5938Efficacy of evinacumab in homozygous familial hypercholesterolemia patients with null or non-null LDL-receptor mutations and on various background therapies
Bibliographic record
Abstract
Background: Patients with homozygous familial hypercholesterolemia (HoFH) are characterized by extremely elevated plasma LDL-C that is refractory to most lipid-lowering therapies (LLTs), putting them at very high risk for premature cardiovascular disease. Despite combination therapy, and often apheresis, HoFH patients, including those with null/null LDL-receptor (LDLR) mutations, often show little response to combination therapies including statins or PCSK9 inhibitors. Blockade of ANGPTL3 is deemed a promising therapy for HoFH, given that loss-of-function mutations in ANGPTL3 result in hypolipidemia and treatment with an ANGPTL3 inhibitor resulted in LDL-C lowering in LDL-R−/− mouse models. Purpose: To assess the efficacy of evinacumab, a monoclonal antibody against ANGPTL3, in HoFH patients, including those with LDLR null/null mutations, treated with different classes of commonly prescribed combinations of LLT. Methods: Nine HoFH patients participated in this ongoing, single-arm, open-label, proof-of-concept study (ClinicalTrial.gov NCT02265952). Current LLT was maintained from 4 weeks before baseline measurement throughout the 26-week treatment and observation period. Evinacumab was dosed as a single 250 mg SC injection at baseline and subsequently as 15 mg/kg IV at week 2. Two patients further received 450 mg SC at weeks 12, 13, 14, and 15. The primary endpoint was the mean percent change in LDL-C levels from baseline to week 4.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".