Variable Lymphocyte Receptor B Technologies – Are They Ready for Prime Time?
Bibliographic record
Abstract
Variable Lymphocyte Receptors (VLRs) are the molecules used by jawless vertebrates, lampreys and hagfish, to recognize antigens, akin to the antibodies (Abs) of jawed vertebrates. The unique architecture and evolutionary distance of VLRB Abs enable their binding to epitopes not readily recognized by conventional mammalian Abs. The single gene-polypeptide nature of the secreted VLRB Abs allows for efficient genetic editing and production of VLRB monoclonal Abs (mAbs), which are stable in diverse environments. VLRB Abs can also be modified into scaffolds, known as Repebodies, for efficient protein targeting.Objective To review the current and the potential research and clinical use of VLRBs.Methods A literature search was conducted for English studies published in the past 20 years using the terms “Variable Lymphocyte Receptor,” “VLR,” “VLRB” or “Repebody.” Only primary reports were included.Results VLRB-based technologies are currently being investigated for diagnosis, imaging, and treatment of diverse conditions including solid organ and hematological malignancies, infectious diseases, autoimmunity, and degenerative and metabolic disorders. VLRB mAbs can be used to directly recognize disease biomarkers, such as B cells from chronic lymphocytic leukemia, or to deliver drugs to the brain or cancer cells. The VLRB C-terminal multimerization domain has been utilized to create vaccines while VLR-based chimeric antigen receptor (CAR) T cell constructs are being investigated for cancer therapies.Conclusions The extensive knowledge gained with VLRB mAbs in diverse in vitro and in vivo models emphasizes their promise for translation into clinical applications and readiness for prime time.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".