Drewe, Jürgen; Schöning, Verena; Danton, Ombeline; Schenk, Alexander; Boonen, Georg (2024). Machine Learning-Based Analysis Reveals Triterpene Saponins and Their Aglycones in Cimicifuga racemosa as Critical Mediators of AMPK Activation. Pharmaceutics, 16(4) MDPI 10.3390/pharmaceutics16040511
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Cimicifuga racemosa (CR) extracts contain diverse constituents such as saponins. These saponins, which act as a defense against herbivores and pathogens also show promise in treating human conditions such as heart failure, pain, hypercholesterolemia, cancer, and inflammation. Some of these effects are mediated by activating AMP-dependent protein kinase (AMPK). Therefore, comprehensive screening for activating constituents in a CR extract is highly desirable. Employing machine learning (ML) techniques such as Deep Neural Networks (DNN), Logistic Regression Classification (LRC), and Random Forest Classification (RFC) with molecular fingerprint MACCS descriptors, 95 CR constituents were classified. Calibration involved 50 randomly chosen positive and negative controls. LRC achieved the highest overall test accuracy (90.2%), but DNN and RFC surpassed it in precision, sensitivity, specificity, and ROC AUC. All CR constituents were predicted as activators, except for three non-triterpene compounds. The validity of these classifications was supported by good calibration, with misclassifications ranging from 3% to 17% across the various models. High sensitivity (84.5-87.2%) and specificity (84.1-91.4%) suggest suitability for screening. The results demonstrate the potential of triterpene saponins and aglycones in activating AMP-dependent protein kinase (AMPK), providing the rationale for further clinical exploration of CR extracts in metabolic pathway-related conditions.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of General Internal Medicine (DAIM) > Clinic of General Internal Medicine |
UniBE Contributor: |
Schöning, Verena |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1999-4923 |
Publisher: |
MDPI |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
30 Apr 2024 14:30 |
Last Modified: |
30 Apr 2024 14:40 |
Publisher DOI: |
10.3390/pharmaceutics16040511 |
PubMed ID: |
38675172 |
Uncontrolled Keywords: |
AMPK activator Cimicifuga racemosa deep neural networks logistic regression classification machine learning polyphenols triterpene saponins |
BORIS DOI: |
10.48350/196304 |
URI: |
https://boris.unibe.ch/id/eprint/196304 |
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