RESUMEN
Background: Exposure-response (E-R) models were developed for the primary endpoint of hospitalization or death in COVID-19 patients from the Phase 3 portion of the MOVe-OUT study (Clinicaltrials.gov NCT04577797). Beyond dose, these models can identify other determinants of response, highlight the relationship of virologic response with clinical outcomes, and provide a basis for differential efficacy across trials. Method(s): Logistic regression models were constructed using a multi-step process with influential covariates identified first using placebo arm data only. Subsequently the assessment of drug effect based on drug exposure was determined using placebo and molnupiravir (MOV) arm data. To validate the models, the rate of hospitalization/death was predicted for published studies of COVID-19 treatment. All work was performed using R Version 3.0 or later. Result(s): A total of 1313 participants were included in the E-R analysis, including subjects having received MOV (N=630) and placebo (N=683). Participants with missing baseline RNA or PK were excluded (79 from MOV and 16 from placebo arms). The covariates shown to be significant determinants of response were baseline viral load, baseline disease severity, age, weight, viral clade, and co-morbidities of active cancer and diabetes. Day 5 and Day 10 viral load were identified as strong on-treatment predictors of hospitalization/death, pointing to sustained high viral load as driving negative outcomes. Estimated AUC50 was 19900 nM*hr with bootstrapped 95% C.I. of (9270, 32700). In an external validation exercise based on baseline characteristics, the E-R model predicted the mean (95% CI) placebo hospitalization rates across trials of 9.3% (7.6%, 11.7%) for MOVe-OUT, 7.2% (5.3%, 9.8%) for the nirmatrelvir/ritonavir EPIC-HR trial, and 3.2% (1.9%, 5.5%) for generic MOV trials by Aurobindo and Hetero, consistent with the differing observed placebo rates in these trials. The relative reduction in hospitalization/death rate predicted with MOV treatment (relative to placebo) also varied with the above patient populations. Conclusion(s): Overall, the exposure-response results support the MOV dose of 800 mg Q12H for treatment of COVID-19. The results further support that many clinical characteristics impacted hospitalization rate beyond drug exposures which can vary widely across studies. These characteristics also influenced the magnitude of relative risk reduction achieved by MOV in the MOVe-OUT study.
RESUMEN
BACKGROUND: Exposure-response (E-R) analysis supported molnupiravir phase 3 dose selection based on viral load (VL) and mechanism of action (MOA) markers from phase 2.1 This analysis evaluated how well these biomarkers predict the E-R for hospitalization or death in phase 3. METHOD(S): The following E-R models were developed and compared: (1) logistic regression of the primary outcome (hospitalization or death) from phase 3, (2) VL change from baseline (CFB) from phase 2 and 3, and (3) low frequency nucleotide substitutions (LNS), a measure of MOA, from phase 2. Individual estimates of exposure were derived from population PK modeling of sparse samples collected in all patients. All work was performed using R v3.0 or later. RESULT(S): All E-R relationships were best represented by an Emax model with AUC50 estimates of 19,900, 10,260, and 4,390 nM*hr for hospitalization, day 5 VL CFB, and LNS mutation rate, respectively. Normalized E-R relationships were overlaid, illustrating consistency in E-R shape (Figure). Plasma NHC AUC0-12 was identified as the PK driver. Patients at 800 mg achieved near maximal response. CONCLUSION(S): E-R results support the dose of 800 mg Q12H for treatment of COVID-19. E-R relationships for MOA and virology biomarkers were consistent with the clinical E-R. (Figure Presented).
RESUMEN
BACKGROUND: The goal of this analysis was to investigate the relationship of molnupiravir pharmacokinetics (PK) and clinical outcomes (primary endpoint of hospitalization or death) in patients with COVID-19 in the phase 3 cohort of MOVe-OUT (clinicaltrials.gov NCT04577797).1 METHODS: Logistic regression models were constructed using a multi-step process with influential covariates identified first using placebo arm data only and subsequently assessment of drug effect as a function of exposures evaluated using placebo and MOV arm data. Individual estimates of exposure were derived from population PK modeling of sparse samples collected in all patients. All work was performed using R v3.0 or later. RESULT(S): A total of 1,313 participants were included in the exposure-response (E-R) analysis, including subjects on MOV (N = 630) and placebo (N = 683). Participants with missing PK or baseline RNA were excluded (79 from MOV and 16 from placebo arms). The covariates shown to be significant determinants of response were baseline viral load, baseline disease severity, age, weight, viral clade, active cancer, and diabetic risk factors. An additive AUC-based Emax model with a fixed hill coefficient of 1 best represented exposure-dependency in drug effect. Estimated AUC50 was 19,900 nM*hr with bootstrapped 95% confidence interval of (9,270, 32,700). Patients at 800 mg achieved near maximal response, which was larger than the response projected for 200 or 400 mg. CONCLUSION(S): Overall, the E-R results support the MOV dose of 800 mg Q12H for treatment of COVID-19. Many patient characteristics, beyond drug exposures, impacted the risk of hospitalization or death.
RESUMEN
Background: SARS-CoV-2, a novel coronavirus, emerged in Wuhan, China in December of 2019, and became a pandemic. Increases in bacterial/fungal co-infections have occurred during influenza pandemics and early data from this pandemic indicate high utilization of antimicrobial therapy. We compared the utilization of antimicrobials and health outcomes between SARS-CoV-2 positive and negative patients. Methods: Patients hospitalized at 271 US acute care facilities from 3/1/20-5/30/20 with ≥1 day length of stay (LOS) and ≥24 hours of antimicrobial therapy tested for SARS-CoV-2 were included in the study (BD Insights Research Database [Becton, Dickinson & Company, Franklin Lakes, NJ]). Demographics, antimicrobial utilization, duration of antimicrobial therapy, hospital LOS and ICU LOS data were analyzed by SARS-CoV-2 test results. Results: 142,054 patients were tested for SARS CoV-2 and 12% (n=17,075) were SARS-CoV-2 positive. SARS-CoV-2 negative and positive patients did not differ regarding presence of a positive bacterial culture. Total LOS, % ICU admission, and ICU LOS were higher among SARS-CoV-2 positive patients (Table). In total 48% of admissions were prescribed antimicrobial therapy;rates were higher in SARS-CoV-2 positive versus negative admissions (68% vs. 46%). The most common antimicrobials and classes are in Table. Antimicrobial therapy and outcomes in hospitalized SARS-CoV-2 tested patients. Conclusion: Almost half of patients tested for SARS-CoV-2 were prescribed antimicrobials, with antimicrobial use higher among those with SARS-CoV-2, despite similar rates of positive cultures. On average, antimicrobials were prescribed within 10 hours from the time to admission among patients tested. These treatment patterns may highlight the difficulties in making treatment decisions and concerns over potential bacterial superinfection in SARS-CoV-2, but also indicate potential overuse of antimicrobials. Collateral damage from antimicrobial overuse include increase selection of antimicrobial resistance, adverse effects of drugs, and unnecessary treatment costs. It will be important to continue to evaluate the utilization and appropriateness of antimicrobial use among SARS-CoV-2 patients.
RESUMEN
Background: Past experiences with viral epidemics have indicated an increased risk for bacterial, fungal, or other viral secondary or co-infections due to patient characteristics, healthcare exposures and biological factors. It is important to understand the epidemiology of these infections to properly treat and manage these complex patients. This study evaluates the frequency, source, and pathogens identified among SARS-CoV-2 tested patients. Methods: This was a multi-center, retrospective cohort analysis of SARS-COV-2 tested patients from 271 US acute care facilities with >1 day inpatient admission with a discharge or death between 3/1/20-5/31/20 (BD Insights Research Database [Becton, Dickinson & Company, Franklin Lakes, NJ]). We evaluated pathogens identified from blood, respiratory tract (upper/lower), urine, intra-abdominal (IA), skin/wound and other sources and classified them with respect to Gram-negative (GN), and Grampositive (GP) bacteria, fungi, and viruses among those SARS-CoV-2 positive and negative. Results: There were 599,709 admissions with 142,054 (23.7%) patients tested. Among those SARS-CoV-2 tested, 17,075 (12%) were positive and 124,979 (78%) were negative. The most common specimen collection sites (Table 1) and pathogens (Table 2) are shown. Higher rates of urine and respiratory cultures and higher rates of P. aeruginosa and fungi were seen in SARS CoV-2 positive patients. The top pathogens for urine cultures were Escherichia coli and Klebsiella pneumoniae, for blood Staphylococcus aureus and Escherichia coli and respiratory Staphylococcus aureus and Pseudomonas aeruginosa. SARS-CoV-2 positive patients had an overall longer length of stay (LOS) than negative, which almost doubled when a positive pathogen was identified. Conclusion: There were similar rates of positive pathogen identification among SARS-CoV-2 test positive and negative patients, which might highlight similarities in clinical presentation. However, SARS-CoV-2 positive patients had longer hospital LOS and LOS increased with positive culture. Sources of infection and pathogens varied based on a positive or negative SARS-CoV-2 result. Identifying likely causative pathogens of co-infections in the era of SARS-CoV-2 is critical for treatment optimization.