Rapid In-Process Monitoring of Lentiviral Vector Particles by High-Performance Liquid Chromatography
Bibliographic record
Abstract
Lentiviral vectors (LVs) are a popular gene delivery tool in cell and gene therapy and they are a primary tool for ex vivo transduction of T cells for expression of chimeric antigen receptor (CAR) in CAR-T cell therapies. Extensive process and product characterization are required in manufacturing virus-based gene vectors to better control batch-to-batch variability. However, it has been an ongoing challenge to make quantitative assessments of LV product because current analytical tools often are low throughput and lack robustness and standardization is still required. This paper presents a high-throughput and robust physico-chemical characterization method that directly assesses total LV particles. With simple sample preparation and fast elution time (6.24 min) of the LV peak in 440 mM NaCl (in 20 mM Tris-HCl [pH 7.5]), this ion exchange high-performance liquid chromatography (IEX-HPLC) method is ideal for routine in-process monitoring to facilitate the development of scalable and robust LV manufacturing processes. Furthermore, this HPLC method is suitable for the analysis of all in-process samples, from crude samples such as LV supernatants to final purified products. The linearity range of the standard curve is 3.13 × 10 8 to 1.0 × 10 10 total particles/mL, and both the intra- and inter-assay variabilities are less than 5%.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".