Directed Evolution Enables Simultaneous Controlled Release of Multiple Therapeutic Proteins from Biopolymer‐Based Hydrogels
Bibliographic record
Abstract
Abstract With the advent of increasingly complex combination strategies of biologics, independent control over their delivery is the key to their efficacy; however, current approaches are hindered by the limited independent tunability of their release rates. To overcome these limitations, directed evolution is used to engineer highly specific, low affinity affibody binding partners to multiple therapeutic proteins to independently control protein release rates. As a proof‐of‐concept, specific affibody binding partners for two proteins with broad therapeutic utility: insulin‐like growth factor‐1 (IGF‐1) and pigment epithelium‐derived factor (PEDF) are identified. Protein–affibody binding interactions specific to these target proteins with equilibrium dissociation constants (KD) between 10−7 and 10−8 m are discovered. The affibodies are covalently bound to the backbone of crosslinked hydrogels using click chemistry, enabling sustained, independent, and simultaneous release of bioactive IGF‐1 and PEDF over 7 days. The system is tested with C57BL/6J mice in vivo, and the affibody‐controlled release of IGF‐1 results in sustained activity when compared to bolus IGF‐1 delivery. This work demonstrates a new, broadly applicable approach to tune the release of therapeutic proteins simultaneously and independently and thus the way for precise control over the delivery of multicomponent therapies is paved.
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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 | 0.000 | 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 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".