Seed‐based expression systems for plant molecular farming
Bibliographic record
Abstract
The evolution of the seed system provides enormous adaptability to the gymnosperms and angiosperms, because of the properties of dormancy, nutrient storage and seedling vigour. Many of the unique properties of seeds can be exploited in molecular farming applications, particularly where it is desirable to produce large quantities of a recombinant protein. Seeds of transgenic plants have been widely used to generate a raw material for the extraction and isolation of proteins and polypeptides, which can be processed into valuable biopharmaceuticals. The factors that control high-level accumulation of recombinant proteins in seed are reviewed in the following paragraphs. These include promoters and enhancers, which regulate transcript abundance. However, it is shown that subcellular trafficking and targeting of the desired polypeptides or proteins play a crucial role in their accumulation at economically useful levels. Seeds have proven to be versatile hosts for recombinant proteins of all types, including peptides or short and long polypeptides as well as complex, noncontiguous proteins like antibodies and other immunoglobulins. The extraction and recovery of recombinant proteins from seeds is greatly assisted by their dormancy properties, because this allows for long-term stability of stored products including recombinant proteins and a decoupling of processing from the growth and harvest cycles. Furthermore, the low water content and relatively low bioload of seeds help greatly in designing cost-effective manufacturing processes for the desired active pharmaceutical ingredient. The development of cGMP processes based on seed-derived materials has only been attempted by a few groups to date, but we provide a review of the key issues and criteria based on interactions with Food and Drug Administration and European Medicines Agency. This article uses 'case studies' to highlight the utility of seeds as vehicles for pharmaceutical production including: insulin, human growth hormone, lysozyme and lactoferrin. These examples serve to illustrate the preclinical and, in one case, clinical information required to move these plant-derived molecules through the research phase and into the regulatory pathway en route to eventual approval.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".