Safety, tolerability, and pharmacokinetics of VV116, an oral nucleoside analog against SARS-CoV-2, in Chinese healthy subjects.

VV116 (JT001) is an oral drug candidate of nucleoside analog against SARS-CoV-2. The purpose of the three phase I studies was to evaluate the safety, tolerability, and pharmacokinetics of single and multiple ascending oral doses of VV116 in healthy subjects, as well as the effect of food on the pharmacokinetics and safety of VV116. Three studies were launched sequentially: Study 1 (single ascending-dose study, SAD), Study 2 (multiple ascending-dose study, MAD), and Study 3 (food-effect study, FE). A total of 86 healthy subjects were enrolled in the studies. VV116 tablets or placebo were administered per protocol requirements. Blood samples were collected at the scheduled time points for pharmacokinetic analysis. 116-N1, the metabolite of VV116, was detected in plasma and calculated for the PK parameters. In SAD, AUC and Cmax increased in an approximately dose-proportional manner in the dose range of 25-800 mg. T1/2 was within 4.80-6.95 h. In MAD, the accumulation ratio for Cmax and AUC indicated a slight accumulation upon repeated dosing of VV116. In FE, the standard meal had no effect on Cmax and AUC of VV116. No serious adverse event occurred in the studies, and no subject withdrew from the studies due to adverse events. Thus, VV116 exhibited satisfactory safety and tolerability in healthy subjects, which supports the continued investigation of VV116 in patients with COVID-19.

Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Clinical characteristics and outcomes in coronavirus disease 2019 (COVID-19) patients with and without hypertension: a retrospective study.

The novel coronavirus disease (COVID-19) has spread all over the world in a short time. Information about the differences between COVID-19 patients with and without hypertension is limited. To explore the characteristics and outcomes differences between COVID-19 patients with and without hypertension, the medical records and cardiac biomarkers of 414 patients were analyzed. A total of 149 patients had a history of hypertension, while 265 patients did not have hypertension, and the groups were compared based on their clinical characteristics and laboratory findings as well as the hazard risk for composite outcomes, including intensive care unit (ICU) admission, mechanical ventilation, or death. The results are as follows. On admission, 22.1% of patients in hypertension group had elevated high sensitivity troponin I (hs-TNI > 26 pg/mL), which was higher than the proportion in the nonhypertension group (6.4%). Median NT-proBNP levels in patients with hypertension (141.9 pg/mL) were higher than those in patients without hypertension (77.3 pg/mL). Patients in the hypertension group had a higher risk for in-hospital death [HR: 2.57, 95% CI (1.46~4.51)]. However, the impact of hypertension on the prognosis was not significant after adjusting for age and sex. Multivariate Cox hazard regression confirmed that NT-proBNP levels in the highest tertile (upper 75 % of patients with hypertension) was an independent risk factor for in-hospital death in all COVID-19 patients. Taken together, hypertension per se had a modest impact on the prognosis in COVID-19 patients. In COVID-19 patients with and without hypertension, NT-proBNP may be a better predictor of prognosis than hs-TNI.