Evaluation of the potential of botanicals and their constituents against the SARS-CoV-2 virus
Bibliographic record
Abstract
The critical challenge that natural products research projects seek to address is identifying biologically active constituents in complex mixtures. The gold standard approach towards this goal is bioassay-guided fractionation, whereby mixtures are successively purified and tested for their ability to achieve a desired biological activity. The success of this approach is reflected by the discovery of many essential drugs, including the antibiotics streptomycin and tetracycline, and the anti-cancer drug Taxol. However, the bioassay-guided fractionation approach is limited by i) its inherent bias towards abundant and easily isolable compounds, and ii) the quality of the biological data used to guide isolation. The Center for High-Throughput Functional Annotation of Natural Products (HiFAN) seeks to address these limitations by developing new tools for the comprehensive evaluation of natural product mixtures. These tools enable the collection of multi-dimensional biological datasets, and the application of untargeted spectrometry metabolomics approaches to comprehensively profile the chemical composition of natural product mixtures. We will highlight the application of these approaches to identify natural product extracts and constituents with potential efficacy against SARS-CoV-2. A panel of botanical extracts and pure natural compounds were screened for blockade of authentic SARS-CoV-2 infection in cell culture. Promising activity was demonstrated by extracts and constituents from the botanical Stephania tetrandra, and the chemical and biological datasets were integrated using a multivariate statistical approach to determine which active constituents were most strongly associated with biological activity. We are currently employing HiFAN’s gene expression and cytological profiling platforms to derive insight into potential anti-viral and anti-inflammatory mechanisms of action for the bioactive constituents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".