Researchers have published a new approach to heparin screening that allows the detection of adulteration within one hour. Published in the journal Analytical Chemistry, the study presents a screening strategy using hydrogen peroxide digestion followed by fast reversed-phase ion pairing liquid chromatography (reversed-phase IP–LC) coupled with tandem mass spectrometry (MS–MS) to detect contamination of heparin samples.
Researchers have published a new approach to heparin screening that allows the detection of adulteration within one hour. Published in the journal Analytical Chemistry, the study presents a screening strategy using hydrogen peroxide digestion followed by fast reversed-phase ion pairing liquid chromatography (reversed-phase IP–LC) coupled with tandem mass spectrometry (MS–MS) to detect contamination of heparin samples.1
Photo Credit: MOLEKUUL/SCIENCE PHOTO LIBRARY/Getty Images
Heparin is a complex carbohydrate that is commonly used as a blood thinner in drug formulation and as a coating on medical devices. Between 2007–2008, contamination of heparin with oversulphated chondroitin sulphates (OSCs) led to 94 deaths and 574 adverse reactions in the USA alone, highlighting a need for accurate and sensitive analytical methods to screen heparin.1 Corresponding author Peter Nemes from George Washington University (Washington D.C., USA) told The Column: “My goal is to develop new analytical technologies and methodologies that advance tests to higher throughput, specificity, and sensitivity so that a larger number of products can be tested to help preserve and advance human health. Encouraged by the performance of pyrolysis mass spectrometry,2 a technology that we also recently developed along this mission, my colleagues and I wanted to further enhance the specificity of detecting a potential contaminant.”
The study authors developed a sample preparation method using chemical treatment using hydrogen peroxide combined with heating to break down 50 μg samples of heparin, including oversulphated chondroitin sulphates (OSCs), into smaller fragments. The resulting oligomers were then separated using reversed-phase IP–LC and analyzed using MS. The method was then applied to samples collected by the FDA during the heparin contamination incident in 2007–2008 to show that it could differentiate between safe and contaminated samples. Nemes told The Column: “In comparison to current mass spectrometry protocols that typically require several hours-to-days for sample preparation and instrumental analysis time, our approach is completed in 60 minutes - from start to finish - allowing us to quality-test a given sample in higher throughput.”
In terms of future work, Nemes told The Column: “My goal is to continue advancing analytical measurements to higher sensitivity, specificity, and compatibility to volume/mass-limited samples so that even trace-level compounds can be measured in extremely small amounts of samples.” - B.D.
References
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.