Details of the purpose and any published outputs from this project can be found at the link above.
The contents of this repository MUST NOT be considered an accurate or valid representation of the study or its purpose. This repository may reflect an incomplete or incorrect analysis with no further ongoing work. The content has ONLY been made public to support the OpenSAFELY open science and transparency principles and to support the sharing of re-usable code for other subsequent users. No clinical, policy or safety conclusions must be drawn from the contents of this repository.
Comparison of the Sustained Effectiveness of Sotrovimab and Molnupiravir in Preventing Severe COVID-19 Outcomes
Qing Wen 1,2 , *Amelia CA Green3 ,*Bang Zhang1, Viyaasan Mahalingasivam1, Ruth E. Costello1, John Tazare1, Edward P K Parker1,Thomas Hartney1, Rosalind M Eggo1, Stephen J W Evans1, Dave Evans3, Becky Smith3, Amir Mehrkar3, Ian J Douglas1, Ben Goldacre3, #Laurie A Tomlinson1
*Contributed equally #Correspondence to: Laurie A Tomlinson
Affiliations:
- London School of Hygiene and Tropical Medicine, London, UK
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK
- Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
**This was an exploratory analysis undertaken to develop and document methods rather than to generate definitive findings. The important limitations in this study include the absence of propensity score matching to control for confounding, due to permissions to access the COVID-related data expiring before the analyses could be fully completed. This report is therefore provided solely for transparency and to document the analytical approach used; it is not intended to support substantive conclusions or interpretation of results.
During the global effort to combat COVID-19, vaccination has been instrumental in reducing viral transmission, disease severity, and mortality. Even so, its protective benefits remain inaccessible to individuals with medical contraindications and to immunocompromised patients who mount inadequate immune responses. This persistent protection gap underscores the critical need for alternative prophylactic interventions specifically tailored to high-risk populations.
Neutralising monoclonal antibodies (mAbs) represent a promising alternative for prophylactic protection against SARS-CoV-2 in these high-risk populations, particularly in exposure-prone settings such as nursing homes and household contacts.1 However, this use case currently lacks sufficient evidence to support clinical recommendation. Whether exposure to mAbs confers prophylactic benefits and how long any such protection lasts is still uncertain and under investigation, as illustrated by the ongoing PROTECT-V trial investigating the preventive potential of a neutralising monoclonal antibody - sotrovimab.2,3
Sotrovimab was authorised by the UK's Medicines and Healthcare products Regulatory Agency (MHRA) in December 2021 as a first-line treatment for high-risk COVID-19 patients, following the approval of the oral antiviral molnupiravir in November 2021.4,5 Pivotal trial data and subsequent real-world evidence demonstrated that sotrovimab significantly reduced severe outcomes in high-risk patients during the Omicron BA.1 and BA.2 waves, whereas molnupiravir developed for a similar indication, exhibited more limited effectiveness in comparable populations.6,7 Notably, recent studies confirm that sotrovimab’s protective efficacy is maintained against emerging subvariants (BN.1, BQ.1.1, XBB.1.5) due to Fc-mediated mechanisms, including antibody-dependent cellular cytotoxicity and phagocytosis.8 While earlier evidence of its approval from the Phase 3 COMET-ICE trial and real-world comparisons with molnupiravir demonstrated consistent effectiveness only within a 29-day or extended 60-day window following treatment initiation,6,9 the long-term clinical benefit of sotrovimab beyond this period is not well understood. We therefore set out to evaluate its durable effectiveness by analysing real-world electronic medical records to gain deeper insight into its potential for prolonged clinical benefit.
By early 2022, both sotrovimab and molnupiravir were among the most frequently prescribed medications by COVID-19 Medicine Delivery Units (CMDUs) to prevent disease progression in the community settings, thereby supporting the comparability between the two treatment groups. A single infusion of sotrovimab is predicted to exert its effect for about 16-48 weeks.2 In contrast, molnupiravir is rapidly absorbed, metabolised, and cleared from the body within 24 to 48 hours, with a half-life of approximately 1 hour.10 The Phase 3 MOVe-AHEAD trial found that molnupiravir prophylaxis did not achieve superiority over placebo in preventing SARS-CoV-2 infection in households settings.11 In the UK PANORAMIC trial, no evidence was found that molnupiravir provided additional protection against COVID-19 hospitalisation or death during the 3-6 month follow-up period.12 In light of the distinct pharmacokinetic profiles and differential clinical trial outcomes of sotrovimab and molnupiravir, as extension of our previous research, we conducted a comparative analysis of their sustained clinical effectiveness employing a trial emulation framework based on the PROTECT-V platform trial. The study period spans the circulation of Omicron subvariants BA.4, BA.5, BN.1, BQ.1.1 and XBB.1.5. in England.13
With the approval of NHS England, our study utilised routine clinical data from NHS England primary care service, managed by The Phoenix Partnership (TPP) securely linked to Office for National Statistics (ONS) death data and other resources via OpenSAFELY. OpenSAFELY is a data analytics platform developed on behalf of NHS England to address urgent COVID-19 research questions, allows secure real-time analysis of pseudonymised primary care records within the EHR vendor’s safeguarded data centre. All data were linked, stored and analysed securely using the OpenSAFELY platform (https://www.opensafely.org), as part of the NHS England OpenSAFELY COVID-19 service. Data includes pseudonymised data such as coded diagnoses, medications and physiological parameters. No free text data are included.
We conducted a population-based cohort study of adult patients who received treatment with either molnupiravir or sotrovimab between December 16, 2021, and February 10, 2022. Patients were aged between 18 and 110 years, registered at GP surgeries, and non-hospitalised for COVID-19 prior to treatment initiation. In accordance with the PROTECT-V trial eligibility criteria, included patients are required to fall into at least one of the following high-risk categories: renal disease (dialysis), kidney transplant, solid organ transplant, haematopoietic stem cell transplant, solid cancer, haematological disease, immune mediated inflammatory disorders, or primary immune deficiencies. Exclusion criteria included patients under 18 years or over 110 years, pregnant, receiving other antivirals or nMAbs, or were recorded to receive more than one treatment during the initial treatment period. COVID-19-related outcomes were defined using four ICD-10 codes (International Classification of Diseases, 10th edition): U07.1(COVID-19, virus identified), U07.2(COVID-19, virus not identified), U09.9 (Post-COVID condition, unspecified), U10.9 (Multisystem inflammatory syndrome associated with COVID-19, unspecified).14
Potential confounding factors included baseline age, sex, ethnicity (White, Black or Black British, Asian or Asian British, Chinese or Other Ethnic Groups, Mixed, Other), Sustainability and Transformation Partnership (STP) of the GP practice’s NHS region, COVID-19 vaccination status (unvaccinated, one dose, two doses, or three or more doses), body mass index (BMI; most recent record, Underweight, Normal, Overweight, or Obese), Index of Multiple Deprivation (IMD; represented as quintiles derived from the patient’s postcode at the Lower Super Output Area level), diagnosis of high-risk conditions, other comorbidities (diabetes, hypertension, chronic heart disease, chronic respiratory disease, autism, serious mental illness), and calendar time (to account for secular trends in prescribing and COVID-19 outcomes). Missing data on ethnicity, IMD, or BMI were classified as "Unknown."
Baseline characteristics of all covariates for each treatment group and for the entire cohort were detailed in the descriptive analysis. T-test, χ² test, or rank-sum test were used as appropriate to compare the distributions of baseline characteristics between the two treatment groups ensuring comparability. The follow-up period for each patient begins 60 days after the start of their treatment, until the date of outcome event, a 6-month period, death, patient deregistration date, or for a 12-month exploratory period, the study end date, whichever comes first. Patients with treatment records of other antivirals or nMAbs after receiving sotrovimab or molnupiravir are censored at the start date of the second treatment.
The risk of the primary outcome (COVID-19 related hospital admission or death) in the two treatment groups were compared using the Cox proportional hazards model, with follow-up period serving as the time scale, and adjustments made for covariates including demographics, high-risk cohort categories, vaccination status, calendar time, body mass index and other comorbidities. Additional models were fitted to evaluate the robustness of the primary findings, progressively incorporating more factors: (1) age and sex; (2) high-risk groups; (3) ethnic background, deprivation, vaccination status, calendar days; (4) body mass index, diabetes, hypertension, and chronic heart and lung diseases. Further sensitivity analyses were conducted using stratified Cox models based on Sustainability and Transformation Partnership (STP) regions and including all-cause deaths within 60 days of treatment initiation as outcomes. Similar analytical procedures were employed to compare the risks of secondary outcomes between the two treatments. Statistical disclosure control methods, including the redaction of any counts ≤7 and rounding to the nearest 10, were applied across the entire report to reduce the disclosure risk.
Data management was performed using Python 3.12 (64-bit), with analysis carried out using R (version 4.3.2). Code for data management and analysis, as well as codelists, are archived online. All code is shared openly for review and re-use under MIT open license [https://github.com/opensafely/prophy_effects_Sotro_Molnup]. Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared.
A total of 4900 high-risk adult patients within OpenSAFELY data platform were identified as having been initially treated for COVID-19 either sotrovimab (n=2735, 55.8%) or molnupiravir (n=2165, 44.2 %) during the designated period. The mean age was 53 (SD: 16), with 2820 (57.5%) aged 50 or older (1730, 35.3% >=60 years). 2840 (58.0%) were female, nearly one-third (1490,30.4%) were from the East region. The majority (4230, 86.3%) patients were recorded as White, followed by Asian or Asian British (n =250, 5.1%), and Black or Black British (160, 3.27%). Hypertension (1880, 38.4%) was the most common comorbidity, with diabetes (1030, 21.0%) and chronic respiratory disease (1030, 21.0%) both equally frequent in the second place. 4800 (98.0%) patients had received COVID-19 vaccinations, of which 88.4% at least three vaccinations.
The average age in patients who received sotrovimab (52 years, SD:16) were slightly younger than those treated with molnupiravir (54, SD: 17). The sotrovimab treatment group exhibited higher rates of dialysis (11.2% vs 7.2%), solid organ transplant (4.57% vs 3.46%), and kidney transplant (2.74% vs 1.62%) compared to the molnupiravir group. While the proportions of immunosupression (+3.9%), imid (+1.9%) and haematological disease (+1.2%) were marginally higher in the molnupiravir cohort. The baseline demographics and clinical characteristics were approximately balanced between the treatment groups.
During the 6 months follow-up after 60 days of treatment initiation, 40 (0.8%) cases of COVID-19 related hospitalisation or death were observed, 25(62.5%) in the molnupiravir group, 15(37.5%) in the sotrovimab group. The results from stratified Cox regression show that no significant differences in the risk of hospitalisation or death between sotrovimab and molnupiravir treatment group within 6-month follow-up observation period after 60 days of treatment. After adjusting for age, gender baseline demographic, and clinic characteristics, high-risk categories, COVID-19 vaccination status, Index of Multiple Deprivation (IMD), STP region of the NHS GP practice, calendar days (to control for underlying trends in prescriptions and COVID-19 outcome), body mass index (BMI) and other commodities (diabetes, hypertension, chronic cardiac disease, chronic respiratory disease), the results remain consistent (hazard ratio 0.98, 95% confidence interval 0.50-1.94, P=0.96). The hazard ratio for these models were close, ranging from 0.76 - 0.98, P>0.05.
Secondary outcomes assessed cumulative COVID-19 related hospitalisations or deaths at 12 and 24 months. At the 12- month follow-up, 80 events were recorded: 35 (43.7%) were treated with molnupiravir, 45 (56.3%) treated with sotrovimab. Stratified Cox regressions revealed no statistically significant difference in risk between the two treatment groups. In the fully adjusted model, sotrovimab was associated with a similar risk as molnupiravir (hazard ratio [HR] 1.22, 95% confidence interval [CI] 0.74-2.01, P=0.43). Hazard ratios across all four models ranged from 1.02 to 1.23. At 24 months (from 60 days post-treatment), 90 cumulative events were observed, with cases equally distributed between the two treatment groups (2.08% for molnupiravir and 1.65% for sotrovimab). Consistent with the 12-month findings, there was no evidence of a differential risk. The fully adjusted Cox regression model yielded an HR of 1.05 (95% CI 0.67–1.65, P=0.81), and the hazard ratios across all models were consistently low, ranging from 0.80 to 0.96.
Over the 6, 12, and 24-month follow-up periods, no statistically significant differences were observed in the risk of COVID-19-related hospitalisation or death between the two treatment groups. The stability of the hazard ratios, consistently approximating unity across all time points, suggests comparable treatment effects over time.
We compared the sustained effectiveness of sotrovimab and molnupiravir in preventing severe outcomes among non-hospitalised high-risk adult COVID-19 patients in England, employing real-time electronic health record data. Our research revealed no significant differences between the two treatments in preventing severe outcomes 60 days after initiation under routine clinical conditions. These results remained consistent after adjusting for a range of potential confounders, in analyses capturing outcomes from treatment initiation through 60 days, and in analyses conducted over extended observation periods, underscoring the robustness of our findings while acknowledging that any potential bias would need to be substantial and highly systematic to account for the observed lack of difference.
The strengths of this study include a large population size and a follow-up period exceeding two years, enabling the assessment of the prolonged protective effectiveness of COVID-19 therapeutic drugs. The OpenSAFELY platform integrates primary care data from 24 million patients with secondary care and COVID-19 records, providing comprehensive and high-quality electronic health data that ensure the feasibility and rigor of the research. The UK NHS system guarantees equitable access to treatment, and detailed CMDUs treatment records provide accurate exposure information in a well-defined study population. Additional strengths include the careful extraction and integration of linked data, adhering to strict research definitions and advanced methodologies, as well as the use of multiple analytical approaches with thorough adjustment for confounders. Nonetheless, this study has several limitations. First, due to inconsistent testing, individuals were included based on high-risk status and COVID-19 treatments without mandatory PCR confirmation, potentially leading to case misidentification. Moreover, the absence of propensity score matching limits control for confounding, and the findings may have restricted generalisability and be susceptible to misclassification due to reliance on coding. Our study is further constrained by its dependence on real-world clinical data, in which treatments followed licensed dosages and clinical guidelines.
Higher doses of sotrovimab have shown promise in clinical trials, including providing at least three months of protection for immunocompromised individuals in a Phase II study (NCT05210101)15 and reducing six-month mortality among hospitalised COVID-19 patients with elevated serum SARS-CoV-2 antigen levels in the RECOVERY trial.16 In contrast, our real-world findings highlight the limited durability of sotrovimab’s effectiveness at the currently approved 500 mg dose, with no significant sustained protection observed beyond 60 days post-treatment in high-risk, non-hospitalised patients when compared with molnupiravir. These results address a critical evidence gap by demonstrating potential limitations in the long-term efficacy of the existing dosing regimen and emphasise the need for further real-world investigation into optimised dosing strategies. Taken together, these findings contribute to a more comprehensive understanding of sotrovimab’s therapeutic potential in mitigating the risk of severe outcomes among high-risk, non-hospitalised populations.
NHS England is the data controller of the NHS England OpenSAFELY COVID-19 Service; TPP is the data processor; all study authors using OpenSAFELY have the approval of NHS England [1]. This implementation of OpenSAFELY is hosted within the TPP environment which is accredited to the ISO 27001 information security standard and is NHS IG Toolkit compliant [2]; Patient data has been pseudonymised for analysis and linkage using industry standard cryptographic hashing techniques; all pseudonymised datasets transmitted for linkage onto OpenSAFELY are encrypted; access to the NHS England OpenSAFELY COVID-19 service is via a virtual private network (VPN) connection; the researchers hold contracts with NHS England and only access the platform to initiate database queries and statistical models; all database activity is logged; only aggregate statistical outputs leave the platform environment following best practice for anonymisation of results such as statistical disclosure control for low cell counts [3].
The service adheres to the obligations of the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018. The service previously operated under notices initially issued in February 2020 by the the Secretary of State under Regulation 3(4) of the Health Service (Control of Patient Information) Regulations 2002 (COPI Regulations), which required organisations to process confidential patient information for COVID-19 purposes; this set aside the requirement for patient consent [4]. As of 1 July 2023, the Secretary of State has requested that NHS England continue to operate the Service under the COVID-19 Directions 2020 [5]. In some cases of data sharing, the common law duty of confidence is met using, for example, patient consent or support from the Health Research Authority Confidentiality Advisory Group [6].
Taken together, these provide the legal bases to link patient datasets using the service. GP practices, which provide access to the primary care data, are required to share relevant health information to support the public health response to the pandemic, and have been informed of how the service operates.
This study was approved by the Health Research Authority [REC reference 20/LO/0651] and by the London School of Hygiene & Tropical Medicine's Ethics Board [reference 21863].
[1] The NHS England OpenSAFELY COVID-19 service - privacy notice. NHS England Digital. Accessed March 2, 2025. https://digital.nhs.uk/coronavirus/coronavirus-covid-19-response-information-governance-hub/the-nhs-england-opensafely-covid-19-service-privacy-notice [2] Data Security and Protection Toolkit. NHS England Digital. Accessed March 2, 2025. https://digital.nhs.uk/services/data-security-and-protection-toolkit/data-security-and-protection-toolkit [3] ISB1523: Anonymisation Standard for Publishing Health and Social Care Data. NHS England Digital. Accessed March 2, 2025. https://digital.nhs.uk/data-and-information/information-standards/information-standards-and-data-collections-including-extractions/publications-and-notifications/standards-and-collections/isb1523-anonymisation-standard-for-publishing-health-and-social-care-data [4] Coronavirus (COVID-19): notice under regulation 3(4) of the Health Service (Control of Patient Information) Regulations 2002 – general. GOV.UK. July 1, 2022. Accessed March 2, 2025. https://www.gov.uk/government/publications/coronavirus-covid-19-notification-of-data-controllers-to-share-information/5de48745-c52e-4799-898f-f617c43c3930 [5] Secretary of State for Health and Social Care - UK Government. COVID-19 Public Health Directions 2020. NHS England Digital. Accessed March 2, 2025. https://digital.nhs.uk/about-nhs-digital/corporate-information-and-documents/directions-and-data-provision-notices/secretary-of-state-directions/covid-19-public-health-directions-2020 [6] Confidentiality Advisory Group. Confidentiality Advisory Group. Health Research Authority. Accessed March 2, 2025. https://www.hra.nhs.uk/about-us/committees-and-services/confidentiality-advisory-group/
Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data is tightly governed by various legislative and regulatory frameworks, and restricted by best practice. The data in the NHS England OpenSAFELY COVID-19 service is drawn from General Practice data across England where TPP is the data processor.
TPP developers initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These pseudonymised records are linked onto key external data resources that have also been pseudonymised via SHA-512 one-way hashing of NHS numbers using a shared salt. University of Oxford, Bennett Institute for Applied Data Science developers and PIs, who hold contracts with NHS England, have access to the OpenSAFELY pseudonymised data tables to develop the OpenSAFELY tools.
These tools in turn enable researchers with OpenSAFELY data access agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All code for the full data management pipeline — from raw data to completed results for this analysis — and for the OpenSAFELY platform as a whole is available for review at github.com/OpenSAFELY.
The data management and analysis code for this paper was led by Qing Wen.
Contributors: LAT, BG, QW, BZ, and REC conceptualised and designed the study. QW, ACAG, and BZ conducted statistical analysis, contributed to data management and coding. QW, ACAG, BZ, VM, REC and TH accessed and verified the underlying data. AM, BG, BS and DE contributed to information governance and project administration. QW and LAT drafted the original version of the manuscript. All authors had full access to the data, contributed to interpretation of the results, reviewed, edited, and approved the final manuscript. All authors accepted responsibility to submit for publication.
BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he has previously been a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science.AM has represented the RCGP in the health informatics group and the Profession Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR); the latter was a paid role. AM is a former employee and interim Chief Medical Officer of NHS Digital. AM has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY.
The OpenSAFELY platform is principally funded by grants from: NHS England [2023-2025]; The Wellcome Trust (222097/Z/20/Z) [2020-2024]; MRC (MR/V015737/1) [2020-2021]. Additional contributions to OpenSAFELY have been funded by grants from: MRC via the National Core Study programme, Longitudinal Health and Wellbeing strand (MC_PC_20030, MC_PC_20059) [2020-2022] and the Data and Connectivity strand (MC_PC_20058) [2021-2022]; NIHR and MRC via the CONVALESCENCE programme (COV-LT-0009, MC_PC_20051) [2021-2024]; NHS England via the Primary Care Medicines Analytics Unit [2021-2024]. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency (UKHSA), the Department of Health and Social Care, or other funders. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
We are very grateful for all the support received from the TPP Technical Operations team throughout this work, and for generous assistance from the information governance and database teams at NHS England and the NHS England Transformation Directorate.
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The OpenSAFELY framework is a Trusted Research Environment (TRE) for electronic health records research in the NHS, with a focus on public accountability and research quality.
Read more at OpenSAFELY.org.
As standard, research projects have a MIT license.