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Record W3104114400 · doi:10.1101/2020.11.09.20228098

Peginterferon-lambda for the treatment of COVID-19 in outpatients

2020· preprint· en· W3104114400 on OpenAlexafffundabout
Jordan J. Feld, Christopher Kandel, Mia J. Biondi, Robert Kozak, Muhammad Atif Zahoor, Camille Lemieux, Sergio Borgia, Andrea K. Boggild, Jeff Powis, Janine McCready, Darrell H. S. Tan, Tiffany Chan, Bryan Coburn, Deepali Kumar, Atul Humar, Adrienne K Chan, Braden O’Neil, Seham Noureldin, Joshua Booth, Rachel Hong, David Smookler, Wesam Aleyadeh, Anjali Patel, Bethany Barber, Julia Casey, Ryan Hiebert, Henna Mistry, Ingrid Choong, Colin Hislop, Deanna M. Santer, D. Lorne Tyrrell, Jeffrey S. Glenn, Adam J. Gehring, Harry L.A. Janssen, Bettina E. Hansen

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsSt. Michael's HospitalMcMaster UniversityUniversity of AlbertaTrillium Health CentreWilliam Osler Health SystemSunnybrook Health Science CentreToronto East General HospitalUniversity Health NetworkToronto General HospitalUniversity of TorontoHealth Sciences Centre
FundersUniversity of TorontoSanofi
KeywordsViral loadPlaceboAsymptomaticClinical endpointInternal medicineMedicineGastroenterologyAdverse effectViral sheddingVirologyRandomized controlled trialImmunologyVirusPathology

Abstract

fetched live from OpenAlex

Summary Background There are currently no effective treatments for outpatients with coronavirus disease 2019 (COVID-19). Interferon-lambda-1 is a Type III interferon involved in the innate antiviral response with activity against respiratory pathogens. Methods In this double-blind, placebo-controlled trial, outpatients with laboratory-confirmed COVID-19 were randomized to a single subcutaneous injection of peginterferon-lambda 180μg or placebo within 7 days of symptom onset or first positive swab if asymptomatic. The primary endpoint was proportion negative for SARS-CoV-2 RNA on Day 7 post-injection. Findings There were 30 patients per arm, with median baseline SARS-CoV-2 viral load of 6.71 (IQR 1.3-8.0) log copies/mL. The decline in SARS-CoV-2 RNA was greater in those treated with peginterferon-lambda than placebo (p=0.04). On Day 7, 24 participants (80%) in the peginterferon-lambda group had an undetectable viral load compared to 19 (63%) in the placebo arm (p=0.15). After controlling for baseline viral load, peginterferon lambda treatment resulted in a 4.12-fold (95CI 1.15-16.7, p=0.029) higher likelihood of viral clearance by Day 7. Of those with baseline viral load above 10E6 copies/mL, 15/19 (79%) in the peginterferon-lambda group were undetectable on Day 7 compared to 6/16 (38%) in the placebo group (p=0.012). Adverse events were similar between groups with only mild reversible transaminase elevations more frequently observed in the peginterferon-lambda group. Interpretation Peginterferon-lambda accelerated viral decline in outpatients with COVID-19 resulting in a greater proportion with viral clearance by Day 7, particularly in those with high baseline viral load. Peginterferon-lambda may have potential to prevent clinical deterioration and shorten duration of viral shedding. ( NCT04354259 ) Funding This study was supported by the Toronto COVID-19 Action Initiative, University of Toronto and the Ontario First COVID-19 Rapid Research Fund. Medication was supplied by Eiger BioPharma. Research in Context Treatment trials for COVID-19 have largely focused on hospitalized patients and no treatments are approved for people with mild to moderate disease in the outpatient setting. A number of studies in ambulatory populations have been registered but no controlled studies in the outpatient setting have been reported to date (Pubmed Search October 20, 2020, COVID-19 treatment; controlled trials). Uncontrolled case series of hydroxychloroquine with or without azithromycin have been reported with mixed results but no clear signal of efficacy and some concerns raised about cardiac toxicity. Treamtent in the outpatient setting has potential to prevent infected individuals from deteriorating and perhaps more importantly, may shorten the duration of viral shedding, reducing the risk of transmission and the duration required for self-isolation, with significant public health and societal impact. Added value of this study This is the first study to show an antiviral effect in outpatients with COVID-19. After controlling for baseline viral load, those treated with peginterferon-lambda had a 4.12-fold (95%CI 1.15-16.7, p=0.029) higher odds of viral clearance by Day 7 compared to those who received placebo. The viral load decline was faster with pegterferon-lambda and the effect was most pronounced in those with high viral loads. In individuals with a baseline viral load of 10E6 copies/mL or higher, 15/19 (79%) in the peginterferon-lambda arm cleared by Day 7 compared to 6/16 (38%) (p=0.012) in the placebo arm (OR 6.25, 95%CI 1.49-31.1, p=0.012), translating to a median time to viral clearance of 7 days (95%CI 6.2-7.8 days) with peginterferon-lambda compared to 10 days (95%CI 7.8-12.2 days) with placebo (p=0.038). Those with low viral loads (<10E6 copies/mL) cleared quickly in both groups. Peginterferon-lambda was well-tolerated with a similar side effect profile to placebo and no concerning laboratory adverse events. Implications of all available evidence There is no currently approved therapy for outpatients with COVID-19. This study showed that peginterferon-lambda accelerated viral clearance, particularly in those with high baseline viral loads, highlighting the importance of quantitative viral load testing in the evaluation of antiviral agents for COVID-19. Treatment early in the course of disease may prevent clinical deterioration and shorenting of the duration of viral shedding may have important public health impact by limiting transmission and reducing the duration required for self-isolation. Additional trials of peginterferon-lambda and other antiviral strategies in the outpatient setting are required.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.212
GPT teacher head0.488
Teacher spread0.276 · 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 teacher head, not a consensus.

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".

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Citations9
Published2020
Admission routes3
Has abstractyes

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