Green Biologics: Harnessing the Power of Plants to Produce Pharmaceuticals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plants are increasingly used for the production of high-quality biological molecules for use as pharmaceuticals and biomaterials in industry. Plants have proved that they can produce life-saving therapeutic proteins (Elelyso™-Gaucher's disease treatment, ZMapp™-anti-Ebola monoclonal antibodies, seasonal flu vaccine, Covifenz™-SARS-CoV-2 virus-like particle vaccine); however, some of these therapeutic proteins are difficult to bring to market, which leads to serious difficulties for the manufacturing companies. The closure of one of the leading companies in the sector (the Canadian biotech company Medicago Inc., producer of Covifenz) as a result of the withdrawal of investments from the parent company has led to the serious question: What is hindering the exploitation of plant-made biologics to improve health outcomes? Exploring the vast potential of plants as biological factories, this review provides an updated perspective on plant-derived biologics (PDB). A key focus is placed on the advancements in plant-based expression systems and highlighting cutting-edge technologies that streamline the production of complex protein-based biologics. The versatility of plant-derived biologics across diverse fields, such as human and animal health, industry, and agriculture, is emphasized. This review also meticulously examines regulatory considerations specific to plant-derived biologics, shedding light on the disparities faced compared to biologics produced in other systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 | 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 it