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Record W3014622477 · doi:10.1093/cvr/cvaa077

Effect of inclisiran, the small-interfering RNA against proprotein convertase subtilisin/kexin type 9, on platelets, immune cells, and immunological biomarkers: a pre-specified analysis from ORION-1

2020· article· en· W3014622477 on OpenAlexaff
Ulf Landmesser, Arash Haghikia, Lawrence A. Leiter, R. Scott Wright, David Kallend, Peter Wijngaard, Robert M. Stoekenbroek, John J.P. Kastelein, Kausik K. Ray

Bibliographic record

VenueCardiovascular Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersDaiichi-SankyoMedicines CompanyCSL BehringDaiichi Sankyo EuropeAstraZeneca
KeywordsImmunogenicityMedicineImmune systemPCSK9ImmunologyKexinProprotein convertasePharmacologyAntibodyInternal medicineLipoproteinCholesterolLDL receptor

Abstract

fetched live from OpenAlex

AIMS: Small-interfering RNA (siRNA)-based targeting of proprotein convertase subtilisin/kexin type 9 (PCSK9) represents a novel therapeutic approach that may provide a convenient, infrequent, and safe dosing schedule to robustly lower low-density lipoprotein cholesterol (LDL-C). Given the long duration of action, however, establishing safety in particular with respect to immunogenicity is of paramount importance. In earlier clinical studies of other RNA-targeted treatment approaches (antisense oligonucleotide therapy) immunological and haematological adverse effects, in particular thrombocytopenia and pro-inflammatory effects, have been reported. Here, we present the pre-specified safety analysis from ORION-1 evaluating platelets, immune cells, immunological markers, antidrug antibodies, and clinical immunogenicity adverse events (AEs) under PCSK9 siRNA treatment with inclisiran. METHODS AND RESULTS: The pre-specified safety analysis from ORION-1 was performed in six different inclisiran dosing regimens in patients at high risk of cardiovascular disease with elevated LDL-C levels. Patients received either a single dose (SD: 200 mg, n = 60; 300 mg, n = 62 or 500 mg, n = 66) or double-dose starting regimen (DD: 100 mg, n = 62; 200 mg, n = 63; or 300 mg, n = 61 on days 1 and 90) of inclisiran or placebo (SD: n = 65; DD: n = 62). The effects of inclisiran on haematological parameters including platelet counts, lymphocytes, and monocytes as well as on the immune markers interleukin 6 (IL-6) and tumour necrosis factor-α (TNF-α) were examined after 180 days. Immunogenicity was further evaluated by analysis of anti-drug-antibodies (ADAs) towards inclisiran in 6068 study samples and by careful analysis of immunogenicity AEs as part of the pharmacovigilance strategy. At day 180, no significant alterations of platelet counts were observed in any of the dosing groups (change from baseline, SD: 200 mg: 0.8%; 300 mg: -0.5%; 500 mg: -1.8%; DD: 100 mg: 1.3%; 200 mg: -0.5%; 300 mg: 1.0%; no significant difference for any group as compared with placebo). No significant effects on other immune cells, including leucocytes, monocytes, or neutrophils were detected. Notably, no significant increase of inflammatory biomarkers (IL-6 or TNF-α) with either the SD or DD regimen became evident. There was no evidence for immunogenicity based on ADA level analysis and careful review of clinical immunogenicity AEs in none of the treatment regimens. CONCLUSION: In this pre-specified safety analysis of ORION-1 for the siRNA therapeutic inclisiran, no adverse effects on measures of inflammation or immune activation nor adverse effects on platelets or clinical immunogenicity AEs were observed over at least 6-month treatment. These safety findings in the largest analysis of an RNAi study in humans to date provide strong reassurance about the safety of inclisiran and the potential of cardiovascular RNA-targeted therapies.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.059
GPT teacher head0.316
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations81
Published2020
Admission routes1
Has abstractyes

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