A huge congratulations to our graduate student Ellie Abraham, who has been awarded an NIH Ruth L. Kirschstein National Research Service Award (F31 predoctoral fellowship) from the National Center for Complementary and Integrative Health (NCCIH). Ellie’s project looks at new machine learning approaches to better identify botanical dietary supplement samples, as well as key bioactive molecules from active supplements and herbal medicines. Well deserved!!

As the demand for medicinal plants and botanical dietary products increase, so do the incentives to adulterate products for economic gain, at the expense of product efficacy and consumer safety. However, plant-derived products are inherently complex, and also come in many different forms (dried herbs, extracts, tinctures, dried formulations, etc.); this complexity hinders traditional methods of authentication and quality control. Furthermore, identifying the molecules of interest that underpin the desired bioactivity is a long-known challenge of natural product discovery. Ellie’s project aims to employ advanced machine learning models and multi-omics approaches to better classify unknown samples, as well as improve the ability to detect bioactive molecules from botanicals. Using basil (Ocimum spp.) as a model organism, the study will employ molecular and genetic methods to characterize known samples, and then apply that model to unknown commercial samples to test its rigor and applicability in real-world situations. This will improve herbal product authentication, an important task considering misrepresentation of products can result in a loss of medicinal effect, consumer trust, and potentially jeopardize consumer safety. Furthermore, the ability to identify compounds quickly and reliably with multiple medicinal properties will contribute to the discovery of therapeutic compounds from a variety of natural product sources.