Research Project
9679781
Abstract Halo Labs proposes to develop ?Partelligence? a particle ID technique that enables accurate and rapid identification of contaminating particles in biopharmaceutical formulations. Protein therapeutics currently represent between 15 and 30% of the overall pharmaceutical market. The primary concern for this class of therapeutics is that they can elicit an immune response from patients who develop anti- drug antibodies. The drug?s effect is therefore eliminated between 1 and 10 percent of patients who return to their original disease state. The presence of particulate matter in these therapeutics (e.g. shed glass from a syringe or a protein aggregate) can enhance this immune response and, due to the patient safety risk the FDA regulates the amount of particles that can be present. There are always some number of particles in each injected sample, and although their presence can be detected, they don?t know what the particles actually are. A QC tool that can identify the particles would help manufactures trace them back to their source (e.g. a bad lot of syringes) and eliminate them. Partelligence aims to make particle identification routine in biopharma QC. The technology builds off our current instrument, Horizon, which was launched in mid-2017 and already sold to some of the world?s largest pharmaceutical companies. The technique works by analyzing several combinatorial features including size, morphology, optical contrast, and intrinsic fluorescence, and in this proposal we will test which features are key to enable the most accurate and rapid particle recognition. To date, we have performed feasibility experiments validating our ability to identify a few commonly found particles in biopharma solutions. Given this, our goals in Phase I are to expand on these studies by building a comprehensive training set and by testing a number of different algorithms. We will first start with reference samples, and then move to real biopharmaceutical samples provided by our pharma collaborators. At the end of the study, we will do a feasibility analysis to determine if the throughput, specificity and reliability meets the needs of the industry.
