REGN-COV2, a Neutralizing Antibody Cocktail, in Outpatients with Covid-19.

BACKGROUND: Recent data suggest that complications and death from coronavirus disease 2019 (Covid-19) may be related to high viral loads. METHODS: In this ongoing, double-blind, phase 1-3 trial involving nonhospitalized patients with Covid-19, we investigated two fully human, neutralizing monoclonal antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, used in a combined cocktail (REGN-COV2) to reduce the risk of the emergence of treatment-resistant mutant virus. Patients were randomly assigned (1:1:1) to receive placebo, 2.4 g of REGN-COV2, or 8.0 g of REGN-COV2 and were prospectively characterized at baseline for endogenous immune response against SARS-CoV-2 (serum antibody-positive or serum antibody-negative). Key end points included the time-weighted average change in viral load from baseline (day 1) through day 7 and the percentage of patients with at least one Covid-19-related medically attended visit through day 29. Safety was assessed in all patients. RESULTS: Data from 275 patients are reported. The least-squares mean difference (combined REGN-COV2 dose groups vs. placebo group) in the time-weighted average change in viral load from day 1 through day 7 was -0.56 log10 copies per milliliter (95% confidence interval [CI], -1.02 to -0.11) among patients who were serum antibody-negative at baseline and -0.41 log10 copies per milliliter (95% CI, -0.71 to -0.10) in the overall trial population. In the overall trial population, 6% of the patients in the placebo group and 3% of the patients in the combined REGN-COV2 dose groups reported at least one medically attended visit; among patients who were serum antibody-negative at baseline, the corresponding percentages were 15% and 6% (difference, -9 percentage points; 95% CI, -29 to 11). The percentages of patients with hypersensitivity reactions, infusion-related reactions, and other adverse events were similar in the combined REGN-COV2 dose groups and the placebo group. CONCLUSIONS: In this interim analysis, the REGN-COV2 antibody cocktail reduced viral load, with a greater effect in patients whose immune response had not yet been initiated or who had a high viral load at baseline. Safety outcomes were similar in the combined REGN-COV2 dose groups and the placebo group. (Funded by Regeneron Pharmaceuticals and the Biomedical and Advanced Research and Development Authority of the Department of Health and Human Services; ClinicalTrials.gov number, NCT04425629.).

REGEN-COV Antibody Combination and Outcomes in Outpatients with Covid-19.

BACKGROUND: In the phase 1-2 portion of an adaptive trial, REGEN-COV, a combination of the monoclonal antibodies casirivimab and imdevimab, reduced the viral load and number of medical visits in patients with coronavirus disease 2019 (Covid-19). REGEN-COV has activity in vitro against current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern. METHODS: In the phase 3 portion of an adaptive trial, we randomly assigned outpatients with Covid-19 and risk factors for severe disease to receive various doses of intravenous REGEN-COV or placebo. Patients were followed through day 29. A prespecified hierarchical analysis was used to assess the end points of hospitalization or death and the time to resolution of symptoms. Safety was also evaluated. RESULTS: Covid-19-related hospitalization or death from any cause occurred in 18 of 1355 patients in the REGEN-COV 2400-mg group (1.3%) and in 62 of 1341 patients in the placebo group who underwent randomization concurrently (4.6%) (relative risk reduction [1 minus the relative risk], 71.3%; P<0.001); these outcomes occurred in 7 of 736 patients in the REGEN-COV 1200-mg group (1.0%) and in 24 of 748 patients in the placebo group who underwent randomization concurrently (3.2%) (relative risk reduction, 70.4%; P = 0.002). The median time to resolution of symptoms was 4 days shorter with each REGEN-COV dose than with placebo (10 days vs. 14 days; P<0.001 for both comparisons). REGEN-COV was efficacious across various subgroups, including patients who were SARS-CoV-2 serum antibody-positive at baseline. Both REGEN-COV doses reduced viral load faster than placebo; the least-squares mean difference in viral load from baseline through day 7 was -0.71 log10 copies per milliliter (95% confidence interval [CI], -0.90 to -0.53) in the 1200-mg group and -0.86 log10 copies per milliliter (95% CI, -1.00 to -0.72) in the 2400-mg group. Serious adverse events occurred more frequently in the placebo group (4.0%) than in the 1200-mg group (1.1%) and the 2400-mg group (1.3%); infusion-related reactions of grade 2 or higher occurred in less than 0.3% of the patients in all groups. CONCLUSIONS: REGEN-COV reduced the risk of Covid-19-related hospitalization or death from any cause, and it resolved symptoms and reduced the SARS-CoV-2 viral load more rapidly than placebo. (Funded by Regeneron Pharmaceuticals and others; ClinicalTrials.gov number, NCT04425629.).

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

The way we walk ­ our 'gait' ­ is a key indicator of health. Gait irregularities like limping, shuffling or a slow pace can be signs of muscle or joint problems. Assessing a patient's gait is therefore an important element in diagnosing these conditions, and in evaluating whether treatments are working. Gait is often assessed via a simple visual inspection, with patients being asked to walk back and forth in a doctor's office. While quick and easy, this approach is highly subjective and therefore imprecise. 'Objective gait analysis' is a more accurate alternative, but it relies on tests being conducted in specialised laboratories with large-scale, expensive equipment operated by highly trained staff. Unfortunately, this means that gait laboratories are not accessible for everyday clinical use. In response, Wipperman et al. aimed to develop a low-cost alternative to the complex equipment used in gait laboratories. To do this, they harnessed wearable sensor technologies ­ devices that can directly measure physiological data while embedded in clothing or attached to the user. Wearable sensors have the advantage of being cheap, easy to use, and able to provide clinically useful information without specially trained staff. Wipperman et al. analysed data from classic gait laboratory devices, as well as 'digital insoles' equipped with sensors that captured foot movements and pressure as participants walked. The analysis first 'trained' on data from gait laboratories (called force plates) and then applied the method to gait measurements obtained from digital insoles worn by either healthy participants or patients with knee problems. Analysis of the pressure data from the insoles confirmed that they could accurately predict which measurements were from healthy individuals, and which were from patients. The gait characteristics detected by the insoles were also comparable to lab-based measurements ­ in other words, the insoles provided similar type and quality of data as a gait laboratory. Further analysis revealed that information from just a single step could reveal additional information about the subject's walking. These results support the use of wearable devices as a simple and relatively inexpensive way to measure gait in everyday clinical practice, without the need for specialised laboratories and visits to the doctor's office. Although the digital insoles will require further analytical and clinical study before they can be widely used, Wipperman et al. hope they will eventually make monitoring muscle and joint conditions easier and more affordable.

Development and content validation of the Symptoms Evolution of COVID-19: a patient-reported electronic daily diary in clinical and real-world studies.

BACKGROUND: At the onset of the COVID-19 pandemic, there was limited understanding of symptom experience and disease progression. We developed and validated a fit-for-purpose disease-specific instrument to assess symptoms in patients with COVID-19 to inform endpoints in an interventional trial for non-hospitalized patients. METHODS: The initial drafting of the 23-item Symptoms Evolution of COVID-19 (SE-C19) Instrument was developed based on the Centers for Disease Control and Prevention symptom list and available published literature specific to patients with COVID-19 as of Spring 2020. The measurement principles outlined in the Food and Drug Administration (FDA) Patient-Reported Outcomes (PRO) guidance and the FDA's series of four methodological Patient-Focused Drug Development guidance documents were also considered. Following initial development, semi-structured qualitative interviews were conducted with a purposive sample of 30 non-hospitalized COVID-19 patients. Interviews involved two stages: (1) concept elicitation, to obtain information about the symptoms experienced as a result of COVID-19 in the patients' own words, and (2) cognitive debriefing, for patients to describe their understanding of the SE-C19 instructions, specific symptoms, response options, and recall period to ensure the content of the SE-C19 is relevant and comprehensive. Five clinicians treating COVID-19 outpatients were also interviewed to obtain their insights on symptoms experienced by patients and provide input on the SE-C19. RESULTS: Patients reported no issues regarding the relevance or appropriateness of the SE-C19 instructions, including the 24-h recall period. The comprehensiveness of the SE-C19 was confirmed against the conceptualization of the patient experience of symptoms developed in the qualitative research. Minor conceptual gaps were revealed to capture nuances in the experience of nasal and gustatory symptoms and systemic manifestations of sickness. Almost all items were endorsed by patients as being appropriate, well understood, and easy to respond to. The clinicians largely approved all items, response options, and recall period. CONCLUSIONS: The qualitative research provided supportive evidence of the content validity of the SE-C19 to assess the symptoms of outpatients with COVID-19, and its use in clinical trials to evaluate the benefit of treatment. Minor changes may be considered to improve conceptual clarity and ease of responding.