Factors Influencing Covid-19 Risk: Insights from Molnupiravir Exposure-Response

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.

Immune-Viral Dynamics Modeling of the Basis for Individual Variation in Covid-19

Background: Viral dynamics models provide mechanistic insights into viral disease and therapeutic interventions. A detailed, mechanistic model of COVID-19 was developed and fit to data from molnupiravir (MOV) trials to characterize the SARS-CoV-2 viral dynamics in MOV-treated and untreated participants and describe the basis for variation across individuals. Method(s): An Immune-Viral Dynamics Model (IVDM) incorporating mechanisms of viral infection, viral replication, and induced innate and adaptive immune response described the dynamics of viral load (VL) from pooled data from MOV Phase 2 and 3 trials (N=1958). Population approaches were incorporated to estimate variation across individuals and to conduct an extensive covariate analysis. Nineteen parameters in a system of five differential equations described SARS-CoV-2 viral dynamics in humans. Six population parameters were successfully informed through fitting to observed trial data while the remaining parameters were fixed based on literature values or calibrated via sensitivity analysis. Result(s): Final viral dynamics and immune response parameters were all estimated with high certainty and reasonable inter-individual variabilities. The model captured the viral load profiles across a wide range of subpopulations and predicted lymphocyte dynamics without using this data to inform the parameters, suggesting inferred immune response curves from this model were accurate. This mechanistic representation of COVID-19 disease indicated that the processes of cellular infection, viral production, and immune response are in a time-varying, non-equilibrium state throughout the course of infection. MOV mechanism of action was best described as an inhibitory process on the infectivity term with estimated AUC50 of 10.5 muM*hr. Covariates identified included baseline viral load on infectivity and age, baseline disease severity, viral clade, baseline viral load, and diabetes on immune response parameters. Greater variation was identified for immune parameters than viral kinetic parameters. Conclusion(s): These findings show that the variation in the human response (e.g., immune response) is more influential in COVID-19 disease than variations in the virus kinetics. The model indicates that immunocompromised patients (due to HIV, organ transplant, active cancer, immunosuppressive therapies) develop an immune response to SARS-CoV-2, albeit more slowly than in immunocompetent, and MOV is effective in further reducing viral loads in the immunocompromised.

Molnupiravir Exposure-Response Relationships for Clinical Outcomes, Virology, and Mechanism of Action Biomarkers Are Consistent in Treatment of Covid-19

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).

Molnupiravir Exposure-Response of Clinical Outcomes in Treatment of Covid-19

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.

Highly Sensitive and Selective Sensing Probe for Determination of Anti-Covid-19 Remdesvir: Application to Pharmaceutical Dosage Form and Biological Fluids

We reported the first investigational electrochemical study for Remdesvir (REM). REM is a promising antiviral agent used recently for the treatment of the most dangerous pandemic disease nowadays (COVID-19). Anionic surfactant, silica nanoparticles, and multiwall carbon nanotubes modified carbon paste (SDS/SiO2/MWCNT/CPE) sensor was designed to introduce our approach. The results revealed irreversible diffusion oxidative reaction of REM with two well-defined peaks (E1/V = 1.19, E2/V = 1.35) in 0.1 M phosphate buffer of pH 6 using differential pulse (DP) voltammetry. A linear relationship between the peak current and the drug concentration was established over the concentration range of 1.66 × 10-7-3.52 × 10-6 M (100-200 ng ml-1) with a limit of detection (LOD) of 4.80 × 10-8 M and limit of quantitation (LOQ) of 8.0 × 10-8 M and mean % recovery ± % RSD of 99.05 ± 1.94. The proposed method succeeded in the determination of the drug in its pharmaceutical dosage form, in human plasma with and human urine samples. Finally, the method was validated according to ICH guidelines and FDA guidance for the determination of the drug in biological fluids. The developed data was found to be in good agreement with a validated reported method. © 2022 The Electrochemical Society ("ECS").

Diagnosing Pneumonia from Chest X-Rays Using Optimized Residual Network

Chest diseases are thought to be among the most lethal. When we examine the death rate and the enormous number of people who suffer from pneumonia, it becomes clear how critical it is to diagnose the disease early. Recently, academics have started to use deep learning to diagnose medical diseases, while others are working to improve the performance of deep learning neural networks. For many academics and practitioners, optimizing hyperparameters in Convolutional Neural Networks is a time-consuming task. Experts must manually configure a set of hyperparameter options to obtain superior performance hyperparameters. Convolutional Neural Network is used to model and apply the best results of this manual configuration. Various datasets, on the other hand, necessitate different models or a mix of hyperparameters, which can be time-consuming and tiresome. Several models have been developed to handle this, including grid search and random selection. We propose two Residual Networks hyperparameters optimization systems to meet the aims. In order to improve existing diagnosis methods, these optimization techniques are applied to diagnose pneumonia from chest X-rays. To test the method, we employed these algorithms to categorize a COVID-19 and pneumonia dataset made up of X-ray images. The suggested systems demonstrated that adjusting hyperparameters for the ResNet using random search and hyperband optimization algorithms produces better accuracy than other algorithms, with accuracies of 98.84975% and 98.4184%, respectively. We therefore conclude that employing random search or hyperband to optimize ResNet hyperparameters yields better outcomes than other methods. © 2021 IEEE.

Metabolic disorders associated with coronavirus disease-2019 in conjunction with different chronic diseases and the increased vulnerability to infection

The severe acute respiratory-syndrome coronavirus 2 is a viral pathogen that is responsible for the coronavirus disease-2019. Since first being reported, severe acute respiratory-syndrome coronavirus 2 has infected millions of people and eventually caused millions of deaths worldwide, with these numbers rising daily during successive waves. So far, the risk factors associated with poor clinical outcomes (death or admission to an ICU) have been reported to be old age and several comorbidities associated with compromised immune system to help the patient fight the infection. The most common of these comorbidities are obesity, hypertension, diabetes, cardiovascular diseases, dementia, and malignancies. These comorbidities, individually or in combination with age, were reported to be linked with poor prognoses. In the present review, vulnerability of patients with different chronic diseases to infection with coronavirus disease-2019 is discussed with different treatment strategies during coexistence of viral infection with any of these diseases. Also, biochemical markers (e.g., angiotensin-converting enzyme 2, cytokine storm, or inflammatory markers) and the underlying mechanisms associated with viral infection together with the different chronic diseases are described. © COLING 2018.All right reserved.

Smart autonomous thermometer system to assist COVID-19 pandemic monitoring in smart campuses

COVID-19 pandemic has impacted human lives from different aspects during the last months. The number of recorder cases is increasingly growing. The infection rate spikes due to unprotected contact with people with fever or respiratory symptoms. Fever is one of the common symptoms of corona virus infection. Thus, measuring the body temperature and tracing its change is an important indicator. It is a recommended practice to inspect suspected cases. For campuses with big number of residents and visitors, monitoring the temperature arises a new challenge as it requires huge number of trained medical staff and imposes the direct contact between the medical staff and the person under test. Also, this practice requires the direct contact between the medical staff and the person under test, which may cause the transmission of the virus. In this paper a non-contact autonomous smart thermometer system is proposed. The thermometer measures forehead temperature remotely, without the need for human operation. The system indicates the person identity and records the measured temperature enabling the tracing of temperature of engaged persons and further analysis of gathered data. In this paper, the system architecture and the implementation of the proposed system is described. Testing and verification processes demonstrate its effectiveness and feasibility. © 2020 Institution of Engineering and Technology. All rights reserved.

Two Epidemics and a Pandemic: The Collision of Prescription Drug Misuse and Racism during COVID-19.

The present study investigated the relationship between perceived racial discrimination and prescription drug misuse (PDM) among Asian, Black, and Latinx Americans during the COVID-19 crisis. U.S. racial/ethnic minorities may have been uniquely affected by two national and one global pandemic: the opioid crisis, racism, and COVID-19. Opioid death rates increased among many groups prior to the pandemic. This country witnessed an increase in racialized acts against people of color across the spectrum in the spring and summer months of the world's COVID-19 outbreak. While studies have shown a clear link between perceived racial discrimination and substance abuse outside of the global pandemic, no identified studies have done so against the backdrop of a global health pandemic. Separate hierarchical regressions revealed a significant association between perceived racial discrimination and PDM for Black Americans, Asian Americans, and Latinx individuals. Findings build on the scant literature on PDM in diverse samples and establish a relationship between perceived racial discrimination and PDM, as previously identified for other abused substances. Future post-pandemic substance misuse interventions should consider the influence of perceived racial discrimination as they help individuals recover from the aftermath of this stressful trifecta.

Hyperparameters optimization for ResNet and Xception in the purpose of diagnosing COVID-19

COVID-19 has been considered as a global pandemic. Recently, researchers are using deep learning networks for medical diseases' diagnosis. Some of these researches focuses on optimizing deep learning neural networks for enhancing the network accuracy. Optimizing the Convolutional Neural Network includes testing various networks which are obtained through manually configuring their hyperparameters, then the configuration with the highest accuracy is implemented. Each time a different database is used, a different combination of the hyperparameters is required. This paper introduces two COVID-19 diagnosing systems using both Residual Network and Xception Network optimized by random search in the purpose of finding optimal models that give better diagnosis rates for COVID-19. The proposed systems showed that hyperparameters tuning for the ResNet and the Xception Net using random search optimization give more accurate results than other techniques with accuracies 99.27536% and 100 % respectively. We can conclude that hyperparameters tuning using random search optimization for either the tuned Residual Network or the tuned Xception Network gives better accuracies than other techniques diagnosing COVID-19.

COVID-19 forecasting based on an improved interior search algorithm and multi-layer feed forward neural network (preprint)

COVID-19 is a novel coronavirus that was emerged in December 2019 within Wuhan, China. As the crisis of its serious increasing dynamic outbreak in all parts of the globe, the forecast maps and analysis of confirmed cases (CS) becomes a vital great changeling task. In this study, a new forecasting model is presented to analyze and forecast the CS of COVID-19 for the coming days based on the reported data since 22 Jan 2020. The proposed forecasting model, named ISACL-MFNN, integrates an improved interior search algorithm (ISA) based on chaotic learning (CL) strategy into a multi-layer feed-forward neural network (MFNN). The ISACL incorporates the CL strategy to enhance the performance of ISA and avoid the trapping in the local optima. By this methodology, it is intended to train the neural network by tuning its parameters to optimal values and thus achieving high-accuracy level regarding forecasted results. The ISACL-MFNN model is investigated on the official data of the COVID-19 reported by the World Health Organization (WHO) to analyze the confirmed cases for the upcoming days. The performance regarding the proposed forecasting model is validated and assessed by introducing some indices including the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) and the comparisons with other optimization algorithms are presented. The proposed model is investigated in the most affected countries (i.e., USA, Italy, and Spain). The experimental simulations illustrate that the proposed ISACL-MFNN provides promising performance rather than the other algorithms while forecasting task for the candidate countries.