Safety and Pharmacokinetics of Islatravir in Individuals with Severe Renal Insufficiency.

Islatravir (MK-8591) is a high-potency reverse transcriptase translocation inhibitor in development for the treatment of HIV-1 infection. Data from preclinical and clinical studies suggest that ~30% to 60% of islatravir is excreted renally and that islatravir is not a substrate of renal transporters. To assess the impact of renal impairment on the pharmacokinetics of islatravir, an open-label phase 1 trial was conducted with individuals with severe renal insufficiency (RI). A single dose of islatravir 60 mg was administered orally to individuals with severe RI (estimated glomerular filtration rate [eGFR] <30 mL/min/1.73 m2) and to healthy individuals without renal impairment (matched control group; eGFR ≥90 mL/min/1.73 m2). Safety and tolerability were assessed, and blood samples were collected to measure the pharmacokinetics of islatravir and its major metabolite 4'-ethynyl-2-fluoro-2'deoxyinosine (M4) in plasma, as well as active islatravir-triphosphate (TP) in peripheral blood mononuclear cells (PBMCs). Plasma islatravir and M4 area under the concentration-time curve from zero to infinity (AUC0-∞) were ~2-fold and ~5-fold higher, respectively, in participants with severe RI relative to controls, whereas islatravir-TP AUC0-∞ was ~1.5-fold higher in the RI group than in the control group. The half-lives of islatravir in plasma and islatravir-TP in PBMCs were longer in participants with severe RI than in controls. These findings are consistent with renal excretion playing a major role in islatravir elimination. A single oral dose of islatravir 60 mg was generally well tolerated. These data provide guidance regarding administration of islatravir in individuals with impaired renal function. (This study has been registered at ClinicalTrials.gov under registration no. NCT04303156.).

Applications of Bayesian shrinkage prior models in clinical research with categorical responses.

BACKGROUND: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions such as diabetes, colon cancer, and Alzheimer's disease. Developing accurate prediction and classification methods benefits personalized medicine. Building an excellent predictive model involves selecting the features that are most significantly associated with the outcome. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. This work discusses variable selection with three shrinkage priors and illustrates its application to clinical data such as Pima Indians Diabetes, Colon cancer, ADNI, and OASIS Alzheimer's real-world data. METHODS: A unified Bayesian hierarchical framework that implements and compares shrinkage priors in binary and multinomial logistic regression models is presented. The key feature is the representation of the likelihood by a Polya-Gamma data augmentation, which admits a natural integration with a family of shrinkage priors, specifically focusing on Horseshoe, Dirichlet Laplace, and Double Pareto priors. Extensive simulation studies are conducted to assess the performances under different data dimensions and parameter settings. Measures of accuracy, AUC, brier score, L1 error, cross-entropy, and ROC surface plots are used as evaluation criteria comparing the priors with frequentist methods as Lasso, Elastic-Net, and Ridge regression. RESULTS: All three priors can be used for robust prediction on significant metrics, irrespective of their categorical response model choices. Simulation studies could achieve the mean prediction accuracy of 91.6% (95% CI: 88.5, 94.7) and 76.5% (95% CI: 69.3, 83.8) for logistic regression and multinomial logistic models, respectively. The model can identify significant variables for disease risk prediction and is computationally efficient. CONCLUSIONS: The models are robust enough to conduct both variable selection and prediction because of their high shrinkage properties and applicability to a broad range of classification problems.

Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model.

An unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic's progress was uncertain, and thus, predicting it became crucial for public health researchers. These predictions help the effective allocation of health-care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, the UK, and Canada. A novel hybrid approach based on the Theta method and autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus, a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test datasets on an average.

The effects of long COVID-19, its severity, and the need for immediate attention: Analysis of clinical trials and Twitter data.

Background: The coronavirus disease 2019 (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization; identifying the disease progression, predicting patient outcomes early, the possibility of long-term adverse events through effective modeling, and the use of real-world data are of immense importance to effective treatment, resource allocation, and prevention of severe adverse events of grade 4 or 5. Methods: First, we raise awareness about the different clinical trials on long COVID-19. The trials were selected with the search term "long COVID-19" available in ClinicalTrials.gov. Second, we curated the recent tweets on long-haul COVID-19 and gave an overview of the sentiments of the people. The tweets obtained with the query term #long COVID-19 consisted of 8,436 tweets between 28 August 2022 and 06 September 2022. We utilized the National Research Council (NRC) Emotion Lexicon method for sentiment analysis. Finally, we analyze the retweet and favorite counts are associated with the sentiments of the tweeters via a negative binomial regression model. Results: Our results find that there are two types of clinical trials being conducted: observational and interventional. The retweet counts and favorite counts are associated with the sentiments and emotions, such as disgust, joy, sadness, surprise, trust, negative, and positive. Conclusion: We need resources and further research in the area of long COVID-19.

Coronavirus (COVID-19): A Systematic Review and Meta-analysis to Evaluate the Significance of Demographics and Comorbidities.

Background: The unprecedented outbreak of a contagious respiratory disease caused by a novel coronavirus has led to a pandemic since December 2019, claiming millions of lives. The study systematically reviews and summarizes COVID-19's impact based on symptoms, demographics, comorbidities, and demonstrates the association of demographics in cases and mortality in the United States. Methods: PubMed and Google Scholar were searched from December 2019- August 2020, and articles restricted to the English language were collected following PRISMA guidelines. US CDC data was used for establishing statistical significance of age, sex, and race. Results: Among 3745 patients in China, mean age is 50.63 (95% CI: 36.84, 64.42) years, and 55.7 % (95% CI: 52.2, 59.2) were males. Symptoms included fever 86.5% (82.7, 90.0), fatigue 41.9% (32.7, 51.4), dyspnea 29.0% (21.2, 37.5), cough 66.0% (61.3, 70.6), mucus 66% (61.3, 70.6), lymphopenia 18.9% (5.2, 38.0). Prevalent comorbidities were hypertension 16.4% (12.5, 20.8), diabetes 8.9% (7.0, 11.1), CVD 10.9% (6.1, 16.7), ARDS 14.6% (4.9, 27.8), malignancy 1.5 (0.05, 2.8), 1.3% (0.08, 1.9), COPD 1.3 (0.08, 1.9). 63.5 % (33.5, 88.7) received oxygen therapy, 20.8% (8.9, 35.7) were in ventilation, 23.5% (5.9, 47.8) were at the ICU. 86.5% (76.8, 94) had antiviral, 73.9% (55.3, 89.0) had antibiotics, 30% (20.6, 40.2) corticosteroids treatment.In the US, the odds ratio of infection in males to females is 0.873 (CI: 0.052,14.791), while the odds of dying from infection is 1.378 (CI: 0.081, 23.528) for males. The prevalence of infection is higher in females; case and death rates are higher in whites and Hispanics than other races; the death rate is higher in males irrespective of race and age; death rate per 100,000 population increases monotonically with age. Conclusion: Results showed that metabolic diseases comprising CVD, diabetes, hypertension, and respiratory diseases, including COPD, ARDS, are the most common comorbidities to severe condition and poor prognosis in covid-19 patients. Following the recent FDA's guidance for designing Covid-19 vaccine trials, stratification factors of age, race, sex, and comorbidities need consideration in allocation. This study aimed to provide clinical researchers, health policy planners a detailed insight into the coronavirus disease.

Bayesian Decision-Theoretic Methods for Survival Data using Stochastic Optimization.

We introduce a principled method for Bayesian subgroup analysis. The approach is based on casting subgroup analysis as a Bayesian decision problem. The two main innovations are: (1) the explicit consideration of a "subgroup report," comprising multiple subpopulations; and (2) adapting an inhomogeneous Markov chain Monte Carlo simulation scheme to implement stochastic optimization. The latter makes the search for "subgroup reports" practically feasible.

Adaptive Signature Design- review of the biomarker guided adaptive phase -III controlled design.

Genomics having a profound impact on oncology drug development necessitates the use of genomic signatures for therapeutic strategy and emerging medicine proposals. Since its advent in the arena of clinical trials biomarker-related predictive methods for the identification and selection of patient subgroups, with optimal treatment response, are widely used. Genetic signatures which are accountable for the differential response to treatments are experimentally recognizable and analytically validated in phase II stage of clinical trials. The availability of robust and validated biomarkers in phase III is limited. Hence, the development of a clinical trial design without the availability of biomarker identity for treatment-sensitive patients becomes indispensable. Adaptive Signature Design (ASD) is a design procedure of developing and validating a predictive classifier (diagnostic testing strategy) when the signature of subjects responding differentially to treatment is remote in the context of the study. This review provides a detailed methodology and statistical background of this pioneering design developed by Freidlin and Simon (2005). In addition, it concentrates on the advances in ASD regarding statistical issues such as predictive assay identification, classification techniques, statistical methods, subgroup search, choice of differentially expressed genes, and multiplicity correction. The statistical methodology behind the design is explained with the intent of building the ground steps for future research approachable, especially for beginning researchers. Most of the existing research articles give a microcosmic view of the design and lack in describing the details behind the methodology. This study covers those details and marks the novelty of our research.