Bibliographic record
Abstract
Antibody-based therapeutics constitute a major growth area in medicine today. However, antibodies as drugs present significant analytical challenges as they are large, complex and heterogenous molecules produced in living cells. The key attributes that affect safety, stability and efficacy must be identified and controlled to ensure regulatory compliance. Liquid Chromatography-Mass Spectrometry (LC-MS) is a powerful analytical technology that is well suited to the task of analyzing antibody-based therapeutics. LC-MS is used to characterize antibody features ranging from the relatively simple (e.g. intact molecular weight determination and post-translational modification analysis) to the complex (e.g., higher order structure analysis and epitope identification). Few other analytical technologies are as versatile as LC-MS for monitoring a wide range of attributes or as capable of keeping pace with the innovation happening today in biotherapeutic design. In this chapter we will provide an overview of the LC-MS methods currently used for the characterization of antibody-based therapeutics, with an emphasis on the analysis of post-translational modifications. We will also highlight some recent innovations, new technologies and trends that are likely to significantly impact the manner in which antibody-based therapeutics are analyzed in the future.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".