Pharmacokinetics of nirmatrelvir and ritonavir in COVID-19 patients with end stage renal disease on intermittent haemodialysis (preprint)

Background: Nirmatrelvir/ritonavir is an effective therapy against SARS-CoV-2. Patients with end-stage renal disease (ESRD) are at high risk for severe COVID-19 and show impaired vaccine responses underlining the importance of antiviral therapy. However, use of nirmatrelvir/ritonavir is not recommended in these patients due to lack of clinical and pharmacokinetic data. Objective: To investigate pharmacokinetics and hepatic tolerance of nirmatrelvir/ritonavir in patients with ESRD and haemodialysis (HD). Patients and methods: Four patients diagnosed with SARS-CoV-2 infection received nirmatrelvir/ritonavir 150/100mg twice daily as recommended for renal impairment; HD ran in two- to three-day intervals. Plasma and serum samples were drawn before and after each HD during the 5-day treatment and for ensuing 3-5 days. Results: Median peak levels of nirmatrelvir obtained two hours after medication pre-HD in three patients were 7745ng/mL on day 3 and 6653ng/mL on day 5; median post-HD levels (C6h) declined to 5765ng/mL (74%) and 5521ng/mL (83%), on days 3 and 5 of treatment, respectively. Three days after end of treatment, median levels were 365ng/mL pre-HD and 30ng/mL post-HD. Measurements of the fourth patient, six hours after drug intake pre-HD showed nirmatrelvir-levels of 3704ng/mL on treatment day 3 which fell to 2308ng/mL post-HD, at one hour before intake of the next dose (Cmin). Conclusion: Use of nirmatrelvir/ritonavir in patients with ESRD results in high nirmatrelvir blood concentrations, which are still within the range known from patients without renal failure. No accumulation of nirmatrelvir took place and levels declined to zero within few days after end of treatment.

A proteomic survival predictor for COVID-19 patients in intensive care (preprint)

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.

Clinical and Virological Characteristics of Hospitalized COVID-19 Patients in a German Tertiary Care Center during the First Wave of the SARS-CoV-2 Pandemic (preprint)

BackgroundAdequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. MethodsA cohort of 168 hospitalized adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care center was analyzed. ResultsForty-four percent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95%CI 1.10-1.37, p<0.01), age 60-69 as compared to 18-59 years (aOR 4.33, 95%CI 1.07-20.10, p=0.04), and history of hypertension (aOR 5.55, 95%CI 2.00-16.82, p<0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p<0.01). Median duration of hospitalization was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV-patients. ConclusionOur results indicate a short duration of symptoms before admission as a risk factor for severe disease and different viral load kinetics in severely affected patients.

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome (preprint)

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.