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medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.09.07.22279497


Background Sotrovimab, a recombinant human monoclonal antibody (mAb) against SARS-CoV-2 had US FDA Emergency Use Authorization (EUA) for the treatment of high-risk outpatients with mild-to-moderate COVID-19 from May 26, 2021, to April 5, 2022. The study objective was to evaluate the real-world effectiveness of sotrovimab in reducing the risk of 30-day all-cause hospitalization and/or mortality during the time period when the prevalence of circulating SARS-CoV-2 variants was changing between Delta and Omicron sub-lineages in the US. Methods A retrospective analysis was conducted on de-identified claims data for 1,530,501 patients diagnosed with COVID-19 (ICD-10: U07.1) from September 1, 2021, to April 30, 2022, in the FAIR Health National Private Insurance Claims (FH NPIC(R)) database. Patients meeting EUA high-risk criteria were identified via pre-specified ICD-10-CM diagnoses in records [≤]24 months prior to their first COVID-19 diagnosis and divided into two cohorts based on claimed procedural codes: treated with sotrovimab (''sotrovimab'') and not treated with a mAb (''no mAb''). All-cause hospitalizations and facility-reported all-cause mortality within 30 days of diagnosis (''30-day hospitalization or mortality'') were identified. Multivariable and propensity score-matched Poisson and logistic regressions were conducted to estimate the adjusted relative risk (RR) and odds of 30-day hospitalization or mortality among those treated with sotrovimab compared with those not treated with a mAb. Results Of the high-risk COVID-19 patients identified, 15,633 were treated with sotrovimab and 1,514,868 were not treated with a mAb. Compared with the no mAb cohort, the sotrovimab cohort was older and had a higher proportion of patients across the majority of high-risk conditions. In the no mAb cohort, 84,307 (5.57%) patients were hospitalized and 8,167 (0.54%) deaths were identified, while in the sotrovimab cohort, 418 (2.67%) patients were hospitalized and 13 (0.08%) deaths were identified. After adjusting for potential confounders, high-risk COVID-19 patients treated with sotrovimab had a 55% relative risk reduction of 30-day hospitalization or mortality (RR: 0.45, 95% CI: 0.41,0.49) and an 85% relative risk reduction of 30-day mortality (RR: 0.15, 95% CI: 0.08, 0.29) compared with high-risk patients not treated with a mAb. From September 2021 to April 2022, sotrovimab maintained clinical effectiveness with relative risk reductions of 30-day hospitalization or mortality ranging from 46% to 71%. Stratifying by high-risk condition, sotrovimab-treated patients exhibited statistically significant relative risk reductions of 30-day hospitalization or mortality compared with the no mAb cohort across all high-risk conditions (P<0.0001), ranging from 44% among pregnant women to 70% among patients 65 years and older. Conclusion In this large, US real-world, observational study of high-risk COVID-19 patients with reported diagnosis between September 2021 and April 2022 during the Delta and early Omicron variant waves, treatment with sotrovimab was associated with reduced risk of 30-day all-cause hospitalization and facility-reported mortality compared with no mAb treatment. Sotrovimab clinical effectiveness persisted throughout the months when Delta and early Omicron sub-lineages were the predominant circulating variants in the US, though there was an uncertain RR estimate in April 2022 with wide confidence intervals due to the small sample size. Sotrovimab clinical effectiveness also persisted among all high-risk subgroups assessed.

medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.12.21.21268197


Understanding who is at risk of progression to severe COVID-19 is key to effective treatment. We studied correlates of disease severity in the COMET-ICE clinical trial that randomized 1:1 to placebo or to sotrovimab, a monoclonal antibody for the treatment of SARS-CoV-2 infection. Several laboratory parameters identified study participants at greater risk of severe disease, including a high neutrophil-lymphocyte ratio (NLR), a negative SARS-CoV-2 serologic test and whole blood transcriptome profiles. Sotrovimab treatment in these groups was associated with normalization of NLR and the transcriptomic profile, and with a decrease of viral RNA in nasopharyngeal samples. Transcriptomics provided the most sensitive detection of participants who would go on to be hospitalized or die. To facilitate timely measurement, we identified a 10-gene signature with similar predictive accuracy. In summary, we identified markers of risk for disease progression and demonstrated that normalization of these parameters occurs with antibody treatment of established infection.

medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.06.21.21259286


SARS-CoV-2 evolution threatens vaccine- and natural infection-derived immunity, and the efficacy of therapeutic antibodies. Herein we sought to predict Spike amino acid changes that could contribute to future variants of concern. We tested the importance of features comprising epidemiology, evolution, immunology, and neural network-based protein sequence modeling. This resulted in identification of the primary biological drivers of SARS-CoV-2 intra-pandemic evolution. We found evidence that resistance to population-level host immunity has increasingly shaped SARS-CoV-2 evolution over time. We identified with high accuracy mutations that will spread, at up to four months in advance, across different phases of the pandemic. Behavior of the model was consistent with a plausible causal structure wherein epidemiological variables integrate the effects of diverse and shifting drivers of viral fitness. We applied our model to forecast mutations that will spread in the future, and characterize how these mutations affect the binding of therapeutic antibodies. These findings demonstrate that it is possible to forecast the driver mutations that could appear in emerging SARS-CoV-2 variants of concern. This modeling approach may be applied to any pathogen with genomic surveillance data, and so may address other rapidly evolving pathogens such as influenza, and unknown future pandemic viruses.