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Codexis, Inc. is a protein engineering company that develops enzymes for pharmaceutical, food and medical applications.[2][3]
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| Company type | Public |
|---|---|
| Nasdaq: CDXS Russell 2000 Component | |
| Industry | Protein engineering, biocatalysis, industrial enzymes, fine chemicals |
| Founded | 2002; 22 years ago (2002) |
| Headquarters | Redwood City, California, U.S. |
Key people |
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| Revenue |  US$68.5 million (FY 2019) |
Number of employees | 165 (as of April 2020)[1] |
| Website | codexis |
Codexis is based in Redwood City, CA and was incorporated in 2002. It went public in April 2010 on NASDAQ,[4] and in October, acquired Maxygen's MolecularBreeding technology portfolio.[5]
Codexis won the Presidential Green Chemistry Challenge Award from the U.S. Environmental Protection Agency (EPA) in 2006 for its work on a building block of Lipitor.[6] It then won a second time in 2010 for its work with Merck & Co. on the active ingredient in Januvia.[7]
In 2017, the company entered a partnership with Tate & Lyle to provide research and development for the production of new ingredients.[8] That same year, Codexis announced a collaboration with Nestle to provide enzymes for metabolic disorders.[9]
In 2017, Codexis developed a recombinant phenylalanine ammonia-lyase (PAL) enzyme, to act as a substitute phenylalanine hydroxylase (PAH) enzyme for people who suffer from phenylketonuria.[3] The enzyme was in-licensed by Nestle Health Sciences.[3]
In 2020, Takeda Pharmaceutical announced a collaboration with Codexis to research and create gene therapies for rare diseases, including lysosomal storage disorders.[10]
In June 2020, they announced a partnership with Molecular Assemblies to engineer enzymes for DNA synthesis.[11]
Codexis uses directed evolution to develop its enzymes.[12][13] Using this method, scientists genetically engineer genes, then screen the enzymes produced to see if it creates the properties needed for a specific reaction.[13][7] Their protein engineering platform, called CodeEvolver, uses machine learning and high-throughput experimentation to learn protein sequence changes and their impacts on protein function.[3][14]
