Rapid response platform to manufacture human immunoglobulins
Bibliographic record
Abstract
Abstract A rapidly deployable drug product for use in the early stages of a disease outbreak could prevent further spread of the pathogen. Emergent BioSolutions is developing a rapid response platform (RRP) to manufacture human immunoglobulins (HIG) for use as a passive immunotherapy during public health emergencies. Passive immunotherapies derived from either convalescent plasma or purified immunoglobulins (Ig) have a long history of success in treating a range of viral, bacterial and toxin diseases, as they provide the immediate benefit of a protective response. Emergent's RRP is being developed to address the need for front-line therapy/prophylaxis with immediate benefit with the potential to overcome the current limitations of convalescent plasma. Emergent's proposed RRP begins with identification of plasma donors with measurable levels of pathogen-specific antibodies using a field deployable real-time donor screening assay. Plasma collected from eligible donors is treated using the Mirasol® Pathogen Reduction Technology, pooled, purified and concentrated into HIG using Emergent's rapid HIG manufacturing process to produce product formulated for intramuscular administration. Our rapid HIG process is chromatography based and each 30 L run aims to purify, formulate, and fill ∼65 doses (1 g of total IgG) with testing and release onsite for immediate use. Multiple innovations to streamline operations, including single-use technologies and minimal human interfaces, allow for the manufacture of HIG product in ∼30 hours. To house the process, Emergent has designed and constructed a working modular manufacturing unit (MMU) prototype. The MMU consists of a modified 53-foot shipping container designed for multiple modes of transport, to meet ISO 8 environment requirements with self-contained utilities, including HVAC, and can run off a diesel generator or local power. Product from multiple development runs within the MMU have been characterized and validation work is ongoing. Key messages A modular manufacturing unit is being developed with the potential to provide a local capability to produce health solutions for endemic diseases. A modular manufacturing unit is being developed with the potential to rapidly manufacture human immunoglobulins during public health emergencies and pandemics.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".