Founded in 2016, by Nathan Magarvey, PhD, Chief Scientific Officer, Adapsyn is developing a bioinformatics platform devoted to discovering previously unidentified natural product drug candidates by selectively isolating new agents with the potential to interrogate difficult-to-drug phenotypes. The company applies proprietary machine learning to genomic and metabolomic data from microbes to discover new natural products encoded within microbial genomes.
Adapsyn has ongoing collaborations with leading pharmaceutical companies and is actively developing an internal pipeline of therapeutic candidates.
Adapsyn’s platform analyses metabolomic and genomic data to identify, isolate, and assay novel drug-like small molecules from bacteria.
Our metabolomic pipeline identifies all novel molecules in a given extract sample using high-throughput high-resolution LCMS, predicts additional chemical characteristics of those molecules, and use an automated workflow to isolate molecules that meet a development program’s parameters. Once isolated, candidate molecules can be assayed for bioactivity to identify hits for downstream development.
This can be done both on material from a pre-existing extract library or from material collected from new fermentations. Our in-house fermentation workflow utilizes high-throughput culturing to define media conditions that optimize natural target molecule production for scale-up, without the need for heterologous gene expression. Strain selection to hit molecule isolation and assay can be completed in approximately six weeks.
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