspectral imaging with artificial intelligence

Botanical drugs hold great potential to prevent and treat complex diseases. Quality control is essential in ensuring the safety, efficacy, and therapeutic consistency of these drug products. The quality of a botanical drug product can be assessed using a variety of analytical methods based on criteria that judge the identity, strength, purity, and potency. However, most of these methods are developed on separate analytical platforms, and few approaches are available for in-process monitoring of multiple quality properties in a non-destructive manner. Here, we present a hyperspectral imaging-based strategy for online measurement of physical, chemical, and biological properties of botanical drugs using artificial intelligence algorithms. An end-to-end convolutional neural network (CNN) model was established to accurately determine phytochemicals and bioactivities based on the spectra. Besides, a new dual-scale anomaly (DSA) detection algorithm was proposed for visible particle inspection based on the images. The strategy was exemplified on Shuxuening Injection, a Ginkgo biloba-derived drug used in the treatment of cerebrovascular and cardiovascular diseases. Four quality metrics of the injection, including total flavonol, total ginkgolides, antioxidant activity, and anticoagulant activity, were successfully predicted by the CNN model with validation R² of 0.922, 0.921, 0.880, and 0.913 respectively, showing better performance than the other models. Unqualified samples with visible particles could be detected by DSA with a low false alarm rate of 9.38 %. Chromaticity results indicated that the inter-company variations of color were significant, while intra-company variations were relatively small. This demonstrates a real application of integrating hyperspectral imaging with artificial intelligence to provide a rapid, accurate, and non-destructive approach for process analysis of botanical drugs.

Minimizing transient microenvironment-associated variability for analysis of environmental anthropogenic contaminants via ambient ionization

The rapid and quantitative analysis of anthropogenic contaminants in environmental matrices is crucial for regulatory testing and to elucidate the environmental fate of these pollutants. Direct ambient mass spectrometry (AMS) methodologies greatly increase sample throughput, can be adapted for onsite analysis and are often regarded as semi-quantitative by most developed protocols. One of the limitations of AMS, especially for on site analysis applications, is the irreproducibility of the measurements related to the occurrence of transient microenvironments (TME) and variable background interferences. In this work we report an effective strategy to minimize these effects by hyphenating, for the first time, solid phase microextraction (SPME) arrow to mass spectrometry via a thermal desorption unit (TDU) and direct analysis in real time (DART) source. The developed method was optimized for the extraction and analysis of pesticides and pharmaceuticals from surface water. It was demonstrated that the hyphenation of the SPME and TDU-DART resulted in reduced background contamination, indicating the suitability of the method for onsite analysis even in variable and non-ideal environments. Model analytes were quantitated in the low μg/L range with a total analysis time of less than 5 min, linear dynamic ranges (LDR) and interday reproducibility for most compounds being 2.5-500 μg/L and lower than 10%, respectively. The developed approach provides an excellent analytical tool that can be applied for the onsite high-throughput analysis of water samples as well as air and aereosols. Considering the tunability of our extraction process, time-resolved environmental monitoring can be achieved onsite within minutes.