Increased Throughput of Combined Stability Testing and Metabolite Identification Using a Sample Multiplexing Strategy for the Optimization of Engineered Cyclic Peptide Drugs
Bibliographic record
Abstract
Engineered cyclic peptides (ECPs) have been in the spotlight as novel drug modalities for challenging therapeutic targets. Oral delivery of engineered cyclic peptides benefits from ease of administration. However, one of the main hurdles in developing orally effective peptide drugs is their potential metabolic instability due to enzymatic degradation. To that end, in vitro experiments with simulated intestinal fluid (SIF) have been used to assess drug metabolic stability in the gastrointestinal tract. Currently, metabolic stability evaluations and biotransformation assessments are performed separately, which can be time-consuming and result in complex data analysis. Presented here is a sample multiplexing strategy to address these challenges by leveraging a Thermo Scientific Orbitrap Astral mass spectrometer with two complementary analyzers, enabling the simultaneous analysis of metabolic stability from the Orbitrap full scan and biotransformation from the Astral analyzer from one sample injection. Furthermore, we demonstrate that 10 engineered cyclic peptides can be pooled into one sample injection without compromising the data quality to decrease the instrument run time and improve the throughput of the assay.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".