Co-stimulatory pathway competitive assay development using Liquid chromatography–tandem mass spectrometry (LC-MS/MS)
Bibliographic record
Abstract
T-cells play a significant role in the development of autoimmune diseases. The CD28-B7 costimulatory pathway is crucial for activating T-cells, and blocking this pathway is essential for treating autoimmune diseases. Therapeutic antibodies and fusion proteins that target costimulatory molecules like CD80, CD86, CTLA-4, and CD28 have been developed to explore the costimulation process and as targeted treatments. To advance our understanding of costimulation in autoimmunity and the inhibition of the costimulatory pathway, it is crucial to have an accurate, precise, and direct method for detecting and quantifying the soluble form of these molecules in body fluids and various biological systems. Herein, we developed a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantifying the four costimulatory proteins depending on the signature peptides derived from the soluble isoform of these proteins in multiple reaction monitoring (MRM) mode. The method was validated using the US FDA guidelines. The LOQ was determined as ∼0.5 nM for the four analytes, with quantification extended to 20 nM with a correlation coefficient of R2>0.998. The developed MRM method was used to analyze on-bead digested protein mixtures to establish a competitive assay for the CD28-B7 costimulatory pathway using CTLA4-Ig (Abatacept TM) as an FDA-approved drug for rheumatoid arthritis. The IC50 was determined to be 2.99 and 159.8 nM for sCD80 and sCD86, respectively. A straightforward MRM-based competitive assay will advance the knowledge about the costimulatory role in autoimmunity and the autoimmune therapeutic drug discovery, with the need for broad application on different in vitro and in vivo models to discover new targeted inhibitors.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".