Agonistic Human Antibodies Binding to Lecithin-Cholesterol Acyltransferase Modulate High Density Lipoprotein Metabolism
Bibliographic record
Abstract
Drug discovery opportunities where loss-of-function alleles of a target gene link to a disease-relevant phenotype often require an agonism approach to up-regulate or re-establish the activity of the target gene. Antibody therapy is increasingly recognized as a favored drug modality due to multiple desirable pharmacological properties. However, agonistic antibodies that enhance the activities of the target enzymes are rarely developed because the discovery of agonistic antibodies remains elusive. Here we report an innovative scheme of discovery and characterization of human antibodies capable of binding to and agonizing a circulating enzyme lecithin cholesterol acyltransferase (LCAT). Utilizing a modified human LCAT protein with enhanced enzymatic activity as an immunogen, we generated fully human monoclonal antibodies using the XenoMouse(TM) platform. One of the resultant agonistic antibodies, 27C3, binds to and substantially enhances the activity of LCAT from humans and cynomolgus macaques. X-ray crystallographic analysis of the 2.45 Å LCAT-27C3 complex shows that 27C3 binding does not induce notable structural changes in LCAT. A single administration of 27C3 to cynomolgus monkeys led to a rapid increase of plasma LCAT enzymatic activity and a 35% increase of the high density lipoprotein cholesterol that was observed up to 32 days after 27C3 administration. Thus, this novel scheme of immunization in conjunction with high throughput screening may represent an effective strategy for discovering agonistic antibodies against other enzyme targets. 27C3 and other agonistic human anti-human LCAT monoclonal antibodies described herein hold potential for therapeutic development for the treatment of dyslipidemia and cardiovascular disease.
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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) | 0.001 | 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".