Generic serial and parallel on‐line direct‐injection using turbulent flow liquid chromatography/tandem mass spectrometry
Bibliographic record
Abstract
The development of turbulent flow chromatography (TFC) has enabled considerable growth in the utility of on-line direct-injection technologies. TFC has now become established in a large number of varied analytical environments, particularly drug discovery/pharmacokinetics, metabolite profiling, combinatorial library purification, pre-clinical and clinical GLP applications. The utility of turbulent flow technology for in-house pre-clinical and clinical quantitative applications has necessitated extensive valve-cleaning procedures, and consequently lengthy cycle-times, to effectively remove the system carry-over. In-house requirements for assay validation require carry-over less than 20% of the lowest level of quantification (LLOQ), corresponding to 0.02% carry-over for a linear calibration range incorporating 3 orders. A generic turbulent flow chromatography protocol has been developed for drug discovery that incorporates polymeric turbulent flow extraction (cyclone) with C18-based reverse-phase chromatography. Further, multiple wash steps are incorporated within the methodology to meet in-house requirements for carry-over. Selection of novel switching-valve materials based on polyarylethyl ketone (PAEK) and Hastelloy/Valcon E autosampler injection hardware has enabled us to significantly impact the cycle-time required to reduce carry-over. Consequently, optimal usage of switching valves has enabled parallel operation for a generic on-line direct-injection methodology to successfully reduce the total cycle-time. Overall reductions from 4 min per sample to 90 s per sample are shown with comparable data quality using a proprietary target molecule from 0.1-100 ng/mL. This paper describes the hardware configuration and methodologies utilized to perform generic serial and parallel on-line direct-injection using a Turboflow HTLC 2300 system.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".