Fluence Analytics, a market leader in continuous polymer reaction monitoring and control, has announced the launch of ARGEN-LT, a new high-throughput static light scattering test unit with eight independent sample cells that measure the stability of biopolymers under thermal, chemical, and mechanical stress. A new version of the existing ARGEN product is also being released, which is a 16-sample cell instrument with temperatures ranging from 18°C to 100°C.
Alex Reed, co-founder and President of Fluence Analytics, said “As the industry continues to grow the pipeline of therapeutics utilizing proteins, peptides, mRNA and DNA based technologies, assessing shelf-life for these hypersensitive biological drugs is critical to delivering lifesaving treatments to patients around the world. Being able to simulate these conditions, while continuously detecting even the slightest changes instability, is a significant leap forward. This effort is the culmination of collaboration with global experts and customers to deliver a new tool to the development arsenal.”
ARGEN-LT was developed in response to feedback from biopharma industry experts and customers seeking an instrument capable of confirming the shelf-life viability of biopolymers at low temperature(s). A major European biopharma company purchased the first ARGEN-LT developed by Fluence Analytics.
The only commercially available biopharma products that can simultaneously test proteins, peptides, mRNA, DNA, and other biopolymers for stability are ARGEN and ARGEN-LT. Through the use of continuous light scattering, ARGEN can provide real-time visibility into biopolymers and be sensitive to small changes in oligomerization states and molecular weights, which helps companies speed up formulation development. ARGEN’s ability to detect aggregation and degradation early in the process saves time and resources. In addition to providing an intuitive interface for all aspects of experimental design, the proprietary software allows independent control of each cell for parameter adjustment parallel to real-time data processing.
