Next-generation Metabolomics in the Development of New Antidepressants: Using Albiflorin as an Example
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Depression is a highly prevalent disorder that affects more than 300 million adults worldwide in 2015. Depression also frequently coexists with many other conditions such as osteoporosis and one-third of the Intensive Care Unit (ICU) survivors had depressive symptoms. Antidepressants have become the most commonly prescribed drugs in the United States. In addition to the regular process, drug discovery and development (R&D) for depression presents extra challenges because of the heterogeneity of the symptoms and various co-occurring disorders. Botanical medicine with multi-functional nature has been proposed to be more effective, providing rapid control of core and comorbid conditions of depression. With the technical advances in analytical instruments, metabolomics is entering into a "new generation". Next-generation metabolomics (NGM) has the capability to comprehensively characterize drug-induced metabolic changes in the biological systems. NGM has demonstrated great potential in all the stages of pharmaceutical R&D in the last 10 years. Albiflorin isolated from Peony roots is a promising drug candidate with multi-target for depression and is currently under development by Beijing Wonner Biotech. In this work, we summarized the common analytical platforms for NGM and its main applications in drug R&D. We used albiflorin as an example to illustrate how NGM improves our understanding of drug candidate actions and facilitates drug safety evaluation. Future directions on how to expand the use of NGM for new antidepressant development in pharmaceutical industry were also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it