Charles River Acquires Retrogenix, Partners Valence
US CDMO Charles River Laboratories has acquired Retrogenix, a UK-based early-stage contract research organization that provides specialized bioanalytical services, for approximately £35 million in cash. The transaction also includes a potential additional payment of up to £5 million based on future performance.
“The acquisition of Retrogenix strategically expands Charles River’s existing discovery capabilities by adding a proprietary cell microarray technology to accelerate target identification and provide preclinical safety assurance for novel therapies,” said James Foster, chairman, president and CEO of Charles River Laboratories. The purchase, he added, “enhances our ability to support clients’ early-stage drug research efforts in advanced drug modalities, including cell therapies.”
Retrogenix also offers a large protein library with more than 6,200 human plasma membrane and secreted protein clones, which it said provides a unique screening tool for discovering primary target receptors and assessing potential off-target binding issues.
The deal follows Charles River’s purchase last December of California, US-based Distributed Bio, a next-generation antibody discovery company. Charles River said the combination of Retrogenix’s capabilities with Distributed Bio’s large-molecule discovery platform will further strengthen its integrated, end-to-end solution for therapeutic antibody and cell and gene therapy discovery and development.
Partnership with Valence Discovery
In separate news, Charles River has formed a strategic partnership with Valence Discovery, gaining access to the latter’s artificial intelligence (AI) platform for molecular property prediction, generative chemistry and multiparameter optimization.
The Valence platform enables the design of small molecule drug candidates in novel regions of chemical space, followed by rapid optimization against project-specific potency, selectivity, safety and pharmacology criteria.
Valence regards itself as a pioneer in applying few-shot learning to drug design. This, it said, allows the unlocking of prediction tasks for which only small amounts of training data are available and overcomes a critical limitation of existing machine learning technologies in drug discovery. Few shot learning is where a model is fed with a very small amount of training data compared with the usual practice of using large datasets.
Charles River said the AI platform will allow clients to access increased diversity in the chemical matter being investigated, together with more rapid optimization against complex, project-specific design criteria, which will ultimately reduce timelines and improve success rates for drug discovery projects.
Author: Elaine Burridge, Freelance Journalist
