IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0236878.html
   My bibliography  Save this article

Modeling cannabinoids from a large-scale sample of Cannabis sativa chemotypes

Author

Listed:
  • Daniela Vergara
  • Reggie Gaudino
  • Thomas Blank
  • Brian Keegan

Abstract

The widespread legalization of Cannabis has opened the industry to using contemporary analytical techniques for chemotype analysis. Chemotypic data has been collected on a large variety of oil profiles inherent to the cultivars that are commercially available. The unknown gene regulation and pharmacokinetics of dozens of cannabinoids offer opportunities of high interest in pharmacology research. Retailers in many medical and recreational jurisdictions are typically required to report chemical concentrations of at least some cannabinoids. Commercial cannabis laboratories have collected large chemotype datasets of diverse Cannabis cultivars. In this work a data set of 17,600 cultivars tested by Steep Hill Inc., is examined using machine learning techniques to interpolate missing chemotype observations and cluster cultivars into groups based on chemotype similarity. The results indicate cultivars cluster based on their chemotypes, and that some imputation methods work better than others at grouping these cultivars based on chemotypic identity. Due to the missing data and to the low signal to noise ratio for some less common cannabinoids, their behavior could not be accurately predicted. These findings have implications for characterizing complex interactions in cannabinoid biosynthesis and improving phenotypical classification of Cannabis cultivars.

Suggested Citation

  • Daniela Vergara & Reggie Gaudino & Thomas Blank & Brian Keegan, 2020. "Modeling cannabinoids from a large-scale sample of Cannabis sativa chemotypes," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-17, September.
  • Handle: RePEc:plo:pone00:0236878
    DOI: 10.1371/journal.pone.0236878
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236878
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0236878&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0236878?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Meaghan A. Valliere & Tyler P. Korman & Nicholas B. Woodall & Gregory A. Khitrov & Robert E. Taylor & David Baker & James U. Bowie, 2019. "A cell-free platform for the prenylation of natural products and application to cannabinoid production," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    2. Meaghan A. Valliere & Tyler P. Korman & Nicholas B. Woodall & Gregory A. Khitrov & Robert E. Taylor & David Baker & James U. Bowie, 2019. "Author Correction: A cell-free platform for the prenylation of natural products and application to cannabinoid production," Nature Communications, Nature, vol. 10(1), pages 1-1, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qian Kang & Huan Fang & Mengjie Xiang & Kaixing Xiao & Pingtao Jiang & Chun You & Sang Yup Lee & Dawei Zhang, 2023. "A synthetic cell-free 36-enzyme reaction system for vitamin B12 production," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Xinlei Wei & Xue Yang & Congcong Hu & Qiangzi Li & Qianqian Liu & Yue Wu & Leipeng Xie & Xiao Ning & Fei Li & Tao Cai & Zhiguang Zhu & Yi-Heng P. Job Zhang & Yanfei Zhang & Xuejun Chen & Chun You, 2024. "ATP-free in vitro biotransformation of starch-derived maltodextrin into poly-3-hydroxybutyrate via acetyl-CoA," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0236878. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.