NGS and IVD Manufacturer Team up to Improve Clinical Trials
What happens when a Next Generation Sequencing (NGS) company and an in vitro diagnostics (IVD) Manufacturer join forces? The answer is stronger companion diagnostics that could help improve the efficiency of clinical trials.
Cellgen Diagnostics, an IVD specialist and Genomic Testing Cooperative (GTC), a firm that’s focused on molecular testing, are answering this question with a new collaboration. The two companies said the collaboration will provide the pharmaceutical industry a seamless integration of biomarker discovery, validation, and development of IVDs.
Lavance Northington, founder and CEO of Cellgen Diagnostics said generally speaking, NGS providers and IVD manufacturers compete against each other in the marketplace, but Cellgen and GTC see our two offerings as complimentary..
The collaboration will initially focus on next generation sequencing applications in early-stage clinical programs to allow researchers comprehensive information from a single sample for molecular classification. Synergies between both companies will enable biopharmaceutical companies to make more informed decisions regarding their immuno-oncology pipelines.
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“[The collaboration] provides a seamless transition in improving the pace in which clinical trials are being conducted,” Northington said. “The goal is to reduce the timeframe and reduce the cost [of clinical trials], and a lot of that depends on patient recruitment; patient stratification; and making sure you get the right person into the [trial]. With our collaboration I think we’ll be able to bring a technical harmonization to the process that doesn’t exist today.”
Cellgen will provide a fully automated companion diagnostic platform that can be leveraged across multiple drug pipelines. Cellgen Immune Panel assays will allow pharma groups in R&D to use a standard approach to patient stratification and therapeutic response monitoring during clinical trials and into the clinic.
GTC combines next generation sequencing, machine learning and deep-learning to comprehensively profile the tumor microenvironment and create unique patient phenotypic profiles.