Supplementary Appendix
Supplement to: Levin MJ, Ustianowski A, De Wit S, et al. Intramuscular AZD7442 (tixagevimab–cilgavimab) for
prevention of Covid-19. N Engl J Med. DOI: 10.1056/NEJMoa2116620
This appendix has been provided by the authors to give readers additional information about the work.
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Table of contents
List of study sites and investigators ............................................................................. 4
Supplementary methods ............................................................................................ 10
Description of the adjudication committee ......................................................... 10
Study randomization and blinding ..................................................................... 12
Participant follow-up .......................................................................................... 13
Key protocol amendments ................................................................................ 13
Full inclusion criteria .......................................................................................... 14
Full exclusion criteria ......................................................................................... 16
Analyses and endpoints .................................................................................... 18
Serum sampling and bioanalytical analyses ..................................................... 19
Sequencing of SARS-CoV-2 samples ............................................................... 20
Statistical analysis ............................................................................................. 21
Supplementary results ............................................................................................... 27
Missing data analysis ........................................................................................ 27
Covid-19‒related hospitalizations ..................................................................... 27
Supplementary figures ............................................................................................... 28
Figure S1. Participant flow through trial (CONSORT flow diagram) .................. 28
Figure S2. Pharmacokinetic and anti–SARS-CoV-2 neutralizing antibody analyses:
(A) serum AZD7442 geometric mean concentration ± SD, and (B) SARS-CoV-2
neutralizing antibody geometric mean titers with 95% CI .................................. 30
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Supplementary tables ................................................................................................ 32
Table S1. Definition of symptomatic Covid-19 (qualifying symptoms) ............... 32
Table S2. Censoring category breakdown for primary endpoint ....................... 33
Table S3. Participant demographics and baseline clinical characteristics by
outcome category .............................................................................................. 34
Table S4. Representativeness of study participants ......................................... 36
Table S5. Number of participants with SAEs by system organ class, primary data
cut (SAS) ........................................................................................................... 38
Table S6. Safety data, median 6-month data cut (SAS) .................................... 40
Table S7. Number of participants with SAEs by system organ class, median
6-month data cut (SAS) ..................................................................................... 42
Table S8. Key secondary efficacy endpoint ...................................................... 44
Table S9. Definition of SARS-CoV-2 RT-PCR‒positive severe or critical il ness45
Table S10. Post hoc analysis of primary efficacy endpoint events (first SARS-CoV-
2 RT-PCR‒positive symptomatic il ness, censored at unblinding or receipt of Covid-
19 vaccine) ........................................................................................................ 46
Table S11. Summary of detected SARS-CoV-2 spike-based lineages, median
6-month data cut ............................................................................................... 47
References .................................................................................................................... 48
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List of study sites and investigators
Al ianz Research Institute, Inc., Westminster, CA: David Pham, Luis T Casanova
Anima, Alken, Belgium: Hilde Bollen, Ine Vercammen, Mirjam de Maeyer, Erik
Buntinx, Linde Buntinx
Aventiv Research Inc., Columbus, OH: Samir Arora, Lindsay Newton
Baptist Health Center for Clinical Research, Little Rock, AR: Priyantha
Wijewardane
BVBA Dr. Luc Capiau, Wetteren, Belgium: Luc Capiau, Christiane Snoeck
Central Valley Research – Modesto, Modesto, CA: Faisal Amin, Haroon Hafeez
Centre Hospitalier Départemental Les Oudairies, La Roche S/ Yon Cedex 9,
France: Thomas Guimard
Chicago Clinical Research Institute, Chicago, IL: Akash G Manjunathappa
CHU Clermont Ferrand – Hôpital Gabriel Montpied, Clermont-Ferrand Cedex,
Clermont-Ferrand, France: Henri Laurichesse
CHU de Limoges – Hôpital Dupuytren, Limoges Cedex, Limoges, France:
Christine Vallejo, Rachel Froget, Caroline Cail e-Fenerol, Clémentine Ruch
CHU Dijon – Hôpital du Bocage, Dijon Cedex, Dijon, France: Maxime Luu, Marc
Bardou, Audrey Guil ier, Hervé Devil iers, Rogier Thomas
CHU Nantes – Hôtel Dieu, Nantes Cedex 1, Nantes, France: Francois Raffi,
Clotilde Al avena, Anne-Sophie Lecompte
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CHU Saint Etienne – Hôpital Nord, Saint Etienne Cedex 2, Saint Etienne,
France: Elisabeth Botelho-Nevers, José Fernandes, Frédérique Bertholon
Columbus Clinical Services, LLC, Miami, FL: Sara Llerena, Eileen Jimenez,
Bertha M Cano, Veronica Bello, Lidice Chateloin, Maria V Cano, Patricia González
Cediel, Gricel Cano, Marilda Cano Zaldivar, Felisa Aleman
Cozy Research, LLC, Wesley Chapel, FL: Jonathan Yousef, Melissa Vila, Ryan J
DeWeese
DaVita Clinical Research – Las Vegas, Las Vegas, NV: Mark Vishnepolsky
DaVita Clinical Research College Park, The Woodlands, TX: Adam Frome
DaVita Clinical Research Hartford, Hartford, CT: Sharad Sathyan
DaVita Clinical Research Middlebury, Middlebury, CT: Sina Raissi, Helen Brickel
DaVita Clinical Research, Bronx, NY: Robert Lynn, Melissa Marine, Gabriela
Mendoza
DaVita Clinical Research, Houston, TX: Ronald Ralph, Cynthia Moka, Heather
DiMarco, Michelle Cordero
DaVita Minneapolis Dialysis Unit, Minneapolis, MN: Jeffrey Connaire
Davita Columbus Dialysis, Bradley Park, Columbus, GA: Vinayak Ramanath,
Ferdinand Alcaide, Rajendran Alappan, Tamorie Smith
El Paso Kidney Specialists, El Paso, TX: German Hernandez
Elixia Covid-19, Hollywood, FL: Steven Zeig, Neal Patel
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Hopital Cardiologique – CHU Lille pt, Lille, France: Dominique Deplanque, Elise
Elrezzi, Romain Barus, Juliette De Langhe, Maria-Claire Migaud, Stéphanie Somers
Hôpital Saint-Louis, Paris Cedex 10, Paris, France: Jean-Michel Molina, Dyhia
Sardou-Rabia
Hospital Clinic de Barcelona, Barcelona, Barcelona, Spain: Leal Alexander,
Florencia Etcheverry, Marta Aldea, Jocelyn Nava, Omar Anagua, Jesse Anagua
Melendres
Hospital Quironsalud Marbella, Marbella, Málaga, Spain: José Maria Ignacio
García
Hospital Universitario Clinico San Carlos, Madrid, Madrid, Spain: Vicente
Estrada Perez, Eva Santiago
Hospital Universitario Quironsalud Madrid, Pozuelo de Alarcon, Madrid, Spain:
Jose María Echave-Sustaeta, Lorena Comeche Casanova
Hospital Universitario Ramon y Cajal, Madrid, Spain: Alfonso Cruz Jentoft, Jesús
Mateos del Nozal, María Dolores Ochoa Castil o
Invictus Clinical Research Group, LLC, Pompano Beach, FL: Juan F Zapata,
Clarence McMillan
Kansas Nephrology Research Institute, LLC, Wichita, KS: Dennis L Ross, Angie
Anderson
Marvel Clinical Research, Huntington Beach, CA: Brian Siu, David Wilson,
Yelena Quintanil a
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Medif, Gozée, Thuin, Belgium: Marc de Meulemeester
MedResearch, Inc., El Paso, TX: Ogechika Alozie, Jose Burgos
Meridian Clinical Research, LLC, Omaha, NE: Brandon J Essink, Laura Falcone,
Roni Gray
Midwest Clinical Research LLC, St. Louis, MO: Robert Onder
National Institute of Clinical Research, Garden Grove, CA: Michael Dao
Ormond Beach Clinical Research, Ormond Beach, FL: Diego Torres
Palm Beach Research Center, West Palm Beach, FL: Mira Baron
Palmetto Clinical Research, Summervil e, SC: Eric Bolster
Panthera Biopartners Ltd (London), Enfield, Greater London, UK: John Ndikum,
David Biles
Panthera Biopartners Ltd (North Manchester), Rochdale, Greater Manchester,
UK: David Ball
Panthera Biopartners Ltd (Preston), Preston, Lancaster, UK: Mahadev Ramjee,
Liana Dunn, El ie Howie, Siobhan Reil y
Parkway Medical Center, Birmingham, AL: Greg Sullivan
Physicians Research Group, Tempe, AZ: Rajiv Parikh
Pinderfields Hospital, Wakefield, West Yorkshire, UK: Nathanael Wright
Private Practice RESPISOM Namur, Namur, Belgium: Jean-Benoit Martinot
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Project 4 Research, Miami, FL: Ibrahim Menendez-Perez
Rame Medical Limited, Torpoint, Cornwall, UK: Lawrence Barnes, Susie Keast,
Nicola Donlin, Amelia Lewis
Renal Medicine Associates, Albuquerque, NM: Jayant Kumar
Research Al iance Inc., Clearwater, FL: Edison Tan
Research Management, Inc., Austin, TX: Judith Betts
Ridgewood Dialysis Center, Ridgewood, NY: Jodumutt Bhat
Salford Royal, Salford, Greater Manchester, UK: Alison Uriel
South Florida Nephrology Group, P.A., Coral Springs, FL: Asghar Chaudhry,
Radu Jacob
South Florida Research Institute, Lauderdale Lakes, FL: Edouard Martin, Ojeifo
Akharia
Synexus Clinical Research US, Inc., Atlanta, GA: Sherif George Naguib
Synexus Clinical Research US, Inc., Cerritos, KY: Ghazaleh Bahrami, Nelson So
Synexus Clinical Research US, Inc., Jamaica, NY: Margarita Nunez
Triad Clinical Trials, LLC, Greensboro, NC: Richard L. Montgomery, Wil iam F
Hopper III, Shavonna Haamid, Stephanie Riggs, Dominique Smith, Lisa Crihfield,
Julia Kordsmeier
University College London Hospitals, London, Greater London, UK: Nicola
Longley, Tommy Rampling, Vincenzo Libri, Shama Hamal, Sarah Whittley, Marivic
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Ricamara, Claudia Ismail, Yee Ting Nicole Yim, Yee-Chin Lee, Holly Baker, Todd
Rawlins, Kirsty Adams, Catherine Houlihan
Vista Del Sol Dialysis, Victorvil e, CA: Edgard Vera, Michelle Owens
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Supplementary methods
Description of the adjudication committee
The adjudication committee was an independent and external committee convened
to provide a systematic blinded assessment of whether any deaths during the study
were associated with coronavirus disease 2019 (Covid-19). A Charter was
developed to document the Committee members’ roles, responsibilities, and decision
pathways.
The adjudication committee was composed of three members: one Chairperson and
two additional physicians, all with expertise in infectious diseases, pulmonary
disease, critical care, or virology. The Chairperson was selected based on their
expertise in pulmonary critical care.
The Chairperson was responsible for overseeing the operations of the adjudication
committee, overseeing meetings, and supervising the flow of data from the
committee back to the study sponsor. Committee members were responsible for
independently adjudicating deaths occurring in the study according to the clinical trial
protocol and adjudication committee Charter.
Adjudication committee members were not study investigators or members of other
committees associated with the protocol or study program (e.g., the Data Safety
Monitoring Board Committee). Adjudication committee members did not have any
serious conflicts of interest that would bias their review of trial data (e.g., financial
interests that could be substantially affected by the outcome of the study) and were
asked to disclose any conflicts of interest prior to selection. Any conflicts of interest
that arose during the study were disclosed at the time of identification. The study
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sponsor and Chairperson were notified of any conflicts. Committee members were
blinded to participant treatment assignment throughout the adjudication process.
In the event of a study death, two adjudication committee members independently
reviewed the complete clinical event packet and rendered their adjudication (Part 1).
If the results were concordant, the Chairperson reviewed the event dossier and Part
1 adjudication forms to determine the relatedness of the death to severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. If this adjudication was
concordant, the Chairperson completed the final outcome form, and the adjudication
was deemed complete.
If the independent adjudication results from the two adjudication committee members
were discordant, the complete clinical event packet was sent to the Chairperson to
independently review (Part 2). If this third adjudication was not concordant with either
of the first two reviewers, a moderated committee meeting was convened to discuss
the relatedness of the death to SARS-CoV-2. Discussion continued until a
consensus was reached or members agreed that they were unable to reach final
consensus. Requests to the study site to provide additional information could be
used to resolve any discordance. If consensus could not be reached through
discussion, the Chairperson rendered the final decision.
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Study randomization and blinding
Randomization was stratified within each of two cohorts, both capped not to exceed
80% of the total participants randomized. Cohort 1 consisted of adults ≥60 years of
age with randomization stratified by long-term care facility residence. Cohort 2
consisted of adults <60 years of age with randomization stratified by risk of exposure
to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
Participants were centrally assigned to AZD7442 or saline placebo using interactive
response technology (IRT). An external third-party vendor (Signant Health; Blue Bell,
PA, USA) generated the randomization list using SAS software (SAS Institute, Cary,
NC, USA) using random number generation with stratified randomization and
random block sizes within each stratum. Before the study was initiated, user guides,
log-in information, and directions for the IRT were provided to each study site.
AZD7442 was supplied in individual kits of a single-use vial of each of the
component monoclonal antibodies (mAbs). Study sites sourced their own saline
placebo. An unblinded pharmacist or equivalent at each site prepared and masked
dosages and provided syringes to blinded study site staff for administration.
Al participants and investigators involved in the dosing, clinical evaluation, and
monitoring of the participants were blinded to which randomized drug was received.
Blinding could be broken at the investigator’s discretion if required, or alternatively at
the request of a participant to unblind to determine eligibility for Covid-19
vaccination.
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Participant follow-up
For all efficacy endpoints, participants were contacted weekly—by telephone, email,
or text message—from baseline with reminders to monitor for Covid-19 symptoms to
determine infection incidence. During these weekly contacts, Covid-19 symptoms
from the past 7 days were discussed, and illness visits were initiated within 3 days if
qualifying symptoms were reported (Table S1). Participants who experienced at least
one Covid-19 qualifying symptom were instructed to contact the study site, and
participants who presented with a qualifying symptom after day 1 were tested for
SARS-CoV-2 at an additional il ness visit. Nasopharyngeal swab samples were
collected for central SARS-CoV-2 reverse-transcription polymerase chain reaction
(RT-PCR) testing; if positive, the participant was instructed to continue il ness visits
up to day 28; if negative, the participant was instructed to stop illness visits and
continue with the main scheduled assessments.
Key protocol amendments
The protocol was amended on several occasions as the pandemic evolved. Version
9 (July 26, 2021) was the final version used for analysis. In the original protocol
(October 7, 2020), primary analysis of the primary endpoint was scheduled to be
conducted when the last dosed participant had been followed through day 183. This
was updated to the current analysis from Version 7 (April 7, 2021) onwards. Another
amendment (Version 4, December 21, 2020, onwards) allowed participants to
unblind if they wished to consider Covid-19 vaccination. A full list of protocol
amendments can be found in Version 9 of the protocol which is available online at
NEJM.org.
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Full inclusion criteria
Participants were eligible for trial inclusion only if all the below criteria were met:
1.
Aged ≥18 years at the time of signing the informed consent.
2.
Candidate for benefit from passive immunization with antibodies, defined as:
(a)
Increased risk for inadequate response to immunization (predicted poor
responder to vaccines):
- Elderly, i.e., ≥60 years old
- Obese, i.e., body mass index ≥30 kg/m2
- Congestive heart failure
- Chronic obstructive pulmonary disease
- Chronic kidney disease, i.e., glomerular filtration rate
<30 mL/min/1.73 m2
- Chronic liver disease
- Immunocompromised state from solid organ transplant, blood or
bone marrow transplant, immune deficiencies, HIV, use of
corticosteroids, or use of other immunosuppressive medicines
- Intolerant of vaccine (defined as previous history of severe adverse
event [AE] or serious AE [SAE] after receiving any approved
vaccine)
(b)
Increased risk for SARS-CoV-2 infection, defined as individuals whose
locations or circumstances put them at appreciable risk of exposure to
SARS-CoV-2 and Covid-19, based on available risk assessment at
time of enrollment. Examples include:
- Health care workers, including staff of long-term care facilities
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- Workers in industrial settings shown to be at high risk for SARS-
CoV-2 transmission
- Military personnel residing or working in high-density settings
- Students living in dormitory settings
- Others living in settings of similar close or high-density proximity
3.
Medically stable, defined as disease not requiring significant change in
therapy or hospitalization for worsening disease during the 1 month prior to
enrollment, with no acute change in condition at the time of study enrollment
as judged by the investigator.
4.
A negative result from point-of-care SARS-CoV-2 serology testing at
screening, using the FaStep Assure tech Point-of-Care (POC)/Fingerstick
Fastep® Covid-19 IgG/IgM Rapid Test Device (Assure Tech, Hangzhou,
China).
5.
Using a predefined method of contraception:
(a)
Male participants must use a condom from day 1 and agree to continue
through 365 days following dosing.
(b)
Female participants must either:
- Not be of childbearing potential (either permanently sterilized
[hysterectomy, bilateral oophorectomy, or bilateral salpingectomy]
or postmenopausal), or
- If of childbearing potential, agree to use one highly effective form of
contraception (one that can achieve a failure rate of <1% per year
when used consistently and correctly) from day 1 and agree to
continue through 365 days following dosing, and
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- If of childbearing potential, have a negative urine pregnancy test
result at visit 1 and throughout the study.
6.
Able to understand and comply with study requirements and procedures (if
applicable, with assistance by caregiver, surrogate, or legally authorized
representative or equivalent representative as locally defined) based on the
assessment of the investigator.
7.
Have signed informed consent, if able (participants who were considered by
the investigator to be clinically unable to consent at screening and who were
entered into the study by the consent of a legally acceptable representative
must have shown evidence of assent, as applicable in accordance with local
regulations).
Full exclusion criteria
Participants were excluded from the study if any of the below criteria applied:
1.
Significant infection or other acute il ness, including fever >100°F (>37.8°C)
on the day prior to or day of randomization.
2.
History of laboratory-confirmed SARS-CoV-2 infection or any positive SARS-
CoV-2 result based on available data at screening.
3.
History of infection with severe acute respiratory syndrome or Middle East
respiratory syndrome.
4.
Known history of allergy or reaction to any component of the study drug
formulation.
5.
Previous hypersensitivity, infusion-related reaction, or severe adverse reaction
following administration of a mAb.
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6.
Any prior receipt of investigational or licensed vaccine or other mAb/biologic
indicated for the prevention of SARS-CoV-2 or Covid-19, or expected receipt
during the period of study follow-up.
7.
Clinical y significant bleeding disorder (e.g., factor deficiency, coagulopathy, or
platelet disorder), or prior history of significant bleeding or bruising following
intramuscular (IM) injections or venipuncture.
8.
Any other significant disease, disorder, or finding that may significantly
increase the risk to the participant because of participation in the study, affect
the ability of the participant to participate in the study, or impair interpretation
of the study data.
9.
Receipt of any investigational medicinal product in the preceding 90 days or
expected receipt of investigational medicinal product during the period of
study follow-up, or concurrent participation in another interventional study.
10. For women only, currently pregnant (confirmed with positive pregnancy test)
or breastfeeding.
11. Blood drawn >450 mL (1 unit) for any reason within 30 days prior to
randomization.
12. Employees of the sponsor involved in planning, executing, supervising, or
reviewing the AZD7442 program, clinical study site staff, or any other
individuals involved with the conduct of the study, or immediate family
members of such individuals.
13. In nations, states, or other jurisdictions that for legal or ethical reasons bar the
enrollment of participants who lack capacity to provide their own informed
consent, such participants were excluded.
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Exclusion criterion 8 was written to allow investigators to use their own judgment
about whether a participant should be included in the study for any condition that
was not specified in the inclusion and exclusion criteria. Examples might be very sick
individuals (e.g., end of life) who would not benefit from participating in the study, or
participants with such psychological issues that, based on investigator judgment,
would mean they would not be able to comply with study requirements.
Analyses and endpoints
The two key supportive analyses of the primary efficacy endpoint were the first case
of SARS-CoV-2 RT-PCR–positive symptomatic il ness (regardless of unblinding or
receipt of a Covid-19 vaccine), and the first case of SARS-CoV-2 RT-PCR–positive
symptomatic il ness including all deaths.
The key secondary efficacy endpoint was the incidence of participants who had a
post-dose response (negative at baseline to positive at any time post baseline) for
SARS-CoV-2 nucleocapsid antibodies.
There were two secondary efficacy endpoints: incidence of SARS-CoV-2 RT-PCR–
positive severe or critical il ness occurring post dose, and incidence of Covid-19–
related emergency room (ER) visits occurring post dose. Severe Covid-19 was
characterized by a minimum of either pneumonia (fever, cough, tachypnea or
dyspnea, and lung infiltrates) or hypoxemia (oxygen saturation [SpO2] <90% in room
air or severe respiratory distress) and a World Health Organization (WHO) Clinical
Progression Scale1 score of 5 or higher (Table S7) prior to unblinding or vaccination.
The secondary pharmacokinetic endpoint was measurement of serum AZD7442
concentrations. Exploratory endpoints were post-dose geometric mean titers of
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SARS-CoV-2 neutralizing antibodies after a single IM dose of AZD7442, and
genotypic analysis of SARS-CoV-2 variants.
A post hoc analysis of primary efficacy endpoint events (first post-dose occurrence of
SARS-CoV-2 RT-PCR–positive symptomatic il ness) was performed in which the
efficacy observed within the first 3 months (0–3-month time period) was compared
with the efficacy observed within the 3–6-month time period.
A post hoc analysis of the number of participants hospitalized due to Covid-19,
regardless of prior vaccination or unblinding, was performed for the primary and
median 6-month follow-up analyses.
Serum sampling and bioanalytical analyses
Serum samples for anti-nucleocapsid antibody, neutralizing antibody, and AZD7442
pharmacokinetic assessments were collected predose and at days 8, 29, 58, 92, and
183. Samples wil also be collected at days 366 (scheduled) and 457 (optional) for
neutralizing antibody and AZD7442 pharmacokinetic assessments. For participants
who developed Covid-19, serum samples for neutralizing antibody and AZD7442
pharmacokinetic assessments were collected at illness visit days 1, 14, 21, and 28.
SARS-CoV-2 nucleocapsid antibodies were measured for all participants using the
Elecsys® anti-SARS-CoV-2 nucleocapsid serology test (Roche Diagnostics, Vienna,
Austria), an electroluminescence immunoassay-based modality that allows for the
qualitative detection of IgG reactive to the SARS-CoV-2 nucleoprotein in human
serum. Anti–SARS-CoV-2 specific antibodies were captured to streptavidin-coated
solid phase microparticles with biotinylated SARS-CoV-2–specific antigen and
qualitative results were determined via a two-point calibration and a cutoff formula.
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The assay was validated and performed by LabCorp Drug Development
(Indianapolis, IN, USA).
Neutralizing antibody titers against SARS-CoV-2 were assessed in serum samples
collected in a validated live neutralization assay (plaque reduction neutralization test
[PRNT]80) by Viroclinics Biosciences (Rotterdam, Netherlands), after administration
of AZD7442, as described previously.2
Serum samples were analyzed for tixagevimab and cilgavimab concentrations by
PPD Laboratories (Richmond, VA, USA) using a validated ultra-high performance
liquid chromatography method coupled with tandem mass spectrometry with positive
electrospray. As previously described, 20 -μL samples were diluted and extracted
with streptavidin magnetic beads coated with biotinylated SARS-CoV-2 receptor-
binding domain. Isolated analytes were digested (denaturation, reduction, alkylation,
and trypsin digestion) and the extract fortified with stable isotope-labeled peptide
internal standard working solution. Unknown samples were quantified using a linear,
1/concentration² weighted, least-squares regression algorithm.2
Sequencing of SARS-CoV-2 samples
The full-length viral spike gene (AA 1-1274) was amplified from SARS-CoV-2 RT-
PCR–positive nasopharyngeal swabs collected at il ness visits using a standard,
single-tube population-based RT-PCR method and sequenced in a validated
GenoSure SARS-CoV-2 spike next-generation sequencing assay at Monogram
Biosciences (South San Francisco, CA, USA). Sequence files were analyzed to
determine frequency of amino acid polymorphisms (consensus; reported at ≥25%
frequency). For participants who developed SARS-CoV-2 RT-PCR–positive
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symptomatic illness, SARS-CoV-2 spike protein sequences were available on illness
visit days 1 or 14.
The Pango dynamic nomenclature is a system for identifying and naming distinct
SARS-CoV-2 lineages of epidemiological relevance, based on SARS-CoV-2 whole
genome sequences.3,4 A spike-only version of the Pangolin Covid-19 lineage
assigner (Hedgehog), under development by the academic developers of Pangolin at
the University of Edinburgh and Oxford University
(https://github.com/aineniamh/hedgehog), was used to classify SARS-CoV-2 spike
sequences from the PROVENT study to current Pango lineages (version 1.2.6) or
sets of lineages.5
Statistical analysis
Hypotheses and sample size
The null hypothesis for the primary endpoint was: efficacy (calculated as 1 – relative
risk) of AZD7442 compared to placebo in preventing Covid-19 is equal to 0. The
alternative hypothesis was: efficacy of AZD7442 compared to placebo in preventing
Covid-19 is not equal to 0.
Version 7 of the protocol amended the timing of the primary analysis to occur after
approximately 24 primary endpoint events were observed or 30% of trial participants
elected to become unblinded. The statistical rationale for this protocol amendment
was an observed increase in unblinding rate in the study population, which indicated
that event accrual would slow significantly owing to unblinding and vaccination, and
also that power for one of the key supportive estimands (intent-to-treat analysis
without censoring) would decrease owing to vaccine efficacy. The 30% unblinding
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value was therefore chosen to achieve reporting in a timely manner, providing an
analysis estimating the treatment effect in the randomized target population with
relevant congruency between the primary and supportive estimands. Simulations
were performed using the overall observed rates of unblinding and primary endpoint
events across arms, an assumed AZD7442 efficacy of 80%, and an assumed Covid-
19 vaccine efficacy of 90% against symptomatic Covid-19.6-10
For analysis of the primary efficacy endpoint, a study population of approximately
5150 participants randomized in a 2:1 ratio with a minimum of 18 observed events,
assuming 80% true efficacy and 0.74% observed attack rate in the placebo arm at
the time of the analysis, was estimated to provide approximately 90% power to
demonstrate the lower bound of the two-sided 95% confidence interval (CI) for
efficacy to be >0.
Given the variable follow-up that would be available at the primary analysis, the
attack rates used in sample size determination and power calculations used an
observed attack rate based on expected follow-up rather than an annualized attack
rate. Ten thousand simulations of trials were performed to estimate power, using
Poisson regression model with robust variance, with no participants lost to
follow-up.11
Statistical methods
Demographics and baseline clinical characteristics analyses used the full analysis
set (FAS): all participants who were randomized and received at least one of the two
planned injections, with a full dose being two injections. Participants were classified
according to their randomized study drug regardless of what was actually received.
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The primary and secondary efficacy analyses used the full pre-exposure analysis set
(FPAS): all participants in the FAS who did not have a prior SARS-CoV-2 RT-PCR–
positive confirmed Covid-19 infection. Participants were classified according to their
randomized study drug regardless of what was actually received.
The safety analyses used the safety analysis set (SAS): all participants who were
randomized and received at least one of the two planned injections. Participants
were classified according to study drug received.
The pharmacokinetic analyses used the pharmacokinetic analysis set: all
participants who were randomized and received at least one injection of AZD7442
and from whom blood samples were assumed not to be affected by factors such as
protocol violations, and who had at least one quantifiable serum pharmacokinetic
observation post dose. A dose was two injections (one tixagevimab and one
cilgavimab injection) as per the protocol. Participants receiving placebo were not
included in these analyses.
Statistical tests
Statistical tests were conducted at the two-sided 5% significance level; 95% CIs
were two-sided. The primary analysis used a while-on-treatment estimand strategy,
in which data from participants whose randomized assignment was unblinded or
from participants who received a Covid-19 vaccine were censored at the date of
unblinding or vaccine administration, whichever was earlier. Participants without
events prior to day 183 were censored at the earlier date of study discontinuation or
data cutoff date. Al deaths were independently determined to be related or not
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related to Covid-19 by the external MAC. Deaths that were adjudicated as related to
Covid-19 were included as a primary efficacy endpoint event.
The primary efficacy endpoint was a binary response, whereby a participant’s status
was classified as symptomatic Covid-19 or not prior to day 183.
A Poisson regression model with robust variance was used as the primary efficacy
analysis model to estimate the relative risk of symptomatic infection in the AZD7442
group compared with the placebo group. The model included group (AZD7442
versus placebo) and age at informed consent (≥60 years versus <60 years) as
covariates, with the log of the follow-up time used as an offset. An unstructured
correlation matrix was specified for the model. For participants who met the primary
endpoint before day 183, follow-up was calculated as (date of onset of primary
endpoint) – (date of dosing) + 1. For participants who did not experience a primary
endpoint event before day 183, efficacy follow-up time was considered censored and
calculated as (date of end of study or date of last assessment, whichever is later) –
(date of dosing) + 1. End of study dates occurring after day 183 were censored at
day 183.
Efficacy was calculated as relative risk reduction (RRR) = 100% × (1 – relative risk),
which was the incidence of infection in the AZD7442 group relative to that in the
placebo group, expressed as a percentage.
To support the primary analysis, a Cox proportional hazard model giving the hazard
ratio (HR) was fitted to the data, along with Kaplan-Meier curves for the active and
control groups, showing the cumulative incidence of the first case of SARS-CoV-2
RT-PCR–positive symptomatic il ness occurring post dose and prior to day 183.
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There was no evidence of violation of the proportional hazard assumption following
evaluation of log-log survival curves and through fitting time-dependent covariates.
Two key supportive analyses were prespecified in the study protocol and included in
the multiple testing framework to control the type I error rate. The first key supportive
analysis of the primary efficacy endpoint used a treatment policy estimand strategy,
in which data from participants whose randomized assignment was unblinded or
from participants who received a Covid-19 vaccine were included and analyzed
regardless of their unblinding or vaccination status. The endpoint definition was
expanded to include deaths from any cause post dose of AZD7442 or placebo and
prior to day 183 in the second key supportive analysis.
For missing data, participants who discontinued early from the study or were lost to
follow-up before experiencing a primary endpoint event were censored in the Kaplan
-Meier and Poisson regression analyses. Censoring due to loss to follow up or early
discontinuation was considered to be noninformative. Participants who were
unblinded or vaccinated before experiencing a primary endpoint event were
censored in the Kaplan -Meier and Poisson regression analyses. Censoring arising
from unblinding or vaccination was considered independent censoring (i.e.,
censoring was noninformative within the subgroup of interest). A key supportive
analysis using an intent-to-treat policy in which unblinding or vaccination did not
result in censoring (unblinding or vaccination event ignored) was conducted to
assess the impact of independent censoring (Table 3).
A hierarchical approach was used to control for multiplicity of the primary, key
supportive, and key secondary analyses on the basis of a two-sided alpha level
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of 0.05. The hierarchical approach at the primary analysis was conducted in the
following order:
1. Primary efficacy endpoint analyzed using the while-on-treatment estimand.
2. First key supportive analysis: primary efficacy endpoint analyzed using the
treatment policy estimand.
3. second key supportive analysis: primary endpoint definition expanded to
include death due to any cause (using the while-on-treatment estimand).
4. key secondary efficacy endpoint.
Statistical significance of the primary, key supportive, and key secondary efficacy
analyses was considered achieved if the observed P value was <0.05. No statistical
testing was performed for the safety endpoints.
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Supplementary results
Missing data analysis
Missing data frequency was small and balanced between treatment arms (Table S2).
Participant demographics and baseline clinical characteristics were generally
balanced between censoring subgroups (Table S3). Increased censoring due to
unblinding or vaccination was seen in participants aged ≥60, likely reflecting
prioritization of this age group for Covid-19 vaccination.
Covid-19‒related hospitalizations
At the time of the primary data cut, 0 and 3 (0.2%) participants in the AZD7442 and
placebo groups, respectively, had been hospitalized due to Covid-19, regardless of
prior vaccination or unblinding.
At the time of the median 6-month follow-up data cut, 0 and 7 (0.4%) participants in
the AZD7442 and placebo groups, respectively, had been hospitalized due to Covid-
19, regardless of prior vaccination or unblinding.
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Supplementary figures
Figure S1. Participant flow through trial (CONSORT flow diagram)
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*Screening failed because of inclusion criterion 4: A negative result from point-of-care SARS-CoV-2 serology testing at screening, using the FaStep Assure tech Point-of-Care
(POC)/Fingerstick Fastep® Covid-19 IgG/IgM Rapid Test Device (Assure Tech, Hangzhou, China).
†Includes 40 participants in the AZD7442 group and 17 in the placebo group who discontinued from the study before dosing. Al participants who discontinued at any time after
dosing were included in the SAS. Participants who discontinued after dosing were included in the FPAS if they had a negative SARS-CoV-2 RT-PCR test at baseline.
‡Nineteen participants in the AZD7442 group and 6 in the placebo group had a positive SARS-CoV-2 RT-PCR test at baseline and per study protocol were excluded from the
FPAS.
§One participant was randomized to placebo and incorrectly received AZD7442; per study protocol this participant was assessed in the AZD7442 group for the SAS.
In the PROVENT FPAS study population, 3430 and 1700 participants in the AZD7442 and placebo groups, respectively, did not have a primary endpoint event (SARS-CoV-2
RT-PCR‒positive symptomatic il ness). A breakdown of how participants who did not meet the primary endpoint were censored (not observed to have event; lost to follow
up/early discontinuation; censored due to unblinding; censored due to vaccination) is available in Table S2. A comparison of participant characteristics between censoring
categories is available in Table S3.
AE, adverse event; Covid-19, coronavirus disease 2019; FPAS, full pre-exposure analysis set; RT-PCR, reverse-transcription polymerase chain reaction; SARS-CoV-2, severe
acute respiratory syndrome coronavirus 2; SAS, safety analysis set.
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Figure S2. Pharmacokinetic and anti–SARS-CoV-2 neutralizing antibody
analyses: (A) serum AZD7442 geometric mean concentration ± SD, and (B)
SARS-CoV-2 neutralizing antibody geometric mean titers with 95% CI
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Per protocol, all participants who received AZD7442 and from whom PK blood samples were assumed not to be
affected by factors such as protocol violations and who had at least one quantifiable serum PK observation post
dose were included in the pharmacokinetic analysis set.
(A) Values are GMC ± gSD. Individual serum concentrations with levels <LLOQ were set to 50% of LLOQ
(0.3 μg/mL). Individual serum concentrations that were not reportable (NR) were reported as NR and missing
values were reported as no sample (NS); any values reported as NR or NS were excluded from analysis. Of the
3500 randomized participants who received AZD7442, 1607 (45.9%) did not have evaluable plasma
concentration data at the time of this analysis, 40 (1.1%) were not dosed, and 1 (<0.1%) had an exclusionary
protocol violation at baseline.
(B) Values are GMT with 95% CI. Data were only available for 43 and 6 participants at days 58 and 92,
respectively, and so are not reported here. The dashed line represents the GMT of neutralizing antibody from 28
convalescent plasma samples from patients with Covid-19.2 Of the 3500 randomized participants who received
AZD7442, 2389 (68.3%) did not have evaluable plasma concentration data at the time of this analysis, 40 (1.1%)
were not dosed, and 1 (<0.1%) had an exclusionary protocol violation at baseline.
BL, baseline; CI confidence interval; gSD, geometric standard deviation; GMC, geometric mean concentration;
GMT, geometric mean titer; LLOQ, lower limit of quantification; PK, pharmacokinetics; PRNT, plaque reduction
neutralization test; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
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Supplementary tables
Table S1. Definition of symptomatic Covid-19 (qualifying symptoms)
Participant must present with at least one of the following symptoms:
No minimum duration
Must be present for ≥2 days
Fever
Runny nose
Shortness of breath
Congestion
Difficulty breathing
New loss of smell
New onset confusion (only for participants
New loss of taste
≥60 years old)
Headache
Appetite loss or decreased food intake (only
for participants ≥60 years old)
Sore throat
Increased supplemental oxygen
Body aches
requirement (only for participants ≥60 years Chills
old on baseline supplemental oxygen)
Cough
Diarrhea
Muscle aches
Fatigue
Nausea
Vomiting
Covid-19, coronavirus disease 2019.
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Table S2. Censoring category breakdown for primary endpoint
Category
AZD7442
Placebo
SARS-CoV-2 RT-PCR‒positive symptomatic il ness
(primary endpoint event), n/N (%)
11/3441 (0.3) 31/1731 (1.8)
Did not have primary endpoint event (FPAS:
censored participants), N
3430
1700
Not observed to have event, n (%)
1549 (45.2)
713 (41.9)
Lost to follow up/early discontinuation, n (%)
83 (2.4)
37 (2.2)
Censored due to unblinding, n (%)
1346 (39.2)
688 (40.5)
Censored due to vaccination, n (%)*
452 (13.2)
262 (15.4)
*Some participants were vaccinated without unblinding.
FPAS, ful pre-exposure analysis set; RT-PCR, reverse-transcription polymerase chain reaction; SARS-CoV-2,
severe acute respiratory syndrome coronavirus 2.
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Table S3. Participant demographics and baseline clinical characteristics by outcome category
Censoring reason
Events
First SARS-CoV-2 RT-
Lost to follow
PCR‒positive
Characteristic, Not observed to have up/early
Censored due to
Censored due to
symptomatic illness
n (%)
event
discontinuation
unblinding
vaccination
(censored at
(n=2262)
(missing)
(n=2034)
(n=714)
unblinding or receipt
(n=120)
of Covid-19 vaccine)
(n=42)
AZD7442 Placebo
AZD7442 Placebo
AZD7442 Placebo
AZD7442 Placebo
AZD7442 Placebo
Treatment
group*
1549
713
83
37
1346
688
452
262
11
31
Age group
≥60 years
535 (34.5)
248 (34.8)
26 (31.3)
16 (43.2)
695 (51.6)
360 (52.3)
237 (52.4)
119 (45.4)
3 (27.3)
12 (38.7)
<60 years
1014 (65.5) 465 (65.2)
57 (68.7)
21 (56.8)
651 (48.4)
328 (47.7)
215 (47.6)
143 (54.6)
8 (72.7)
19 (61.3)
Sex
Male
913 (58.9)
413 (57.9)
56 (67.5)
20 (54.1)
639 (47.5)
337 (49.0)
246 (54.4) 148 (56.5)
2 (18.2)
16 (51.6)
Female
636 (41.1)
300 (42.1)
27 (32.5)
17 (45.9)
707 (52.5)
351 (51.0)
206 (45.6) 114 (43.5)
9 (81.8)
15 (48.4)
Ethnicity
Not
Hispanic/Latino 1140 (73.6) 542 (76.0)
61 (73.5)
28 (75.7)
1138 (84.5) 580 (84.3)
373 (82.5)
231 (88.2)
9 (81.8)
25 (80.6)
Hispanic/Latino 335 (21.6)
128 (18.0)
16 (19.3)
8 (21.6)
119 (8.8)
52 (7.6)
59 (13.1)
22 (8.4)
2 (18.2)
5 (16.1)
Not reported/
unknown
74 (4.8)
43 (6.0)
6 (7.2)
1 (2.7)
89 (6.6)
56 (8.1)
20 (4.4)
9 (3.4)
0
1 (3.2)
Race
White
962 (62.1)
405 (56.8)
54 (65.1)
30 (81.1)
1169 (86.8) 597 (86.8)
338 (74.8)
187 (71.4)
10 (90.9)
24 (77.4)
Black/African
American
417 (26.9)
212 (29.7)
20 (24.1)
4 (10.8)
79 (5.9)
34 (4.9)
77 (17.0)
48 (18.3)
0
4 (12.9)
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Other†
170 (11.0)
96 (13.5)
9 (10.8)
3 (8.1)
98 (7.3)
57 (8.3)
37 (8.2)
27 (10.3)
1 (9.1)
3 (9.7)
SARS-CoV-2 status at baseline
Positive
0
0
0
0
0
0
0
0
0
0
Negative
1485 (95.9) 683 (95.8)
80 (96.4)
36 (97.3)
1312 (97.5) 669 (97.2)
447 (98.9)
255 (97.3)
11 (100)
30 (96.8)
Missing
64 (4.1)
30 (4.2)
3 (3.6)
1 (2.7)
34 (2.5)
19 (2.8)
5 (1.1)
7 (2.7)
0
1 (3.2)
High risk for severe Covid-19 at baseline
Any
1187 (76.6) 567 (79.5)
65 (78.3)
27 (73.0)
1044 (77.6) 534 (77.6)
349 (77.2)
210 (80.2)
11 (100)
21 (67.7)
Obesity (BMI
≥30 kg/m2)
617 (39.8)
278 (39.0)
33 (39.8)
16 (43.2)
598 (44.4)
294 (42.7)
195 (43.1)
106 (40.5)
7 (63.6)
14 (45.2)
Hypertension
538 (34.7)
275 (38.6)
32 (38.6)
9 (24.3)
469 (34.8)
236 (34.3)
184 (40.7)
104 (39.7)
4 (36.4)
10 (32.3)
Smoking
430 (27.8)
204 (28.6)
29 (34.9)
9 (24.3)
179 (13.3)
99 (14.4)
76 (16.8)
53 (20.2)
2 (18.2)
5 (16.1)
Diabetes
212 (13.7)
106 (14.9)
12 (14.5)
5 (13.5)
178 (13.2)
93 (13.5)
83 (18.4)
35 (13.4)
1 (9.1)
3 (9.7)
Asthma
130 (8.4)
66 (9.3)
8 (9.6)
2 (5.4)
184 (13.7)
98 (14.2)
53 (11.7)
29 (11.1)
2 (18.2)
3 (9.7)
* Participants were randomized 2:1 to AZD7442 and placebo
†Includes participants identifying as Asian, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander, and unknown/not reported/multiple/missing data.
BMI, body mass index; Covid-19, coronavirus disease 2019; RT-PCR, reverse-transcription polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
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Table S4. Representativeness of study participants
Category
Example
Disease, problem, or
SARS-CoV-2 infection; Covid-19
condition under
investigation
Special considerations
related to:
Sex and gender
Higher Covid-19 case fatality rates have been reported for male
sex compared with female sex in some countries, which may be
impacted by additional variables such as infection/exposure risk
and comorbidities.
Age
Older age is associated with more severe Covid-19 outcomes
and death.
Race or ethnic
Black, Latino, and other ethnic/racial groups are
group
disproportionately affected by Covid-19 in countries including
the United States and United Kingdom.
Geography
Covid-19 prevalence has been variable throughout the world
depending on regional/country social distancing measures,
travel restrictions, and vaccination rates.
Other considerations
Medical comorbidities, including diabetes, cardiovascular
disease, and obesity, are associated with more severe Covid-19
outcomes.
Risk of SARS-CoV-2 exposure and infection can be affected by
location or circumstance, such as health care workers, military
personnel in high -density settings, and workers in industrial
settings.
Overall
The participants in the present study demonstrated a high
representativeness of
proportion of adults aged ≥60 years (43% overall) and
this trial
individuals with comorbidities placing them at high risk of severe
Covid-19 (78%). Overall, 53% of participants in the study were
considered at increased risk of exposure to SARS-CoV-2.
The proportion of Hispanic/Latino participants (15%) was
representative of the US population.
Overall, 17% of participants were Black or African American,
representing a slightly higher proportion than Black populations
in the US and UK. The proportion of Asian participants in the
study overal (3%) appears slightly lower than the proportion of
Asian populations within the US and UK, and the proportion of
American Indian/Alaska Native participants (0.6%) was also
lower compared with the proportion among the US population.
Potential study participants were directed to the study website to complete a set of prescreen questions to
determine their pre-eligibility.
Potential participants were asked to identify their age category as “Yes, I am between 18 and 59 years of age
(inclusive),” “Yes, I am 60 years of age or older,” or “No”.
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Potential participants were asked prescreen questions to determine whether they had an increased risk of getting
Covid-19 (due to location, employment, or personal circumstances) OR were less likely than most adults to
benefit from a vaccine (e.g., due to older age, obesity, or immunosuppression from a health condition or
medication).
Potential participants were asked to choose the race or ethnicity that describes them (choose all that apply):
Hispanic or Latino; American Indian or Alaskan Native; Asian; Black or African American; Native Hawai an or
other Pacific Islander; White; Other; Prefer not to say.
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Table S5. Number of participants with SAEs by system organ class, primary
data cut (SAS)
Participants with at least one SAE, n (%)
AZD7442 Placebo
Total
(n=3461) (n=1736) (N=5197)
Any SAE
50 (1.4)
23 (1.3)
73 (1.4)
Infections and infestations*
8 (0.2)
5 (0.3)
13 (0.3)
Injury, poisoning, and procedural complications†
4 (0.1)
8 (0.5)
12 (0.2)
Nervous system disorders‡
9 (0.3)
0
9 (0.2)
Cardiac disorders§
6 (0.2)
1 (0.1)
7 (0.1)
Gastrointestinal disorders║
6 (0.2)
1 (0.1)
7 (0.1)
Renal and urinary disorders
6 (0.2)
1 (0.1)
7 (0.1)
Musculoskeletal and connective tissue disorders
4 (0.1)
1 (0.1)
5 (0.1)
Hepatobiliary disorders
3 (0.1)
1 (0.1)
4 (0.1)
Metabolism and nutrition disorders
3 (0.1)
0
3 (0.1)
Neoplasms benign, malignant, and unspecified
(including cysts and polyps)
0
3 (0.2)
3 (0.1)
Respiratory, thoracic, and mediastinal disorders
1 (<0.1)
2 (0.1)
3 (0.1)
Vascular disorders
2 (0.1)
1 (0.1)
3 (0.1)
Blood and lymphatic system disorders
2 (0.1)
0
2 (<0.1)
Clinical laboratory tests
1 (<0.1)
1 (0.1)
2 (<0.1)
Pregnancy, puerperium, and perinatal conditions
1 (<0.1)
0
1 (<0.1)
Psychiatric disorders
1 (<0.1)
0
1 (<0.1)
Reproductive system and breast disorders
1 (<0.1)
0
1 (<0.1)
*Includes appendicitis (perforated), cellulitis, Covid-19, Covid-19 pneumonia, cystitis, diverticulitis, gastroenteritis,
osteomyelitis, peritonitis, postoperative wound infection, sepsis, and staphylococcal infection.
†Includes concussion, femur fracture, fibula fracture, gunshot wound, incisional hernia (obstructive), joint injury,
multiple injuries, overdose, procedural pain, subdural hemorrhage, tendon rupture, and tibia fracture.
‡Includes Bell’s palsy, cerebrovascular accident, complex regional pain syndrome, metabolic encephalopathy,
migraine, partial seizures, syncope, and transient ischemic attack.
§Includes acute left ventricular failure, acute myocardial infarction, myocardial infarction, and paroxysmal
atrioventricular block.
║Includes abdominal hernia, abdominal pain, acute pancreatitis, chronic pancreatitis, gastrointestinal ulcer
hemorrhage, irritable bowel syndrome, and mesenteric artery thrombosis.
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SAEs were coded using the Medical Dictionary for Regulatory Activities, version 24.0
Covid-19, coronavirus disease 2019; SAE, serious adverse event; SAS, safety analysis set.
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Table S6. Safety data, median 6-month data cut (SAS)
Participants with at least one event,
AZD7442
Placebo
Total
n (%)*
(n=3461)†
(n=1736)†
(N=5197)
AEs
1579 (45.6)
790 (45.5)
2369 (45.6)
Mild AEs
835 (24.1)
419 (24.1)
1254 (24.1)
Moderate AEs
596 (17.2)
295 (17.0)
891 (17.1)
Severe AEs
128 (3.7)
65 (3.7)
193 (3.7)
SAEs
130 (3.8)
58 (3.3)
188 (3.6)
Intervention-related‡ SAEs
1 (<0.1)
0
1 (<0.1)
AEs leading to study discontinuation
2 (0.1)
1 (0.1)
3 (0.1)
Medically attended AEs
641 (18.5)
280 (16.1)
921 (17.7)
AEs of special interest
92 (2.7)
37 (2.1)
129 (2.5)
Injection site reaction
82 (2.4)
36 (2.1)
118 (2.3)
Anaphylaxis
1 (<0.1)
0
1 (<0.1)
Immune complex disease§
0
0
0
Other
9 (0.3)
2 (0.1)
11 (0.2)
Intervention-related‡ AEs of special
87 (2.5)
36 (2.1)
123 (2.4)
interest
All AEs with outcome of death║
9 (0.3)
7 (0.4)
16 (0.3)
Illicit drug overdose
2 (0.1)
1 (0.1)
3 (0.1)
Narcotic toxicity¶
0
1 (0.1)
1 (<0.1)
Covid-19**
0
1 (0.1)
1 (<0.1)
Covid-19 ARDS**
0
1 (0.1)
1 (<0.1)
Septic shock
1 (<0.1)
0
1 (<0.1)
Arrhythmia
1 (<0.1)
0
1 (<0.1)
Cardio-respiratory arrest
1 (<0.1)
0
1 (<0.1)
Congestive cardiac failure
1 (<0.1)
0
1 (<0.1)
Myocardial infarction
1 (<0.1)
0
1 (<0.1)
End-stage renal disease
1 (<0.1)
0
1 (<0.1)
Renal failure
1 (<0.1)
0
1 (<0.1)
Hepatic cirrhosis
0
1 (0.1)
1 (<0.1)
Malignant neoplasm (unknown primary
0
1 (0.1)
1 (<0.1)
site)
Dementia (Alzheimer’s type)
0
1 (0.1)
1 (<0.1)
*Participants may have had more than one event.
†One participant was randomized to placebo and incorrectly received AZD7442; per study protocol this
participant was assessed in the AZD7442 group for the SAS.
‡Events were determined to be intervention-related by investigators based on their judgment.
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§Immune complex disease was removed as an AEs of special interest following adjudication.
║Al deaths were determined by the investigator to not be related to the study drug received.
¶Participant died as a result of accidental exposure to two substances controlled under Schedule I of the 1961
United Nations Single Convention on Narcotic Drugs.12
**Cases were adjudicated to be Covid-19 related by the independent and external Morbidity Adjudication
Committee.
AEs were coded using the Medical Dictionary for Regulatory Activities, version 24.0.
AE, adverse event; ARDS, acute respiratory distress syndrome; Covid-19, coronavirus disease 2019; SAE,
serious adverse event; SAS, safety analysis set.
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Table S7. Number of participants with SAEs by system organ class, median
6-month data cut (SAS)
Participants with at least one SAE, n (%)
AZD7442 Placebo
Total
(n=3461) (n=1736) (N=5197)
Any SAE
130 (3.8) 58 (3.3) 188 (3.6)
Infections and infestations*
31 (0.9)
15 (0.9)
46 (0.9)
Cardiac disorders†
23 (0.7)
5 (0.3)
28 (0.5)
Nervous system disorders‡
18 (0.5)
5 (0.3)
23 (0.4)
Injury, poisoning, and procedural complications§
11 (0.3)
12 (0.7)
23 (0.4)
Gastrointestinal disorders║
12 (0.3)
6 (0.3)
18 (0.3)
Hepatobiliary disorders
8 (0.2)
5 (0.3)
13 (0.3)
Neoplasms benign, malignant, and unspecified
(including cysts and polyps)
5 (0.1)
7 (0.4)
12 (0.2)
Renal and urinary disorders
8 (0.2)
1 (0.1)
9 (0.2)
Respiratory, thoracic, and mediastinal disorders
5 (0.1)
4 (0.2)
9 (0.2)
Psychiatric disorders
5 (0.1)
3 (0.2)
8 (0.2)
Vascular disorders
4 (0.1)
4 (0.2)
8 (0.2)
Metabolism and nutrition disorders
6 (0.2)
0
6 (0.1)
Musculoskeletal and connective tissue disorders
5 (0.1)
1 (0.1)
6 (0.1)
General disorders and administration site conditions
2 (0.1)
3 (0.2)
5 (0.1)
Clinical laboratory tests
3 (0.1)
1 (0.1)
4 (0.1)
Blood and lymphatic system disorders
2 (0.1)
0
2 (<0.1)
Reproductive system and breast disorders
2 (0.1)
0
2 (<0.1)
Pregnancy, puerperium, and perinatal conditions
1 (<0.1)
0
1 (<0.1)
Skin and subcutaneous tissue disorders
1 (<0.1)
0
1 (<0.1)
Ear and labyrinth disorders
0
1 (0.1)
1 (<0.1)
Eye disorders
0
1 (0.1)
1 (<0.1)
*Includes abdominal abscess, abscess limb, appendicitis, arteriovenous graft site infection, cellulitis, Covid-19,
Covid-19 pneumonia, cystitis, device related infection, diverticulitis, enterococcal bacteremia, gastroenteritis,
influenza, localized infection, lower respiratory tract infection, lung abscess, osteomyelitis, peritonitis, pneumonia,
postoperative wound infection, sepsis, septic shock, sialadenitis, soft tissue infection, staphylococcal infection,
urinary tract infection, and urosepsis.
†Includes acute left ventricular failure, angina pectoris, arrhythmia, arteriosclerosis coronary artery, atrial
fibril ation, cardiac failure, cardiomegaly, cardiomyopathy, cardio-respiratory arrest, congestive cardiac failure,
coronary artery disease, myocardial infarction, and paroxysmal atrioventricular block.
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‡Includes Bell's palsy, carotid artery stenosis, cerebral infarction, cerebrovascular accident, complex regional
pain syndrome, dementia Alzheimer's type, dizziness, epilepsy, hepatic encephalopathy, lacunar infarction, loss
of consciousness, metabolic encephalopathy, migraine, partial seizures, presyncope, ruptured cerebral
aneurysm, seizure, syncope, and transient ischemic attack.
§Includes ankle fracture, concussion, fall, femur fracture, fibula fracture, gunshot wound, incisional hernia, joint
injury, lower limb fracture, multiple injuries, overdose, peritoneal dialysis complication, procedural pain, road
traffic accident, skin laceration, subdural hemorrhage, tendon rupture, tibia fracture, toxicity to various agents,
and wound.
║Includes abdominal hernia, abdominal pain, acute pancreatitis, diarrhea, discolored feces, esophageal
hemorrhage, gastric ulcer, gastritis, gastrointestinal hemorrhage, gastrointestinal ulcer hemorrhage, hemorrhoids,
irritable bowel syndrome, mesenteric artery thrombosis, pancreatitis, peritoneal cyst, small intestinal obstruction,
and vomiting.
SAEs were coded using the Medical Dictionary for Regulatory Activities, version 24.0.
Covid-19, coronavirus disease 2019; SAE, serious adverse event; SAS, safety analysis set.
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Table S8. Key secondary efficacy endpoint
Endpoint
Primary analysis
Median 6-month follow-up*
AZD7442
Placebo
AZD7442
Placebo
Key secondary: Post-dose SARS-CoV-2 nucleocapsid antibody–positive (censored at
unblinding or receipt of Covid-19 vaccine)†
N
3123
1564
3121
1564
n (%)
21 (0.7)
21 (1.3)
38 (1.2)
42 (2.7)
RRR (95% CI)
51.1% (10.6‒73.2)
57.7% (34.7‒72.7)
P value
0.020
—
*Analysis not prespecified in protocol; P values not computed.
†Defined as seronegative at baseline and seropositive at any time post baseline. Antibody testing was conducted
at prespecified study days and was not dependent on participants reporting symptoms of Covid-19.
Estimates were based on a Poisson regression with robust variance, with the model including group (AZD7442
versus placebo) and age at informed consent (≥60 years versus <60 years), with the log of the follow-up time as
an offset.
Estimated RRR >0 provides evidence in favor of AZD7442 with P<0.05 indicating statistical significance.
Percentages were based on the number of participants in the analysis by group (N).
CI, confidence interval; Covid-19, coronavirus disease 2019; RRR, relative risk reduction; RT-PCR, reverse-
transcription polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
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Table S9. Definition of SARS-CoV-2 RT-PCR‒positive severe or critical il ness
Either pneumonia (fever, cough, tachypnea or dyspnea, and lung infiltrates), or hypoxemia
(SpO2 <90% or severe respiratory distress), plus a WHO Clinical Progression Scale [below]
score of ≥5 prior to unblinding or vaccination
WHO Clinical Progression Scale
Patient state
Descriptor
Score
Uninfected
Uninfected; no viral RNA detected
0
Asymptomatic; viral RNA detected
1
Ambulatory:
mild disease
Symptomatic; independent
2
Symptomatic; assistance needed
3
Hospitalized:
Hospitalized; no oxygen therapy*
4
moderate
disease
Hospitalized; oxygen by mask or nasal prongs
5
Hospitalized; oxygen by NIV or high flow
6
Intubation and mechanical ventilation; pO2/FiO2 ≥150 or
Hospitalized:
SpO2/FiO2 ≥200
7
severe disease Mechanical ventilation; pO2/FiO2 <150 (SpO2/FiO2 <200) or
vasopressors
8
Mechanical ventilation; pO2/FiO2 <150 and vasopressors,
dialysis, or ECMO
9
Dead
Death
10
*If hospitalized for isolation only, status recorded as for ambulatory patient.
The WHO Clinical Progression Scale provides a measure of il ness severity across a range from 0 (not infected)
to 10 (dead).1
ECMO, extracorporeal membrane oxygenation; FiO2, fraction of inspired oxygen; NIV, noninvasive ventilation;
pO2, partial pressure of oxygen; SpO2, oxygen saturation; WHO, World Health Organization.
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Table S10. Post hoc analysis of primary efficacy endpoint events (first SARS-
CoV-2 RT-PCR‒positive symptomatic il ness, censored at unblinding or receipt
of Covid-19 vaccine)
Time period
0–3 months
3–6 months
AZD7442
Placebo
AZD7442
Placebo
N
3441
1731
2003
960
n (%)
8 (0.2)
19 (1.1)
3 (0.1)
12 (1.2)
RRR (95% CI)
79% (52‒91)
88% (58‒97)
Estimated RRR >0 provides evidence in favor of AZD7442. The analysis was not prespecified in the study
protocol, so P values were not computed.
Estimates were based on a Poisson regression with robust variance, with the model including group (AZD7442
versus placebo) and age at informed consent (≥60 years versus <60 years), with the log of the follow-up time as
an offset.
Percentages were based on the number of participants in the analysis by group (N).
CI, confidence interval; Covid-19, coronavirus disease 2019; RRR, relative risk reduction; RT-PCR, reverse-
transcription polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
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Table S11. Summary of detected SARS-CoV-2 spike-based lineages, median
6-month data cut
Participants with data, n (%)
SARS-CoV-2 spike-based lineage*
AZD7442
Placebo
Total
(n=11)
(n=31)
(n=42)
B.1.1.7_1 (Alpha†)
0
5 (11.9)
5 (11.9)
B.1.351 (Beta†)
1 (2.4)
0
1 (2.4)
B.1.617.2‡ (Delta§)
0
5 (11.9)
5 (11.9)
A_1
1 (2.4)
0
1 (2.4)
A_22
1 (2.4)
2 (4.8)
3 (7.1)
AY.3.1
1 (2.4)
0
1 (2.4)
B.1.1.315_1
1 (2.4)
0
1 (2.4)
B.1.429║
2 (4.8)
0
2 (4.8)
B.1.526¶
0
1 (2.4)
1 (2.4)
RNA insufficient for sequencing
4 (9.5)
18 (42.8)
22 (52.4)
*Lineage nomenclature from WHO. The Omicron variant (currently circulating VoC), Gamma variant (previously
circulating VoC), and the Zeta, Eta, Theta, Kappa, Lambda, and Mu variants (previously circulating VoIs) were
not identified in the PROVENT study population.13
†The Alpha and Beta variants were designated as currently circulating VoCs during the PROVENT study and
were redesignated as previously circulating VoCs as of March 9, 2022.
‡Includes subvariants B.1.617.2_1, _2, _3, and _4.
§The Delta variant was designated as a current circulating VoC on May 11, 2021.
║Former VoI Epsilon; designated as previously circulating VOI as of July 6, 2021.
¶Former VoI Iota; designated as previously circulating VOI as of September 20, 2021.
Al dates correct as of April 5, 2022.
QNS, quantity not sufficient; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; VoC, Variant of
Concern; VoI, Variant of Interest; VUM; Variant Under Monitoring; WHO, World Health Organization.
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