Continuous flow microreactor for protein PEGylation
Bibliographic record
Abstract
PEGylation is increasingly being utilized to enhance the therapeutic efficacy of biopharmaceuticals. Various chemistries and reaction conditions have been established to synthesize PEGylated proteins and more are being developed. Both the extent of conversion and selectivity of protein PEGylation are highly sensitive to process variables and parameters. Therefore, microfluidic-based high-throughput screening platforms would be highly suitable for optimization of protein PEGylation. As part of this study, a poly-dimethylsiloxane-based continuous flow microreactor system was designed and its performance was compared head-to-head with a batch reactor. The reactants within the microreactor were contacted by passive micromixing based on chaotic advection generated by staggered herringbone grooves embedded in serpentine microchannels. The microreactor system was provided with means for on-chip reaction quenching. Lysozyme was used as the model protein while methoxy-polyethylene glycol-(CH2)5COO-NHS was used as the PEGylation reagent. Full mixing was achieved close to the microreactor inlet, making the device suitable for protein PEGylation. The effect of mixing type, i.e., simple stirring versus chaotic laminar mixing on PEGylation, was investigated. Higher selectivity (as high as 100% selectivity) was obtained with the microreactor while the conversion was marginally lower.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".