Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally and poses a major challenge to healthcare systems worldwide. A high incidence of hospitalisation and death due to COVID-19 has been reported among older patients and those with certain coexisting conditions, such as obesity, diabetes mellitus, cardiovascular disease, chronic obstructive pulmonary disease, and chronic kidney disease (Petrilli et al., 2020; Huang et al., 2020). The implementation of mass vaccination campaigns has markedly reduced the healthcare burden related to COVID-19. Nevertheless, SARS-CoV-2 vaccination rates differ considerably across countries, and growing evidence suggests a reduced efficacy of vaccines against new viral variants of concern (VOC) (Cao et al., 2022; Planas et al., 2022; Dejnirattisai et al., 2022; Andrews et al., 2022).

Therapeutic agents directed against SARS-CoV-2 have been developed to prevent the COVID-19 progression, especially addressing high-risk groups of patients. Neutralising monoclonal antibodies (mAbs) target the spike protein of SARS-CoV-2 that mediates viral entry into host cells (Benton et al., 2020). Based on the results of randomised placebo-controlled trials showing the efficacy in preventing COVID-19 progression, drug regulatory authorities, such as the US Food and Drug Administration (FDA), the European Medicines Agency, and the Italian Medicines Agency (AIFA), had granted the emergency use authorisation status for bamlanivimab 700 mg combined with etesevimab 1400 mg, casirivimab 600 mg combined with imdevimab 600 mg, and sotrovimab 500 mg to treat early COVID-19 in patients at high risk of progression (Dougan et al., 2021; Weinreich et al., 2021; Gupta et al., 2021).

To date, two randomised trials have compared the clinical outcomes of these mAbs in preventing severe COVID-19, showing similar effectiveness of bamlanivimab/etesevimab vs casirivimab/imdevimab in patients infected with the alpha VOC (McCreary et al., 2022) and casirivimab/imdevimab vs sotrovimab in patients infected with the Delta VOC, respectively (Huang et al., 2022).

This paper reports the results of the MANTICO trial, a non-inferiority randomised controlled trial comparing the clinical efficacy of routinely-used mAbs in a real-life setting of outpatients aged 50 or older with early mild-to-moderate COVID-19. The patient enrolment started in December 2021 and was interrupted after the publication of in-vitro evidence that two treatments under investigation (bamlanivimab/etesevimab and casirivimab/imdevimab) were not effective against the new emerging viral Omicron VOC (Cao et al., 2022; Planas et al., 2022; Dejnirattisai et al., 2022). The analysis is therefore restricted to 319 randomised patients, who were enrolled up to the interruption for possible futility, and was performed according to the SARS-CoV-2 VOC (Delta and Omicron).

The first patient was enrolled on 9 December 2021. Overall, 319 patients underwent randomisation by 20 January 2022 and were assigned to receive bamlanivimab/etesevimab (106 patients), sotrovimab (107 patients), or casirivimab/imdevimab (106 patients). No patients reported previous SARS-CoV-2 infections. No patients were lost to follow-up. VOC data were available for 311 patients: 170 (53.3%) were infected with Omicron and 141 (44.2%) with Delta. Eight (2.5%) patients were excluded from this analysis due to the lack of SARS-CoV-2 VOC identification. Figure 1 shows the flow diagram of the progress through the trial phases. Baseline characteristics of the population by type of SARS-CoV-2 VOC are reported in Table 1.

Figure 1

Download asset Open asset

Comparing symptoms at enrolment by VOC, anosmia/ageusia (p<0.001), nausea/vomiting (p<0.001), and feeling feverish or hot (p<0.01) were significantly more frequent among patients infected with Delta, while sore throat (p<0.001) was significantly more frequent among patients infected with Omicron. Serological positivity to anti-SARS-CoV-2 antibodies (p<0.001) and complete primary COVID-19 vaccination series within 120 days of the enrolment or booster vaccination (p<0.001) were significantly more frequent among patients infected with Omicron. Table 2 shows the bivariate Cox regression of symptom resolution predictors by type of SARS-CoV-2 VOC. No predictors were associated with the time to symptom resolution in both SARS-CoV-2 VOC.

Delta VOC

Baseline characteristics of 141 patients infected with Delta VOC by type of treatment are reported in Table 3. The main detected lineages were 34 AY.4 (24.1%), 33 AY.43 (23.4%), and 26 AY.122 (18.4%). 69 (48.9%) were male, median age was 65.7 years (IQR ±15.4), 115 (78.8%) had at least one comorbidity, 70 (49.6%) were serum antibody-positive at the enrolment, and 23 (16.3%) received complete primary COVID-19 vaccination series within 120 days of the enrolment or booster vaccination.

Table 3

Characteristic	TotalN=141	SotrovimabN=43	Bamlanivimab/etesevimabN=48	Casirivimab/imdevimabN=50
Sex (male) – n (%)	69 (48.94)	22 (51.16)	21 (43.75)	26 (52.00)
Age – median (IQR) (range)	65.7 (15.4) (50–92)	65.8 (16.4) (50–90)	68.6 (11.8) (50–92)	63.2 (12) (50–89)
Smoking status – n (%)
Smoker	8 (5.67)	2 (4.65)	4 (8.33)	2 (4.00)
Former smoker	32 (22.70)	8 (18.60)	11 (22.92)	13 (26.00)
Non-smoker	101 (71.63)	33 (76.74)	33 (68.75)	35 (70.00)
BMI – n (%)
≤29	101 (71.63)	29 (67.44)	36 (75.00)	36 (72.00)
≥30	40 (28.37)	14 (32.56)	12 (25.00)	14 (28.00)
SARS-CoV-2 serological status – n (%)
Antibody-positive	70 (49.65)	20 (46.51)	29 (61.70)	21 (43.75)
Antibody-negative	68 (48.23)	23 (53.49)	18 (38.30)	27 (56.25)
Other	3 (2.13)	0	1 (2.08)	2 (4.00)
Anti-SARS-CoV-2 vaccination status – n (%)
3 doses	16 (11.35)	6 (13.95)	3 (6.25)	7 (14.00)
2 doses ≤120 days	7 (4.96)	2 (4.65)	2 (4.17)	3 (6.00)
1 or 2 doses ≥120 days	54 (38.30)	14 (32.56)	26 (54.17)	14 (28.00)
Not vaccinated	59 (41.84)	19 (44.19)	15 (31.25)	25 (50.00)
Other	5 (3.55)	2 (4.65)	2 (4.17)	1 (2.00)
Comorbidities – n (%)
Diabetes	3 (2.13)	0	2 (4.17)	1 (2.00)
Cardiovascular disease	56 (39.72)	18 (41.86)	20 (41.67)	18 (36.00)
Chronic kidney disease	7 (4.96)	1 (2.33)	2 (4.17)	4 (8.00)
Chronic liver disease	3 (2.13)	0	1 (2.08)	2 (4.00)
Chronic pulmonary disease	16 (11.35)	6 (13.95)	4 (8.33)	6 (12.00)
Immunocompromising conditions	17 (12.06)	6 (13.95)	6 (12.50)	5 (10.00)
Symptoms at enrolment – n (%)
Cough	96 (68.09)	28 (65.12)	36 (75.00)	32 (64.00)
Nasal congestion	69 (48.94)	20 (46.51)	22 (45.83)	27 (54.00)
Sore throat	32 (22.70)	10 (23.26)	8 (16.67)	14 (28.00)
Feeling hot or feverish	103 (73.05)	31 (72.09)	36 (75.00)	36 (72.00)
Myalgia	46 (32.62)	11 (25.58)	16 (33.33)	19 (38.00)
Fatigue	47 (33.33)	13 (30.23)	15 (31.25)	19 (38.00)
Headache	59 (41.84)	15 (34.88)	15 (31.25)	29 (58.00)
Anosmia/ageusia	39 (27.66)	12 (27.91)	15 (31.25)	12 (24.00)
Nausea/vomiting	28 (19.86)	6 (13.95)	9 (18.75)	13 (26.00)
Diarrhoea	15 (10.64)	1 (2.33)	5 (10.42)	9 (18.00)
Serum C-reactive protein level – n	136	41	46	49
Mean (SD), mg/L	20.58 (29.00)	22.84 (33.70)	25.27 (34.20)	14.29 (15.99)

Primary and secondary outcomes of the study population infected with Delta VOC by type of treatment are reported in Table 4 with the exclusion of time to sustained patient-reported symptom resolution. No COVID-19 progression was recorded in Delta infections. All-cause mortality through day 28 was the same as that through day 14. An emergency department visit without hospitalisation was observed once in one patient in the casirivimab/imdevimab group. This visit was deemed to be unrelated to COVID-19.

Table 4

Outcome	TotalN=141	SotrovimabN=44	Bamlanivimab/etesevimabN=47	Casirivimab/imdevimabN=50
Composite primary outcome – n (%)	0	0	0	0
Hospitalisation	0	0	0	0
Need of supplemental oxygen therapy	0	0	0	0
Death from any cause through day 14	0	0	0	0
Secondary outcomes
Emergency department visits through day 28 – n (%)	1 (0.71)	0	0	1 (2)
All-cause mortality through day 28 – n (%)	0	0	0	0
Duration of supplemental oxygen therapy – days	0	0	0	0
Rate of non-invasive ventilation – n (%)	0	0	0	0
Duration of non-invasive ventilation – days	0	0	0	0
Rate of mechanical ventilation – n (%)	0	0	0	0
Duration of mechanical ventilation – days	0	0	0	0

The median time to symptom resolution was 7 days (95% CI 6–13) in the bamlanivimab/etesevimab group, 10 days (95% CI 7–14) in the sotrovimab group, and 10 days (95% CI 7–15) in the casirivimab/imdevimab group, not differing significantly across the overall groups of treatment (Log-rank Chi-square 0.22, p 0.895) and for each comparison between treatment groups, namely bamlanivimab/etesevimab with casirivimab/imdevimab (Log-rank Chi-square 0.08, p 0.776), sotrovimab with casirivimab/imdevimab (Log-rank Chi-square 0.40, p 0.527), and bamlanivimab/etesevimab with sotrovimab (Log-rank Chi-square 0.01, p 0.92). Figure 2A shows the time to symptom resolution by type of treatment in the Delta study population. The Cox regression analysis confirmed the non-significantly different effects upon the time to symptom resolution between casirivimab/imdevimab (reference standard according to the original trial protocol) and both bamlanivimab/etesevimab and sotrovimab (HR 1.052 and HR 1.097, 95% CI 0.70–1.57 and 0.73–1.65, p 0.805 and 0.657, respectively).

Figure 2

Download asset Open asset

During the SARS-CoV-2 pandemic, the paradigm of discovering and implementing mAb and antiviral treatments based on randomised controlled trials has lagged significantly behind the new evidence coming from in-vitro studies, which has driven clinical recommendations causing ethical dilemmas on the continuation of ongoing trials. At the time of approving the MANTICO trial protocol (November 2021), casirivimab/imdevimab, bamlanivimab/etesevimab, and, later, sotrovimab were the only therapies recommended by the COVID-19 treatment guidelines for outpatients with mild-to-moderate COVID-19 at high risk of progressing to severe COVID-19. Delta was the SARS-CoV-2 dominant VOC worldwide, and the selection of the study mAbs was based on their in-vitro activity against the circulating variants and on the existing evidence of their clinical efficacy. Since mid-December 2021, the Omicron VOC has been spreading worldwide, rapidly becoming the dominant VOC. Preliminary in-vitro studies on Omicron demonstrated numerous mutations in the gene encoding the spike protein, predicting a markedly reduced susceptibility to bamlanivimab/etesevimab and casirivimab/imdevimab (Cao et al., 2022; Planas et al., 2022; Dejnirattisai et al., 2022). According to these findings, FDA and AIFA have revised the emergency use authorisation for bamlanivimab/etesevimab and casirivimab/imdevimab, halting their use, in line with the National Institutes of Health COVID-19 Treatment Guidelines Panel, which advised against the use of these mAbs due to reduced activity against Omicron and because real-time testing to identify rare, non-Omicron variants is not readily available (National Institutes of Health, 2022). Therefore, the study enrolment in a real-life outpatient setting was prematurely discontinued for possible futility, after the inclusion of barely one fourth of the predefined sample size (1260 patients). Nevertheless, the recruitment timeframe provided a unique opportunity to collect data on the clinical efficacy of bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab in patients infected with Omicron.

Overall, the three treatment groups appeared to be balanced with respect to the predictors of outcomes in both Delta and Omicron population, as expected under the randomised allocation design. As reported by previous studies, patients infected with Omicron, compared with patients infected with Delta, were more likely to present with symptoms limited to the upper respiratory tract and to have pre-existing immunity, considering that Omicron is better equipped than Delta to infect people with pre-existing immunity (Nyberg et al., 2022).

Considering the time to symptom resolution, no differences in the effect between treatment groups were found in Delta infections, whereas sotrovimab seems to show a benefit in patients infected with Omicron BA.1 and BA.1.1. This benefit was consistent across all Omicron subgroups, regardless of the SARS-CoV-2 serology and vaccination status, confirming the preliminary in-vitro evidence on the mAbs activity against Omicron BA.1 and BA.1.1 (Cao et al., 2022; Planas et al., 2022; Dejnirattisai et al., 2022).

The COVID-19 progression was recorded in two patients infected with Omicron, who were both randomised to receive bamlanivimab/etesevimab. On the one hand, these findings seem consistent with recent in-vitro data showing that all study treatments were active against Delta, and both casirivimab/imdevimab and sotrovimab retained a residual neutralising activity against Omicron BA.1/BA.1.1, whereas bamlanivimab/etesevimab did not neutralise Omicron (Takashita et al., 2022b; Iketani et al., 2022; Takashita et al., 2022a; Arora et al., 2022). Nevertheless, the above-mentioned results are severely limited by the early discontinuation of the trial, and firm conclusions on the primary outcome parameters cannot be drawn. Furthermore, the observed rate of COVID-19 progression (2/319, 0.6%) was lower than the one used to inform the sample size calculation (5% in the casirivimab/imdevimab arm, reference standard; NCT05205759). This overestimation of the primary outcome could be influenced by the lower intrinsic-severity of Omicron, the high vaccination rate in Italy, and the prioritisation of the booster vaccination for the elderly (Bhattacharyya and Hanage, 2022). Another limitation of this study is the lack of data on the clinical efficacy of the study mAbs, as well as other commercially available early COVID-19 treatments (mAbs, such as tixagevimab/cilgavimab and bebtelovimab, and antiviral drugs, such as remdesivir, nirmatrelvir/ritonavir, and molnupiravir), against the currently circulating VOC (BA.2 subvariants, BA.4, or BA.5; CoVariants, 2022). Following an adaptive design in a real-life setting, the MANTICO trial is actively recruiting to compare the clinical efficacy of commercially available early COVID-19 treatments against the currently circulating VOC (tixagevimab/cilgavimab, nirmatrelvir/ritonavir, and sotrovimab; NCT05321394).

Additional clinical studies with an adequate sample size are required to determine whether casirivimab/imdevimab and sotrovimab are indeed effective in preventing COVID-19 progression due to Omicron infection. Should the role of casirivimab/imdevimab in preventing severe COVID-19 due to Omicron infections be confirmed, this mAb could represent a readily available and well-tolerated treatment option in case of shortages of mAbs supplies and contraindication to other early COVID-19 treatments.

The MANTICO trial provides the first data on the clinical efficacy of bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab against Omicron VOC. There is an urgent need for adaptive clinical trials comparing anti-SARS-CoV-2 treatments by the currently circulating VOC to promptly inform recommendations for the management of early COVID-19.

The trial dataset has been uploaded to the Dryad repository (https://doi.org/10.5061/dryad.tdz08kq2w). As per predefined protocol, personally identifiable information (such as gender, date of birth, age, and weight) has been removed from the dataset to keep the records completely anonymous. In addition, the dataset record order was randomised so the resulting dataset is a file very similar in terms of length, fields and content to the original version, except for row order which is now completely random and the record id variable deleted.

1. 1. Tacconelli E

   (2022) Dryad Digital Repository

   Adaptive, Randomized, Non-inferiority Trial to Evaluate the Efficacy of Monoclonal Antibodies in Outpatients With Mild or Moderate COVID-19.

   https://doi.org/10.5061/dryad.tdz08kq2w

1. 1. Andrews N
   2. Stowe J
   3. Kirsebom F
   4. Toffa S
   5. Rickeard T
   6. Gallagher E
   7. Gower C
   8. Kall M
   9. Groves N
   10. O’Connell A-M
   11. Simons D
   12. Blomquist PB
   13. Zaidi A
   14. Nash S
   15. Iwani Binti Abdul Aziz N
   16. Thelwall S
   17. Dabrera G
   18. Myers R
   19. Amirthalingam G
   20. Gharbia S
   21. Barrett JC
   22. Elson R
   23. Ladhani SN
   24. Ferguson N
   25. Zambon M
   26. Campbell CNJ
   27. Brown K
   28. Hopkins S
   29. Chand M
   30. Ramsay M
   31. Lopez Bernal J

   (2022) Covid-19 vaccine effectiveness against the omicron (b.1.1.529) variant

   The New England Journal of Medicine 386:1532–1546.

   https://doi.org/10.1056/NEJMoa2119451

   • PubMed
   • Google Scholar
2. 1. Arora P
   2. Kempf A
   3. Nehlmeier I
   4. Schulz SR
   5. Cossmann A
   6. Stankov MV
   7. Jäck H-M
   8. Behrens GMN
   9. Pöhlmann S
   10. Hoffmann M

   (2022) Augmented neutralisation resistance of emerging omicron subvariants BA.2.12.1, BA.4, and BA.5

   The Lancet. Infectious Diseases 22:1117–1118.

   https://doi.org/10.1016/S1473-3099(22)00422-4

   • PubMed
   • Google Scholar
3. 1. Benton DJ
   2. Wrobel AG
   3. Xu P
   4. Roustan C
   5. Martin SR
   6. Rosenthal PB
   7. Skehel JJ
   8. Gamblin SJ

   (2020) Receptor binding and priming of the spike protein of SARS-cov-2 for membrane fusion

   Nature 588:327–330.

   https://doi.org/10.1038/s41586-020-2772-0

   • PubMed
   • Google Scholar
4. 1. Bhattacharyya RP
   2. Hanage WP

   (2022) Challenges in inferring intrinsic severity of the SARS-cov-2 omicron variant

   The New England Journal of Medicine 386:e14.

   https://doi.org/10.1056/NEJMp2119682

   • PubMed
   • Google Scholar
5. 1. Cao Y
   2. Wang J
   3. Jian F
   4. Xiao T
   5. Song W
   6. Yisimayi A
   7. Huang W
   8. Li Q
   9. Wang P
   10. An R
   11. Wang J
   12. Wang Y
   13. Niu X
   14. Yang S
   15. Liang H
   16. Sun H
   17. Li T
   18. Yu Y
   19. Cui Q
   20. Liu S
   21. Yang X
   22. Du S
   23. Zhang Z
   24. Hao X
   25. Shao F
   26. Jin R
   27. Wang X
   28. Xiao J
   29. Wang Y
   30. Xie XS

   (2022) Omicron escapes the majority of existing SARS-cov-2 neutralizing antibodies

   Nature 602:657–663.

   https://doi.org/10.1038/s41586-021-04385-3

   • PubMed
   • Google Scholar
6. Website

   1. CoVariants

   (2022) Overview of variants/mutations

   Accessed August 12, 2022.

   https://covariants.org/per-variant
7. Website

   1. COVID-19 Treatment Guidelines Panel

   (2019) Coronavirus Disease 2019 (COVID-19) Treatment Guidelines: clinical spectrum of SARS-CoV-2 infection. National Institutes of Health

   Accessed April 17, 2022.

   https://www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum
8. 1. Dejnirattisai W
   2. Huo J
   3. Zhou D
   4. Zahradník J
   5. Supasa P
   6. Liu C
   7. Duyvesteyn HME
   8. Ginn HM
   9. Mentzer AJ
   10. Tuekprakhon A
   11. Nutalai R
   12. Wang B
   13. Dijokaite A
   14. Khan S
   15. Avinoam O
   16. Bahar M
   17. Skelly D
   18. Adele S
   19. Johnson SA
   20. Amini A
   21. Ritter TG
   22. Mason C
   23. Dold C
   24. Pan D
   25. Assadi S
   26. Bellass A
   27. Omo-Dare N
   28. Koeckerling D
   29. Flaxman A
   30. Jenkin D
   31. Aley PK
   32. Voysey M
   33. Costa Clemens SA
   34. Naveca FG
   35. Nascimento V
   36. Nascimento F
   37. Fernandes da Costa C
   38. Resende PC
   39. Pauvolid-Correa A
   40. Siqueira MM
   41. Baillie V
   42. Serafin N
   43. Kwatra G
   44. Da Silva K
   45. Madhi SA
   46. Nunes MC
   47. Malik T
   48. Openshaw PJM
   49. Baillie JK
   50. Semple MG
   51. Townsend AR
   52. Huang K-YA
   53. Tan TK
   54. Carroll MW
   55. Klenerman P
   56. Barnes E
   57. Dunachie SJ
   58. Constantinides B
   59. Webster H
   60. Crook D
   61. Pollard AJ
   62. Lambe T
   63. OPTIC Consortium
   64. ISARIC4C Consortium
   65. Paterson NG
   66. Williams MA
   67. Hall DR
   68. Fry EE
   69. Mongkolsapaya J
   70. Ren J
   71. Schreiber G
   72. Stuart DI
   73. Screaton GR

   (2022) SARS-cov-2 omicron-B.1.1.529 leads to widespread escape from neutralizing antibody responses

   Cell 185:467–484.

   https://doi.org/10.1016/j.cell.2021.12.046

   • PubMed
   • Google Scholar
9. 1. Dougan M
   2. Nirula A
   3. Azizad M
   4. Mocherla B
   5. Gottlieb RL
   6. Chen P
   7. Hebert C
   8. Perry R
   9. Boscia J
   10. Heller B
   11. Morris J
   12. Crystal C
   13. Igbinadolor A
   14. Huhn G
   15. Cardona J
   16. Shawa I
   17. Kumar P
   18. Adams AC
   19. Van Naarden J
   20. Custer KL
   21. Durante M
   22. Oakley G
   23. Schade AE
   24. Holzer TR
   25. Ebert PJ
   26. Higgs RE
   27. Kallewaard NL
   28. Sabo J
   29. Patel DR
   30. Dabora MC
   31. Klekotka P
   32. Shen L
   33. Skovronsky DM
   34. BLAZE-1 Investigators

   (2021) Bamlanivimab plus etesevimab in mild or moderate covid-19

   The New England Journal of Medicine 385:1382–1392.

   https://doi.org/10.1056/NEJMoa2102685

   • PubMed
   • Google Scholar
10. 1. Gupta A
    2. Gonzalez-Rojas Y
    3. Juarez E
    4. Crespo Casal M
    5. Moya J
    6. Falci DR
    7. Sarkis E
    8. Solis J
    9. Zheng H
    10. Scott N
    11. Cathcart AL
    12. Hebner CM
    13. Sager J
    14. Mogalian E
    15. Tipple C
    16. Peppercorn A
    17. Alexander E
    18. Pang PS
    19. Free A
    20. Brinson C
    21. Aldinger M
    22. Shapiro AE
    23. COMET-ICE Investigators

    (2021) Early treatment for covid-19 with SARS-cov-2 neutralizing antibody sotrovimab

    The New England Journal of Medicine 385:1941–1950.

    https://doi.org/10.1056/NEJMoa2107934

    • PubMed
    • Google Scholar
11. 1. Harris PA
    2. Taylor R
    3. Thielke R
    4. Payne J
    5. Gonzalez N
    6. Conde JG

    (2009) Research electronic data capture (redcap) -- a metadata-driven methodology and workflow process for providing translational research informatics support

    Journal of Biomedical Informatics 42:377–381.

    https://doi.org/10.1016/j.jbi.2008.08.010

    • PubMed
    • Google Scholar
12. 1. Huang C
    2. Wang Y
    3. Li X
    4. Ren L
    5. Zhao J
    6. Hu Y
    7. Zhang L
    8. Fan G
    9. Xu J
    10. Gu X
    11. Cheng Z
    12. Yu T
    13. Xia J
    14. Wei Y
    15. Wu W
    16. Xie X
    17. Yin W
    18. Li H
    19. Liu M
    20. Xiao Y
    21. Gao H
    22. Guo L
    23. Xie J
    24. Wang G
    25. Jiang R
    26. Gao Z
    27. Jin Q
    28. Wang J
    29. Cao B

    (2020) Clinical features of patients infected with 2019 novel coronavirus in wuhan, china

    Lancet 395:497–506.

    https://doi.org/10.1016/S0140-6736(20)30183-5

    • PubMed
    • Google Scholar
13. 1. Huang DT
    2. McCreary EK
    3. Bariola JR
    4. Minnier TE
    5. Wadas RJ
    6. Shovel JA
    7. Albin D
    8. Marroquin OC
    9. Kip KE
    10. Collins K
    11. Schmidhofer M
    12. Wisniewski MK
    13. Nace DA
    14. Sullivan C
    15. Axe M
    16. Meyers R
    17. Weissman A
    18. Garrard W
    19. Peck-Palmer OM
    20. Wells A
    21. Bart RD
    22. Yang A
    23. Berry LR
    24. Berry S
    25. Crawford AM
    26. McGlothlin A
    27. Khadem T
    28. Linstrum K
    29. Montgomery SK
    30. Ricketts D
    31. Kennedy JN
    32. Pidro CJ
    33. Nakayama A
    34. Zapf RL
    35. Kip PL
    36. Haidar G
    37. Snyder GM
    38. McVerry BJ
    39. Yealy DM
    40. Angus DC
    41. Seymour CW

    (2022) Effectiveness of casirivimab-imdevimab and sotrovimab during a SARS-cov-2 delta variant surge: a cohort study and randomized comparative effectiveness trial

    JAMA Network Open 5:e2220957.

    https://doi.org/10.1001/jamanetworkopen.2022.20957

    • PubMed
    • Google Scholar
14. 1. Iketani S
    2. Liu L
    3. Guo Y
    4. Liu L
    5. Chan JF-W
    6. Huang Y
    7. Wang M
    8. Luo Y
    9. Yu J
    10. Chu H
    11. Chik KK-H
    12. Yuen TT-T
    13. Yin MT
    14. Sobieszczyk ME
    15. Huang Y
    16. Yuen K-Y
    17. Wang HH
    18. Sheng Z
    19. Ho DD

    (2022) Antibody evasion properties of SARS-cov-2 omicron sublineages

    Nature 604:553–556.

    https://doi.org/10.1038/s41586-022-04594-4

    • PubMed
    • Google Scholar
15. Website

    1. Istituto Superiore di Sanità

    (2021) Epidemia COVID-19, aggiornamento nazionale

    Accessed September 30, 2022.

    https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_3-febbraio-2021.pdf
16. 1. McCreary EK
    2. Bariola JR
    3. Minnier TE
    4. Wadas RJ
    5. Shovel JA
    6. Albin D
    7. Marroquin OC
    8. Kip KE
    9. Collins K
    10. Schmidhofer M
    11. Wisniewski MK
    12. Nace DA
    13. Sullivan C
    14. Axe M
    15. Meyers R
    16. Weissman A
    17. Garrard W
    18. Peck-Palmer OM
    19. Wells A
    20. Bart RD
    21. Yang A
    22. Berry LR
    23. Berry S
    24. Crawford AM
    25. McGlothlin A
    26. Khadem T
    27. Linstrum K
    28. Montgomery SK
    29. Ricketts D
    30. Kennedy JN
    31. Pidro CJ
    32. Haidar G
    33. Snyder GM
    34. McVerry BJ
    35. Yealy DM
    36. Angus DC
    37. Nakayama A
    38. Zapf RL
    39. Kip PL
    40. Seymour CW
    41. Huang DT

    (2022) The comparative effectiveness of COVID-19 monoclonal antibodies: a learning health system randomized clinical trial

    Contemporary Clinical Trials 119:106822.

    https://doi.org/10.1016/j.cct.2022.106822

    • PubMed
    • Google Scholar
17. Website

    1. National Institutes of Health

    (2022) COVID-19 Treatment Guidelines Panel. Coronavirus Disease 2019 (COVID-19) Treatment Guidelines

    Accessed February 13, 2022.

    https://www.covid19treatmentguidelines.nih.gov
18. 1. Nyberg T
    2. Ferguson NM
    3. Nash SG
    4. Webster HH
    5. Flaxman S
    6. Andrews N
    7. Hinsley W
    8. Bernal JL
    9. Kall M
    10. Bhatt S
    11. Blomquist P
    12. Zaidi A
    13. Volz E
    14. Aziz NA
    15. Harman K
    16. Funk S
    17. Abbott S
    18. Hope R
    19. Charlett A
    20. Chand M
    21. Ghani AC
    22. Seaman SR
    23. Dabrera G
    24. De Angelis D
    25. Presanis AM
    26. Thelwall S
    27. COVID-19 Genomics UK (COG-UK) consortium

    (2022) Comparative analysis of the risks of hospitalisation and death associated with SARS-cov-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in england: a cohort study

    Lancet 399:1303–1312.

    https://doi.org/10.1016/S0140-6736(22)00462-7

    • PubMed
    • Google Scholar
19. 1. Petrilli CM
    2. Jones SA
    3. Yang J
    4. Rajagopalan H
    5. O’Donnell L
    6. Chernyak Y
    7. Tobin KA
    8. Cerfolio RJ
    9. Francois F
    10. Horwitz LI

    (2020) Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in new york city: prospective cohort study

    BMJ 369:m1966.

    https://doi.org/10.1136/bmj.m1966

    • PubMed
    • Google Scholar
20. 1. Planas D
    2. Saunders N
    3. Maes P
    4. Guivel-Benhassine F
    5. Planchais C
    6. Buchrieser J
    7. Bolland W-H
    8. Porrot F
    9. Staropoli I
    10. Lemoine F
    11. Péré H
    12. Veyer D
    13. Puech J
    14. Rodary J
    15. Baele G
    16. Dellicour S
    17. Raymenants J
    18. Gorissen S
    19. Geenen C
    20. Vanmechelen B
    21. Wawina-Bokalanga T
    22. Martí-Carreras J
    23. Cuypers L
    24. Sève A
    25. Hocqueloux L
    26. Prazuck T
    27. Rey FA
    28. Simon-Loriere E
    29. Bruel T
    30. Mouquet H
    31. André E
    32. Schwartz O

    (2022) Considerable escape of SARS-cov-2 omicron to antibody neutralization

    Nature 602:671–675.

    https://doi.org/10.1038/s41586-021-04389-z

    • PubMed
    • Google Scholar
21. 1. Takashita E
    2. Kinoshita N
    3. Yamayoshi S
    4. Sakai-Tagawa Y
    5. Fujisaki S
    6. Ito M
    7. Iwatsuki-Horimoto K
    8. Halfmann P
    9. Watanabe S
    10. Maeda K
    11. Imai M
    12. Mitsuya H
    13. Ohmagari N
    14. Takeda M
    15. Hasegawa H
    16. Kawaoka Y

    (2022a) Efficacy of antiviral agents against the SARS-cov-2 omicron subvariant BA.2

    The New England Journal of Medicine 386:1475–1477.

    https://doi.org/10.1056/NEJMc2201933

    • PubMed
    • Google Scholar
22. 1. Takashita E
    2. Kinoshita N
    3. Yamayoshi S
    4. Sakai-Tagawa Y
    5. Fujisaki S
    6. Ito M
    7. Iwatsuki-Horimoto K
    8. Chiba S
    9. Halfmann P
    10. Nagai H
    11. Saito M
    12. Adachi E
    13. Sullivan D
    14. Pekosz A
    15. Watanabe S
    16. Maeda K
    17. Imai M
    18. Yotsuyanagi H
    19. Mitsuya H
    20. Ohmagari N
    21. Takeda M
    22. Hasegawa H
    23. Kawaoka Y

    (2022b) Efficacy of antibodies and antiviral drugs against covid-19 omicron variant

    The New England Journal of Medicine 386:995–998.

    https://doi.org/10.1056/NEJMc2119407

    • PubMed
    • Google Scholar
23. Website

    1. U.S. Department of Health and Human Services Food and Drug Administration

    (2022a) Assessing COVID19-Related Symptoms in Outpatient Adult and Adolescent Subjects in Clinical Trials of Drugs and Biological Products for COVID-19 Prevention or Treatment

    Accessed March 30, 2022.

    https://www.fda.gov/regulatory-information/search-fda-guidance-documents/assessing-covid-19-related-symptoms-outpatient-adult-and-adolescent-subjects-clinical-trials-drugs
24. Website

    1. U.S. Department of Health and Human Services Food and Drug Administration

    (2022b) COVID-19: developing drugs and biological products for treatment or prevention

    Accessed March 30, 2022.

    https://www.fda.gov/regulatory-information/search-fda-guidance-documents/covid-19-developing-drugs-and-biological-products-treatment-or-prevention
25. 1. Weinreich DM
    2. Sivapalasingam S
    3. Norton T
    4. Ali S
    5. Gao H
    6. Bhore R
    7. Xiao J
    8. Hooper AT
    9. Hamilton JD
    10. Musser BJ
    11. Rofail D
    12. Hussein M
    13. Im J
    14. Atmodjo DY
    15. Perry C
    16. Pan C
    17. Mahmood A
    18. Hosain R
    19. Davis JD
    20. Turner KC
    21. Baum A
    22. Kyratsous CA
    23. Kim Y
    24. Cook A
    25. Kampman W
    26. Roque-Guerrero L
    27. Acloque G
    28. Aazami H
    29. Cannon K
    30. Simón-Campos JA
    31. Bocchini JA
    32. Kowal B
    33. DiCioccio AT
    34. Soo Y
    35. Geba GP
    36. Stahl N
    37. Lipsich L
    38. Braunstein N
    39. Herman G
    40. Yancopoulos GD
    41. Trial Investigators

    (2021) REGEN-COV antibody combination and outcomes in outpatients with covid-19

    The New England Journal of Medicine 385:e81.

    https://doi.org/10.1056/NEJMoa2108163

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Exploratory data on the clinical efficacy of monoclonal antibodies against SARS-CoV-2 Omicron Variant of Concern" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Miles Davenport as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: David Huang (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) As this is a report of a randomised trial, please report according to CONSORT guidelines/checklist and provide a CONSORT figure.

2) Clearer presentation of primary and secondary outcome data.

3) Revision of the discussion and conclusion to ensure that all statements regarding the efficacy of interventions and interpretations of findings are supported and consistent with the data presented (see reviewer comments below for examples).

4) Inclusion of discussion of limitations of the study, including potential sources of bias

Reviewer #1 (Recommendations for the authors):

The authors followed STROBE guidelines. However, reporting an RCT and not an observational study, they should have followed CONSORT guidelines.

Could the authors explain why the type of administered mAb was blinded to the patient?

Reviewer #2 (Recommendations for the authors):

Thank you for the opportunity to review this manuscript. I provide the below feedback in an effort to improve the manuscript and in addition to my evaluation summary and public review.

The authors could be more consistent with regard to the description of the primary outcome (e.g. "COVID-19 progression" on lines 38/39 of the abstract cf "disease progression" on line 44).

On lines 46/47, the authors could consider editing the statement "two disease progressions were recorded in the bamlanivimab/etesevimab group" to specify that there were no cases of disease progression in the other groups.

On lines 54/55, the evidence for the claim re: Casirivimab/imdevimab is insufficient (see statements above) but even as it is currently written it is not quite accurate. The manuscript states "Casirivimab/imdevimab seems to maintain a role in preventing severe COVID-19 in the Omicron population" whereas it could be better worded in terms of preventing progression of COVID-19 in adults with mild-moderate COVID-19 due to the omicron variant of SARS-CoV-2.

The ClinicalTrials.gov entry for your trial says 320 participants were recruited. Is there a reason that this manuscript says 319?

Is there a CONSORT diagram? Is there a CONSORT checklist that could be submitted with this manuscript? I see there is a STROBE checklist, and I guess in some ways this is an observational (δ vs omicron) study housed within an RCT. But I think it would be useful to understand (and report) more about the recruitment to the RCT, even as an appendix, and include a CONSORT diagram.

Were any participants lost to follow-up? Were any data points missing?

Figure 1 would usually show the primary outcome, rather than a secondary outcome. I can see why the authors would choose to show the secondary outcome (time to symptom resolution) in the figure, as this is a useful way of showing those data. But the discussion is focused on a signal in the primary outcome (which I do not think is justified) and I think if the authors wish to focus on that (hypothesis-generating only) finding of only 2 cases of COVID-19 progression, only occurring in the bamlanivimab/etesevimab group, the readers should see how these groups 'differ' visually too.

Could the word 'survival' lead to misinterpretation of the data in Figure 1? I would suggest removing 'survival' from the figure title (currently: "Figure 1 – Survival time to symptom resolution by type of treatment in the study population infected with Δ". Same for Figure 2).

Could Figure 1 and Figure 2 be combined into a multi-panel figure so the data are next to each other for comparison?

On lines 222/223, this statement is redundant when you have presented the days of symptoms in each group "turning out to be 5 days shorter in the sotrovimab group compared to both bamlanivimab/etesevimab and casirivimab/imdevimab groups"

I was confused about the presentation of the statistical analyses of the time to symptom resolution by treatment assignment in the two cohorts (δ and omicron). For the δ data, an analysis was presented assessing whether the duration of symptoms differed by treatment assignment (log-rank chi-square), and then each arm vs arm comparison was also presented as log-rank chi-square (lines 189-194). Cox regression was then used to confirm no difference with casirivimab/imdevimab as the reference. For omicron, where the data suggest there may be a difference, we do not present the chi-square analyses (lines 220-225). Is there a reason for this? The data should be presented the same way for both VOC.

Lines 224-226 too strongly imply a benefit here with casirivimab/imdevimab. And could be read as suggesting both casirivimab/imdevimab and sotrovimab retain comparable activity in vitro. Of course, it depends on the assay used, etc. And these in vitro assays have significant limitations, in terms of their utility to make treatment decisions (and decisions re: guideline recommendations!). But I think this section could be re-worded to improve accuracy.

When this is published, we will be on to the next variant (BA.2, BA.4, etc), in most places we already are. Therefore, consideration of how the authors frame the results in that context, ensuring we do not necessarily imply that all variants termed 'omicron' are equal with regards to mAb neutralisation, will be important (and challenging).

The authors could consider more of a discussion on the paucity of clinical data on these mAbs and these variants from well-designed trials, and that we do not know whether the in vitro findings have informed so many treatment/guideline decisions, do correlate with in vivo activity. Is this the first study to report clinical outcomes randomising patients across δ and omicron with these mAb agents? The authors could consider referring to this study, the other large comparative effectiveness study comparing monoclonal antibodies (particularly if published soon). https://www.medrxiv.org/content/10.1101/2021.09.03.21262551v1

Reviewer #3 (Recommendations for the authors):

My overall review is in the public review. What follows is details. Also, authors should check and review extensively for improvements to the use of English.

Abstract

– Notes AIFA and ORCHESTRA funding, but I believe the electronic form says no funding.

Introduction

– Other comparative effectiveness trials have been conducted.

https://www.medrxiv.org/content/10.1101/2021.12.23.21268244v1

https://www.sciencedirect.com/science/article/pii/S1551714422001483

Methods

– Please describe which centers participated and where they were located.

Results

– 319 pts enrolled in 5 weeks, over the winter holidays is impressive.

– Leveraging of the ~50/50 δ/omicron breakdown is very nice.

– For completeness, would note the #s of all the secondary outcomes, even if they are very small in # or even zero.

Discussion

– Would note that there was a signal in only 1 secondary outcome – could note it is perhaps due to the fact that very few patients had "progression" – but that nonetheless this signal is from 1 of multiple examined outcomes

– On page 10, line 255, "significant benefit" is somewhat problematic – statistically for the reason noted above, and 5d less symptoms is of debatable significance if ultimate outcome is the same. Would use more objective/neutral words.

– On page 11, line 265 – what are the specific findings that support that all study treatments were active against Δ (when there was no "control" arm) – the fact that no disease progression occurred – if so, pls say so?

Also, what are the specific findings that support that C-I and S both retain activity against Omicron when S was superior to both C-I and B-E for symptom resolution, and C-I and B-E had the same 12d median symptom duration? Ie. what is evidence that C-I retained activity v Omicron?

– Would note that currently the dominant Omicron variants are now BA.2 and beyond, and for this reason, FDA revoked all EUAs for mAbs, except for bebtelovimb.

Tables + Figures

– Please shorten everything to 1 or 2 decimal points.

– The K-M curves are hard to read – try dropping the 95% CIs.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Exploratory data on the clinical efficacy of monoclonal antibodies against SARS-CoV-2 Omicron Variant of Concern" for further consideration by eLife. Your revised article has been evaluated by Miles Davenport (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As recommended by the reviewers, please edit the abstract to ensure the conclusion reflects the study design and data. The abstract should state that the trial was ceased early and the conclusion amended as per reviewer 2 comments below.

For the study methods, the original sample size calculation should be included in the methods section (rather than referring readers to clinical trial register).

Reviewer #2 (Recommendations for the authors):

Thank you for submitting your revised manuscript and for addressing the suggestions raised in previous reviews. The revised manuscript is clearer, in terms of the study design and the important limitations, and the conclusions are now better supported by the data.

Given the uncertainty, the limitations, etc, the conclusion of the abstract is important. I suggest a minor edit of this sentence:

"Casirivimab/imdevimab seems to maintain a role in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 infection due to Omicron."

Which could be changed to:

"Casirivimab/imdevimab may maintain a role in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 infection due to Omicron."

This is particularly true when you are not suggesting it is used for treatment (which could be implied by "seems to maintain a role") and your overarching conclusion is that adaptive clinical studies are required (although casirivimab/imdevimab is not included in the next phase of MANTICO, presumably because these data are only available now and the VOCs have continued to change).

Reviewer #3 (Recommendations for the authors):

Revisions are appropriate, however, manuscript revisions (esp Discussion revisions) have not extended to the Abstract.

For Abstract, I recommend:

1. Cut decimal places to two.

2. Also add to the 2nd Conclusion sentence the specific Omicron variants analyzed in this study. Or reword so it's clear that the specific variants apply to all Conclusion sentences / mAbs.

3. Add to Conclusion a sentence reflective of the "results are severely limited" sentence added to Discussion.

E.g., could note that time to symptom resolution was 1 of X secondary outcomes, and no conclusions could be made about the primary outcome.

Specific text up to the authors – my main residual concern is simply that the Abstract's Conclusion is considerably "stronger" than the manuscript's conclusion/discussion, and should be appropriately caveated.

Essential revisions:

1) As this is a report of a randomised trial, please report according to CONSORT guidelines/checklist and provide a CONSORT figure.

The CONSORT flow diagram has been added as Figure 1 and the CONSORT checklist has been included among the uploaded files.

2) Clearer presentation of primary and secondary outcome data.

Primary and secondary outcome data have been reported in Table 4 (Δ variant) and Table 6 (Omicron variant) of the revised version of the manuscript (pages 20 and 22).

The previous Figure 1 and 2 have been combined into a multi-panel figure, now renamed Figure 2A/2B (without 95% CIs, as suggested by Reviewer 3).

3) Revision of the discussion and conclusion to ensure that all statements regarding the efficacy of interventions and interpretations of findings are supported and consistent with the data presented (see reviewer comments below for examples).

The discussion and conclusion have been revised accordingly (page 12).

4) Inclusion of discussion of limitations of the study, including potential sources of bias

Limitations of the study, including potential sources of bias, have been included in the discussion of the revised version of the manuscript (page 12, lines 290-301).

Reviewer #1 (Recommendations for the authors):

The authors followed STROBE guidelines. However, reporting an RCT and not an observational study, they should have followed CONSORT guidelines.

Could the authors explain why the type of administered mAb was blinded to the patient?

The CONSORT flow diagram has been added as Figure 1 and the CONSORT checklist has been included among the uploaded files.

The type of administered mAb was blinded to the patient to reduce the risk of performance bias.

Reviewer #2 (Recommendations for the authors):

Thank you for the opportunity to review this manuscript. I provide the below feedback in an effort to improve the manuscript and in addition to my evaluation summary and public review.

The authors could be more consistent with regard to the description of the primary outcome (e.g. "COVID-19 progression" on lines 38/39 of the abstract cf "disease progression" on line 44).

The primary outcome has been consistently reported as “COVID-19 progression” all throughout the revised version of the manuscript.

On lines 46/47, the authors could consider editing the statement "two disease progressions were recorded in the bamlanivimab/etesevimab group" to specify that there were no cases of disease progression in the other groups.

The sentence has been amended accordingly in the revised version of the manuscript (page 2, line 47).

On lines 54/55, the evidence for the claim re: Casirivimab/imdevimab is insufficient (see statements above) but even as it is currently written it is not quite accurate. The manuscript states "Casirivimab/imdevimab seems to maintain a role in preventing severe COVID-19 in the Omicron population" whereas it could be better worded in terms of preventing progression of COVID-19 in adults with mild-moderate COVID-19 due to the omicron variant of SARS-CoV-2.

The sentence has been amended accordingly in the revised version of the manuscript (page 2, line 55-56).

The ClinicalTrials.gov entry for your trial says 320 participants were recruited. Is there a reason that this manuscript says 319?

Thanks for pointing out this typo. The right number is 319, as reported in the manuscript. The ClinicalTrials.gov entry has been corrected.

Is there a CONSORT diagram? Is there a CONSORT checklist that could be submitted with this manuscript? I see there is a STROBE checklist, and I guess in some ways this is an observational (δ vs omicron) study housed within an RCT. But I think it would be useful to understand (and report) more about the recruitment to the RCT, even as an appendix, and include a CONSORT diagram.

The CONSORT flow diagram has been added as Figure 1 and the CONSORT checklist has been included among the uploaded files.

Were any participants lost to follow-up? Were any data points missing?

No patients were lost to follow-up and no data points were missing.

Figure 1 would usually show the primary outcome, rather than a secondary outcome. I can see why the authors would choose to show the secondary outcome (time to symptom resolution) in the figure, as this is a useful way of showing those data. But the discussion is focused on a signal in the primary outcome (which I do not think is justified) and I think if the authors wish to focus on that (hypothesis-generating only) finding of only 2 cases of COVID-19 progression, only occurring in the bamlanivimab/etesevimab group, the readers should see how these groups 'differ' visually too.

We thank the reviewer for the suggestion. Primary and secondary outcomes have been reported in Table 4 (Δ variant) and Table 6 (Omicron variant) of the revised version of the manuscript (pages 20 and 22).

Could the word 'survival' lead to misinterpretation of the data in Figure 1? I would suggest removing 'survival' from the figure title (currently: "Figure 1 – Survival time to symptom resolution by type of treatment in the study population infected with Δ". Same for Figure 2).

We do agree that the word “survival” can be misleading. The word has been deleted from footnotes and figures.

Could Figure 1 and Figure 2 be combined into a multi-panel figure so the data are next to each other for comparison?

The previous Figure 1 and 2 have been combined into a multi-panel figure, now renamed Figure 2A/2B (without 95% CIs, as suggested by Reviewer 3).

On lines 222/223, this statement is redundant when you have presented the days of symptoms in each group "turning out to be 5 days shorter in the sotrovimab group compared to both bamlanivimab/etesevimab and casirivimab/imdevimab groups"

The statement has been removed.

I was confused about the presentation of the statistical analyses of the time to symptom resolution by treatment assignment in the two cohorts (δ and omicron). For the δ data, an analysis was presented assessing whether the duration of symptoms differed by treatment assignment (log-rank chi-square), and then each arm vs arm comparison was also presented as log-rank chi-square (lines 189-194). Cox regression was then used to confirm no difference with casirivimab/imdevimab as the reference. For omicron, where the data suggest there may be a difference, we do not present the chi-square analyses (lines 220-225). Is there a reason for this? The data should be presented the same way for both VOC.

We thank the reviewer for the comment. The Chi-square analyses on the Omicron group, which were improperly missing in the previous version of the manuscript, have been added (page 10, lines 237-242).

Lines 224-226 too strongly imply a benefit here with casirivimab/imdevimab. And could be read as suggesting both casirivimab/imdevimab and sotrovimab retain comparable activity in vitro. Of course, it depends on the assay used, etc. And these in vitro assays have significant limitations, in terms of their utility to make treatment decisions (and decisions re: guideline recommendations!). But I think this section could be re-worded to improve accuracy.

We do agree with the reviewer. The trial is underpowered to reach any firm conclusion. This major limitation has been better pointed out in the discussion of the revised version of the manuscript (page 12, lines 290-298).

When this is published, we will be on to the next variant (BA.2, BA.4, etc), in most places we already are. Therefore, consideration of how the authors frame the results in that context, ensuring we do not necessarily imply that all variants termed 'omicron' are equal with regards to mAb neutralisation, will be important (and challenging).

We thank the reviewer for the suggestion. This limitation has been pointed out in the discussion of the revised version of the manuscript (page 12, lines 298-301).

The authors could consider more of a discussion on the paucity of clinical data on these mAbs and these variants from well-designed trials, and that we do not know whether the in vitro findings have informed so many treatment/guideline decisions, do correlate with in vivo activity. Is this the first study to report clinical outcomes randomising patients across δ and omicron with these mAb agents? The authors could consider referring to this study, the other large comparative effectiveness study comparing monoclonal antibodies (particularly if published soon). https://www.medrxiv.org/content/10.1101/2021.09.03.21262551v1

We thank the reviewer for the suggested reference, which has been added in the revised version of the manuscript (page 4, lines 85-86). This study reports results as of June 25, 2021. During the trial the Α variant was the dominant variant of concern, while the Δ variant became more prevalent in the final time period. To date, our study is actually the first one to report clinical outcomes against the Omicron variant.

Reviewer #3 (Recommendations for the authors):

My overall review is in the public review. What follows is details. Also, authors should check and review extensively for improvements to the use of English.

Abstract

– Notes AIFA and ORCHESTRA funding, but I believe the electronic form says no funding.

As stated in the manuscript, AIFA and ORCHESTRA provided funding for the study. This information has been added in the electronic form as well.

Introduction

– Other comparative effectiveness trials have been conducted.

https://www.medrxiv.org/content/10.1101/2021.12.23.21268244v1

https://www.sciencedirect.com/science/article/pii/S1551714422001483

Thanks for the suggestion. The references have been added in the revised version of the manuscript (page 4, lines 84-87).

Methods

– Please describe which centers participated and where they were located.

The information on the trial centers has been added in the revised version of the manuscript (page 5, lines 98-99).

Results

– 319 pts enrolled in 5 weeks, over the winter holidays is impressive.

– Leveraging of the ~50/50 δ/omicron breakdown is very nice.

– For completeness, would note the #s of all the secondary outcomes, even if they are very small in # or even zero.

The numbers of all secondary outcomes have been reported in Table 4 (Δ variant) and Table 6 (Omicron variant) of the revised version of the manuscript (pages 20 and 22).

Discussion

– Would note that there was a signal in only 1 secondary outcome – could note it is perhaps due to the fact that very few patients had "progression" – but that nonetheless this signal is from 1 of multiple examined outcomes

We do agree with the reviewer. The trial is underpowered to reach any firm conclusion. This major limitation has been better pointed out in the discussion of the revised version of the manuscript (page 12, lines 290-298).

– On page 10, line 255, "significant benefit" is somewhat problematic – statistically for the reason noted above, and 5d less symptoms is of debatable significance if ultimate outcome is the same. Would use more objective/neutral words.

We thank the reviewer for the suggestion. The statement has been reworded accordingly, replacing “sotrovimab showed a significant benefit” with “sotrovimab seemed to show a benefit” in the revised version of the manuscript (page 11, line 277).

– On page 11, line 265 – what are the specific findings that support that all study treatments were active against Δ (when there was no "control" arm) – the fact that no disease progression occurred – if so, pls say so?

We are sorry for the misleading sentence, which has been rephrased in the revised version of the manuscript (page 12, lines 283-290).

We fully agree with the reviewer on the limitation of the study design.

Also, what are the specific findings that support that C-I and S both retain activity against Omicron when S was superior to both C-I and B-E for symptom resolution, and C-I and B-E had the same 12d median symptom duration? Ie. what is evidence that C-I retained activity v Omicron?

– Would note that currently the dominant Omicron variants are now BA.2 and beyond, and for this reason, FDA revoked all EUAs for mAbs, except for bebtelovimb.

We thank the reviewer for the suggestion. This limitation has been pointed out in the discussion of the revised version of the manuscript (page 12, lines 298-301).

Tables + Figures

– Please shorten everything to 1 or 2 decimal points.

All numbers in Tables and Figures have been shortened to 2 decimal points (p value excluded).

– The K-M curves are hard to read – try dropping the 95% CIs.

We have provided Kaplan-Meier curves without 95% CIs (Figure 2A/2B).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As recommended by the reviewers, please edit the abstract to ensure the conclusion reflects the study design and data. The abstract should state that the trial was ceased early and the conclusion amended as per reviewer 2 comments below.

The abstract has been revised accordingly.

For the study methods, the original sample size calculation should be included in the methods section (rather than referring readers to clinical trial register).

The original sample size calculation has been included in the method section.

Reviewer #2 (Recommendations for the authors):

Thank you for submitting your revised manuscript and for addressing the suggestions raised in previous reviews. The revised manuscript is clearer, in terms of the study design and the important limitations, and the conclusions are now better supported by the data.

Given the uncertainty, the limitations, etc, the conclusion of the abstract is important. I suggest a minor edit of this sentence:

"Casirivimab/imdevimab seems to maintain a role in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 infection due to Omicron."

Which could be changed to:

"Casirivimab/imdevimab may maintain a role in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 infection due to Omicron."

This is particularly true when you are not suggesting it is used for treatment (which could be implied by "seems to maintain a role") and your overarching conclusion is that adaptive clinical studies are required (although casirivimab/imdevimab is not included in the next phase of MANTICO, presumably because these data are only available now and the VOCs have continued to change).

We thank the reviewer for the suggestion. The sentence has been amended accordingly in the revised version of the abstract.

Reviewer #3 (Recommendations for the authors):

Revisions are appropriate, however, manuscript revisions (esp Discussion revisions) have not extended to the Abstract.

For Abstract, I recommend:

1. Cut decimal places to two.

All numbers have been shortened to 2 decimal points in the revised version of the abstract (p value included).

2. Also add to the 2nd Conclusion sentence the specific Omicron variants analyzed in this study. Or reword so it's clear that the specific variants apply to all Conclusion sentences / mAbs.

The conclusion of the abstract has been reworded to clarify that the Omicron variants analysed in this study apply to all sentences

3. Add to Conclusion a sentence reflective of the "results are severely limited" sentence added to Discussion.

E.g., could note that time to symptom resolution was 1 of X secondary outcomes, and no conclusions could be made about the primary outcome.

Specific text up to the authors – my main residual concern is simply that the Abstract's Conclusion is considerably "stronger" than the manuscript's conclusion/discussion, and should be appropriately caveated.

We do agree with the reviewer. A sentence pointing out the severe limitations of the results has been added to the abstract.

Author details

1. Fulvia Mazzaferri

   Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Massimo Mirandola

   Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2342-5867

3. Alessia Savoldi

   Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

   Contribution

   Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

4. Pasquale De Nardo

   Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

   Contribution

   Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

5. Matteo Morra

   Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

   Contribution

   Data curation, Software, Supervision, Validation, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

6. Maela Tebon

   Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

   Contribution

   Data curation, Software, Supervision, Validation, Investigation, Project administration

   Competing interests

   No competing interests declared

7. Maddalena Armellini

   Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

   Contribution

   Data curation, Validation, Investigation, Project administration

   Competing interests

   No competing interests declared

8. Giulia De Luca

   Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

   Contribution

   Data curation, Validation, Investigation

   Competing interests

   No competing interests declared

9. Lucrezia Calandrino

   Infectious Disease Unit, Padova University Hospital, Padua, Italy

   Contribution

   Data curation, Supervision, Investigation

   Competing interests

   No competing interests declared

10. Lolita Sasset

    Infectious Disease Unit, Padova University Hospital, Padua, Italy

    Contribution

    Data curation, Supervision, Investigation

    Competing interests

    Lolita Sasset has served as a paid consultant to Abbvie, Janssen, MSD, Gilead Sciences, Janssen, MSD and ViiV Healthcare; she does not have any financial competing interests with this study

11. Denise D'Elia

    Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy

    Contribution

    Resources, Data curation, Supervision, Investigation

    Competing interests

    No competing interests declared

12. Emanuela Sozio

    Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy

    Contribution

    Resources, Data curation, Formal analysis, Supervision, Investigation, Writing - original draft

    Competing interests

    No competing interests declared

13. Elisa Danese

    Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy

    Contribution

    Resources, Validation, Investigation

    Competing interests

    No competing interests declared

14. Davide Gibellini

    Microbiology and Virology Unit, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

    Contribution

    Resources, Supervision, Validation, Investigation

    Competing interests

    No competing interests declared

15. Isabella Monne

    Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy

    Contribution

    Resources, Supervision, Investigation, Writing - original draft

    Competing interests

    No competing interests declared

16. Giovanna Scroccaro

    Direzione Farmaceutico, Protesica, Dispositivi Medici, Regione del Veneto, Venice, Italy

    Contribution

    Conceptualization, Resources, Supervision, Project administration

    Competing interests

    No competing interests declared

17. Nicola Magrini

    Italian Medicines Agency, Rome, Italy

    Contribution

    Conceptualization, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

18. Annamaria Cattelan

    Infectious Disease Unit, Padova University Hospital, Padua, Italy

    Contribution

    Resources, Supervision, Writing – review and editing

    Competing interests

    Annamaria Cattelan has served as a paid consultant to Abbvie, Janssen, MSD, and received research fundings from Gilead Sciences, Janssen, MSD and ViiV Healthcare; she does not have any financial competing interests with this study

19. Carlo Tascini

    Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy

    Contribution

    Conceptualization, Supervision, Writing – review and editing

    Competing interests

    Carlo Tascini has received grants from Correvio, Biotest, Biomerieux, Gilead, Angelini, MSD, Pfizer, Thermofisher, Zambon, Shionogi, Avir Pharma and Hikma outside the submitted work in the last two years

20. MANTICO Working Group

    Contribution

    Data curation, Investigation, Validation

    Competing interests

    No competing interests declared

    1. Francesca Simbeni, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    2. Alessandro Castelli, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    3. Ilaria Dalla Vecchia, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    4. Chiara Konishi de Toffoli, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    5. Gaia Maccarrone, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    6. Marco Meroi, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    7. Chiara Perlini, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    8. Matilde Rocchi, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    9. Giulia Rosini, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    10. Laura Rovigo, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    11. Lorenzo Tavernaro, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    12. Amina Zaffagnini, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    13. Ilaria Currò, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    14. Ruth Joanna Davis, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    15. Elena Agostini, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    16. Carla Benfatto, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    17. Nicola Bonadiman, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    18. Marco Canova, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    19. Giuseppe Lombardo, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    20. Daniele Mengato, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    21. Danilo Puntrello, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    22. Francesca Prataviera, Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    23. Tosca Semenzin, Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    24. Dario Carloni, Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    25. Giuseppe Lippi, Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
    26. Davide Negrini, Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
    27. Martina Montagnana, Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
    28. Riccardo Cecchetto, Microbiology and Virology Unit, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    29. Alice Fusaro, Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy
    30. Francesco Bonfante, Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy
    31. Elisa Palumbo, Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy
    32. Edoardo Giussani, Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy

21. Evelina Tacconelli

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Visualization, Methodology, Project administration, Writing – review and editing

    For correspondence

    evelina.tacconelli@univr.it

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2010-4977

• 451

  Page views

• 111

  Downloads

• 6

  Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.