Kinetic characterization and modeling of sequentially entrapped enzymes in 3D‐printed PMMA microfluidic reactors for the synthesis of amorphadiene via the isopentenol utilization pathway
Bibliographic record
Abstract
The development of cascade cell-free systems reduces the requirement for extensive metabolic engineering and optimization to increase in vivo pathway flux. For continuous operation and increased stability, direct enzyme entrapment during reactor fabrication by three-dimensional (3D)-printing allows for simple immobilization procedures without enzyme-specific optimization. In this study, the isopentenol utilization pathway (IUP) was selected for the synthesis of amorphadiene, an antimalaria drug precursor, using a 3D-printed, sequentially immobilized, microfluidic reactor. As an initial proof-of-concept, alkaline phosphatase (ALP) was entrapped in a poly(methyl methacrylate) (PMMA)-based matrix during stereolithographic 3D-printing and was kinetically characterized. No significant shift of the kinetically modeled substrate binding affinity was observed during immobilization and continuous operation of an entrapped ALP microfluidic reactor displayed high stability. The IUP enzymes retained moderate activity during entrapment (6.6%-9.6%) relative to the free enzyme solutions, however the sequentially immobilized IUP microfluidic reactor was severely limited by low pathway flux due to the use of stereolithographic 3D-printing which significantly diluted enzyme concentrations for printing. Although this study demonstrated the use of additive manufacturing for the synthesis of amorphadiene using a complex five-enzyme cascade microfluidic reactor, stereolithographic enzyme entrapment remains limited in scope and dependent on advancements to additive manufacturing technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".